%%% -*-BibTeX-*- %%% ==================================================================== %%% BibTeX-file{ %%% author = "Nelson H. F. Beebe", %%% version = "1.07", %%% date = "30 April 2024", %%% time = "13:35:35 MST", %%% filename = "tsas.bib", %%% address = "University of Utah %%% Department of Mathematics, 110 LCB %%% 155 S 1400 E RM 233 %%% Salt Lake City, UT 84112-0090 %%% USA", %%% telephone = "+1 801 581 5254", %%% URL = "http://www.math.utah.edu/~beebe", %%% checksum = "32973 9162 46339 435882", %%% email = "beebe at math.utah.edu, beebe at acm.org, %%% beebe at computer.org (Internet)", %%% codetable = "ISO/ASCII", %%% keywords = "ACM Transactions on Spatial Algorithms and %%% Systems (TSAS); bibliography; BibTeX", %%% license = "public domain", %%% supported = "yes", %%% docstring = "This is a COMPLETE BibTeX bibliography for %%% ACM Transactions on Spatial Algorithms and %%% Systems (TSAS) (CODEN ????, ISSN 2374-0353 %%% (print), 2374-0361 (electronic)). The %%% journal appears quarterly, and publication %%% began with volume 1, number 1, in August %%% 2015. %%% %%% At version 1.07, the COMPLETE journal %%% coverage looked like this: %%% %%% 2015 ( 8) 2019 ( 26) 2023 ( 30) %%% 2016 ( 16) 2020 ( 28) 2024 ( 6) %%% 2017 ( 10) 2021 ( 23) %%% 2018 ( 15) 2022 ( 33) %%% %%% Article: 195 %%% %%% Total entries: 195 %%% %%% The journal Web pages can be found at: %%% %%% http://tsas.acm.org/ %%% http://tsas.acm.org/archive-toc.cfm %%% %%% The journal table of contents page is at: %%% %%% http://dl.acm.org/pub.cfm?id=J1514 %%% %%% Qualified subscribers can retrieve the full %%% text of recent articles in PDF form. %%% %%% The initial draft was extracted from the ACM %%% Web pages. %%% %%% ACM copyrights explicitly permit abstracting %%% with credit, so article abstracts, keywords, %%% and subject classifications have been %%% included in this bibliography wherever %%% available. Article reviews have been %%% omitted, until their copyright status has %%% been clarified. %%% %%% bibsource keys in the bibliography entries %%% below indicate the entry originally came %%% from the computer science bibliography %%% archive, even though it has likely since %%% been corrected and updated. %%% %%% URL keys in the bibliography point to %%% World Wide Web locations of additional %%% information about the entry. %%% %%% BibTeX citation tags are uniformly chosen %%% as name:year:abbrev, where name is the %%% family name of the first author or editor, %%% year is a 4-digit number, and abbrev is a %%% 3-letter condensation of important title %%% words. Citation tags were automatically %%% generated by software developed for the %%% BibNet Project. %%% %%% In this bibliography, entries are sorted in %%% publication order, using ``bibsort -byvolume.'' %%% %%% The checksum field above contains a CRC-16 %%% checksum as the first value, followed by the %%% equivalent of the standard UNIX wc (word %%% count) utility output of lines, words, and %%% characters. This is produced by Robert %%% Solovay's checksum utility.", %%% } %%% ==================================================================== @Preamble{"\input bibnames.sty" # "\def \TM {${}^{\sc TM}$}" } %%% ==================================================================== %%% Acknowledgement abbreviations: @String{ack-nhfb = "Nelson H. F. Beebe, University of Utah, Department of Mathematics, 110 LCB, 155 S 1400 E RM 233, Salt Lake City, UT 84112-0090, USA, Tel: +1 801 581 5254, e-mail: \path|beebe@math.utah.edu|, \path|beebe@acm.org|, \path|beebe@computer.org| (Internet), URL: \path|http://www.math.utah.edu/~beebe/|"} %%% ==================================================================== %%% Journal abbreviations: @String{j-TSAS = "ACM Transactions on Spatial Algorithms and Systems (TSAS)"} %%% ==================================================================== %%% Bibliography entries: @Article{Gemsa:2015:MBL, author = "Andreas Gemsa and Jan-Henrik Haunert and Martin N{\"o}llenburg", title = "Multirow Boundary-Labeling Algorithms for Panorama Images", journal = j-TSAS, volume = "1", number = "1", pages = "1:1--1:30", month = aug, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2794299", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 14:51:00 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=2794299", abstract = "Boundary labeling deals with placing annotations for objects in an image on the boundary of that image. This problem occurs frequently in situations in which placing labels directly in the image is impossible or produces too much visual clutter. Examples are annotating maps, photos, or technical/medical illustrations. Previous algorithmic results for boundary labeling consider a single layer of labels along some or all sides of a rectangular image. If, however, the number of labels is large or the labels are too long, multiple layers of labels are needed. In this article, we study boundary labeling for panorama images, where $ n $ points in a rectangle R are to be annotated by disjoint unit-height rectangular labels placed above R in K different rows (or layers). Each point is connected to its label by a vertical leader that does not intersect any other label. We present polynomial time algorithms based on dynamic programming that either minimize the number of rows to place all n labels or maximize the number (or total weight) of labels that can be placed in K rows for a given integer K. For weighted labels, the problem is shown to be (weakly) NP-hard; in this case, we give a pseudo-polynomial algorithm to maximize the weight of the selected labels. We have implemented our algorithms; the experimental results show that solutions for realistically sized instances are computed instantaneously. We have also investigated two-sided panorama labeling, for which the labels may be placed above or below the panorama image. In this model, all of the aforementioned problems are NP-hard. For solving them, we propose mixed-integer linear program formulations.", acknowledgement = ack-nhfb, articleno = "1", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{To:2015:SAS, author = "Hien To and Cyrus Shahabi and Leyla Kazemi", title = "A Server-Assigned Spatial Crowdsourcing Framework", journal = j-TSAS, volume = "1", number = "1", pages = "2:1--2:28", month = aug, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2729713", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 14:51:00 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=2729713", abstract = "With the popularity of mobile devices, spatial crowdsourcing is rising as a new framework that enables human workers to solve tasks in the physical world. With spatial crowdsourcing, the goal is to crowdsource a set of spatiotemporal tasks (i.e., tasks related to time and location) to a set of workers, which requires the workers to physically travel to those locations in order to perform the tasks. In this article, we focus on one class of spatial crowdsourcing, in which the workers send their locations to the server and thereafter the server assigns to every worker tasks in proximity to the worker's location with the aim of maximizing the overall number of assigned tasks. We formally define this maximum task assignment (MTA) problem in spatial crowdsourcing, and identify its challenges. We propose alternative solutions to address these challenges by exploiting the spatial properties of the problem space, including the spatial distribution and the travel cost of the workers. MTA is based on the assumptions that all tasks are of the same type and all workers are equally qualified in performing the tasks. Meanwhile, different types of tasks may require workers with various skill sets or expertise. Subsequently, we extend MTA by taking the expertise of the workers into consideration. We refer to this problem as the maximum score assignment (MSA) problem and show its practicality and generality. Extensive experiments with various synthetic and two real-world datasets show the applicability of our proposed framework.", acknowledgement = ack-nhfb, articleno = "2", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Ahmed:2015:PBD, author = "Mahmuda Ahmed and Brittany Terese Fasy and Kyle S. Hickmann and Carola Wenk", title = "A Path-Based Distance for Street Map Comparison", journal = j-TSAS, volume = "1", number = "1", pages = "3:1--3:28", month = aug, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2729977", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 14:51:00 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=2729977", abstract = "Comparing two geometric graphs embedded in space is important in the field of transportation network analysis. Given street maps of the same city collected from different sources, researchers often need to know how and where they differ. However, the majority of current graph comparison algorithms are based on structural properties of graphs, such as their degree distribution or their local connectivity properties, and do not consider their spatial embedding. This ignores a key property of road networks since the similarity of travel over two road networks is intimately tied to the specific spatial embedding. Likewise, many current algorithms specific to street map comparison either do not provide quality guarantees or focus on spatial embeddings only. Motivated by road network comparison, we propose a new path-based distance measure between two planar geometric graphs that is based on comparing sets of travel paths generated over the graphs. Surprisingly, we are able to show that using paths of bounded link-length, we can capture global structural and spatial differences between the graphs. We show how to utilize our distance measure as a local signature in order to identify and visualize portions of high similarity in the maps. Finally, we present an experimental evaluation of our distance measure and its local signature on street map data from Berlin, Germany and Athens, Greece.", acknowledgement = ack-nhfb, articleno = "3", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Mckennney:2015:GMR, author = "Mark Mckennney and Roger Frye", title = "Generating Moving Regions from Snapshots of Complex Regions", journal = j-TSAS, volume = "1", number = "1", pages = "4:1--4:30", month = aug, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2774220", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 14:51:00 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=2774220", abstract = "Moving regions are a form of spatiotemporal data in which a region changes in shape and/or position over time. In many fields, moving regions representing real-world phenomena are collected using sensors that take temporally encoded snapshots of regions. We provide a novel algorithm that creates a moving region between any two complex regions. The proposed algorithm has worst-case time bounds of O; ( n; 2 ), but can use approximation techniques to achieve O ( $ n $ lg $ n $ ) in practice, space bounds of O; ( n; ), and output size bounded by O; ( n; ) (where n; is the number of line segments that define the boundaries of the regions).", acknowledgement = ack-nhfb, articleno = "4", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Tang:2015:EGF, author = "Suhua Tang and Yi Yu and Roger Zimmermann and Sadao Obana", title = "Efficient Geo-Fencing via Hybrid Hashing: A Combination of Bucket Selection and In-Bucket Binary Search", journal = j-TSAS, volume = "1", number = "2", pages = "5:1--5:22", month = nov, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2774219", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 14:51:01 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/hash.bib; http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=2774219", abstract = "Geo-fencing, as a spatial join between points (moving objects) and polygons (spatial range), is widely used in emerging location-based services to trigger context-aware events. It faces the challenge of real-time processing a large number of time-variant complex polygons, when points are constantly moving. Following the filter-and-refine policy, in our previous work, we proposed to organize edges per polygon in hash tables to improve the performance of the refining stage. The number of edges, however, is uneven among buckets. As a result, some points that happen to match big buckets with many edges will have much longer responses than usual. In this article, we solve this problem from two aspects: (i) Constructing multiple parallel hash tables and dynamically selecting the bucket with fewest edges and (ii) sorting edges in a bucket so as to realize the crossing number algorithm by binary search. We further combine the two to suggest a hybrid hashing scheme that takes a better tradeoff between real-time pairing points with polygons and system overhead of building hash tables. Extensive analyses and evaluations on two real-world datasets confirm that the proposed scheme can effectively reduce the pairing time in terms of both the average and distribution.", acknowledgement = ack-nhfb, articleno = "5", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{That:2015:TGT, author = "Dai Hai Ton That and Iulian Sandu Popa and Karine Zeitouni", title = "{TRIFL}: A Generic Trajectory Index for Flash Storage", journal = j-TSAS, volume = "1", number = "2", pages = "6:1--6:44", month = nov, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2786758", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 14:51:01 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=2786758", abstract = "Due to several important features, such as high performance, low power consumption, and shock resistance, NAND flash has become a very popular stable storage medium for embedded mobile devices, personal computers, and even enterprise servers. However, the peculiar characteristics of flash memory require redesigning the existing data storage and indexing techniques that were devised for magnetic hard disks. In this article, we propose TRIFL, an efficient and generic TRajectory Index for FLash. TRIFL is designed around the key requirements of trajectory indexing and flash storage. TRIFL is generic in the sense that it is efficient for both simple flash storage devices such as SD cards and more powerful devices such as solid state drives. In addition, TRIFL is supplied with an online self-tuning algorithm that allows adapting the index structure to the workload and the technical specifications of the flash storage device to maximize the index performance. Moreover, TRIFL achieves good performance with relatively low memory requirements, which makes the index appropriate for many application scenarios. The experimental evaluation shows that TRIFL outperforms the representative indexing methods on magnetic disks and flash disks.", acknowledgement = ack-nhfb, articleno = "6", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Guting:2015:ST, author = "Ralf Hartmut G{\"u}ting and Fabio Vald{\'e}s and Maria Luisa Damiani", title = "Symbolic Trajectories", journal = j-TSAS, volume = "1", number = "2", pages = "7:1--7:51", month = nov, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2786756", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 14:51:01 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=2786756", abstract = "Due to the proliferation of GPS-enabled devices in vehicles or with people, large amounts of position data are recorded every day and the management of such mobility data, also called trajectories, is a very active research field. A lot of effort has gone into discovering ``semantics'' from the raw geometric trajectories by relating them to the spatial environment or finding patterns, for example, by data mining techniques. A question is how the resulting ``meaningful'' trajectories can be represented or further queried. In this article, we propose a systematic study of annotated trajectory databases. We define a very simple generic model called symbolic trajectory to capture a wide range of meanings derived from a geometric trajectory. Essentially, a symbolic trajectory is just a time-dependent label; variants have sets of labels, places, or sets of places. They are modeled as abstract data types and integrated into a well-established framework of data types and operations for moving objects. Symbolic trajectories can represent, for example, the names of roads traversed obtained by map matching, transportation modes, speed profile, cells of a cellular network, behaviors of animals, cinemas within 2km distance, and so forth. Symbolic trajectories can be combined with geometric trajectories to obtain annotated trajectories. Besides the model, the main technical contribution of the article is a language for pattern matching and rewriting of symbolic trajectories. A symbolic trajectory can be represented as a sequence of pairs (called units) consisting of a time interval and a label. A pattern consists of unit patterns (specifications for time interval and/or label) and wildcards, matching units and sequences of units, respectively, and regular expressions over such elements. It may further contain variables that can be used in conditions and in rewriting. Conditions and expressions in rewriting may use arbitrary operations available for querying in the host DBMS environment, which makes the language extensible and quite powerful. We formally define the data model and syntax and semantics of the pattern language. Query operations are offered to integrate pattern matching, rewriting, and classification of symbolic trajectories into a DBMS querying environment. Implementation of the model using finite state machines is described in detail. An experimental evaluation demonstrates the efficiency of the implementation. In particular, it shows dramatic improvements in storage space and response time in a comparison of symbolic and geometric trajectories for some simple queries that can be executed on both symbolic and raw trajectories.", acknowledgement = ack-nhfb, articleno = "7", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Kovanen:2015:TAC, author = "Janne Kovanen and Tapani Sarjakoski", title = "Tilewise Accumulated Cost Surface Computation with Graphics Processing Units", journal = j-TSAS, volume = "1", number = "2", pages = "8:1--8:27", month = nov, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2803172", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 14:51:01 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/pvm.bib; http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=2803172", abstract = "Accumulated cost surfaces are used in a variety of fields that employ spatial analysis. Several algorithms have been suggested in the past for solving them efficiently or with minimal errors. Meanwhile, a new wave on the technological frontier has brought about general-purpose computing on GPUs. In this article, we describe how accumulated cost surfaces can be solved with CUDA. To verify the performance of our solution, we performed an experimental comparison against implementations run on a CPU. Our results with realistic cost models indicate that the move to GPUs can engender a speed-up of an order of magnitude.", acknowledgement = ack-nhfb, articleno = "8", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Belussi:2016:SRR, author = "Alberto Belussi and Sara Migliorini and Mauro Negri and Giuseppe Pelagatti", title = "Snap Rounding with Restore: An Algorithm for Producing Robust Geometric Datasets", journal = j-TSAS, volume = "2", number = "1", pages = "1:1--1:36", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2811256", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 14:51:01 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=2811256", abstract = "This article presents a new algorithm called Snap Rounding with Restore (SRR), which aims to make geometric datasets robust and to increase the quality of geometric approximation and the preservation of topological structure. It is based on the well-known Snap Rounding algorithm but improves it by eliminating from the snap rounded arrangement the configurations in which the distance between a vertex and a nonincident edge is smaller than half the width of a pixel of the rounding grid. Therefore, the goal of SRR is exactly the same as the goal of another algorithm, Iterated Snap Rounding (ISR), and of its evolution, Iterated Snap Rounding with Bounded Drift (ISRBD). However, SRR produces an output with a quality of approximation that is on average better than ISRBD, under the viewpoint both of the distance from the original segments and of the conservation of their topological structure. The article also reports some cases where ISRBD, notwithstanding the bounded drift, produces strong topological modifications while SRR does not. A statistical analysis on a large collection of input datasets confirms these differences. It follows that the proposed Snap Rounding with Restore algorithm is suitable for applications that require robustness, a guaranteed geometric approximation, and a good topological approximation.", acknowledgement = ack-nhfb, articleno = "1", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Buchin:2016:APS, author = "Kevin Buchin and Wouter Meulemans and Andr{\'e} {Van Renssen} and Bettina Speckmann", title = "Area-Preserving Simplification and Schematization of Polygonal Subdivisions", journal = j-TSAS, volume = "2", number = "1", pages = "2:1--2:36", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2818373", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 14:51:01 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=2818373", abstract = "In this article, we study automated simplification and schematization of territorial outlines. We present a quadratic-time simplification algorithm based on an operation called edge-move. We prove that the number of edges of any nonconvex simple polygon can be reduced with this operation. Moreover, edge-moves preserve area and topology and do not introduce new orientations. The latter property in particular makes the algorithm highly suitable for schematization in which all resulting lines are required to be parallel to one of a given set of lines (orientations). To obtain such a result, we need only to preprocess the input to use only lines that are parallel to one of the given set. We present an algorithm to enforce such orientation restrictions, again without changing area or topology. Experiments show that our algorithms obtain results of high visual quality.", acknowledgement = ack-nhfb, articleno = "2", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Ali:2016:SCQ, author = "Mohammed Eunus Ali and Egemen Tanin and Peter Scheuermann and Sarana Nutanong and Lars Kulik", title = "Spatial Consensus Queries in a Collaborative Environment", journal = j-TSAS, volume = "2", number = "1", pages = "3:1--3:37", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2829943", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 14:51:01 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=2829943", abstract = "We introduce a new type of query for a location-based social network platform. Consider a scenario in which a group of users is trying to find a common meeting location, yet attempting to include all group members is introducing a significant traveling cost to most of them. In this article, we formulate a new query type called the consensus query, which can be used to help users explore these trade-off options to find a solution upon which everyone can agree. Specifically, we study the problem of evaluating consensus queries in the context of nearest neighbor queries, where the group is interested in finding a meeting place that minimizes the travel distance for at least a specified number of group members. To help the group in selecting a suitable solution, the major challenge is to find optimal subgroups of all allowable subgroup sizes, i.e., greater or equal to the minimum specified subgroup size, that minimize the travel distances. We develop incremental algorithms to evaluate in one pass the optimal query subgroups of different sizes along with their corresponding nearest data points. These subsets, which are evaluated by the location-based service provider, constitute the answer set that is returned to the group. The group then collaboratively selects the final answer from the candidate answer set. An extensive experimental study shows the efficiency and effectiveness of our proposed techniques.", acknowledgement = ack-nhfb, articleno = "3", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Goel:2016:PAD, author = "Preeti Goel and Lars Kulik and Kotagiri Ramamohanarao", title = "Privacy-Aware Dynamic Ride Sharing", journal = j-TSAS, volume = "2", number = "1", pages = "4:1--4:41", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2845080", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 14:51:01 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=2845080", abstract = "Dynamic ride sharing is a service that enables shared vehicle rides in real time and on short notice. It can be an effective solution to counter the problem of increasing traffic jams at peak hours in cities. The growing use and popularity of smart phones and GPS-enabled devices provides us with tools required to efficiently implement ride sharing and significantly enhance carpooling. However, privacy and safety concerns are the main obstacles faced when encouraging people to use such a service. In this work, we present ``Match Maker,'' a negotiation-based model that hides exact location information data for system participants while implementing privacy preserving ride sharing. We use the concept of imprecision (not being precise about location of the user out of set of $ n $ locations) and follow the idea of obfuscation, which equates a higher degree of imprecision with a higher degree of privacy. We identify two attack types that could circumvent privacy preserving ride sharing. We compare the Match Maker model with the standard central trusted server model collecting precise location data, which we term eBay model. We provide the first comprehensive approach that integrates privacy, safety and trust in a single model. We present a recursive ellipse-based algorithm to compute an optimal driver path as well as three negotiation strategies for drivers and passengers. We conduct extensive experiments on real road networks and compare the strategies for privacy and effectiveness of ride sharing in terms of traffic load and vehicle km reduction. We show that ride sharing saves between 9\% and 21\% (on average 12\%) of vehicle km if drivers are only prepared to accept slight detours of their usual trips. In the city of Melbourne, with 11.6 million trips a weekday and an average trip length of 10.2 km, this would save 14.2 million km per weekday.", acknowledgement = ack-nhfb, articleno = "4", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Skoumas:2016:LEU, author = "Georgios Skoumas and Dieter Pfoser and Anastasios Kyrillidis and Timos Sellis", title = "Location Estimation Using Crowdsourced Spatial Relations", journal = j-TSAS, volume = "2", number = "2", pages = "5:1--5:23", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2894745", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 15:01:39 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=2894745", abstract = "The ``crowd'' has become a very important geospatial data provider. Specifically, nonexpert users have been providing a wealth of quantitative geospatial data (e.g., geotagged tweets or photos, online). With spatial reasoning being a basic form of human cognition, textual narratives expressing user travel experiences (e.g., travel blogs) would provide an even bigger source of geospatial data. Narratives typically contain qualitative geospatial data in the form of objects and spatial relations (e.g., ``St. John's church is to the North of the Acropolis museum.''). The scope of this work is (i) to extract these spatial relations from textual narratives, (ii) to quantify (model) them, and (iii) to reason about object locations based only on the quantified spatial relations. We use information extraction methods to identify toponyms and spatial relations, and we formulate a quantitative approach based on distance and orientation features to represent the latter. Probability density functions (PDFs) for spatial relations are determined by means of a greedy expectation maximization (EM)-based algorithm. These PDFs are then used to estimate unknown object locations. Experiments using a text corpus harvested from travel blog sites establish the considerable location estimation accuracy of the proposed approach on synthetic and real-world scenarios.", acknowledgement = ack-nhfb, articleno = "5", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Ferreira:2016:EEM, author = "Chaulio R. Ferreira and Marcus V. A. Andrade and Salles V. G. Magalh{\~a}es and W. Randolph Franklin", title = "An Efficient External Memory Algorithm for Terrain Viewshed Computation", journal = j-TSAS, volume = "2", number = "2", pages = "6:1--6:17", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2903206", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 15:01:39 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=2903206", abstract = "This article presents TiledVS, a fast external algorithm and implementation for computing viewsheds. TiledVS is intended for terrains that are too large for internal memory, even more than 100,000 $ \times $ 100,000 points. It subdivides the terrain into tiles that are stored compressed on disk and then paged into memory with a custom cache data structure and least recently used algorithm. If there is sufficient available memory to store a whole row of tiles, which is easy, then this specialized data management is faster than relying on the operating system's virtual memory management. Applications of viewshed computation include siting radio transmitters, surveillance, and visual environmental impact measurement. TiledVS runs a rotating line of sight from the observer to points on the region boundary. For each boundary point, it computes the visibility of all terrain points close to the line of sight. The running time is linear in the number of points. No terrain tile is read more than twice. TiledVS is very fast, for instance, processing a 104,000 $ \times $ 104,000 terrain on a modest computer with only 512MB of RAM took only $ 1.5 $ hours. On large datasets, TiledVS was several times faster than competing algorithms, such as the ones included in GRASS. The source code of TiledVS is freely available for nonprofit researchers to study, use, and extend. A preliminary version of this algorithm appeared in a four-page ACM SIGSPATIAL GIS 2012 conference paper, ``More Efficient Terrain Viewshed Computation on Massive Datasets Using External Memory.'' This more detailed version adds the fast lossless compression stage that reduces the time by 30\% to 40\%, and many more experiments and comparisons.", acknowledgement = ack-nhfb, articleno = "6", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Agarwal:2016:TNN, author = "Pankaj K. Agarwal and Alex Beutel and Thomas M{\o}lhave", title = "{TerraNNI}: Natural Neighbor Interpolation on {$2$D} and {$3$D} Grids Using a {GPU}", journal = j-TSAS, volume = "2", number = "2", pages = "7:1--7:31", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2786757", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 15:01:39 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=2786757", abstract = "With modern focus on remote sensing technology, such as LiDAR, the amount of spatial data, in the form of massive point clouds, has increased dramatically. Furthermore, repeated surveys of the same areas are becoming more common. This trend will only increase as topographic changes prompt surveys over already scanned areas, in which case we obtain large spatiotemporal datasets. An initial step in the analysis of such spatial data is to create a digital elevation model representing the terrain, possibly over time. In the case of spatial (spatiotemporal, respectively) datasets, these models often represent elevation on a 2D (3D, respectively) grid. This involves interpolating the elevation of LiDAR points on these grid points. In this article, we show how to efficiently perform natural neighbor interpolation over a 2D and 3D grid. Using a graphics processing unit (GPU), we describe different algorithms to attain speed and GPU-memory tradeoffs. Our experimental results demonstrate that our algorithms not only are significantly faster than earlier ones but also scale to much bigger datasets that previous algorithms were unable to handle.", acknowledgement = ack-nhfb, articleno = "7", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Ghinita:2016:PAV, author = "Gabriel Ghinita and Maria Luisa Damiani and Claudio Silvestri and Elisa Bertino", title = "Protecting Against Velocity-Based, Proximity-Based, and External Event Attacks in Location-Centric Social Networks", journal = j-TSAS, volume = "2", number = "2", pages = "8:1--8:36", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2910580", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 15:01:39 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=2910580", abstract = "Mobile devices with positioning capabilities allow users to participate in novel and exciting location-based applications. For instance, users may track the whereabouts of their acquaintances in location-aware social networking applications (e.g., Foursquare). Furthermore, users can request information about landmarks in their proximity. Such scenarios require users to report their coordinates to other parties, which may not be fully trusted. Reporting precise locations may result in serious privacy violations, such as disclosure of lifestyle details, sexual orientation, and so forth. A typical approach to preserve location privacy is to generate a cloaking region (CR) that encloses the user position. However, if locations are continuously reported, an attacker can correlate CRs from multiple timestamps to accurately pinpoint the user position within a CR. In this work, we protect against a broad range of attacks that breach location privacy using knowledge about (1) maximum user velocity, (2) external events that may occur outside the process of self-reporting locations (e.g., social network posts tagged by peers), and (3) information about mutual proximity between users. Assume user u who reports two consecutive cloaked regions A and B. We consider two distinct protection scenarios: in the first case, the attacker does not have information about the sensitive locations on the map, and the objective is to ensure that u can reach some point in B from any point in A; in the second case, the attacker knows the placement of sensitive locations, and the objective is to ensure that u can reach any point in B from any point in A. We propose spatial and temporal cloaking transformations to preserve user privacy, and we show experimentally that privacy can be achieved without significant quality-of-service deterioration.", acknowledgement = ack-nhfb, articleno = "8", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Efstathiades:2016:EPR, author = "Christodoulos Efstathiades and Alexandros Efentakis and Dieter Pfoser", title = "Efficient Processing of Relevant Nearest-Neighbor Queries", journal = j-TSAS, volume = "2", number = "3", pages = "9:1--9:28", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2934675", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 14:51:02 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=2934675", abstract = "Novel Web technologies and resulting applications have led to a participatory data ecosystem that, when utilized properly, will lead to more rewarding services. In this work, we investigate the case of Location-Based Services, specifically how to improve the typical location-based Point-of-Interest (POI) request processed as a k -Nearest-Neighbor query. This work introduces Links-of-Interest (LOI) between POIs as a means to increase the relevance and overall result quality of such queries. By analyzing user-contributed content in the form of travel blogs, we establish the overall popularity of an LOI, that is, how frequently the respective POI pair was visited and is mentioned in the same context. Our contribution is a query-processing method for so-called k -Relevant Nearest Neighbor ( k -RNN) queries that considers spatial proximity in combination with LOI information to retrieve close-by and relevant (as judged by the crowd) POIs. Our method is based on intelligently combining indices for spatial data (a spatial grid) and for relevance data (a graph) during query processing. Using landmarks as a means to prune the search space in the Relevance Graph, we improve the proposed methods. Using in addition A*-directed search, the query performance can be further improved. An experimental evaluation using real and synthetic data establishes that our approach efficiently solves the k -RNN problem.", acknowledgement = ack-nhfb, articleno = "9", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Pillai:2016:MMT, author = "Karthik Ganesan Pillai and Rafal A. Angryk and Juan M. Banda and Dustin Kempton and Berkay Aydin and Petrus C. Martens", title = "Mining At Most Top-{$K$ \%} Spatiotemporal Co-occurrence Patterns in Datasets with Extended Spatial Representations", journal = j-TSAS, volume = "2", number = "3", pages = "10:1--10:27", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2936775", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 14:51:02 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=2936775", abstract = "Spatiotemporal co-occurrence patterns (STCOPs) in datasets with extended spatial representations are two or more different event types, represented as polygons evolving in time, whose instances often occur together in both space and time. Finding STCOPs is an important problem in domains such as weather monitoring, wildlife migration, and solar physics. Nevertheless, in real life, it is difficult to find a suitable prevalence threshold without prior domain-specific knowledge. In this article, we focus our work on the problem of mining at most top-K\% of STCOPs from continuously evolving spatiotemporal events that have polygon-like representations, without using a user-specified prevalence threshold.", acknowledgement = ack-nhfb, articleno = "10", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Pelekis:2016:SOL, author = "Nikos Pelekis and Stylianos Sideridis and Panagiotis Tampakis and Yannis Theodoridis", title = "Simulating Our {LifeSteps} by Example", journal = j-TSAS, volume = "2", number = "3", pages = "11:1--11:39", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2937753", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 14:51:02 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=2937753", abstract = "During the past few decades, a number of effective methods for indexing, query processing, and knowledge discovery in moving object databases have been proposed. An interesting research direction that has recently emerged handles semantics of movement instead of raw spatio-temporal data. Semantic annotations, such as ``stop,'' ``move,'' ``at home,'' ``shopping,'' ``driving,'' and so on, are either declared by the users (e.g., through social network apps) or automatically inferred by some annotation method and are typically presented as textual counterparts along with spatial and temporal information of raw trajectories. It is natural to argue that such ``spatio-temporal-textual'' sequences, called semantic trajectories, form a realistic representation model of the complex everyday life (hence, mobility) of individuals. Towards handling semantic trajectories of moving objects in Semantic Mobility Databases, the lack of real datasets leads to the need to design realistic simulators. In the context of the above discussion, the goal of this work is to realistically simulate the mobility life of a large-scale population of moving objects in an urban environment. Two simulator variations are presented: the core Hermoupolis simulator is parametric driven (i.e., user-defined parameters tune every movement aspect), whereas the expansion of the former, called Hermoupolis by-example, follows the generate-by-example paradigm and is self-tuned by looking inside a real small (sample) dataset. We stress test our proposal and demonstrate its novel characteristics with respect to related work.", acknowledgement = ack-nhfb, articleno = "11", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Hung:2016:SIA, author = "Hui-Ju Hung and De-Nian Yang and Wang-Chien Lee", title = "Social Influence-Aware Reverse Nearest Neighbor Search", journal = j-TSAS, volume = "2", number = "3", pages = "12:1--12:35", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2964906", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 14:51:02 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=2964906", abstract = "Business-location planning, critical to the success of many businesses, can be addressed by the reverse nearest neighbors (RNN) query using geographical proximity to the customers as the main metric to find a store location close to many customers. Nevertheless, we argue that other marketing factors, such as social influence, could be considered in the process of business-location planning. In this article, we propose a framework for business-location planning that takes into account both factors of geographical proximity and social influence. An essential task in this framework is to compute the ``influence spread'' of RNNs for candidate locations. Here, the influence spread refers to the number of people influenced via the word-of-mouth effect. To alleviate the excessive computational overhead and long latency in the framework, we trade storage overhead for processing speed by precomputing and storing the social influence between pairs of customers. Based on Targeted Region (TR)-Oriented and RNN-Oriented processing strategies, we develop two suites of algorithms that incorporate various efficient pruning and segmentation techniques to enhance our framework. Experiments validate our ideas and evaluate the efficiency of the proposed algorithms over various parameter settings. The experimental results show that (a) TR-oriented and RNN-oriented processing are feasible for supporting the task of location planning; (b) RNN-oriented processing is more efficient than TR-oriented processing; and (c) the optimization technique that we developed significantly improves the efficiency of RNN-oriented and TR-oriented processing.", acknowledgement = ack-nhfb, articleno = "12", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Budig:2016:MLM, author = "Benedikt Budig and Thomas C. {Van Dijk} and Alexander Wolff", title = "Matching Labels and Markers in Historical Maps: An Algorithm with Interactive Postprocessing", journal = j-TSAS, volume = "2", number = "4", pages = "13:1--13:24", month = nov, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2994598", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 14:51:03 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=2994598", abstract = "In this article, we present an algorithmic system for determining the proper correspondence between place markers and their labels in historical maps. We assume that the locations of place markers (usually pictographs) and labels (pieces of text) have already been determined -- either algorithmically or by hand -- and we want to match the labels to the markers. This time-consuming step in the digitization process of historical maps is nontrivial even for humans but provides valuable metadata (e.g., when subsequently georeferencing the map). To speed up this process, we model the problem in terms of combinatorial optimization, solve that problem efficiently, and show how user interaction can be used to improve the quality of the results. We also consider a version of the model where we are given label fragments and additionally have to decide which fragments go together. We show that this problem is NP-hard. However, we give a polynomial-time algorithm for a restricted version of this fragment assignment problem. We have implemented the algorithm for the main problem and tested it on a manually extracted ground truth for eight historical maps with a combined total of more than 12,800 markers and labels. On average, the algorithm correctly matches 96\% of the labels and is robust against noisy input. It furthermore performs a sensitivity analysis and in this way computes a measure of confidence for each of the matches. We use this as the basis for an interactive system where the user's effort is directed to checking those parts of the map where the algorithm is unsure; any corrections the user makes are propagated by the algorithm. We discuss a prototype of this system and statistically confirm that it successfully locates those areas on the map where the algorithm needs help.", acknowledgement = ack-nhfb, articleno = "13", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Niu:2016:LED, author = "Wei Niu and Zhijiao Liu and James Caverlee", title = "On Local Expert Discovery via Geo-Located Crowds, Queries, and Candidates", journal = j-TSAS, volume = "2", number = "4", pages = "14:1--14:24", month = nov, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2994599", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 14:51:03 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=2994599", abstract = "Local experts are critical for many location-sensitive information needs, and yet there is a research gap in our understanding of the factors impacting who is recognized as a local expert and in methods for discovering local experts. Hence, in this article, we explore a geo-spatial learning-to-rank framework for identifying local experts. Three of the key features of the proposed approach are: (i) a learning-based framework for integrating multiple user-based, content-based, list-based, and crowd-based factors impacting local expertise that leverages the fine-grained GPS coordinates of millions of social media users; (ii) a location-sensitive random walk that propagates crowd knowledge of a candidate's expertise; and (iii) a comprehensive controlled study over AMT-labeled local experts on eight topics and in four cities. We find significant improvements of local expert finding versus two state-of-the-art alternatives, as well as evidence for the generalizability of local expert ranking models to new topics and new locations.", acknowledgement = ack-nhfb, articleno = "14", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Zhao:2016:OSE, author = "Liang Zhao and Feng Chen and Chang-Tien Lu and Naren Ramakrishnan", title = "Online Spatial Event Forecasting in Microblogs", journal = j-TSAS, volume = "2", number = "4", pages = "15:1--15:39", month = nov, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2997642", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 14:51:03 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=2997642", abstract = "Event forecasting from social media data streams has many applications. Existing approaches focus on forecasting temporal events (such as elections and sports) but as yet cannot forecast spatiotemporal events such as civil unrest and influenza outbreaks, which are much more challenging. To achieve spatiotemporal event forecasting, spatial features that evolve with time and their underlying correlations need to be considered and characterized. In this article, we propose novel batch and online approaches for spatiotemporal event forecasting in social media such as Twitter. Our models characterize the underlying development of future events by simultaneously modeling the structural contexts and their spatiotemporal burstiness based on different strategies. Both batch and online-based inference algorithms are developed to optimize the model parameters. Utilizing the trained model, the alignment likelihood of tweet sequences is calculated by dynamic programming. Extensive experimental evaluations on two different domains demonstrate the effectiveness of our proposed approach.", acknowledgement = ack-nhfb, articleno = "15", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Purushotham:2016:PGR, author = "Sanjay Purushotham and C.-C. Jay Kuo", title = "Personalized Group Recommender Systems for Location- and Event-Based Social Networks", journal = j-TSAS, volume = "2", number = "4", pages = "16:1--16:29", month = nov, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2987381", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 14:51:03 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=2987381", abstract = "Location-Based Social Networks (LBSNs) such as Foursquare, Google+ Local, and so on, and Event-Based Social Networks (EBSNs) such as Meetup, Plancast, and so on, have become popular platforms for users to plan, organize, and attend social events with friends and acquaintances. These LBSNs and EBSNs provide rich content such as online and offline user interactions, location/event descriptions that can be leveraged for personalized group recommendations. In this article, we propose novel Collaborative Filtering-based Bayesian models to capture the location or event semantics and group dynamics such as user interactions, user group membership, user influence, and the like for personalized group recommendations. Empirical experiments on two large real-world datasets (Gowalla LBSN dataset and Meetup EBSN dataset) show that our models outperform the state-of-the-art group recommender systems. We discuss the group characteristics of our datasets and show that modeling of group dynamics learns better group preferences than aggregating individual user preferences. Moreover, our model provides human interpretable results that can be used to understand group participation behavior and location/event popularity.", acknowledgement = ack-nhfb, articleno = "16", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Amagata:2017:GFM, author = "Daichi Amagata and Takahiro Hara", title = "A General Framework for {MaxRS} and {MaxCRS} Monitoring in Spatial Data Streams", journal = j-TSAS, volume = "3", number = "1", pages = "1:1--1:34", month = may, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3080554", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 14:51:03 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=3080554", abstract = "This article addresses the MaxRS (Maximizing Range Sum) monitoring problem. Given a set of weighted spatial stream objects, this problem is to monitor a location of a user-specified sized rectangle where the sum of the weights of the objects covered by the rectangle is maximized. This problem supports modern applications (e.g., traffic analysis and event detection in urban sensing) but has not yet been addressed. Although some algorithms for static objects have been proposed, such algorithms are not scalable to stream environments. These motivate us to devise an algorithm for efficient MaxRS monitoring. We first propose G2 (Graph in Grid index) and a G2-based algorithm to incrementally update the result. We then propose aG2 (aggregate G2), by enhancing G2, and a branch-and-bound algorithm that employs aG2 and can deal with error-guaranteed approximation. We also address MaxCRS monitoring, which is the circle version of the aforementioned problem. Its importance is evident from the fact that distance is also popular as a range criterion. We then have an emerging challenge of developing a general and efficient solution for both continuous MaxRS and MaxCRS queries. Based on a common property of the two problems, we generalize aG2 so as to be employed in both continuous MaxRS and MaxCRS queries. The branch-and-bound algorithm is also extended to suit the generalized index. We conduct extensive experiments using synthetic and real datasets. The experimental results show that our algorithms support a fast result update and significantly outperform the algorithms for static data.", acknowledgement = ack-nhfb, articleno = "1", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Iwata:2017:EPF, author = "Tomoharu Iwata and Hitoshi Shimizu and Futoshi Naya and Naonori Ueda", title = "Estimating People Flow from Spatiotemporal Population Data via Collective Graphical Mixture Models", journal = j-TSAS, volume = "3", number = "1", pages = "2:1--2:18", month = may, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3080555", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 14:51:03 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=3080555", abstract = "Thanks to the prevalence of mobile phones and GPS devices, spatiotemporal population data can be obtained easily. In this article, we propose a mixture of collective graphical models for estimating people flow from spatiotemporal population data. The spatiotemporal population data we use as input is the number of people in each grid cell area over time, which is aggregated information about many individuals; to preserve privacy, they do not contain trajectories of each individual. Therefore, it is impossible to directly estimate people flow. To overcome this problem, the proposed model assumes that transition populations are hidden variables and estimates the hidden transition populations and transition probabilities simultaneously. The proposed model can handle changes of people flow over time by segmenting time-of-day points into multiple clusters, where different clusters have different flow patterns. We develop an efficient variational Bayesian inference procedure for the collective graphical mixture model. In our experiments, the effectiveness of the proposed method is demonstrated by using four real-world spatiotemporal population datasets in Tokyo, Osaka, Nagoya, and Beijing.", acknowledgement = ack-nhfb, articleno = "2", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Karagiorgou:2017:LAM, author = "Sophia Karagiorgou and Dieter Pfoser and Dimitrios Skoutas", title = "A Layered Approach for More Robust Generation of Road Network Maps from Vehicle Tracking Data", journal = j-TSAS, volume = "3", number = "1", pages = "3:1--3:21", month = may, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3061713", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Thu Jun 15 14:51:03 MDT 2017", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "http://dl.acm.org/citation.cfm?id=3061713", abstract = "Nowadays, large amounts of tracking data are generated via GPS-enabled devices and other advanced tracking technologies. These constitute a rich source for inferring the structure of transportation networks. In this work, we present a novel methodology for revealing a road network map from vehicle trajectories. Specifically, we propose an enhanced and robust map construction algorithm that is based on segmenting the original tracking data according to different types of movement and then constructing the topology of the road network hierarchically. The segmentation produces separate road network layers, which are then fused into a single network. This provides a more efficient way to addresses the challenges imposed by noisy and low sampling rate trajectories. It also allows for a mechanism to accommodate automatic map maintenance on updates. Thus, the proposed approach overcomes the limitations of existing methods and introduces a map construction algorithm that is robust against heterogeneous and sparse data and capable to incorporate changes and improvements. An experimental evaluation extensively assesses the quality of the proposed methodology by constructing large parts of the road networks of four major cities, namely Athens, Berlin, Vienna, and Chicago, using as input GPS tracking data of utility vehicles and taxi fleets. Our results show significant improvements concerning the spatial accuracy and the quality of the constructed road network over the current state of the art.", acknowledgement = ack-nhfb, articleno = "3", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Aly:2017:AEE, author = "Heba Aly and Anas Basalamah and Moustafa Youssef", title = "Accurate and Energy-Efficient {GPS}-Less Outdoor Localization", journal = j-TSAS, volume = "3", number = "2", pages = "4:1--4:??", month = aug, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3085575", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:48 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3085575", abstract = "Location-based services have become an important part of our daily lives. However, such services require continuous user tracking while preserving the scarce cell-phone battery resource. In this article, we present Dejavu, a system that uses standard cell-phone sensors to provide accurate and energy-efficient outdoor localization. Dejavu is capable of localizing and navigating both pedestrian and in-vehicle users in real time. Our analysis shows that, whether walking or in-vehicle, when the user encounters a road landmark such as going inside a tunnel, ascending a staircase, or even moving over a bump, all these different landmarks affect the inertial sensors on the phone in a unique pattern. Dejavu employs a dead-reckoning localization approach and leverages these road landmarks, among other automatically discovered virtual landmarks, to reset the dead-reckoning accumulated error and achieve accurate localization. To maintain a low energy profile, Dejavu uses only energy-efficient sensors or sensors that are already running for other purposes. Moreover, Dejavu provides a localization confidence measure along with its predicted location. This improves the usability of the predicted location from end users' perspective. We present the design of Dejavu and how it leverages crowd-sourcing to automatically learn virtual landmarks and their locations. Our evaluation results from implementation on different Android devices using different testbeds showing that Dejavu can localize cell-phones in vehicles with a median error of 8.4 m in city roads and 16.6 m on highways and can localize cell-phones carried by pedestrians with a median error of 3.0m. Moreover, compared to the global position system (GPS) and other state-of-the-art systems, Dejavu can extend the battery lifetime by up to 347\%, while achieving even better localization results than GPS in the more challenging in-city areas. In addition, Dejavu estimates the localization confidence measure accurately with a median error of 2.3m and 31cm for in-vehicle and pedestrian users, respectively.", acknowledgement = ack-nhfb, articleno = "4", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Long:2017:SPB, author = "Yuan Long and Xiaolin Hu", title = "Spatial Partition-Based Particle Filtering for Data Assimilation in Wildfire Spread Simulation", journal = j-TSAS, volume = "3", number = "2", pages = "5:1--5:??", month = aug, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3099471", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:48 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3099471", abstract = "This article develops a spatial partition-based particle filtering framework to support data assimilation for large-scale wildfire spread simulation effectively. The developed spatial partition-based particle filtering framework breaks the system state and observation data into smaller spatial regions and then carries out localized particle filtering based on these spatial regions. Particle Filters (PFs) hold great promise to support data assimilation for spatial temporal simulations, such as wildfire spread simulation, to achieve more accurate simulation or prediction results. However, PFs face major challenges to work effectively for complex spatial temporal simulations due to the high-dimensional state space of the simulation models, which typically cover large areas and have a large number of spatially dependent state variables. The developed framework exploits the spatial locality property of system state and observation data and employs the divide-and-conquer principle to reduce state dimension and data complexity. This framework is especially developed for a discrete event cellular space model (the wildfire simulation model), which significantly differs from prior works that use numerical models specified by partial differential equations (PDEs) with continuous variables. Within this framework, a two-level automated spatial partitioning method is presented to provide automated and balanced spatial partitions with fewer boundary sensors. The developed framework is applied to a wildfire spread simulation and achieved improved results compared to using standard PF-based data assimilation methods.", acknowledgement = ack-nhfb, articleno = "5", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Chawla:2017:CPF, author = "Sanjay Chawla and Jo{\"e}l Estephan and Joachim Gudmundsson and Michael Horton", title = "Classification of Passes in Football Matches Using Spatiotemporal Data", journal = j-TSAS, volume = "3", number = "2", pages = "6:1--6:??", month = aug, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3105576", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:48 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3105576", abstract = "A knowledgeable observer of a game of football (soccer) can make a subjective evaluation of the quality of passes made between players during the game, such as rating them as Good, OK, or Bad. In this article, we consider the problem of producing an automated system to make the same evaluation of passes and present a model to solve this problem. Recently, many professional football leagues have installed object tracking systems in their stadiums that generate high-resolution and high-frequency spatiotemporal trajectories of the players and the ball. Beginning with the thesis that much of the information required to make the pass ratings is available in the trajectory signal, we further postulated that using complex data structures derived from computational geometry would enable domain football knowledge to be included in the model by computing metric variables in a principled and efficient manner. We designed a model that computes a vector of predictor variables for each pass made and uses machine learning techniques to determine a classification function that can accurately rate passes based only on the predictor variable vector. Experimental results show that the learned classification functions can rate passes with 90.2\% accuracy. The agreement between the classifier ratings and the ratings made by a human observer is comparable to the agreement between the ratings made by human observers, and suggests that significantly higher accuracy is unlikely to be achieved. Furthermore, we show that the predictor variables computed using methods from computational geometry are among the most important to the learned classifiers.", acknowledgement = ack-nhfb, articleno = "6", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Teng:2017:TMS, author = "Shan-Yun Teng and Wei-Shinn Ku and Kun-Ta Chuang", title = "Toward Mining Stop-by Behaviors in Indoor Space", journal = j-TSAS, volume = "3", number = "2", pages = "7:1--7:??", month = aug, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3106736", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:48 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3106736", abstract = "In this article, we explore a new mining paradigm, called Indoor Stop-by Patterns (ISP), to discover user stop-by behavior in mall-like indoor environments. The discovery of ISPs enables new marketing collaborations, such as a joint coupon promotion, among stores in indoor spaces (e.g., shopping malls). Moreover, it can also help in eliminating the overcrowding situation. To pursue better practicability, we consider the cost-effective wireless sensor-based environment and conduct the analysis of indoor stop-by behaviors on real data. However, it is a highly challenging issue, in indoor environments, to retrieve frequent ISPs, especially when the issue of user privacy is highlighted nowadays. The mining of ISPs will face a critical challenge from spatial uncertainty. Previous work on mining indoor movement patterns usually relies on precise spatio-temporal information by a specific deployment of positioning devices, which cannot be directly applied. In this article, the proposed Probabilistic Top- k Indoor Stop-by Patterns Discovery (PTkISP) framework incorporates the probabilistic model to identify top- k ISPs over uncertain data collected from sensing logs. Moreover, we develop an uncertain model and devise an Index 1-itemset (IIS) algorithm to enhance the accuracy and efficiency. Our experimental studies show that the proposed PTkISP framework can efficiently discover high-quality ISPs and can provide insightful observations for marketing collaborations.", acknowledgement = ack-nhfb, articleno = "7", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Mariescu-Istodor:2017:GBM, author = "Radu Mariescu-Istodor and Pasi Fr{\"a}nti", title = "Grid-Based Method for {GPS} Route Analysis for Retrieval", journal = j-TSAS, volume = "3", number = "3", pages = "8:1--8:??", month = nov, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3125634", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:48 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3125634", abstract = "Grids are commonly used as histograms to process spatial data in order to detect frequent patterns, predict destinations, or to infer popular places. However, they have not been previously used for GPS trajectory similarity searches or retrieval in general. Instead, slower and more complicated algorithms based on individual point-pair comparison have been used. We demonstrate how a grid representation can be used to compute four different route measures: novelty, noteworthiness, similarity, and inclusion. The measures may be used in several applications such as identifying taxi fraud, automatically updating GPS navigation software, optimizing traffic, and identifying commuting patterns. We compare our proposed route similarity measure, C-SIM, to eight popular alternatives including Edit Distance on Real sequence (EDR) and Frechet distance. The proposed measure is simple to implement and we give a fast, linear time algorithm for the task. It works well under noise, changes in sampling rate, and point shifting. We demonstrate that by using the grid, a route similarity ranking can be computed in real-time on the Mopsi2014 1 route dataset, which consists of over 6,000 routes. This ranking is an extension of the most similar route search and contains an ordered list of all similar routes from the database. The real-time search is due to indexing the cell database and comes at the cost of spending 80\% more memory space for the index. The methods are implemented inside the Mopsi 2 route module.", acknowledgement = ack-nhfb, articleno = "8", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Aydin:2017:MSS, author = "Berkay Aydin and Ahmet Kucuk and Rafal A. Angryk and Petrus C. Martens", title = "Measuring the Significance of Spatiotemporal Co-Occurrences", journal = j-TSAS, volume = "3", number = "3", pages = "9:1--9:??", month = nov, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3139351", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:48 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3139351", abstract = "Spatiotemporal co-occurrences are the appearances of spatial and temporal overlap relationships among trajectory-based spatiotemporal instances with region-based geometric representations. Assessing the significance of spatiotemporal co-occurrences plays an important role in the spatiotemporal frequent pattern mining applications of moving region objects. A spatiotemporal version of the popular Jaccard measure has been used for measuring the strength of spatiotemporal co-occurrences. We will demonstrate the shortcomings of the Jaccard (J) measure when it is used for assessing the significance of co-occurrences among spatiotemporal instances with highly different spatiotemporal evolution characteristics. We will present two extended novel measures (J + and J *) that address the problems linked to the J measure. Our work includes algorithms for the significance measure calculations, the proofs and explanations about the key properties of measures, and a detailed experimental evaluation section. Our experiments include in-depth relevancy and running time analyses demonstrating the suitability of our proposed measures for spatiotemporal frequent pattern mining algorithms.", acknowledgement = ack-nhfb, articleno = "9", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Robles-Ortega:2017:EVD, author = "M. D. Robles-Ortega and L. Ortega and F. R. Feito", title = "Efficient Visibility Determination in Urban Scenes Considering Terrain Information", journal = j-TSAS, volume = "3", number = "3", pages = "10:1--10:??", month = nov, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3152536", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:48 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3152536", abstract = "In this article, we introduce a novel occlusion culling method working on the server side to provide real-time navigation on web-based systems. Nowadays, virtual navigation in urban environments is a rising trend in several contexts such as tourism, GPS navigation systems, and video games. A city environment is usually associated with a complex data model that is better stored, maintained, and updated on a server system. Mobile devices are regular clients in these cases, demanding this information in a fast, reliable, and engaging way. Even though these gadgets have been increasing their capabilities in computation and visualization, the bottleneck is still the transmission of information over the network. The advantage of urban environments is that, from a user viewpoint, only a small portion of the scene is visible. This feature makes crucial the use of occlusion culling techniques working on the server side in order to transmit to the client side only the small set of visible elements compared to the whole scene. The input data are the city geometry from the 2D cadastral information system, the building textures, and DEM (Digital Elevation Model) files with the urban terrain features. In a first stage, the process creates a 2.5D urban model with all these data in preprocessing time. Then the client provides the user location point, and the server sends back the exact portion of visible city. This approach is implemented using polar diagrams for visibility determination and LOD (Level of Detail) techniques for further geometry reduction.", acknowledgement = ack-nhfb, articleno = "10", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Ayala:2018:STM, author = "Daniel Ayala and Ouri Wolfson and Bhaskar Dasgupta and Jie Lin and Bo Xu", title = "Spatio-Temporal Matching for Urban Transportation Applications", journal = j-TSAS, volume = "3", number = "4", pages = "11:1--11:??", month = may, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3183344", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:49 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3183344", abstract = "In this article, we present a search problem in which mobile agents are searching for static resources. Each agent wants to obtain exactly one resource. Both agents and resources are spatially located on a road network and the movement of the agents is constrained to the road network. This problem applies to various transportation applications including: vehicles (agents) searching for parking (resources) and taxicabs (agents) searching for clients to pick up (resources). In this work, we design search algorithms for such scenarios. We model the problem in different scenarios that vary based on the level of information that is available to the agents. These scenarios vary from scenarios in which agents have complete information about other agents and resources, to scenarios in which agents only have access to a fraction of the data about the availability of resources (uncertain data). We also propose pricing schemes that incentivize vehicles to search for resources in a way that benefits the system and the environment. Our proposed algorithms were tested in a simulation environment that uses real-world data. We were able to attain up to 40\% improvements over other approaches that were tested against our algorithms.", acknowledgement = ack-nhfb, articleno = "11", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Belesiotis:2018:APS, author = "Alexandros Belesiotis and George Papadakis and Dimitrios Skoutas", title = "Analyzing and Predicting Spatial Crime Distribution Using Crowdsourced and Open Data", journal = j-TSAS, volume = "3", number = "4", pages = "12:1--12:??", month = may, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3190345", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:49 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3190345", abstract = "Data analytics has an ever increasing impact on tackling various societal challenges. In this article, we investigate how data from several heterogeneous online sources can be used to discover insights and make predictions about the spatial distribution of crime in large urban environments. A series of important research questions is addressed, following a purely data-driven approach and methodology. First, we examine how useful different types of data are for the task of crime levels prediction, focusing especially on how prediction accuracy can be improved by combining data from multiple information sources. To that end, we not only investigate prediction accuracy across all individual areas studied, but also examine how these predictions affect the accuracy of identified crime hotspots. Then, we look into individual features, aiming to identify and quantify the most important factors. Finally, we drill down to different crime types, elaborating on how the prediction accuracy and the importance of individual features vary across them. Our analysis involves six different datasets, from which more than 3,000 features are extracted, filtered, and used to learn models for predicting crime rates across 14 different crime categories. Our results indicate that combining data from multiple information sources can significantly improve prediction accuracy. They also highlight which features affect prediction accuracy the most, as well as for which particular crime categories the predictions are more accurate.", acknowledgement = ack-nhfb, articleno = "12", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Choudhury:2018:BPT, author = "Farhana M. Choudhury and J. Shane Culpepper and Zhifeng Bao and Timos Sellis", title = "Batch Processing of Top-$k$ Spatial-Textual Queries", journal = j-TSAS, volume = "3", number = "4", pages = "13:1--13:??", month = may, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3196155", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:49 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3196155", abstract = "Since the mid-2000s, several indexing techniques have been proposed to efficiently answer top-$k$ spatial-textual queries. However, all of these approaches focus on answering one query at a time. In contrast, how to design efficient algorithms that can exploit similarities between incoming queries to improve performance has received little attention. In this article, we study a series of efficient approaches to batch process multiple top-$k$ spatial-textual queries concurrently. We carefully design a variety of indexing structures for the problem space by exploring the effect of prioritizing spatial and textual properties on system performance. Specifically, we present an efficient traversal method, SF-S ep, over an existing space-prioritized index structure. Then, we propose a new space-prioritized index structure, the MIR-Tree to support a filter-and-refine based technique, SF-Grp. To support the processing of text-intensive data, we propose an augmented, inverted indexing structure that can easily be added into existing text search engine architectures and a novel traversal method for batch processing of the queries. In all of these approaches, the goal is to improve the overall performance by sharing the I/O costs of similar queries. Finally, we demonstrate significant I/O savings in our algorithms over traditional approaches by extensive experiments on three real datasets and compare how properties of different datasets affect the performance. Many applications in streaming, micro-batching of continuous queries, and privacy-aware search can benefit from this line of work.", acknowledgement = ack-nhfb, articleno = "13", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Fujino:2018:DDI, author = "Takumi Fujino and Atsushi Hashimoto and Hidekazu Kasahara and Mikihiko Mori and Masaaki Iiyama and Michihiko Minoh", title = "Detecting Deviations from Intended Routes Using Vehicular {GPS} Tracks", journal = j-TSAS, volume = "4", number = "1", pages = "1:1--1:??", month = jun, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3204455", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:49 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3204455", abstract = "This article proposes a method to find intersections at which cars tend to deviate from the optimal route based on global positioning system (GPS) tracking data under the assumption that such deviations indicate that car navigation systems (CNSs) and road signage are not readily available. If the intended route is known, deviations can be enumerated by comparing the intended route with the vehicle's actual route as observed by a GPS; however, the intended route is unknown and can differ from the route suggested by a CNS. To identify intersections with high deviation rates without knowing intended routes, we exhaustively sampled subsequences from each vehicular GPS track, and detected deviations from the optimal route for the subsequences. Although the detected deviations are not always caused by driver confusion, accumulating such erroneous detection results would yield a meaningful difference in the number of accumulated deviations at each intersection. We applied the proposed method to 3,843 GPS tracks collected from visitor drivers in the city of Kyoto. Thresholding the estimated deviation rate yielded 39 intersections from 14,543 candidates. The results show a certain level of correlation between obtained deviations and rerouting locations from actual CNS data. We also found several intersections where faulty route suggestions are provided by CNSs.", acknowledgement = ack-nhfb, articleno = "1", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Toll:2018:MAM, author = "Wouter Van Toll and Atlas F. Cook Iv and Marc J. Van Kreveld and Roland Geraerts", title = "The Medial Axis of a Multi-Layered Environment and Its Application as a Navigation Mesh", journal = j-TSAS, volume = "4", number = "1", pages = "2:1--2:??", month = jun, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3204456", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:49 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3204456", abstract = "Path planning for walking characters in complicated virtual environments is a fundamental task in simulations and games. A navigation mesh is a data structure that allows efficient path planning. The Explicit Corridor Map (ECM) is a navigation mesh based on the medial axis. It enables path planning for disk-shaped characters of any radius. In this article, we formally extend the medial axis (and therefore the ECM) to 3D environments in which characters are constrained to walkable surfaces. Typical examples of such environments are multi-storey buildings, train stations, and sports stadiums. We give improved definitions of a walkable environment (WE: a description of walkable surfaces in 3D) and a multi-layered environment (MLE: a subdivision of a WE into connected layers). We define the medial axis of such environments based on projected distances on the ground plane. For an MLE with $n$ boundary vertices and k connections, we show that the medial axis has size O (n), and we present an improved algorithm that constructs the medial axis in O (n \log $n$ \log k) time. The medial axis can be annotated with nearest-obstacle information to obtain the ECM navigation mesh. Our implementations show that the ECM can be computed efficiently for large 2D and multi-layered environments and that it can be used to compute paths within milliseconds. This enables simulations of large virtual crowds of heterogeneous characters in real-time.", acknowledgement = ack-nhfb, articleno = "2", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Koide:2018:EIQ, author = "Satoshi Koide and Yukihiro Tadokoro and Takayoshi Yoshimura and Chuan Xiao and Yoshiharu Ishikawa", title = "Enhanced Indexing and Querying of Trajectories in Road Networks via String Algorithms", journal = j-TSAS, volume = "4", number = "1", pages = "3:1--3:??", month = jun, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3200200", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:49 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3200200", abstract = "In this article, we propose a novel indexing and querying method for trajectories constrained in a road network. We aim to provide efficient algorithms for various types of spatiotemporal queries that involve routing in road networks, such as (1) finding moving objects that have traveled along a given path during a given time interval, (2) extracting all paths traveled after a given spatiotemporal context, and (3) enumerating all paths between two locations traveled during a certain time interval. Unlike the existing methods in spatial database research, we employ indexing techniques and algorithms from string processing. This idea is based on the fact that we can represent spatial paths as strings, because trajectories in a network are represented as sequences of road segment IDs. The proposed SNT-index (suffix-array-based network-constrained trajectory index) introduces two novel concepts to trajectory indexing. The first is FM-index, which is a compact in-memory data structure for pattern matching. The second is an inverse suffix array, which allows the FM-index to be integrated with the temporal information stored in a forest of B + -trees. Thanks to these concepts, we can reduce the number of B + -tree accesses required by the query processing algorithms to a constant number, something that cannot be achieved with existing methods. Although an FM-index is essentially a static index, we also propose a practical method of appending new data to the index. Finally, experiments show that our method can process the target queries for more than 1 million trajectories in a few tens of milliseconds, which is significantly faster than what the baseline algorithms can achieve without string algorithms.", acknowledgement = ack-nhfb, articleno = "3", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Yin:2018:FBM, author = "Yifang Yin and Rajiv Ratn Shah and Guanfeng Wang and Roger Zimmermann", title = "Feature-based Map Matching for Low-Sampling-Rate {GPS} Trajectories", journal = j-TSAS, volume = "4", number = "2", pages = "4:1--4:??", month = aug, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3223049", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:49 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3223049", abstract = "With the increasing availability of GPS-equipped mobile devices, location-based services have become an integral part of everyday life. Among one of the initial steps of positioning data management, map matching aims to reduce the uncertainty in a trajectory by matching the GPS points to the road network on a digital map. Most existing work has focused on estimating the likelihood of a candidate route based on the GPS observations, while neglecting to model the probability of a route choice from the perspective of drivers. In this work, we propose a novel feature-based map matching algorithm that estimates the cost of a candidate route based on both GPS observations and human factors. To take human factors into consideration is highly important, especially when dealing with low sampling rate data where most of the movement details are lost. Additionally, we simultaneously analyze a subsequence of coherent GPS points by utilizing a new segment-based probabilistic map matching strategy, which is less susceptible to the noisiness of the positioning data. We have evaluated both the offline and the online versions of our proposed approach on a public large-scale GPS dataset, which consists of 100 trajectories distributed all over the world. The experimental results show that our method is robust to sparse data with large sampling intervals (e.g., 60s--300s) and challenging track features (e.g., u-turns and loops). Measurements including map matching accuracy and system efficiency have been thoroughly evaluated and discussed. Compared with two state-of-the-art map matching algorithms, our method substantially reduces the route mismatch error by 6.4\%--32.3\% (either offline or online with the window size set to 360s), with a slight increase in terms of the processing time. The experimental results show that our proposed method obtains the state-of-the-art map matching results in all the different combinations of sampling rates and challenging features.", acknowledgement = ack-nhfb, articleno = "4", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Dong:2018:WAR, author = "Yuyang Dong and Hanxiong Chen and Jeffrey Xu Yu and Kazutaka Furuse and Hiroyuki Kitagawa", title = "Weighted Aggregate Reverse Rank Queries", journal = j-TSAS, volume = "4", number = "2", pages = "5:1--5:??", month = aug, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3225216", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:49 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3225216", abstract = "In marketing, helping manufacturers to find the matching preferences of potential customers for their products is an essential work, especially in e-commerce analyzing with big data. The aggregate reverse rank query has been proposed to return top-$k$ customers who have more potential to buy a given product bundling than other customers, where the potential is evaluated by the aggregate rank, which is defined as the sum of each product's rank. This query correctly reflects the request only when the customers consider the products in the product bundling equally. Unfortunately, rather than thinking products equally, in most cases, people buy a product bundling because they appreciate a special part of the bundling. Manufacturers, such as video games companies and cable television industries, are also willing to bundle some attractive products with less popular products for the purpose of maximum benefits or inventory liquidation. Inspired by the necessity of general aggregate reverse rank query for unequal thinking, we propose a weighted aggregate reverse rank query, which treats the elements in product bundling with different weights to target customers from all aspects of thought. To solve this query efficiently, we first try a straightforward extension. Then, we rebuild the bound-and-filter framework for the weighted aggregate reverse rank query. We prove, theoretically, that the new approach finds the optimal bounds, and we develop the highly efficient algorithm based on these bounds. The theoretical analysis and experimental results demonstrated the efficacy of the proposed methods.", acknowledgement = ack-nhfb, articleno = "5", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Both:2018:ISE, author = "Alan Both and Matt Duckham and Michael F. Worboys", title = "Identifying Surrounds and Engulfs Relations in Mobile and Coordinate-Free Geosensor Networks", journal = j-TSAS, volume = "4", number = "2", pages = "6:1--6:??", month = aug, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3234505", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:49 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3234505", abstract = "This article concerns the definition and identification of qualitative spatial relationships for the full and partial enclosure of spatial regions. The article precisely defines three relationships between regions-``surrounds,'' ``engulfs,'' and ``envelops''-highlighting the correspondence to similar definitions in the literature. An efficient algorithm capable of identifying these qualitative spatial relations in a network of dynamic (mobile) geosensor nodes is developed and tested. The algorithms are wholly decentralized, and operate in-network with no centralized control. The algorithms are also ``coordinate-free,'' able to operate in distributed spatial computing environments where coordinate locations are expensive to capture or otherwise unavailable. Experimental evaluation of the algorithms designed demonstrates the efficiency of the approach. Although the algorithm communication complexity is dominated by an overall worst-case O (n 2) leader election algorithm, the experiments show in practice an average-case complexity approaching linear, O (n 1.1).", acknowledgement = ack-nhfb, articleno = "6", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Mahmood:2018:DBI, author = "Ahmed R. Mahmood and Ahmed M. Aly and Tatiana Kuznetsova and Saleh Basalamah and Walid G. Aref", title = "Disk-Based Indexing of Recent Trajectories", journal = j-TSAS, volume = "4", number = "3", pages = "7:1--7:??", month = sep, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3234941", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:50 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3234941", abstract = "The plethora of location-aware devices has led to countless location-based services in which huge amounts of spatiotemporal data get created every day. Several applications require efficient processing of queries on the locations of moving objects over time, i.e., the moving object trajectories. This calls for efficient trajectory-based indexing methods that capture both the spatial and temporal dimensions of the data in a way that minimizes the number of disk I/Os required for both updating and querying. Most existing spatiotemporal index structures capture either the current locations of the moving objects or the entire history of the moving objects. Historical spatiotemporal indexing methods require multiple disk I/Os to process new updates and use a discrete trajectory representation that may result in incomplete query results. In this article, we introduce the trails-tree, a disk-based data structure for indexing recent trajectories. The trails-tree requires half the number of disk I/Os needed by other historical spatiotemporal indexing methods for the insertion and querying operations. We give a detailed description of the trails-tree, and we mathematically analyze its performance. Moreover, we present a novel query processing algorithm that ensures the completeness of the query result set. We experimentally verify the performance of the trails-tree using various real and synthetic datasets.", acknowledgement = ack-nhfb, articleno = "7", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Mariescu-Istodor:2018:CIR, author = "Radu Mariescu-Istodor and Pasi Fr{\"a}nti", title = "{CellNet}: Inferring Road Networks from {GPS} Trajectories", journal = j-TSAS, volume = "4", number = "3", pages = "8:1--8:??", month = sep, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3234692", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:50 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3234692", abstract = "Road networks are essential nowadays, especially for people travelling to large, unfamiliar cities. Moreover, cities are constantly growing and road networks need periodic updates to provide reliable information. We propose an automatic method to generate the road network using a GPS trajectory dataset. The method, called CellNet, works by first detecting the intersections (junctions) using a clustering-based technique and then creating the road segments in-between. We compare CellNet against conceptually different alternatives using Chicago and Joensuu datasets. The results show that CellNet provides better accuracy and is less sensitive to parameter setup. The size of the generated road network is only 25\% of the networks produced by other methods. This implies that the network provided by CellNet has much less redundancy.", acknowledgement = ack-nhfb, articleno = "8", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Vollmer:2018:HSA, author = "Jan Ole Vollmer and Matthias Trapp and Heidrun Schumann and J{\"u}rgen D{\"o}llner", title = "Hierarchical Spatial Aggregation for Level-of-Detail Visualization of {$3$D} Thematic Data", journal = j-TSAS, volume = "4", number = "3", pages = "9:1--9:??", month = sep, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3234506", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:50 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3234506", abstract = "Thematic maps are a common tool to visualize semantic data with a spatial reference. Combining thematic data with a geometric representation of their natural reference frame aids the viewer's ability in gaining an overview, as well as perceiving patterns with respect to location; however, as the amount of data for visualization continues to increase, problems such as information overload and visual clutter impede perception, requiring data aggregation and level-of-detail visualization techniques. While existing aggregation techniques for thematic data operate in a 2D reference frame (i.e., map), we present two aggregation techniques for 3D spatial and spatiotemporal data mapped onto virtual city models that hierarchically aggregate thematic data in real time during rendering to support on-the-fly and on-demand level-of-detail generation. An object-based technique performs aggregation based on scene-specific objects and their hierarchy to facilitate per-object analysis, while the scene-based technique aggregates data solely based on spatial locations, thus supporting visual analysis of data with arbitrary reference geometry. Both techniques can apply different aggregation functions (mean, minimum, and maximum) for ordinal, interval, and ratio-scaled data and can be easily extended with additional functions. Our implementation utilizes the programmable graphics pipeline and requires suitably encoded data, i.e., textures or vertex attributes. We demonstrate the application of both techniques using real-world datasets, including solar potential analyses and the propagation of pressure waves in a virtual city model.", acknowledgement = ack-nhfb, articleno = "9", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Khan:2018:ECO, author = "A. K. M. Mustafizur Rahman Khan and Lars Kulik and Egemen Tanin and Hua Hua and Tanzima Hashem", title = "Efficient Computation of the Optimal Accessible Location for a Group of Mobile Agents", journal = j-TSAS, volume = "4", number = "4", pages = "10:1--10:??", month = oct, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3239124", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:50 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3239124", abstract = "Nowadays, people can access location-based services (LBSs) as a group via mobile devices to plan their daily activities with friends and relatives. In this article, we introduce an important class of group-oriented LBSs, group optimal accessible location (GOAL) queries that enable users to identify the location of a point of interest (POI) that has the minimum total distance to a given set of paths. GOAL queries have many applications, such as the selection of an optimal location for group meet-ups or for a mobile facility such as a food truck. In a GOAL query, each trip or path is represented as a set of line segments, and the distance of a POI from a path is computed as the minimum distance of the POI to any line segment of the path. We develop an efficient approach to evaluate GOAL queries. The novelty of our GOAL query processing algorithm in contrast to other spatial query processing algorithms is the reformulation of a GOAL query by considering only a subset of path segments from the given set of paths, which is also the key factor behind the efficiency of our proposed algorithm. We exploit geometric properties and develop pruning techniques to eliminate both POIs and path segments that cannot provide the optimal solution for a GOAL query. Our experimental results demonstrate that we provide a readily deployable solution for real-life applications.", acknowledgement = ack-nhfb, articleno = "10", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Nur:2018:GRI, author = "Abdullah Yasin Nur and Mehmet Engin Tozal", title = "Geography and Routing in the {Internet}", journal = j-TSAS, volume = "4", number = "4", pages = "11:1--11:??", month = oct, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3239162", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:50 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3239162", abstract = "The Internet is a network of networks consisting of tens of thousands of Autonomous Systems ({ASes}). These ASes connect to each other in different forms to enable the `global'' Internet communication. In this study, we investigate the geographical characteristics of the visible Internet as well as examine the relation between geography and intra-AS and inter-AS routing policies. We show that the ingress-to-egress subpaths have lower circuitousness compared to the end-to-end paths. Our findings not only demonstrate the efficient backbone infrastructures and routing schemes deployed by ASes but also show the consequences of economical incentives on the adoption of inter-AS paths. We present and examine the existence of a strong correlation between the geographical distance and round trip delay time as well as the lack of a correlation between the geographical distance and hop length in the Internet. We investigate the relation between the geographical distance and intra-AS routing policies by employing cross-AS (X-AS) Internet topology maps. Our results show that more than two thirds of the intra-AS subpaths are congruent with the shortest geographical distance whether or not geographical distance is employed as a custom parameter in routing decisions. Our results provide new insights into the relations between geography and Internet routing, which allow the network researchers and practitioners to improve their networking infrastructures, reevaluate their routing policies, deploy geography-aware network overlays, and develop more realistic network simulation processes.", acknowledgement = ack-nhfb, articleno = "11", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Wang:2018:VMF, author = "Nana Wang and Mohan Kankanhalli", title = "{$2$D} Vector Map Fragile Watermarking with Region Location", journal = j-TSAS, volume = "4", number = "4", pages = "12:1--12:??", month = oct, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3239163", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:50 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3239163", abstract = "Locating the original region of tampered features is a challenging task for existing 2D vector map fragile watermarking methods. This article presents a 2D vector map fragile watermarking framework that locates not only the current but also the original region of tampered feature groups. In particular, we propose dividing the features of the host vector map into groups, and embedding a watermark consisting of location-bits and check-bits into each group at the sender side. At the receiver side, by comparing the extracted and calculated check-bits, one can identify tampered groups and locate their current regions. Then the location-bits extracted from the mapping groups are used to indicate the original regions of the tampered groups. To demonstrate and analyze the applicability of this framework, we instantiate it by proposing a simulated annealing (SA)-based group division method, a group mapping method, a minimum encasing rectangle (MER) based location-bits generation method and a check-bits generation method, and use an existing reversible data hiding method for watermark embedding. The experimental results show that the proposed framework can locate all the regions influenced by tampering, and the SA-based group division method can get a better region location ability.", acknowledgement = ack-nhfb, articleno = "12", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Aref:2019:ISI, author = "Walid G. Aref", title = "Introduction to the Special Issue on the Best Papers from the {2017 ACM SIGSPATIAL Conference}", journal = j-TSAS, volume = "5", number = "1", pages = "1:1--1:??", month = jun, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3325134", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:50 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3325134", acknowledgement = ack-nhfb, articleno = "1", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Rav:2019:FRA, author = "Mathias Rav and Aaron Lowe and Pankaj K. Agarwal", title = "Flood Risk Analysis on Terrains", journal = j-TSAS, volume = "5", number = "1", pages = "2:1--2:??", month = jun, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3295459", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:50 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3295459", abstract = "An important problem in terrain analysis is modeling how water flows across a terrain and creates floods by filling up depressions. In this article, we study the flooding query problem: Preprocess a given terrain $ \Sigma $, represented as a triangulated xy-monotone surface with $n$ vertices, into a data structure so that for a query rain region $R$ and a query point $q$ on $ \Sigma $, one can quickly determine how much rain has to fall in $R$ so that $q$ is flooded. Available terrain data is often subject to uncertainty, which must be incorporated into the terrain analysis. For instance, the digital elevation models of terrains have to be refined to incorporate underground pipes, tunnels, and waterways under bridges, but there is often uncertainty in their existence. By representing the uncertainty in the terrain data explicitly, we can develop methods for flood risk analysis that properly incorporate terrain uncertainty when reporting what areas are at risk of flooding. We present two results. First, we present an $ O (n \log n)$-time algorithm for preprocessing $ \Sigma $ with a linear-size data structure that can answer a flooding query in $ O (| R | + m \log n)$ time, where $ | R |$ is the number of vertices in $R$, $m$ is the number of so-called tributaries of $q$ at which rain is falling, and $n$ is the number of vertices of the terrain. Next, we extend this data structure to handle ``uncertain'' terrains using a standard Monte Carlo method. Given a probability distribution on terrain data, our data structure returns the probability of a query point being flooded if a specified amount of rain falls on a query region. We implement our data structure and test it on real terrains, showing that a small number of samples suffice to accurately estimate the flood risk.", acknowledgement = ack-nhfb, articleno = "2", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Pavlovic:2019:DCP, author = "Mirjana Pavlovic and Kai-Niklas Bastian and Hinnerk Gildhoff and Anastasia Ailamaki", title = "Dictionary Compression in Point Cloud Data Management", journal = j-TSAS, volume = "5", number = "1", pages = "3:1--3:??", month = jun, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3299770", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:50 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/datacompression.bib; http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3299770", abstract = "Nowadays, massive amounts of point cloud data can be collected thanks to advances in data acquisition and processing technologies such as dense image matching and airborne LiDAR scanning. With the increase in volume and precision, point cloud data offers a useful source of information for natural-resource management, urban planning, self-driving cars, and more. At the same time, on the scale that point cloud data is produced, management challenges are introduced: it is important to achieve efficiency both in terms of querying performance and space requirements. Traditional file-based solutions to point cloud management offer space efficiency, however, they cannot scale to such massive data and provide the declarative power of a DBMS. In this article, we propose a time- and space-efficient solution to storing and managing point cloud data in main memory column-store DBMS. Our solution, Space-Filling Curve Dictionary-Based Compression (SFC-DBC), employs dictionary-based compression in the spatial data management domain and enhances it with indexing capabilities by using space-filling curves. SFC-DBC does so by constructing the space-filling curve over a compressed, artificially introduced dictionary space. Consequently, SFC-DBC significantly optimizes query execution and yet does not require additional storage resources, compared to traditional dictionary-based compression. With respect to space-filling-curve-based approaches, it minimizes storage footprint and increases resilience to skew. As a proof of concept, we develop and evaluate our approach as a research prototype in the context of SAP HANA. SFC-DBC outperforms other dictionary-based compression schemes by up to 61\% in terms of space and up to $ 9.4 \times $ in terms of query performance.", acknowledgement = ack-nhfb, articleno = "3", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Li:2019:PTT, author = "Yang Li and Dimitrios Gunopulos and Cewu Lu and Leonidas J. Guibas", title = "Personalized Travel Time Prediction Using a Small Number of Probe Vehicles", journal = j-TSAS, volume = "5", number = "1", pages = "4:1--4:??", month = jun, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3317663", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:50 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3317663", abstract = "Predicting the travel time of a path is an important task in route planning and navigation applications. As more GPS probe data has been collected to monitor urban traffic, GPS trajectories of the probe vehicles have been frequently used to predict path travel time. However, most trajectory-based methods rely on deploying GPS devices and collect real-time data on a large taxi fleet, which can be expensive and unreliable in smaller cities. This work deals with the problem of predicting path travel time when only a small number of cars are available. We propose an algorithm that learns local congestion patterns of a compact set of frequently shared paths from historical data. Given a travel time prediction query, we identify the current congestion patterns around the query path from recent trajectories, then infer its travel time in the near future. Driver identities are also used in predicting personalized travel time. Experimental results using 10--25 taxis in urban areas of Shenzhen, China, show that personal prediction has on average 3.4mins of error on trips of duration 10--75mins. This result improves the baseline approach of using purely historical trajectories by 16.8\% on average, over four regions with various degrees of path regularity. It also outperforms a state-of-the-art travel time prediction method that uses both historical trajectories and real-time trajectories.", acknowledgement = ack-nhfb, articleno = "4", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Correa:2019:CAR, author = "Oscar Correa and A. K. M. Mustafizur Rahman Khan and Egemen Tanin and Lars Kulik and Kotagiri Ramamohanarao", title = "Congestion-Aware Ride-Sharing", journal = j-TSAS, volume = "5", number = "1", pages = "5:1--5:??", month = jun, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3317639", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:50 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3317639", abstract = "In its current form, ride-sharing is responsible for a small fraction of traffic load compared to other transportation modes, especially private vehicles. As its benefits became more evident, and obstacles, e.g., lack of liability legislation, that may hinder its larger scale adoption are being overcome, ride-sharing will be a more common mode of transportation. In particular, autonomous vehicles (AVs) are showing their proficiency on the roads, which may also catalyze ride-sharing ubiquity. For example, while an AV owner is at work, he may find it appealing to offer his AV as a service or rent it to Uber so that the vehicle serves others' transportation requests. Furthermore, this disruptive technology is backed up by companies like Google (Waymo), Tesla, and Uber. Therefore, ride-sharing will soon become a source of traffic congestion itself. In this article, we present an efficient congestion-aware ride-sharing algorithm which, instead of finding optimal travel plans based on traffic load generated by other means of transportation, it computes optimal travel plans for thousands of ride-sharing requests within a time interval. Note that in this problem, an optimal travel plan for a group of requests may affect an already computed travel plan for another concurrent group of requests, therefore plans cannot be isolated from each other.", acknowledgement = ack-nhfb, articleno = "5", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Whitman:2019:DSS, author = "Randall T. Whitman and Bryan G. Marsh and Michael B. Park and Erik G. Hoel", title = "Distributed Spatial and Spatio-Temporal Join on {Apache Spark}", journal = j-TSAS, volume = "5", number = "1", pages = "6:1--6:??", month = jun, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3325135", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:50 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3325135", abstract = "Effective processing of extremely large volumes of spatial data has led to many organizations employing distributed processing frameworks. Apache Spark is one such open source framework that is enjoying widespread adoption. Within this data space, it is important to note that most of the observational data (i.e., data collected by sensors, either moving or stationary) has a temporal component or timestamp. To perform advanced analytics and gain insights, the temporal component becomes equally important as the spatial and attribute components. In this article, we detail several variants of a spatial join operation that addresses both spatial, temporal, and attribute-based joins. Our spatial join technique differs from other approaches in that it combines spatial, temporal, and attribute predicates in the join operator. In addition, our spatio-temporal join algorithm and implementation differs from others in that it runs in commercial off-the-shelf (COTS) application. The users of this functionality are assumed to be GIS analysts with little if any knowledge of the implementation details of spatio-temporal joins or distributed processing. They are comfortable using simple tools that do not provide the ability to tweak the configuration of the algorithm or processing environment. The spatio-temporal join algorithm behind the tool must always succeed, regardless of input data parameters (e.g., it can be highly irregularly distributed, contain large numbers of coincident points, it can be extremely large, etc.). These factors combine to place additional requirements on the algorithm that are uncommonly found in the traditional research environment. Our spatio-temporal join algorithm was shipped as part of the GeoAnalytics Server [12], part of the ArcGIS Enterprise platform from version 10.5 onward.", acknowledgement = ack-nhfb, articleno = "6", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Gollapudi:2019:ISI, author = "Sreenivas Gollapudi", title = "Introduction to the Special Issue on Urban Mobility: Algorithms and Systems", journal = j-TSAS, volume = "5", number = "2", pages = "7:1--7:??", month = aug, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3346023", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:51 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3346023", acknowledgement = ack-nhfb, articleno = "7", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Cao:2019:UMC, author = "Hancheng Cao and Jagan Sankaranarayanan and Jie Feng and Yong Li and Hanan Samet", title = "Understanding Metropolitan Crowd Mobility via Mobile Cellular Accessing Data", journal = j-TSAS, volume = "5", number = "2", pages = "8:1--8:??", month = aug, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3323345", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:51 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3323345", abstract = "Understanding crowd mobility in a metropolitan area is extremely valuable for city planners and decision makers. However, crowd mobility is a relatively new area of research and has significant technical challenges: lack of large-scale fine-grained data, difficulties in large-scale trajectory processing, and issues with spatial resolution. In this article, we propose a novel approach for analyzing crowd mobility on a ``city block'' level. We first propose algorithms to detect homes, working places, and stay regions for individual user trajectories. Next, we propose a method for analyzing commute patterns and spatial correlation at a city block level. Using mobile cellular accessing trace data collected from users in Shanghai, we discover commute patterns, spatial correlation rules, as well as a hidden structure of the city based on crowd mobility analysis. Therefore, our proposed methods contribute to our understanding of human mobility in a large metropolitan area.", acknowledgement = ack-nhfb, articleno = "8", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Cabannes:2019:RRN, author = "Th{\'e}ophile Cabannes and Marco Sangiovanni and Alexander Keimer and Alexandre M. Bayen", title = "Regrets in Routing Networks: Measuring the Impact of Routing Apps in Traffic", journal = j-TSAS, volume = "5", number = "2", pages = "9:1--9:??", month = aug, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3325916", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:51 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3325916", abstract = "The impact of the recent increase in routing apps usage on road traffic remains uncertain to this day. The article introduces, for the first time, a criterion to evaluate a distance between an observed state of traffic and the user equilibrium of the traffic assignment: the average marginal regret. The average marginal regret provides a quantitative measure of the impact of routing apps on traffic using only link flows, link travel times, and travel demand. In non-atomic routing games (or static traffic assignment models), the average marginal regret is a measure of selfish drivers' behaviors. Unlike the price of anarchy, the average marginal regret in the routing game can be computed in polynomial time without any knowledge of user equilibria and socially optimal states of traffic. First, this article demonstrates on a small example that the average marginal regret is more appropriate to define proximity between an observed state of traffic and an user equilibrium state of traffic than comparing flows, travel times, or total cost. Then, experiments on two different models of app usage and three networks (including the whole L.A. network with more than 50,000 nodes) demonstrate that the average marginal regret decreases with an increase of app usage. Sensitivity analysis of the equilibrium state with respect to the app usage ratio proves that the average marginal regret monotonically decreases to 0 with an increase of app usage. Finally, using a toy example, the article concludes that app usage could become the new Braess paradox.", acknowledgement = ack-nhfb, articleno = "9", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Rayhan:2019:ESG, author = "Yeasir Rayhan and Tanzima Hashem and Roksana Jahan and Muhammad Aamir Cheema", title = "Efficient Scheduling of Generalized Group Trips in Road Networks", journal = j-TSAS, volume = "5", number = "2", pages = "10:1--10:??", month = aug, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3325915", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:51 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3325915", abstract = "In this article, we introduce generalized group trip scheduling (GGTS) queries that enable friends and families to perform activities at different points of interest (POIs), such as a shopping center, a restaurant and a pharmacy with the minimum total travel distance. Trip planning and scheduling for groups, an important class of location-based services (LBSs), have recently received attention from researchers. However, both group trip planning (GTP) and group trip scheduling (GTS) queries have restrictions: a GTP query assumes that all group members visit all required POIs together, whereas a GTS query requires that each POI is visited by a single group member. A GGTS query is more general and allows any number of group members to visit a POI together. We propose an efficient algorithm to evaluate the exact answers for GGTS queries in road networks. Since finding the answer for a GGTS query is an NP-hard problem, to reduce the processing overhead for a large group size or a large number of required POI types or a large POI dataset, we propose two heuristic solutions-trip-scheduling heuristic (TSH) and search region refinement heuristic (SRH)-for processing GGTS queries. Extensive experiments with real datasets show that our optimal algorithm is preferable for small parameter settings, and the heuristic solutions reduce the processing overhead significantly in return for sacrificing the accuracy slightly.", acknowledgement = ack-nhfb, articleno = "10", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Wang:2019:ADR, author = "Haiquan Wang and Yilin Li and Guoping Liu and Xiang Wen and Xiaohu Qie", title = "Accurate Detection of Road Network Anomaly by Understanding Crowd's Driving Strategies from Human Mobility", journal = j-TSAS, volume = "5", number = "2", pages = "11:1--11:??", month = aug, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3325913", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:51 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3325913", abstract = "There are thousands of road closures and changed traffic rules that impact vehicle routing every day. Detecting the road closures and traffic rule changes is essential for dynamic route planning and navigation serving. In this article, we propose a driving-behavior modeling-based method for accurately and effectively detecting the road anomalies. In the first step, we detect the areas of anomalies by using the deviation between drivers' actual and expected behaviors. To discover the cause of anomalies, we explore the drivers' short-term destination and find the crucial link pairs in anomalous areas through a novel optimized link entanglement search algorithm, namely, the Select Link Entanglements (SELES) algorithm. Finally, we analyze the crowd's driving patterns to explain the road network anomalies further. Experiments on a very large GPS dataset demonstrate that the proposed approach outperforms the existing methods in terms of both accuracy and effectiveness.", acknowledgement = ack-nhfb, articleno = "11", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Pietrobon:2019:ARC, author = "Davide Pietrobon and Andrew P. Lewis and Gavin S. Heverly-Coulson", title = "An Algorithm for Road Closure Detection from Vehicle Probe Data", journal = j-TSAS, volume = "5", number = "2", pages = "12:1--12:??", month = aug, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3325912", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:51 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3325912", abstract = "We developed an algorithm for automatically detecting road closures by monitoring vehicle probe data. The algorithm applies to a large class of roads and in the implementation presented was optimized for lower-volume roads. It is suitable for batch as well as real-time applications, the latter class being the most valuable to guarantee a continuously up-to-date traffic product. The algorithm compares the likelihood that every road segment meeting certain requirements is closed or open, and it triggers an alert whenever the likelihood of the observed probe activity is too small given a historical model. We implemented the algorithm and tested it on 12 metro areas in Western Europe. After optimizing parameters for performance on lower-volume roads, we obtained a precision of 92\% on those roads and of 80\% overall.", acknowledgement = ack-nhfb, articleno = "12", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Albert:2019:IMD, author = "Marc Albert and Claudio Ruch and Emilio Frazzoli", title = "Imbalance in Mobility-on-Demand Systems: A Stochastic Model and Distributed Control Approach", journal = j-TSAS, volume = "5", number = "2", pages = "13:1--13:??", month = aug, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3325914", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:51 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3325914", abstract = "The control of large-scale mobility-on-demand systems is an emerging topic that has been considered from a system theoretical, transportation scientific, and algorithmic point of view. Existing formulations model mobility-on-demand systems in a queuing theoretical, network flow-based, or continuous, kinematic framework. In this work, we model a mobility-on-demand system as a stochastic differential equation that represents a generalization of previous approaches. Based on the model, we define system imbalance as the difference of the stochastic processes of service request arrival and vehicle arrival. We formally derive the first moment of the system imbalance for an imbalance control strategy that consists of a feedforward control approach (reference trajectory) and an additional feedback component. A distributed feedback control policy is defined that averages the imbalance across the system and therefore aims at a uniform quality of service distribution. Finally, we verify our results in a high-fidelity and large-scale agent-based simulation of a hypothetical mobility-on-demand system.", acknowledgement = ack-nhfb, articleno = "13", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Doocy:2019:RPM, author = "Lauren Doocy and Steven D. Prager and Joseph T. {Kider, Jr.} and R. Paul Wiegand", title = "Robust Path Matching and Anomalous Route Detection Using Posterior Weighted Graphs", journal = j-TSAS, volume = "5", number = "2", pages = "14:1--14:??", month = aug, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3338905", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:51 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3338905", abstract = "Understanding movement behaviors is critical for urban mobility and transport problems, including robust path matching, behavior analysis, and anomaly detection. We investigate a graph-based, probabilistic method for matching behaviors of entities operating on networks embedded in some geographic context (e.g., road networks) under different types of uncertainty. Our method uses a decay function that allows network topology and attribute information associated with that topology (geographic or otherwise) to guide generalizations of the activity patterns and model learning process. This allows the system to recognize when two routes within a network are similar, even when those routes share little explicit path information. We demonstrate this method's robust ability to distinguish between fundamentally different behaviors, even when data are both incomplete and subject to noise. The results show good performance when matching behaviors on different sized and attributed synthetic networks, as well as on a real-world road network; it examines situations in which observed entity behavior is noisy, as well as situations in which observed behaviors differ from learned models as a result of systemic noise in the underlying network. Finally, our approach provides a robust method of detecting anomalous activity patterns on the network.", acknowledgement = ack-nhfb, articleno = "14", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Hemminki:2019:CRS, author = "Samuli Hemminki and Keisuke Kuribayashi and Shin'ichi Konomi and Petteri Nurmi and Sasu Tarkoma", title = "Crowd Replication: Sensing-Assisted Quantification of Human Behavior in Public Spaces", journal = j-TSAS, volume = "5", number = "3", pages = "15:1--15:??", month = sep, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3317666", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:51 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3317666", abstract = "A central challenge for public space design is to evaluate whether a given space promotes different types of activities. In this article, as our first contribution, we develop crowd replication as a novel sensor-assisted method for quantifying human behavior within public spaces. In crowd replication, a researcher is tasked with recording the behavior of people using a space while being instrumented with a mobile device that captures a sensor trace of the replicated movements and activities. Through mathematical modeling, behavioral indicators extracted from the replicated trajectories can be extrapolated to represent a larger target population. As our second contribution, we develop a novel highly accurate pedestrian sensing solution for reconstructing movement trajectories from sensor traces captured during the replication process. Our key insight is to tailor sensing to characteristics of the researcher performing replication, which allows reconstruction to operate robustly against variations in pace and other walking characteristics. We validate crowd replication through a case study carried out within a representative example of a metropolitan-scale public space. Our results show that crowd-replicated data closely mirrors human dynamics in public spaces and reduces overall data collection effort while producing high-quality indicators about behaviors and activities of people within the space. We also validate our pedestrian modeling approach through extensive benchmarks, demonstrating that our approach can reconstruct movement trajectories with high accuracy and robustness (median error below 1\%). Finally, we demonstrate that our contributions enable capturing detailed indicators of liveliness, extent of social interaction, and other factors indicative of public space quality.", acknowledgement = ack-nhfb, articleno = "15", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Suzuki:2019:PVP, author = "Jun Suzuki and Yoshihiko Suhara and Hiroyuki Toda and Kyosuke Nishida", title = "Personalized Visited-{POI} Assignment to Individual Raw {GPS} Trajectories", journal = j-TSAS, volume = "5", number = "3", pages = "16:1--16:??", month = sep, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3317667", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:51 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3317667", abstract = "Knowledge discovery from GPS trajectory data is an essential topic in several scientific areas, including data mining, human behavior analysis, and user modeling. This article proposes a task that assigns personalized visited points of interest (POIs). Its goal is to assign every fine-grain location (i.e., POIs) that a user actually visited, which we call visited-POI, to the corresponding span of his or her (personal) GPS trajectories. We also introduce a novel algorithm to solve this assignment task. First, we exhaustively extract stay-points as span candidates of visits using a variant of a conventional stay-point extraction method and then extract POIs that are located close to the extracted stay-points as visited-POI candidates. Then, we simultaneously predict which stay-points and POIs can be actual user visits by considering various aspects, which we formulate as integer linear programming. Experimental results conducted on a real user dataset show that our method achieves higher accuracy in the visited-POI assignment task than the various cascaded procedures of conventional methods.", acknowledgement = ack-nhfb, articleno = "16", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Jagadeesh:2019:FCC, author = "George R. Jagadeesh and Thambipillai Srikanthan", title = "Fast Computation of Clustered Many-to-many Shortest Paths and Its Application to Map Matching", journal = j-TSAS, volume = "5", number = "3", pages = "17:1--17:??", month = sep, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3329676", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:51 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3329676", abstract = "We examine the problem of computing shortest paths in a transportation network from a set of geographically clustered source nodes to a set of target nodes. Such many-to-many shortest path computations are an essential and computationally expensive part of many emerging applications that involve map matching of imprecise trajectories. Existing solutions to this problem mostly conform to the obvious approach of performing a single-source shortest path computation for each source node. We present an algorithm that exploits the clustered nature of the source nodes. Specifically, we rely on the observation that paths originating from a cluster of nodes typically exit the source region's boundary through a small number of nodes. Evaluations on a real road network show that the proposed algorithm provides a speed-up of several times over the conventional approach when the source nodes are densely clustered in a region. We also demonstrate that the presented technique is capable of substantially accelerating map matching of sparse and noisy trajectories.", acknowledgement = ack-nhfb, articleno = "17", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Brown:2019:RPS, author = "Philip E. Brown and Tamraparni Dasu and Yaron Kanza and Divesh Srivastava", title = "From Rocks to Pebbles: Smoothing Spatiotemporal Data Streams in an Overlay of Sensors", journal = j-TSAS, volume = "5", number = "3", pages = "18:1--18:??", month = sep, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3329677", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:51 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3329677", abstract = "Spatiotemporal streams are prone to data quality issues such as missing, duplicated and delayed data-when data generating sensors malfunction, data transmissions experience problems, or when data are stored or processed improperly. However, many important real-time applications rely on the continuous availability of stream values, e.g., to monitor traffic flow, resource usage, weather phenomena, and so on. Other non real-time applications that support continuous or offline historical analytics also require high quality data to avoid producing misleading output such as false positives, erroneous conclusions, and decisions. In this article, we study the problem of smoothing streams produced by an overlay of sensors. We present nonparametric (data-driven, distribution free) statistical methods to provide an uninterrupted stream of high-quality spatiotemporal data to real-time applications, even when the raw stream suffers data quality issues, such as noise or missing values. Our novel family of robust methods computes smoothed values (SVs) that could be used as proxies for data of questionable quality. The methods make use of a partition of the monitored area into cells to compute SVs based on historical data and the deviation from normalcy in neighboring spatial cells in a way that outperforms standard regression or interpolation. Our methods use incremental computation for efficiency, and they differ in how the deviations are normalized, e.g., with respect to zeroth-order, first-order, and second-order moments. We use three real data sets to run a suite of experiments and empirically demonstrate the superiority of the method that uses normalization with respect to variability.", acknowledgement = ack-nhfb, articleno = "18", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Zhao:2019:SAR, author = "Liang Zhao and Olga Gkountouna and Dieter Pfoser", title = "Spatial Auto-regressive Dependency Interpretable Learning Based on Spatial Topological Constraints", journal = j-TSAS, volume = "5", number = "3", pages = "19:1--19:??", month = sep, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3339823", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:51 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3339823", abstract = "Spatial regression models are widely used in numerous areas, including detecting and predicting traffic volume, air pollution, and housing prices. Unlike conventional regression models, which commonly assume independent and identical distributions among observations, existing spatial regression requires the prior knowledge of spatial dependency among the observations in different spatial locations. Such a spatial dependency is typically predefined by domain experts or heuristics. However, without sufficient consideration on the context of the specific prediction task, it is prohibitively difficult for one to pre-define the numerical values of the spatial dependency without bias. More importantly, in many situations, the existing techniques are insufficient to sense the complete connectivity and topological patterns among spatial locations (e.g., in underground water networks and human brain networks). Until now, these issues have been extremely difficult to address and little attention has been paid to the automatic optimization of spatial dependency in relation to a prediction task, due to three challenges: (1) necessity and complexity of modeling the spatial topological constraints; (2) incomplete prior spatial knowledge; and (3) difficulty in optimizing under spatial topological constraints that are usually discrete or nonconvex. To address these challenges, this article proposes a novel convex framework that automatically jointly learns the prediction mapping and spatial dependency based on spatial topological constraints. There are two different scenarios to be addressed. First, when the prior knowledge on existence of conditional independence among spatial locations is known (e.g., via spatial contiguity), we propose the first model named Spatial-Autoregressive Dependency Learning I (SADL-I) to further quantify such spatial dependency. However, when the knowledge on the conditional independence is unknown or incomplete, our second model named Spatial-Autoregressive Dependency Learning II (SADL-II) is proposed to automatically learn the conditional independence pattern as well as quantify the numerical values of the spatial dependency based on spatial topological constraints. Topological constraints are usually discrete and nonconvex, which makes them extremely difficult to be optimized together with continuous optimization problems of spatial regression. To address this, we propose a convex and continuous equivalence of the original discrete topological constraints with a theoretical guarantee. The proposed models are then transferred to convex problems that can be iteratively optimized by our new efficient algorithms until convergence to a global optimal solution. Extensive experimentation using several real-world datasets demonstrates the outstanding performance of the proposed models. The code of our SADL framework is available at: http://mason.gmu.edu/~lzhao9/materials/codes/SADL.", acknowledgement = ack-nhfb, articleno = "19", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Mahin:2019:AAR, author = "Mehnaz Tabassum Mahin and Tanzima Hashem", title = "Activity-aware Ridesharing Group Trip Planning Queries for Flexible {POIs}", journal = j-TSAS, volume = "5", number = "3", pages = "20:1--20:??", month = sep, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3341818", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:51 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3341818", abstract = "In recent years, ridesharing has become a popular model that enables users to share their rides with others. In this article, we introduce a novel ridesharing service, an Activity-aware Ridesharing Group Trip Planning (ARGTP) query, in road networks that exhibits three novel features: (i) ensures a complete trip for visiting more than two locations, (ii) allows visiting both fixed and flexible locations, and (iii) provides true ridesharing services instead of taxilike ridesourcing services by matching a group of riders' flexible trips with a driver's fixed trip. A trip visits a point-of-interest (POI) like a bank, restaurant, or supermarket for an activity in between source and destination locations. In a fixed trip, the POI is predetermined (e.g., a specific branch of a bank) and in a flexible trip, the POI is a flexible one (e.g., any branch of a bank). Considering the spatial proximity of the riders' trips with a driver's trip, an ARGTP query returns an optimal ridesharing group that minimizes the group cost. We develop the first solution to process ARGTP queries in real time and extend our solution for generalized ARGTP queries with multiple POIs. The efficiency of ARGTP query processing algorithms depends on the number of candidate riders and POIs to be explored. We introduce novel pruning techniques to refine the riders and POI search space. We perform extensive experiments using both real and synthetic datasets to validate the efficiency and effectiveness of our approach and show that it outperforms two baseline approaches with a large margin.", acknowledgement = ack-nhfb, articleno = "20", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Aly:2019:BBC, author = "Heba Aly and John Krumm and Gireeja Ranade and Eric Horvitz", title = "To Buy or Not to Buy: Computing Value of Spatiotemporal Information", journal = j-TSAS, volume = "5", number = "4", pages = "22:1--22:??", month = dec, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3320431", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:51 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3320431", abstract = "Location data from mobile devices is a sensitive yet valuable commodity for location-based services and advertising. We investigate the intrinsic value of location data in the context of strong privacy, where location information is only available from end users via purchase. We present an algorithm to compute the expected value of location data from a user, without access to the specific coordinates of the location data point. We use decision-theoretic techniques to provide a principled way for a potential buyer to make purchasing decisions about private user location data. We illustrate our approach in three scenarios: the delivery of targeted ads specific to a user's home location, the estimation of traffic speed, and location prediction. In all three cases, the methodology leads to quantifiably better purchasing decisions than competing methods.", acknowledgement = ack-nhfb, articleno = "22", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Kannangara:2019:SSG, author = "Sameera Kannangara and Egemen Tanin and Aaron Harwood and Shanika Karunasekera", title = "Stepping Stone Graph: A Graph for Finding Movement Corridors using Sparse Trajectories", journal = j-TSAS, volume = "5", number = "4", pages = "23:1--23:??", month = dec, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3324883", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:51 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3324883", abstract = "There are many real world applications that require identifying public movements such as identifying movement corridors in cities and most popular paths. If one is not given user trajectories but rather sporadic location data, such as location-based social network data, finding movement related information becomes difficult. Rather than processing all points in a dataset given a query, a clever approach is to construct a graph, based on user locations, and query this graph to answer questions such as shortest paths, most popular paths, and movement corridors. Shortest path graph is one of the popular graphs. However, the shortest path graph can be inefficient and ineffective for analysing movement data, as it calculates the graph edges considering the shortest paths over all the points in a dataset. Therefore, edge sets resulting from shortest path graphs are usually very restrictive and not suitable for movement analysis because of its global view of the dataset. We propose the stepping stone graph, which calculates the graph considering point pairs rather than all points; the stepping stone graph focuses on possible local movements, making it both efficient and effective for location-based social network related data. We demonstrate its capabilities by applying it in the Location-Based Social Network domain and comparing with the shortest path graph. We also compare its properties to a range of other graphs and demonstrate how stepping stone graph relates to Gabriel graph, relative neighbourhood graph, and Delaunay triangulation.", acknowledgement = ack-nhfb, articleno = "23", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Ayhan:2019:DDF, author = "Samet Ayhan and Pablo Costas and Hanan Samet", title = "A Data-driven Framework for Long-Range Aircraft Conflict Detection and Resolution", journal = j-TSAS, volume = "5", number = "4", pages = "24:1--24:??", month = dec, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3328832", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:51 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3328832", abstract = "At the present time, there is no mechanism for Air Navigation Service Providers (ANSPs) to probe new flight plans filed by the Airlines Operation Centers (AOCs) against the existing approved flight plans to see if they are likely to cause conflicts or bring sector traffic densities beyond control. In the current Air Traffic Control (ATC) operations, aircraft conflicts and sector traffic densities are resolved tactically, increasing workload and leading to potential safety risks and loss of capacity and efficiency. We propose a novel Data-driven Framework to address a long-range aircraft conflict detection and resolution (CDR) problem. Given a set of predicted trajectories, the framework declares a conflict when a protected zone of an aircraft on its trajectory is infringed upon by another aircraft. The framework resolves the conflict by prescribing an alternative solution that is optimized by perturbing at least one of the trajectories involved in the conflict. To achieve this, the framework learns from descriptive patterns of historical trajectories and pertinent weather observations and builds a Hidden Markov Model (HMM). Using a variant of the Viterbi algorithm, the framework avoids the airspace volume in which the conflict is detected and generates a new optimal trajectory that is conflict free. The key concept upon which the framework is built is the assumption that the airspace is nothing more than a horizontally and vertically concatenated set of spatio-temporal data cubes where each cube is considered as an atomic unit. We evaluate our framework using real trajectory datasets with pertinent weather observations from two continents and demonstrate its effectiveness for strategic CDR.", acknowledgement = ack-nhfb, articleno = "24", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Bast:2019:EGG, author = "Hannah Bast and Patrick Brosi and Sabine Storandt", title = "Efficient Generation of Geographically Accurate Transit Maps", journal = j-TSAS, volume = "5", number = "4", pages = "25:1--25:??", month = dec, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3337790", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:51 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3337790", abstract = "We present LOOM (Line-Ordering Optimized Maps), an automatic generator of geographically accurate transit maps. The input to LOOM is data about the lines of a transit network: for each line, its station sequence and geographical course. LOOM proceeds in three stages: (1) construct a line graph, where edges correspond to network segments with the same set of lines following the same course; (2) apply a set of local transformation rules that compute an optimal partial ordering of the lines and speed up the next stage; (3) construct an Integer Linear Program (ILP) that yields a line ordering for each edge and minimizes the total number of line crossings and line separations; and (4) based on the line graph and the computed line ordering, draw the map. As our maps respect the geography of the transit network, they can be used as overlays in typical map services. Previous research either did not take the network geography into account or was only concerned with schematic metro map layouting. We evaluate LOOM on six real-world transit networks, with line-ordering search-space sizes up to $ 2 \times 10^{267} $. Using our transformation rules and an improved ILP formulation, we compute optimal line orderings in a fraction of a second for all networks. This enables interactive use of our method in map editors.", acknowledgement = ack-nhfb, articleno = "25", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Lowe:2019:FRA, author = "Aaron Lowe and Pankaj K. Agarwal", title = "Flood-Risk Analysis on Terrains under the Multiflow-Direction Model", journal = j-TSAS, volume = "5", number = "4", pages = "26:1--26:??", month = dec, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3340707", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:51 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3340707", abstract = "An important problem in terrain analysis is modeling how water flows across a terrain and creates floods by filling up depressions. In this article, we study a number of flood-risk related problems: Given a terrain $ \Sigma $, represented as a triangulated xy-monotone surface with $n$ vertices, a rain distribution R, and a volume of rain \psi, determine which portions of $ \Sigma $ are flooded. We develop efficient algorithms for flood-risk analysis under the multiflow-directions (MFD) model, in which water at a point can flow along multiple downslope edges and which more accurately represent flooding events. We present three main results: First, we present an O (n \log n)-time algorithm to answer a terrain-flood query: if it rains a volume \psi according to a rain distribution R, determine what regions of $ \Sigma $ will be flooded. Second, we present a $ O (n \log n + n m)$-time algorithm for preprocessing $ \Sigma $ containing $m$ sinks into a data structure of size $ O (n m)$ for answering point-flood queries: Given a rain distribution $R$, a volume of rain $ \psi $ falling according to $R$, and point $q$ \in $ \Sigma $, determine whether $q$ will be flooded. A point-flood query can be answered in $ O (| R | k + k^2)$ time, where $k$ is the number of maximal depressions in $ \Sigma $ containing the query point $q$ and | R | is the number of vertices in $R$ with positive rainfall. Finally, we present algorithms for answering a flood-time query: given a rain distribution $R$ and a point $ q \in \Sigma $, determine the volume of rain that must fall before $q$ is flooded. Assuming that the product of two $ k \times k $ matrices can be computed in $ O (k \omega)$ time, we show that a flood-time query can be answered in $ O (n k + k \omega)$ time. We also give an $ \alpha $-approximation algorithm, for $ \alpha > 1$, which runs in $ O(n \log n \log \alpha \rho)$-time, where $ \rho $ is a variable on the terrain that depends on the ratio between depression volumes. We implemented our algorithms for computing terrain and point-flood queries as well as approximate flood-time queries. We tested the efficacy and efficiency of these algorithms on three real terrains of different types (urban, suburban, and mountainous.)", acknowledgement = ack-nhfb, articleno = "26", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Sabek:2019:RSM, author = "Ibrahim Sabek and Mashaal Musleh and Mohamed F. Mokbel", title = "{RegRocket}: Scalable Multinomial Autologistic Regression with Unordered Categorical Variables Using {Markov} Logic Networks", journal = j-TSAS, volume = "5", number = "4", pages = "27:1--27:??", month = dec, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3366459", ISSN = "2374-0353", bibdate = "Fri Dec 6 16:16:51 MST 2019", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/citation.cfm?id=3366459", abstract = "Autologistic regression is one of the most popular statistical tools to predict spatial phenomena in several applications, including epidemic diseases detection, species occurrence prediction, earth observation, and business management. In general, autologistic regression divides the space into a two-dimensional grid, where the prediction is performed at each cell in the grid. The prediction at any location is based on a set of predictors (i.e., features) at this location and predictions from neighboring locations. In this article, we address the problem of building efficient autologistic models with multinomial (i.e., categorical) prediction and predictor variables, where the categories represented by these variables are unordered. Unfortunately, existing methods to build autologistic models are designed for binary variables in addition to being computationally expensive (i.e., do not scale up for large-scale grid data such as fine-grained satellite images). Therefore, we introduce RegRocket: a scalable framework to build multinomial autologistic models for predicting large-scale spatial phenomena. RegRocket considers both the accuracy and efficiency aspects when learning the regression model parameters. To this end, RegRocket is built on top of Markov Logic Network (MLN), a scalable statistical learning framework, where its internals and data structures are optimized to process spatial data. RegRocket provides an equivalent representation of the multinomial prediction and predictor variables using MLN where the dependencies between these variables are transformed into first-order logic predicates. Then, RegRocket employs an efficient framework that learns the model parameters from the MLN representation in a distributed manner. Extensive experimental results based on two large real datasets show that RegRocket can build multinomial autologistic models, in minutes, for 1 million grid cells with 0.85 average F1-score.", acknowledgement = ack-nhfb, articleno = "27", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "http://dl.acm.org/pub.cfm?id=J1514", } @Article{Li:2020:TGF, author = "Wei Li and Haiquan Chen and Wei-Shinn Ku and Xiao Qin", title = "{Turbo-GTS}: a Fast Framework of Optimizing Task Throughput for Large-Scale Mobile Crowdsourcing", journal = j-TSAS, volume = "6", number = "1", pages = "1:1--1:29", month = feb, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3363450", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 7 07:13:55 MDT 2020", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3363450", abstract = "In mobile crowdsourcing, workers are financially motivated to perform as many self-selected tasks as possible to maximize their revenue. Unfortunately, the existing task scheduling approaches in mobile crowdsourcing fail to consider task execution \ldots{}", acknowledgement = ack-nhfb, articleno = "1", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Zhang:2020:DPS, author = "Rui Zhang and Kevin G. Stanley and Daniel Fuller and Scott Bell", title = "Differentiating Population Spatial Behavior Using Representative Features of Geospatial Mobility {(ReFGeM)}", journal = j-TSAS, volume = "6", number = "1", pages = "2:1--2:25", month = feb, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3362063", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 7 07:13:55 MDT 2020", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3362063", abstract = "Understanding how humans use and consume space by comparing stratified groups, either through observation or controlled study, is key to designing better spaces, cities, and policies. GPS data traces provide detailed movement patterns of individuals but \ldots{}", acknowledgement = ack-nhfb, articleno = "2", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Naghizade:2020:PCA, author = "Elham Naghizade and Lars Kulik and Egemen Tanin and James Bailey", title = "Privacy- and Context-aware Release of Trajectory Data", journal = j-TSAS, volume = "6", number = "1", pages = "3:1--3:25", month = feb, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3363449", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 7 07:13:55 MDT 2020", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3363449", abstract = "The availability of large-scale spatio-temporal datasets along with the advancements in analytical models and tools have created a unique opportunity to create valuable insights into managing key areas of society from transportation and urban planning \ldots{}", acknowledgement = ack-nhfb, articleno = "3", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Souza:2020:STD, author = "Roberto C. S. N. P. Souza and Derick M. Oliveira and Denise E. F. de Brito and Renato M. Assun{\c{c}}{\~a}o and Wagner {Meira Jr.}", title = "Space-Time Drift Point Detection in Mobility Patterns", journal = j-TSAS, volume = "6", number = "1", pages = "4:1--4:24", month = feb, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3360721", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 7 07:13:55 MDT 2020", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3360721", abstract = "Location-aware information is now commonplace, as the ubiquity and pervasiveness of technology enabled its generation and storage at large scale. These data constitute a rich representation of entities' whereabouts and behavior as they move on the map. \ldots{}", acknowledgement = ack-nhfb, articleno = "4", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Mishra:2020:TSP, author = "Suman Mishra and Lina Kattan and S. C. Wirasinghe", title = "Transit Signal Priority Along a Signalized Arterial: a Passenger-based Approach", journal = j-TSAS, volume = "6", number = "1", pages = "5:1--5:19", month = feb, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3355611", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 7 07:13:55 MDT 2020", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3355611", abstract = "This article develops a passenger-based priority for transit buses by balancing the trade-offs between the benefits at major streets and delays on side streets. A rule-based Transit Signal Priority (TSP) is set to assign priority to scheduled-based \ldots{}", acknowledgement = ack-nhfb, articleno = "5", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Chambers:2020:MMU, author = "Erin Chambers and Brittany Terese Fasy and Yusu Wang and Carola Wenk", title = "Map-Matching Using Shortest Paths", journal = j-TSAS, volume = "6", number = "1", pages = "6:1--6:17", month = feb, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3368617", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 7 07:13:55 MDT 2020", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3368617", abstract = "We consider several variants of the map-matching problem, which seeks to find a path Q in graph G that has the smallest distance to a given trajectory P (which is likely not to be exactly on the graph). In a typical application setting, P models a noisy \ldots{}", acknowledgement = ack-nhfb, articleno = "6", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Costa:2020:RTS, author = "Camila F. Costa and Mario A. Nascimento", title = "In-Route Task Selection in Spatial Crowdsourcing", journal = j-TSAS, volume = "6", number = "2", pages = "7:1--7:45", month = feb, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3368268", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:22 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3368268", abstract = "Consider a city's road network and a worker who is traveling on a given path from a starting point s to a destination d (e.g., from school or work to home) in said network. Consider further that there is a set of tasks in the network available to be \ldots{}", acknowledgement = ack-nhfb, articleno = "7", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Tampakis:2020:DSJ, author = "Panagiotis Tampakis and Christos Doulkeridis and Nikos Pelekis and Yannis Theodoridis", title = "Distributed Subtrajectory Join on Massive Datasets", journal = j-TSAS, volume = "6", number = "2", pages = "8:1--8:29", month = feb, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3373642", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:22 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3373642", abstract = "Joining trajectory datasets is a significant operation in mobility data analytics and the cornerstone of various methods that aim to extract knowledge out of them. In the era of Big Data, the production of mobility data has become massive and, \ldots{}", acknowledgement = ack-nhfb, articleno = "8", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Das:2020:EDD, author = "Nabanita Das and Souvik Basu and Sipra Das Bit", title = "Efficient {DropBox} Deployment toward Improving Post-Disaster Information Exchange in a Smart City", journal = j-TSAS, volume = "6", number = "2", pages = "9:1--9:18", month = feb, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3373645", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:22 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3373645", abstract = "In the face of a disaster, the already installed gadgets in a smart city can be leveraged to gather post-disaster situational information. However, owing to the typical disruption of cellular and Internet connectivity during disasters, the possibility \ldots{}", acknowledgement = ack-nhfb, articleno = "9", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Huang:2020:IIT, author = "Xiaohui Huang and Pan He and Anand Rangarajan and Sanjay Ranka", title = "Intelligent Intersection: Two-stream Convolutional Networks for Real-time Near-accident Detection in Traffic Video", journal = j-TSAS, volume = "6", number = "2", pages = "10:1--10:28", month = feb, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3373647", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:22 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3373647", abstract = "Camera-based systems are increasingly used for collecting information on intersections and arterials. Unlike loop controllers that can generally be only used for detection and movement of vehicles, cameras can provide rich information about the traffic \ldots{}", acknowledgement = ack-nhfb, articleno = "10", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Yang:2020:SFB, author = "Dongfang Yang and {\"U}mit {\"O}zg{\"u}ner and Keith Redmill", title = "A Social Force Based Pedestrian Motion Model Considering Multi-Pedestrian Interaction with a Vehicle", journal = j-TSAS, volume = "6", number = "2", pages = "11:1--11:27", month = feb, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3373646", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:22 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3373646", abstract = "Pedestrian motion modeling in mixed traffic scenarios is crucial to the development of autonomous systems in transportation related applications. This work investigated how pedestrian motion is affected by surrounding pedestrians and vehicles, i.e., \ldots{}", acknowledgement = ack-nhfb, articleno = "11", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Qian:2020:UOD, author = "Xinwu Qian and Dheeraj Kumar and Wenbo Zhang and Satish V. Ukkusuri", title = "Understanding the Operational Dynamics of Mobility Service Providers: a Case of {Uber}", journal = j-TSAS, volume = "6", number = "2", pages = "12:1--12:20", month = feb, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3378888", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:22 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3378888", abstract = "The rise of mobility service providers (MSPs) is reforming the traditional taxi service (TTS) market. MSPs differ from TTS with the core idea of using technology to optimally match riders with drivers, features like ride-sharing and surge pricing, and \ldots{}", acknowledgement = ack-nhfb, articleno = "12", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Zhou:2020:SST, author = "Fan Zhou and Hantao Wu and Goce Trajcevski and Ashfaq Khokhar and Kunpeng Zhang", title = "Semi-supervised Trajectory Understanding with {POI} Attention for End-to-End Trip Recommendation", journal = j-TSAS, volume = "6", number = "2", pages = "13:1--13:25", month = feb, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3378890", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:22 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3378890", abstract = "Trip planning/recommendation is an important task for a plethora of applications in urban settings (e.g., tourism, transportation, social outings), relying on services provided by Location-Based Social Networks (LBSN). To provide greater context-. \ldots{}", acknowledgement = ack-nhfb, articleno = "13", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Wang:2020:TTG, author = "Lijing Wang and Jiangzhuo Chen and Madhav Marathe", title = "{TDEFSI}: Theory-guided Deep Learning-based Epidemic Forecasting with Synthetic Information", journal = j-TSAS, volume = "6", number = "3", pages = "15:1--15:39", month = may, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3380971", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:23 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3380971", abstract = "Influenza-like illness (ILI) places a heavy social and economic burden on our society. Traditionally, ILI surveillance data are updated weekly and provided at a spatially coarse resolution. Producing timely and reliable high-resolution spatiotemporal \ldots{}", acknowledgement = ack-nhfb, articleno = "15", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Das:2020:SSA, author = "Monidipa Das and Mahardhika Pratama and Soumya K. Ghosh", title = "{SARDINE}: a Self-Adaptive Recurrent Deep Incremental Network Model for Spatio-Temporal Prediction of Remote Sensing Data", journal = j-TSAS, volume = "6", number = "3", pages = "16:1--16:26", month = may, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3380972", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:23 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3380972", abstract = "The timely and accurate prediction of remote sensing data is of utmost importance especially in a situation where the predicted data is utilized to provide insights into emerging issues, like environmental nowcasting. Significant research progress can \ldots{}", acknowledgement = ack-nhfb, articleno = "16", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Ferreira:2020:DLA, author = "Danielle L. Ferreira and Bruno A. A. Nunes and Carlos Alberto V. Campos and Katia Obraczka", title = "A Deep Learning Approach for Identifying User Communities Based on Geographical Preferences and Its Applications to Urban and Environmental Planning", journal = j-TSAS, volume = "6", number = "3", pages = "17:1--17:24", month = may, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3380970", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:23 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3380970", abstract = "Understanding human mobility plays a vital role in urban and environmental planning as cities continue to grow. Ubiquitous geo-location, localization technology, and availability of big-data-ready computing infrastructure have enabled the development of \ldots{}", acknowledgement = ack-nhfb, articleno = "17", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Jauhri:2020:GRR, author = "Abhinav Jauhri and Brad Stocks and Jian Hui Li and Koichi Yamada and John Paul Shen", title = "Generating Realistic Ride-Hailing Datasets Using {GANs}", journal = j-TSAS, volume = "6", number = "3", pages = "18:1--18:14", month = may, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3380968", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:23 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3380968", abstract = "This article focuses on the synthetic generation of human mobility data in urban areas. We present a novel application of generative adversarial networks (GANs) for modeling and generating human mobility data. We leverage actual ride requests from ride-. \ldots{}", acknowledgement = ack-nhfb, articleno = "18", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Steininger:2020:MEN, author = "Michael Steininger and Konstantin Kobs and Albin Zehe and Florian Lautenschlager and Martin Becker and Andreas Hotho", title = "{MapLUR}: Exploring a New Paradigm for Estimating Air Pollution Using Deep Learning on Map Images", journal = j-TSAS, volume = "6", number = "3", pages = "19:1--19:24", month = may, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3380973", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:23 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3380973", abstract = "Land-use regression (LUR) models are important for the assessment of air pollution concentrations in areas without measurement stations. While many such models exist, they often use manually constructed features based on restricted, locally available \ldots{}", acknowledgement = ack-nhfb, articleno = "19", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Shao:2020:ILA, author = "Wei Shao and Siyu Tan and Sichen Zhao and Kyle Kai Qin and Xinhong Hei and Jeffrey Chan and Flora D. Salim", title = "Incorporating {LSTM} Auto-Encoders in Optimizations to Solve Parking Officer Patrolling Problem", journal = j-TSAS, volume = "6", number = "3", pages = "20:1--20:21", month = may, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3380966", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:23 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3380966", abstract = "The smart parking system is one of the most important problems in smart cities. Recently, an increasing number of sensors installed in parking spaces have provided big spatio-temporal data that be used to analyze parking situations in the city and help \ldots{}", acknowledgement = ack-nhfb, articleno = "20", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Touya:2020:DLE, author = "Guillaume Touya and Imran Lokhat", title = "Deep Learning for Enrichment of Vector Spatial Databases: Application to Highway Interchange", journal = j-TSAS, volume = "6", number = "3", pages = "21:1--21:21", month = may, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3382080", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:23 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3382080", abstract = "Spatial analysis and pattern recognition with vector spatial data is particularly useful to enrich raw data. In road networks, for instance, there are many patterns and structures that are implicit with only road line features, among which highway \ldots{}", acknowledgement = ack-nhfb, articleno = "21", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Youssef:2020:ISI, author = "Moustafa Youssef and John Krum and Muhammad Aamir Cheema", title = "Introduction to the Special Issue on Deep Learning for Spatial Algorithms and Systems", journal = j-TSAS, volume = "6", number = "3", pages = "14:1--14:2", month = may, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3386878", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:23 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3386878", acknowledgement = ack-nhfb, articleno = "14", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Wang:2020:SGS, author = "Senzhang Wang and Jiannong Cao and Hao Chen and Hao Peng and Zhiqiu Huang", title = "{SeqST-GAN}: {Seq2Seq} Generative Adversarial Nets for Multi-step Urban Crowd Flow Prediction", journal = j-TSAS, volume = "6", number = "4", pages = "22:1--22:24", month = aug, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3378889", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:23 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3378889", abstract = "Citywide crowd flow data are ubiquitous nowadays, and forecasting the flow of crowds is of great importance to many real applications such as traffic management and mobility-on-demand (MOD) services. The challenges of accurately predicting urban crowd \ldots{}", acknowledgement = ack-nhfb, articleno = "22", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Fellegara:2020:TTF, author = "Riccardo Fellegara and Leila {De Floriani} and Paola Magillo and Kenneth Weiss", title = "Tetrahedral Trees: a Family of Hierarchical Spatial Indexes for Tetrahedral Meshes", journal = j-TSAS, volume = "6", number = "4", pages = "23:1--23:34", month = aug, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3385851", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:23 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3385851", abstract = "We address the problem of performing efficient spatial and topological queries on large tetrahedral meshes with arbitrary topology and complex boundaries. Such meshes arise in several application domains, such as 3D Geographic Information Systems (GISs), \ldots{}", acknowledgement = ack-nhfb, articleno = "23", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Smolyak:2020:CIG, author = "Daniel Smolyak and Kathryn Gray and Sarkhan Badirli and George Mohler", title = "Coupled {IGMM-GANs} with Applications to Anomaly Detection in Human Mobility Data", journal = j-TSAS, volume = "6", number = "4", pages = "24:1--24:14", month = aug, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3385809", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:23 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3385809", abstract = "Detecting anomalous activity in human mobility data has a number of applications, including road hazard sensing, telematics-based insurance, and fraud detection in taxi services and ride sharing. In this article, we address two challenges that arise in \ldots{}", acknowledgement = ack-nhfb, articleno = "24", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Li:2020:CPM, author = "Qingzhe Li and Liang Zhao and Yi-Ching Lee and Jessica Lin", title = "Contrast Pattern Mining in Paired Multivariate Time Series of a Controlled Driving Behavior Experiment", journal = j-TSAS, volume = "6", number = "4", pages = "25:1--25:28", month = aug, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3397272", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:23 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3397272", abstract = "The controlled experiment is an important scientific method for researchers seeking to determine the influence of the intervention, by interpreting the contrast patterns between the temporal observations from control and experimental groups (i.e., \ldots{})", acknowledgement = ack-nhfb, articleno = "25", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Tokuda:2020:NAP, author = "Eric K. Tokuda and Yitzchak Lockerman and Gabriel B. A. Ferreira and Ethan Sorrelgreen and David Boyle and Roberto M. {Cesar, Jr.} and Claudio T. Silva", title = "A New Approach for Pedestrian Density Estimation Using Moving Sensors and Computer Vision", journal = j-TSAS, volume = "6", number = "4", pages = "26:1--26:20", month = aug, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3397575", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:23 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3397575", abstract = "An understanding of person dynamics is indispensable for numerous urban applications, including the design of transportation networks and planning for business development. Pedestrian counting often requires utilizing manual or technical means to count \ldots{}", acknowledgement = ack-nhfb, articleno = "26", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Emiris:2020:PEM, author = "Ioannis Z. Emiris and Ioannis Psarros", title = "Products of {Euclidean} Metrics, Applied to Proximity Problems among Curves: Unified Treatment of Discrete {Fr{\'e}chet} and Dynamic Time Warping Distances", journal = j-TSAS, volume = "6", number = "4", pages = "27:1--27:20", month = aug, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3397518", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:23 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3397518", abstract = "Approximate Nearest Neighbor (ANN) search is a fundamental computational problem that has benefited from significant progress in the past couple of decades. However, most work has been devoted to pointsets, whereas complex shapes have not been \ldots{}", acknowledgement = ack-nhfb, articleno = "27", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Babu:2020:TTB, author = "Sarath Babu and B. S. Manoj", title = "Toward a Type-based Analysis of Road Networks", journal = j-TSAS, volume = "6", number = "4", pages = "28:1--28:45", month = aug, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3397579", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:23 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3397579", abstract = "Road networks are major influential factors in the development of any nation. Due to different factors involved in their evolution, road networks exhibit complex structures, which result in problems such as inefficient traffic patterns, congestion, and \ldots{}", acknowledgement = ack-nhfb, articleno = "28", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Paoluzzi:2021:TCA, author = "Alberto Paoluzzi and Vadim Shapiro and Antonio Dicarlo and Francesco Furiani and Giulio Martella and Giorgio Scorzelli", title = "Topological Computing of Arrangements with (Co)Chains", journal = j-TSAS, volume = "7", number = "1", pages = "1:1--1:29", month = jan, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3401988", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:24 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3401988", abstract = "In many areas of applied geometric/numeric computational mathematics, including geo-mapping, computer vision, computer graphics, finite element analysis, medical imaging, geometric design, and solid modeling, one has to compute incidences, adjacencies, \ldots{}", acknowledgement = ack-nhfb, articleno = "1", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{DeBock:2021:SDD, author = "Jelle {De Bock} and Steven Verstockt", title = "{SmarterROUTES} --- a Data-driven Context-aware Solution for Personalized Dynamic Routing and Navigation", journal = j-TSAS, volume = "7", number = "1", pages = "2:1--2:25", month = jan, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3402125", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:24 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3402125", abstract = "SmarterROUTES contributes to personalised routing and navigation by data-driven route ranking and an environmentally aware road scene complexity-estimation mechanism. Traditional routing algorithms provide the fastest, shortest, or most ecological route \ldots{}", acknowledgement = ack-nhfb, articleno = "2", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Vu:2021:UDL, author = "Tin Vu and Alberto Belussi and Sara Migliorini and Ahmed Eldway", title = "Using Deep Learning for Big Spatial Data Partitioning", journal = j-TSAS, volume = "7", number = "1", pages = "3:1--3:37", month = jan, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3402126", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:24 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3402126", abstract = "This article explores the use of deep learning to choose an appropriate spatial partitioning technique for big data. The exponential increase in the volumes of spatial datasets resulted in the development of big spatial data frameworks. These systems \ldots{}", acknowledgement = ack-nhfb, articleno = "3", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Peng:2021:FOS, author = "Dongliang Peng and Alexander Wolff and Jan-Henrik Haunert", title = "Finding Optimal Sequences for Area Aggregation --- {$ A^\star $} vs. Integer Linear Programming", journal = j-TSAS, volume = "7", number = "1", pages = "4:1--4:40", month = jan, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3409290", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:24 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3409290", abstract = "To provide users with maps of different scales and to allow them to zoom in and out without losing context, automatic methods for map generalization are needed. We approach this problem for land-cover maps. Given two land-cover maps at two different \ldots{}", acknowledgement = ack-nhfb, articleno = "4", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Brito:2021:SPP, author = "Denise E. F. Brito and Renato M. Assun{\c{c}}{\~a}o and Roberto C. S. N. P. Souza and Wagner {Meira, Jr.}", title = "{SCPP}: a Point Process-based Clustering of Spatial Visiting Patterns", journal = j-TSAS, volume = "7", number = "1", pages = "5:1--5:30", month = jan, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3423405", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:24 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3423405", abstract = "A collection of individuals is represented by point patterns. Each individual is a finite set of geographical locations representing their visiting pattern to places in a region. We present SCPP, an algorithm for clustering these individuals considering \ldots{}", acknowledgement = ack-nhfb, articleno = "5", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Zhang:2021:ADI, author = "Xiaowei Zhang and Aly Shehata and Bedrich Benes and Daniel Aliaga", title = "Automatic Deep Inference of Procedural Cities from Global-scale Spatial Data", journal = j-TSAS, volume = "7", number = "2", pages = "6:1--6:28", month = feb, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3423422", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:24 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3423422", abstract = "Recent advances in big spatial data acquisition and deep learning allow novel algorithms that were not possible several years ago. We introduce a novel inverse procedural modeling algorithm for urban areas that addresses the problem of spatial data \ldots{}", acknowledgement = ack-nhfb, articleno = "6", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Damiani:2021:LRD, author = "Maria Luisa Damiani and Fatima Hachem and Christian Quadri and Matteo Rossini and Sabrina Gaito", title = "On Location Relevance and Diversity in Human Mobility Data", journal = j-TSAS, volume = "7", number = "2", pages = "7:1--7:38", month = feb, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3423404", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:24 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3423404", abstract = "The theme of human mobility is transversal to multiple fields of study and applications, from ad hoc networks to smart cities, from transportation planning to recommendation systems on social networks. Despite the considerable efforts made by a few \ldots{}", acknowledgement = ack-nhfb, articleno = "7", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Zhu:2021:PCK, author = "Huaijie Zhu and Xiaochun Yang and Bin Wang and Wang-Chien Lee and Jian Yin and Jianliang Xu", title = "Processing Continuous k Nearest Neighbor Queries in Obstructed Space with {Voronoi} Diagrams", journal = j-TSAS, volume = "7", number = "2", pages = "8:1--8:27", month = feb, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3425955", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:24 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3425955", abstract = "With the emergence and growing popularity of and location-based service (LBS) technologies, the continuous k nearest neighbor (CO k NN) query in obstructed space is becoming a very important service. In this article, we study the CO k NN in obstructed space,. \ldots{}", acknowledgement = ack-nhfb, articleno = "8", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{He:2021:MIA, author = "Eric He and Fan Bai and Curtis Hay and Jinzhu Chen and Vijayakumar Bhagavatula", title = "A Map Inference Approach Using Signal Processing from Crowd-sourced {GPS} Data", journal = j-TSAS, volume = "7", number = "2", pages = "9:1--9:23", month = feb, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3431785", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:24 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3431785", abstract = "The amount of GPS data that can be collected is increasing tremendously, thanks to the increased popularity of Global Position System (GPS) devices (e.g., smartphones). This article aims to develop novel methods of converting crowd-sourced GPS traces \ldots{}", acknowledgement = ack-nhfb, articleno = "9", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Laoudias:2021:IQP, author = "Christos Laoudias and Artyom Nikitin and Panagiotis Karras and Moustafa Youssef and Demetrios Zeinalipour-Yazti", title = "Indoor Quality-of-position Visual Assessment Using Crowdsourced Fingerprint Maps", journal = j-TSAS, volume = "7", number = "2", pages = "10:1--10:32", month = feb, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3433026", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Sat Mar 27 09:18:24 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3433026", abstract = "Internet-based Indoor Navigation (IIN) architectures organize signals collected by crowdsourcers in Fingerprint Maps (FMs) to improve localization given that satellite-based technologies do not operate accurately in indoor spaces where people spend 80\%-. \ldots{}", acknowledgement = ack-nhfb, articleno = "10", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Bose:2021:PST, author = "Sunanda Bose and Sumit Kumar Paul and Nandini Mukherjee", title = "Predicting Spatio-Temporal Phenomena of Mobile Resources in Sensor Cloud Infrastructure", journal = j-TSAS, volume = "7", number = "3", pages = "11:1--11:38", month = sep, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3446936", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Wed Mar 2 06:18:28 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3446936", abstract = "Integration of sensor and cloud technologies enable distributed sensing and data collection. We consider a scenario when sensing requests are originated from sensor aware applications that are hosted inside sensor-cloud infrastructures. These requests \ldots{}", acknowledgement = ack-nhfb, articleno = "11", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Sun:2021:RTE, author = "Yuhan Sun and Mohamed Sarwat", title = "\pkg{Riso-Tree}: an Efficient and Scalable Index for Spatial Entities in Graph Database Management Systems", journal = j-TSAS, volume = "7", number = "3", pages = "12:1--12:39", month = sep, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3450945", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Wed Mar 2 06:18:28 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3450945", abstract = "With the ubiquity of spatial data, vertexes or edges in graphs can possess spatial location attributes side by side with other non-spatial attributes. For instance, as of June 2018, the Wikidata knowledge graph contains 48,547,142 data items (i.e., \ldots{})", acknowledgement = ack-nhfb, articleno = "12", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Middya:2021:SIT, author = "Asif Iqbal Middya and Sarbani Roy", title = "Spatial Interpolation Techniques on Participatory Sensing Data", journal = j-TSAS, volume = "7", number = "3", pages = "13:1--13:32", month = sep, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3457609", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Wed Mar 2 06:18:28 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3457609", abstract = "Spatial distributions of data of natural phenomena can be estimated by using different spatial interpolation techniques. These techniques can be used for the purpose of developing urban noise pollution monitoring applications, so they can truly describe \ldots{}", acknowledgement = ack-nhfb, articleno = "13", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Daghistani:2021:SAL, author = "Anas Daghistani and Walid G. Aref and Arif Ghafoor and Ahmed R. Mahmood", title = "\pkg{SWARM}: Adaptive Load Balancing in Distributed Streaming Systems for Big Spatial Data", journal = j-TSAS, volume = "7", number = "3", pages = "14:1--14:43", month = sep, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3460013", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Wed Mar 2 06:18:28 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3460013", abstract = "The proliferation of GPS-enabled devices has led to the development of numerous location-based services. These services need to process massive amounts of streamed spatial data in real-time. The current scale of spatial data cannot be handled using \ldots{}", acknowledgement = ack-nhfb, articleno = "14", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Gudmundsson:2021:PIS, author = "Joachim Gudmundsson and Michael Horton and John Pfeifer and Martin P. Seybold", title = "A Practical Index Structure Supporting {Fr{\'e}chet} Proximity Queries among Trajectories", journal = j-TSAS, volume = "7", number = "3", pages = "15:1--15:33", month = sep, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3460121", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Wed Mar 2 06:18:28 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3460121", abstract = "We present a scalable approach for range and k nearest neighbor queries under computationally expensive metrics, like the continuous Fr{\'e}chet distance on trajectory data. Based on clustering for metric indexes, we obtain a dynamic tree structure whose size \ldots{}", acknowledgement = ack-nhfb, articleno = "15", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Aref:2021:ISS, author = "Walid G. Aref", title = "Introduction to the Special Section on the Best Papers from the {2019 ACM SIGSPATIAL Conference}", journal = j-TSAS, volume = "7", number = "4", pages = "16:1--16:2", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3485049", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Wed Mar 2 06:18:29 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3485049", acknowledgement = ack-nhfb, articleno = "16", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Custers:2021:MPC, author = "Bram Custers and Mees Van De Kerkhof and Wouter Meulemans and Bettina Speckmann and Frank Staals", title = "Maximum Physically Consistent Trajectories", journal = j-TSAS, volume = "7", number = "4", pages = "17:1--17:33", month = jun, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3452378", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Wed Mar 2 06:18:29 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3452378", abstract = "Trajectories are usually collected with physical sensors, which are prone to errors and cause outliers in the data. We aim to identify such outliers via the physical properties of the tracked entity, that is, we consider its physical possibility to visit \ldots{}", acknowledgement = ack-nhfb, articleno = "17", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Werner:2021:GAE, author = "Martin Werner", title = "\pkg{GloBiMapsAI}: an {AI}-Enhanced Probabilistic Data Structure for Global Raster Datasets", journal = j-TSAS, volume = "7", number = "4", pages = "18:1--18:24", month = jun, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3453184", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Wed Mar 2 06:18:29 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3453184", abstract = "In the last decade, more and more spatial data has been acquired on a global scale due to satellite missions, social media, and coordinated governmental activities. This observational data suffers from huge storage footprints and makes global analysis \ldots{}", acknowledgement = ack-nhfb, articleno = "18", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Teixeira:2021:ISR, author = "Douglas {Do Couto Teixeira} and Aline {Carneiro Viana} and Jussara M. Almeida and M{\'a}rio S. Alvim", title = "The Impact of Stationarity, Regularity, and Context on the Predictability of Individual Human Mobility", journal = j-TSAS, volume = "7", number = "4", pages = "19:1--19:24", month = jun, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3459625", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Wed Mar 2 06:18:29 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3459625", abstract = "Predicting mobility-related behavior is an important yet challenging task. On the one hand, factors such as one's routine or preferences for a few favorite locations may help in predicting their mobility. On the other hand, several contextual factors, \ldots{}", acknowledgement = ack-nhfb, articleno = "19", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Almaslukh:2021:TGS, author = "Abdulaziz Almaslukh and Yunfan Kang and Amr Magdy", title = "Temporal Geo-Social Personalized Keyword Search Over Streaming Data", journal = j-TSAS, volume = "7", number = "4", pages = "20:1--20:28", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3473006", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Wed Mar 2 06:18:29 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3473006", abstract = "The unprecedented rise of social media platforms, combined with location-aware technologies, has led to continuously producing a significant amount of geo-social data that flows as a user-generated data stream. This data has been exploited in several \ldots{}", acknowledgement = ack-nhfb, articleno = "20", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Petroff:2021:SEA, author = "Matthew A. Petroff", title = "A Square Equal-Area Map Projection with Low Angular Distortion, Minimal Cusps, and Closed-Form Solutions", journal = j-TSAS, volume = "7", number = "4", pages = "21:1--21:16", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3460521", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Wed Mar 2 06:18:29 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3460521", abstract = "A novel square equal-area map projection is proposed. The projection combines closed-form forward and inverse solutions with relatively low angular distortion and minimal cusps, a combination of properties not manifested by any previously published square \ldots{}", acknowledgement = ack-nhfb, articleno = "21", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Cicerone:2021:CPS, author = "Serafino Cicerone and Mattia D'emidio and Daniele Frigioni and Filippo Tirabassi Pascucci", title = "Combining Polygon Schematization and Decomposition Approaches for Solving the Cavity Decomposition Problem", journal = j-TSAS, volume = "7", number = "4", pages = "22:1--22:37", month = jun, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3462760", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Wed Mar 2 06:18:29 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3462760", abstract = "The cavity decomposition problem is a computational geometry problem, arising in the context of modern electronic CAD systems, that concerns detecting the generation and propagation of electromagnetic noise into multi-layer printed circuit boards. \ldots{}", acknowledgement = ack-nhfb, articleno = "22", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Mariescu-Istodor:2021:VDC, author = "Radu Mariescu-Istodor and Alexandru Cristian and Mihai Negrea and Peiwei Cao", title = "\pkg{VRPDiv}: a Divide and Conquer Framework for Large Vehicle Routing Problems", journal = j-TSAS, volume = "7", number = "4", pages = "23:1--23:41", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3474832", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Wed Mar 2 06:18:29 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3474832", abstract = "The Vehicle Routing Problem (VRP) is an NP hard problem where we need to optimize itineraries for agents to visit multiple targets. When considering real-world travel (road-network topology, speed limits and traffic), modern VRP solvers can only process \ldots{}", acknowledgement = ack-nhfb, articleno = "23", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Apon:2022:SSG, author = "Sajid Hasan Apon and Mohammed Eunus Ali and Bishwamittra Ghosh and Timos Sellis", title = "Social-Spatial Group Queries with Keywords", journal = j-TSAS, volume = "8", number = "1", pages = "1:1--1:??", month = mar, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3475962", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:56 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3475962", abstract = "Social networks with location enabling technologies, also known as geo-social networks, allow users to share their location-specific activities and preferences through check-ins. A user in such a geo-social network can be attributed to an associated \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "1", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Dan:2022:IGT, author = "Ovidiu Dan and Vaibhav Parikh and Brian D. Davison", title = "{IP} Geolocation through Geographic Clicks", journal = j-TSAS, volume = "8", number = "1", pages = "2:1--2:??", month = mar, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3476774", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:56 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3476774", abstract = "IP geolocation databases map IP addresses to their physical locations. They are used to determine the location of online users when their precise location is unavailable. These databases are vital for a number of online services, including search engine \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "2", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Leung:2022:BSW, author = "Raymond Leung and Alexander Lowe and Anna Chlingaryan and Arman Melkumyan and John Zigman", title = "{Bayesian} Surface Warping Approach for Rectifying Geological Boundaries Using Displacement Likelihood and Evidence from Geochemical Assays", journal = j-TSAS, volume = "8", number = "1", pages = "3:1--3:??", month = mar, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3476979", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:56 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3476979", abstract = "This article presents a Bayesian framework for manipulating mesh surfaces with the aim of improving the positional integrity of the geological boundaries that they seek to represent. The assumption is that these surfaces, created initially using sparse \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "3", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Ren:2022:URT, author = "Xinyu Ren and Seyyed Mohammadreza Rahimi and Xin Wang", title = "Utilization of Real Time Behavior and Geographical Attraction for Location Recommendation", journal = j-TSAS, volume = "8", number = "1", pages = "4:1--4:??", month = mar, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3484318", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:56 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3484318", abstract = "Personalized location recommendation is an increasingly active topic in recent years, which recommends appropriate locations to users based on their temporal and geospatial visiting patterns. Current location recommendation methods usually estimate the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "4", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Middya:2022:CPS, author = "Asif Iqbal Middya and Sarbani Roy and Debjani Chattopadhyay", title = "{CityLightSense}: a Participatory Sensing-based System for Monitoring and Mapping of Illumination levels", journal = j-TSAS, volume = "8", number = "1", pages = "5:1--5:??", month = mar, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3487364", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:56 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3487364", abstract = "Adequate nighttime lighting of city streets is necessary for safe vehicle and pedestrian movement, deterrent of crime, improvement of the citizens' perceptions of safety, and so on. However, monitoring and mapping of illumination levels in city streets \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "5", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Joshi:2022:FBM, author = "Manas Joshi and Arshdeep Singh and Sayan Ranu and Amitabha Bagchi and Priyank Karia and Puneet Kala", title = "{FoodMatch}: Batching and Matching for Food Delivery in Dynamic Road Networks", journal = j-TSAS, volume = "8", number = "1", pages = "6:1--6:??", month = mar, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3494530", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:56 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3494530", abstract = "Food delivery, today, is a multi-billion dollar industry. Minimizing food delivery time is a key contributor towards building positive customer experiences. More precisely, given a stream of food orders and available delivery vehicles, how should orders \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "6", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Valdes:2022:MMM, author = "Fabio Valdes", title = "{MFPMiner}: Mining Meaningful Frequent Patterns from Spatio-textual Trajectories", journal = j-TSAS, volume = "8", number = "1", pages = "7:1--7:??", month = mar, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3498728", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:56 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3498728", abstract = "In the second decade of this century, technical progress has led to a worldwide proliferation of devices for tracking the movement behavior of a person, a vehicle, or another kind of entity. One of the consequences of this development is a massive and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "7", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Mokbel:2022:ISI, author = "Mohamed F. Mokbel and Li Xiong and Demetrios Zeinalipour-Yazti", title = "Introduction to the Special Issue on Contact Tracing", journal = j-TSAS, volume = "8", number = "2", pages = "8:1--8:??", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3514137", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:57 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3514137", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "8", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Jiang:2022:SCT, author = "Ting Jiang and Yang Zhang and Minhao Zhang and Ting Yu and Yizheng Chen and Chenhao Lu and Ji Zhang and Zhao Li and Jun Gao and Shuigeng Zhou", title = "A Survey on Contact Tracing: The Latest Advancements and Challenges", journal = j-TSAS, volume = "8", number = "2", pages = "9:1--9:??", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3494529", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:57 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3494529", abstract = "Infectious diseases are caused by pathogenic microorganisms, such as bacteria, viruses, parasites or fungi, which can be spread, directly or indirectly, from one person to another. Infectious diseases pose a serious threat to human health, especially \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "9", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Rambhatla:2022:TAS, author = "Sirisha Rambhatla and Sepanta Zeighami and Kameron Shahabi and Cyrus Shahabi and Yan Liu", title = "Toward Accurate Spatiotemporal {COVID-19} Risk Scores Using High-Resolution Real-World Mobility Data", journal = j-TSAS, volume = "8", number = "2", pages = "10:1--10:??", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3481044", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:57 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3481044", abstract = "As countries look toward re-opening of economic activities amidst the ongoing COVID-19 pandemic, ensuring public health has been challenging. While contact tracing only aims to track past activities of infected users, one path to safe reopening is to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "10", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Anastasiou:2022:ARC, author = "Chrysovalantis Anastasiou and Constantinos Costa and Panos K. Chrysanthis and Cyrus Shahabi and Demetrios Zeinalipour-Yazti", title = "{ASTRO}: Reducing {COVID-19} Exposure through Contact Prediction and Avoidance", journal = j-TSAS, volume = "8", number = "2", pages = "11:1--11:??", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3490492", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:57 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3490492", abstract = "The fight against the COVID-19 pandemic has highlighted the importance and benefits of recommending paths that reduce the exposure to and the spread of the SARS-CoV-2 coronavirus by avoiding crowded indoor or outdoor areas. Existing path discovery \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "11", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Tang:2022:ALP, author = "Qiang Tang", title = "Another Look at Privacy-Preserving Automated Contact Tracing", journal = j-TSAS, volume = "8", number = "2", pages = "12:1--12:??", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3490490", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:57 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3490490", abstract = "In the current COVID-19 pandemic, manual contact tracing has been proven to be very helpful to reach close contacts of infected users and slow down spread of the virus. To improve its scalability, a number of automated contact tracing (ACT) solutions have \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "12", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Kato:2022:PTT, author = "Fumiyuki Kato and Yang Cao and Mastoshi Yoshikawa", title = "{PCT-TEE}: Trajectory-based Private Contact Tracing System with Trusted Execution Environment", journal = j-TSAS, volume = "8", number = "2", pages = "13:1--13:??", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3490491", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:57 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3490491", abstract = "Existing Bluetooth-based private contact tracing (PCT) systems can privately detect whether people have come into direct contact with patients with COVID-19. However, we find that the existing systems lack functionality and flexibility, which may hurt the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "13", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Teng:2022:ESQ, author = "Dejun Teng and Yanhui Liang and Hoang Vo and Jun Kong and Fusheng Wang", title = "Efficient {$3$D} Spatial Queries for Complex Objects", journal = j-TSAS, volume = "8", number = "2", pages = "14:1--14:??", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3502221", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:57 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3502221", abstract = "3D spatial data has been generated at an extreme scale from many emerging applications, such as high definition maps for autonomous driving and 3D Human BioMolecular Atlas. In particular, 3D digital pathology provides a revolutionary approach to map human \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "14", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Xiao:2022:REM, author = "Mengbai Xiao and Hao Wang and Liang Geng and Rubao Lee and Xiaodong Zhang", title = "An {RDMA}-enabled In-memory Computing Platform for {R}-tree on Clusters", journal = j-TSAS, volume = "8", number = "2", pages = "15:1--15:??", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3503513", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:57 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3503513", abstract = "R-tree is a foundational data structure used in spatial databases and scientific databases. With the advancement of networks and computer architectures, in-memory data processing for R-tree in distributed systems has become a common platform. We have \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "15", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Papadakis:2022:SDP, author = "George Papadakis and George Mandilaras and Nikos Mamoulis and Manolis Koubarakis", title = "Static and Dynamic Progressive Geospatial Interlinking", journal = j-TSAS, volume = "8", number = "2", pages = "16:1--16:??", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3510025", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:57 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3510025", abstract = "Geospatial data constitute a considerable part of Semantic Web data, but at the moment, its sources are insufficiently interlinked with topological relations in the Linked Open Data cloud. Geospatial Interlinking aims to cover this gap through space \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "16", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Zufle:2022:ISIa, author = "Andreas Z{\"u}fle and Taylor Anderson and Song Gao", title = "Introduction to the Special Issue on Understanding the Spread of {COVID-19}, {Part 1}", journal = j-TSAS, volume = "8", number = "3", pages = "17:1--17:??", month = sep, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3568670", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:58 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3568670", abstract = "Infectious diseases are transmitted between human hosts when in close contact over space and time. Recently, an unprecedented amount of spatial and spatiotemporal data have been made available that can be used to improve our understanding of the spread of \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "17e", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Sydora:2022:BOS, author = "Christoph Sydora and Faiza Nawaz and Leepakshi Bindra and Eleni Stroulia", title = "Building Occupancy Simulation and Analysis under Virus Scenarios", journal = j-TSAS, volume = "8", number = "3", pages = "17:1--17:??", month = sep, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3486898", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:58 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3486898", abstract = "During the COVID-19 pandemic, regulations on building usage and occupancy density were brought to the forefront, as research indicated that transmission was most likely to occur in indoor environments. Public health officials and building managers had to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "17", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Burtner:2022:CMM, author = "Susan Burtner and Alan T. Murray", title = "{COVID-19} and Minimizing Micro-Spatial Interactions", journal = j-TSAS, volume = "8", number = "3", pages = "18:1--18:??", month = sep, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3486970", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:58 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3486970", abstract = "COVID-19, the novel coronavirus that has disrupted lives around the world, continues to challenge how humans interact in public and shared environments. Repopulating the micro-spatial setting of an office building, with virus spread and transmission \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "18", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Fan:2022:HMB, author = "Zipei Fan and Chuang Yang and Zhiwen Zhang and Xuan Song and Yinghao Liu and Renhe Jiang and Quanjun Chen and Ryosuke Shibasaki", title = "Human Mobility-based Individual-level Epidemic Simulation Platform", journal = j-TSAS, volume = "8", number = "3", pages = "19:1--19:??", month = sep, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3491063", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:58 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3491063", abstract = "COVID-19 has spread worldwide, and over 140 million people have been confirmed infected, over 3 million people have died, and the numbers are still increasing dramatically. The consensus has been reached by scientists that COVID-19 can be transmitted in \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "19", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Coro:2022:HRG, author = "Gianpaolo Coro and Pasquale Bove", title = "A High-resolution Global-scale Model for {COVID-19} Infection Rate", journal = j-TSAS, volume = "8", number = "3", pages = "20:1--20:??", month = sep, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3494531", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:58 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3494531", abstract = "Several models have correlated COVID-19 spread with specific climatic, geophysical, and air pollution conditions, and early models had predicted the lowering of infection cases in Summer 2020. These approaches have been criticized for their coarse \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "20", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Pejo:2022:GTC, author = "Bal{\'a}zs Pej{\'o} and Gergely Bicz{\'o}k", title = "Games in the Time of {COVID-19}: Promoting Mechanism Design for Pandemic Response", journal = j-TSAS, volume = "8", number = "3", pages = "21:1--21:??", month = sep, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3503155", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:58 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3503155", abstract = "Most governments employ a set of quasi-standard measures to fight COVID-19, including wearing masks, social distancing, virus testing, contact tracing, and vaccination. However, combining these measures into an efficient holistic pandemic response \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "21", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Zakaria:2022:AIC, author = "Camellia Zakaria and Amee Trivedi and Emmanuel Cecchet and Michael Chee and Prashant Shenoy and Rajesh Balan", title = "Analyzing the Impact of {COVID-19} Control Policies on Campus Occupancy and Mobility via {WiFi} Sensing", journal = j-TSAS, volume = "8", number = "3", pages = "22:1--22:??", month = sep, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3516524", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:58 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3516524", abstract = "Mobile sensing has played a key role in providing digital solutions to aid with COVID-19 containment policies, primarily to automate contact tracing and social distancing measures. As more and more countries reopen from lockdowns, there remains a pressing \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "22", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Fanticelli:2022:DDM, author = "Haron C. Fanticelli and Solohaja Rabenjamina and Aline Carneiro Viana and Razvan Stanica and Lucas {Santos De Oliveira} and Artur Ziviani", title = "Data-driven Mobility Analysis and Modeling: Typical and Confined Life of a Metropolitan Population", journal = j-TSAS, volume = "8", number = "3", pages = "23:1--23:??", month = sep, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3517222", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:58 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3517222", abstract = "The idea of using mobile phone data to understand the impact of the Covid-19 pandemic and that of the sanitary constraints associated with it on human mobility imposed itself as evidence in most countries. This work uses spatiotemporal aggregated mobile \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "23", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Mostafiz:2022:CAO, author = "Rafid Mostafiz and Mohammad Shorif Uddin and Khandaker Mohammad Mohi Uddin and Mohammad Motiur Rahman", title = "{COVID-19} Along with Other Chest Infection Diagnoses Using Faster {R-CNN} and Generative Adversarial Network", journal = j-TSAS, volume = "8", number = "3", pages = "24:1--24:??", month = sep, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3520125", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:58 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3520125", abstract = "The rapid spreading of coronavirus (COVID-19) caused severe respiratory infections affecting the lungs. Automatic diagnosis helps to fight against COVID-19 in community outbreaks. Medical imaging technology can reinforce disease monitoring and detection \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "24", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Zufle:2022:ISIb, author = "Andreas Z{\"u}fle and Song Gao and Taylor Anderson", title = "Introduction to the Special Issue on Understanding the Spread of {COVID-19}, {Part 2}", journal = j-TSAS, volume = "8", number = "4", pages = "25:1--25:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3568669", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:58 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3568669", abstract = "Infectious diseases are transmitted between human hosts when in close contact over space and time. Recently, an unprecedented amount of spatial and spatiotemporal data have been made available that can be used to improve our understanding of the spread of \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "25e", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Lorch:2022:QEC, author = "Lars Lorch and Heiner Kremer and William Trouleau and Stratis Tsirtsis and Aron Szanto and Bernhard Sch{\"o}lkopf and Manuel Gomez-Rodriguez", title = "Quantifying the Effects of Contact Tracing, Testing, and Containment Measures in the Presence of Infection Hotspots", journal = j-TSAS, volume = "8", number = "4", pages = "25:1--25:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3530774", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:58 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3530774", abstract = "Multiple lines of evidence strongly suggest that infection hotspots, where a single individual infects many others, play a key role in the transmission dynamics of COVID-19. However, most of the existing epidemiological models fail to capture this aspect \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "25", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Mehrab:2022:EUH, author = "Zakaria Mehrab and Aniruddha Adiga and Madhav V. Marathe and Srinivasan Venkatramanan and Samarth Swarup", title = "Evaluating the Utility of High-Resolution Proximity Metrics in Predicting the Spread of {COVID-19}", journal = j-TSAS, volume = "8", number = "4", pages = "26:1--26:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3531006", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:58 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3531006", abstract = "High resolution mobility datasets have become increasingly available in the past few years and have enabled detailed models for infectious disease spread including those for COVID-19. However, there are open questions on how such mobility data can be used \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "26", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Behera:2022:EML, author = "Shreetam Behera and Debi Prosad Dogra and Manoranjan Satpathy", title = "Effect of Migrant Labourer Inflow on the Early Spread of {Covid-19} in {Odisha}: a Case Study", journal = j-TSAS, volume = "8", number = "4", pages = "27:1--27:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3558778", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:58 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3558778", abstract = "Odisha is a state in the eastern part of India with a population of 46 million. Annually, a large number of people migrate to financial and industrial centers in other states for their livelihood earning. Bulk of them returned to Odisha during the early \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "27", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Cardoso:2022:MGE, author = "M{\'a}rio Cardoso and Andr{\'e} Cavalheiro and Alexandre Borges and Ana Filipa Duarte and Am{\'\i}lcar Soares and Maria Jo{\~a}o Pereira and Nuno Jardim Nunes and Leonardo Azevedo and Arlindo Oliveira", title = "Modeling the Geospatial Evolution of {COVID-19} using Spatio-temporal Convolutional Sequence-to-sequence Neural Networks", journal = j-TSAS, volume = "8", number = "4", pages = "28:1--28:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3550272", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:58 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3550272", abstract = "Europe was hit hard by the COVID-19 pandemic and Portugal was severely affected, having suffered three waves in the first twelve months. Approximately between January 19th and February 5th 2021 Portugal was the country in the world with the largest \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "28", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Azad:2022:SSI, author = "Fahim Tasneema Azad and Robert W. Dodge and Allen M. Varghese and Jaejin Lee and Giulia Pedrielli and K. Sel{\c{c}}uk Candan and Gerardo Chowell-Puente", title = "{SIRTEM}: Spatially Informed Rapid Testing for Epidemic Modeling and Response to {COVID-19}", journal = j-TSAS, volume = "8", number = "4", pages = "29:1--29:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3555310", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:58 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3555310", abstract = "COVID-19 outbreak was declared a pandemic by the World Health Organization on March 11, 2020. To minimize casualties and the impact on the economy, various mitigation measures have being employed with the purpose to slow the spread of the infection, such \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "29", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Jepsen:2022:UBB, author = "Tobias Skovgaard Jepsen and Christian S. Jensen and Thomas Dyhre Nielsen", title = "{UniTE} --- The Best of Both Worlds: Unifying Function-fitting and Aggregation-based Approaches to Travel Time and Travel Speed Estimation", journal = j-TSAS, volume = "8", number = "4", pages = "30:1--30:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3517335", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:58 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3517335", abstract = "Travel time and speed estimation are part of many intelligent transportation applications. Existing estimation approaches rely on either function fitting or data aggregation and represent different tradeoffs between generalizability and accuracy. Function-. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "30", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Lin:2022:GBT, author = "Fandel Lin and Hsun-Ping Hsieh", title = "A Grid-Based Two-Stage Parallel Matching Framework for {Bi}-Objective {Euclidean} Traveling Salesman Problem", journal = j-TSAS, volume = "8", number = "4", pages = "31:1--31:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3526025", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:58 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3526025", abstract = "Traveling salesman problem (TSP) is one of the most studied combinatorial optimization problems; several exact, heuristic or even learning-based strategies have been proposed to solve this challenging issue. Targeting on the research problem of bi-. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "31", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Aref:2023:ESIa, author = "Walid G. Aref", title = "Editorial: Special Issue on the Best Papers from the {2020 ACM SIGSPATIAL Conference}", journal = j-TSAS, volume = "9", number = "1", pages = "1:1--1:??", month = mar, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3573198", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:59 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3573198", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "1", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Wang:2023:AUP, author = "Dongjie Wang and Yanjie Fu and Kunpeng Liu and Fanglan Chen and Pengyang Wang and Chang-Tien Lu", title = "Automated Urban Planning for Reimagining City Configuration via Adversarial Learning: Quantification, Generation, and Evaluation", journal = j-TSAS, volume = "9", number = "1", pages = "2:1--2:??", month = mar, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3524302", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:59 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3524302", abstract = "Urban planning refers to the efforts of designing land-use configurations given a region. However, to obtain effective urban plans, urban experts have to spend much time and effort analyzing sophisticated planning constraints based on domain knowledge and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "2", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Arge:2023:FRT, author = "Lars Arge and Aaron Lowe and Svend C. Svendsen and Pankaj K. Agarwal", title = "{$1$D} and {$2$D} Flow Routing on a Terrain", journal = j-TSAS, volume = "9", number = "1", pages = "3:1--3:??", month = mar, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3539660", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:59 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3539660", abstract = "An important problem in terrain analysis is modeling how water flows across a terrain creating floods by forming channels and filling depressions. In this article, we study a number of flow-query -related problems: Given a terrain \Sigma , represented as a \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "3", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Rizk:2023:LRS, author = "Hamada Rizk and Hirozumi Yamaguchi and Moustafa Youssef and Teruo Higashino", title = "Laser Range Scanners for Enabling Zero-overhead {WiFi-based} Indoor Localization System", journal = j-TSAS, volume = "9", number = "1", pages = "4:1--4:??", month = mar, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3539659", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:59 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3539659", abstract = "Robust and accurate indoor localization has been the goal of several research efforts over the past decade. Toward achieving this goal, WiFi fingerprinting-based indoor localization systems have been proposed. However, fingerprinting involves significant \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "4", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Biswas:2023:MAS, author = "Subhodip Biswas and Fanglan Chen and Zhiqian Chen and Chang-Tien Lu and Naren Ramakrishnan", title = "Memetic Algorithms for Spatial Partitioning Problems", journal = j-TSAS, volume = "9", number = "1", pages = "5:1--5:??", month = mar, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3544779", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:59 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3544779", abstract = "Spatial optimization problems (SOPs) are characterized by spatial relationships governing the decision variables, objectives, and/or constraint functions. In this article, we focus on a specific type of SOP called spatial partitioning, which is a \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "5", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Akatsuka:2023:AKF, author = "Hiroto Akatsuka and Masayuki Terada", title = "Application of {Kalman} Filter to Large-scale Geospatial Data: Modeling Population Dynamics", journal = j-TSAS, volume = "9", number = "1", pages = "6:1--6:??", month = mar, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3563692", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:59 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3563692", abstract = "To utilize a huge amount of observation data based on real-world events, a data assimilation process is needed to estimate the state of the system behind the observed data. The Kalman filter is a very commonly used technique in data assimilation, but it \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "6", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Krapu:2023:RBN, author = "Christopher Krapu and Robert Stewart and Amy Rose", title = "A Review of {Bayesian} Networks for Spatial Data", journal = j-TSAS, volume = "9", number = "1", pages = "7:1--7:??", month = mar, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3516523", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:59 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3516523", abstract = "Bayesian networks are a popular class of multivariate probabilistic models as they allow for the translation of prior beliefs about conditional dependencies between variables to be easily encoded into their model structure. Due to their widespread usage, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "7", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Brown:2023:PWB, author = "Philip E. Brown and Krystian Czapiga and Arun Jotshi and Yaron Kanza and Velin Kounev and Poornima Suresh", title = "Planning Wireless Backhaul Links by Testing Line of Sight and {Fresnel} Zone Clearance", journal = j-TSAS, volume = "9", number = "1", pages = "8:1--8:??", month = mar, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3517382", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:29:59 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3517382", abstract = "Microwave backhaul links are often used as wireless connections between telecommunication towers, in places where deploying optical fibers is impossible or too expensive. The relatively high frequency of microwaves increases their ability to transfer \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "8", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Uddin:2023:DRG, author = "Reaz Uddin and Mehnaz Tabassum Mahin and Payas Rajan and Chinya V. Ravishankar and Vassilis J. Tsotras", title = "Dwell Regions: Generalized Stay Regions for Streaming and Archival Trajectory Data", journal = j-TSAS, volume = "9", number = "2", pages = "9:1--9:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3543850", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:00 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3543850", abstract = "A region R is a dwell region for a moving object O if, given a threshold distance r$_q$ and duration \tau $_q$, every point of R remains within distance r$_q$ from O for at least time \tau $_q$. Points within R are likely to be of interest to O, so identification of dwell \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "9", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Luo:2023:GTM, author = "Yan Luo and Chak-Tou Leong and Shuhai Jiao and Fu-Lai Chung and Wenjie Li and Guoping Liu", title = "{Geo-Tile2Vec}: a Multi-Modal and Multi-Stage Embedding Framework for Urban Analytics", journal = j-TSAS, volume = "9", number = "2", pages = "10:1--10:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3571741", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:00 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3571741", abstract = "Cities are very complex systems. Representing urban regions are essential for exploring, understanding, and predicting properties and features of cities. The enrichment of multi-modal urban big data has provided opportunities for researchers to enhance \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "10", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{BinKhunayn:2023:DSM, author = "Eman {Bin Khunayn} and Hairuo Xie and Shanika Karunasekera and Kotagiri Ramamohanarao", title = "Dynamic Straggler Mitigation for Large-Scale Spatial Simulations", journal = j-TSAS, volume = "9", number = "2", pages = "11:1--11:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3578933", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:00 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3578933", abstract = "Spatial simulations have been widely used to study real-world environments, such as transportation systems. Applications like prediction and analysis of transportation require the simulation to handle millions of objects while running faster than real \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "11", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Haldar:2023:TCM, author = "Aparajita Haldar and Shuang Wang and Gunduz Vehbi Demirci and Joe Oakley and Hakan Ferhatosmanoglu", title = "Temporal Cascade Model for Analyzing Spread in Evolving Networks", journal = j-TSAS, volume = "9", number = "2", pages = "12:1--12:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3579996", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:00 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3579996", abstract = "Current approaches for modeling propagation in networks (e.g., of diseases, computer viruses, rumors) cannot adequately capture temporal properties such as order/duration of evolving connections or dynamic likelihoods of propagation along connections. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "12", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Rayhan:2023:AIA, author = "Yeasir Rayhan and Tanzima Hashem", title = "{AIST}: an Interpretable Attention-Based Deep Learning Model for Crime Prediction", journal = j-TSAS, volume = "9", number = "2", pages = "13:1--13:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3582274", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:00 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3582274", abstract = "Accuracy and interpretability are two essential properties for a crime prediction model. Accurate prediction of future crime occurrences along with the reason behind a prediction would allow us to plan the crime prevention steps accordingly. The key \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "13", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Gudmundsson:2023:PNS, author = "Joachim Gudmundsson and John Pfeifer and Martin P. Seybold", title = "On Practical Nearest Sub-Trajectory Queries under the {Fr{\'e}chet} Distance", journal = j-TSAS, volume = "9", number = "2", pages = "14:1--14:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3587426", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:00 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3587426", abstract = "We study the problem of sub-trajectory nearest-neighbor queries on polygonal curves under the continuous Fr{\'e}chet distance. Given an n vertex trajectory P and an m vertex query trajectory Q, we seek to report a vertex-aligned sub-trajectory P ' of P that is \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "14", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Qin:2023:MLP, author = "Kyle K. Qin and Yongli Ren and Wei Shao and Brennan Lake and Filippo Privitera and Flora D. Salim", title = "Multiple-level Point Embedding for Solving Human Trajectory Imputation with Prediction", journal = j-TSAS, volume = "9", number = "2", pages = "15:1--15:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3582427", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:00 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3582427", abstract = "Sparsity is a common issue in many trajectory datasets, including human mobility data. This issue frequently brings more difficulty to relevant learning tasks, such as trajectory imputation and prediction. Nowadays, little existing work simultaneously \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "15", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Darji:2023:PSR, author = "Dhruvil Darji and Gustavo Vejarano", title = "Point Set Registration for Target Localization Using Unmanned Aerial Vehicles", journal = j-TSAS, volume = "9", number = "3", pages = "16:1--16:??", month = sep, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3586575", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:01 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3586575", abstract = "The problem of point set registration (PSR) on images obtained using a group of unmanned aerial vehicles (UAVs) is addressed in this article. UAVs are given a flight plan each, which they execute autonomously. A flight plan consists of a series of GPS \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "16", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Adhikari:2023:ABM, author = "Anup Adhikari and Leen-Kiat Soh and Deepti Joshi and Ashok Samal and Regina Werum", title = "Agent Based Modeling of the Spread of Social Unrest Using Infectious Disease Models", journal = j-TSAS, volume = "9", number = "3", pages = "17:1--17:??", month = sep, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3587463", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:01 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3587463", abstract = "Prior research suggests that the timing and location of social unrest may be influenced by similar unrest activities in another nearby region, potentially causing a spread of unrest activities across space and time. In this paper, we model the spread of \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "17", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Teng:2023:DOC, author = "Xu Teng and Goce Trajcevski and Andreas Z{\"u}fle", title = "Distance, Origin and Category Constrained Paths", journal = j-TSAS, volume = "9", number = "3", pages = "18:1--18:??", month = sep, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3596601", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:01 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3596601", abstract = "Recommending a Point of Interest (PoI) or a sequence of PoIs to visit based on user's preferences and geo-locations has been one of the most popular applications of Location-Based Services (LBS). Variants have also been considered which take other factors \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "18", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Bhore:2023:WAP, author = "Sujoy Bhore and Robert Ganian and Guangping Li and Martin N{\"o}llenburg and Jules Wulms", title = "{Worbel}: Aggregating Point Labels into Word Clouds", journal = j-TSAS, volume = "9", number = "3", pages = "19:1--19:??", month = sep, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3603376", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:01 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3603376", abstract = "Point feature labeling is a classical problem in cartography and GIS that has been extensively studied for geospatial point data. At the same time, word clouds are a popular visualization tool to show the most important words in text data which has also \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "19", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Zhang:2023:DSP, author = "Minxing Zhang and Dazhou Yu and Yun Li and Liang Zhao", title = "Deep Spatial Prediction via Heterogeneous Multi-source Self-supervision", journal = j-TSAS, volume = "9", number = "3", pages = "20:1--20:??", month = sep, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3605358", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:01 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3605358", abstract = "Spatial prediction is to predict the values of the targeted variable, such as PM2.5 values and temperature, at arbitrary locations based on the collected geospatial data. It greatly affects the key research topics in geoscience in terms of obtaining \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "20", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Alrashid:2023:PPS, author = "Hussah Alrashid and Yongyi Liu and Amr Magdy", title = "{PAGE}: Parallel Scalable Regionalization Framework", journal = j-TSAS, volume = "9", number = "3", pages = "21:1--21:??", month = sep, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3611011", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:01 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3611011", abstract = "Regionalization techniques group spatial areas into a set of homogeneous regions to analyze and draw conclusions about spatial phenomena. A recent regionalization problem, called MP-regions, groups spatial areas to produce a maximum number of regions by \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "21", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Wolfson:2023:GRM, author = "Ouri Wolfson and Prabin Giri and Sushil Jajodia and Goce Trajcevski", title = "Geographic-Region Monitoring by Drones in Adversarial Environments", journal = j-TSAS, volume = "9", number = "3", pages = "22:1--22:??", month = sep, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3611009", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:01 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3611009", abstract = "We consider surveillance of a geographic region by a collaborative system of drones. The drones assist each other in identifying and managing activities of interest on the ground. We also consider an adversary who can create both genuine and fake \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "22", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Aref:2023:ESIb, author = "Walid G. Aref", title = "Editorial: Special Issue on the Best Papers from the {2021 ACM SIGSPATIAL Conference}", journal = j-TSAS, volume = "9", number = "4", pages = "23:1--23:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3632619", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:01 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3632619", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "23", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Sinop:2023:RRU, author = "Ali Kemal Sinop and Lisa Fawcett and Sreenivas Gollapudi and Kostas Kollias", title = "Robust Routing Using Electrical Flows", journal = j-TSAS, volume = "9", number = "4", pages = "24:1--24:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3567421", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:01 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3567421", abstract = "Generating alternative routes in road networks is an application of significant interest for online navigation systems. A high quality set of diverse alternate routes offers two functionalities --- (a) support multiple (unknown) preferences that the user \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "24", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Shaham:2023:HHT, author = "Sina Shaham and Gabriel Ghinita and Ritesh Ahuja and John Krumm and Cyrus Shahabi", title = "{HTF}: Homogeneous Tree Framework for Differentially Private Release of Large Geospatial Datasets with Self-tuning Structure Height", journal = j-TSAS, volume = "9", number = "4", pages = "25:1--25:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3569087", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:01 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3569087", abstract = "Mobile apps that use location data are pervasive, spanning domains such as transportation, urban planning, and healthcare. Important use cases for location data rely on statistical queries, e.g., identifying hotspots where users work and travel. Such \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "25", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Lin:2023:ENS, author = "Fandel Lin and Hsun-Ping Hsieh", title = "Exploiting Network Structure in Multi-criteria Distributed and Competitive Stationary-resource Searching", journal = j-TSAS, volume = "9", number = "4", pages = "26:1--26:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3569937", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:01 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3569937", abstract = "Transportation between satellite cities or inside the city center has always been a crucial factor in contributing to a better quality of life. This article focuses on multi-criteria distributed and competitive route planning for stationary resources in \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "26", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Yin:2023:MDL, author = "Yifang Yin and Wenmiao Hu and An Tran and Ying Zhang and Guanfeng Wang and Hannes Kruppa and Roger Zimmermann and See-Kiong Ng", title = "Multimodal Deep Learning for Robust Road Attribute Detection", journal = j-TSAS, volume = "9", number = "4", pages = "27:1--27:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3618108", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:01 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3618108", abstract = "Automatic inference of missing road attributes (e.g., road type and speed limit) for enriching digital maps has attracted significant research attention in recent years. A number of machine learning-based approaches have been proposed to detect road \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "27", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Chen:2023:SCB, author = "Zhida Chen and Gao Cong and Walid G. Aref", title = "{STAR}: a Cache-based Stream Warehouse System for Spatial Data", journal = j-TSAS, volume = "9", number = "4", pages = "28:1--28:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3605944", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:01 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3605944", abstract = "The proliferation of mobile phones and location-based services has given rise to an explosive growth in spatial data. To enable spatial data analytics, spatial data needs to be streamed into a data stream warehouse system that can provide real-time \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "28", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Schoemans:2023:CTV, author = "Maxime Schoemans and Mahmoud Sakr and Esteban Zim{\'a}nyi", title = "On Computing the Time-varying Distance between Moving Bodies", journal = j-TSAS, volume = "9", number = "4", pages = "29:1--29:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3611010", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:01 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3611010", abstract = "A moving body is a geometry that may translate and rotate over time. Computing the time-varying distance between moving bodies and surrounding static and moving objects is crucial to many application domains including safety at sea, logistics robots, and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "29", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Pedersen:2023:SRA, author = "Simon Aagaard Pedersen and Bin Yang and Christian S. Jensen and Jesper M{\o}ller", title = "Stochastic Routing with Arrival Windows", journal = j-TSAS, volume = "9", number = "4", pages = "30:1--30:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3617500", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:01 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3617500", abstract = "Arriving at a destination within a specific time window is important in many transportation settings. For example, trucks may be penalized for early or late arrivals at compact terminals, and early and late arrivals at general practitioners, dentists, and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "30", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Sharma:2024:SAD, author = "Praval Sharma and Ashok Samal and Leen-Kiat Soh and Deepti Joshi", title = "A Spatially-Aware Data-Driven Approach to Automatically Geocoding Non-Gazetteer Place Names", journal = j-TSAS, volume = "10", number = "1", pages = "1:1--1:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3627987", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:02 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3627987", abstract = "Human and natural processes such as navigation and natural calamities are intrinsically linked to the geographic space and described using place names. Extraction and subsequent geocoding of place names from text are critical for understanding the onset, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "1", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Rifai:2024:VSD, author = "Mouad Rifai and Lennart Johnsson", title = "{VxH}: a Systematic Determination of Efficient Hierarchical Voxel Structures", journal = j-TSAS, volume = "10", number = "1", pages = "2:1--2:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3632404", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:02 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3632404", abstract = "Three-dimensional (3D) maps with many millions to billions of points are now used in an increasing number of applications, with processing rates in the hundreds of thousands to millions of points per second. In mobile applications, power and energy \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "2", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Huang:2024:SST, author = "Huiqun Huang and Suining He and Xi Yang and Mahan Tabatabaie", title = "{STICAP}: Spatio-temporal Interactive Attention for Citywide Crowd Activity Prediction", journal = j-TSAS, volume = "10", number = "1", pages = "3:1--3:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3603375", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:02 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3603375", abstract = "Accurate citywide crowd activity prediction (CAP) can enable proactive crowd mobility management and timely responses to urban events, which has become increasingly important for a myriad of smart city planning and management purposes. However, complex \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "3", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Lu:2024:FUS, author = "Yi-Ju Lu and Cheng-Te Li", title = "Forecasting Urban Sensory Values through Learning Attention-adjusted Graph Spatio-temporal Networks", journal = j-TSAS, volume = "10", number = "1", pages = "4:1--4:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3635140", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:02 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3635140", abstract = "Forecasting spatio-temporal correlated time series of sensor values is crucial in urban applications, such as air pollution alerts, biking resource management, and intelligent transportation systems. While recent advances exploit graph neural networks \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "4", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Zang:2024:DIH, author = "Andi Zang and Runsheng Xu and Goce Trajcevski and Fan Zhou", title = "Data Issues in High-Definition Maps Furniture --- A Survey", journal = j-TSAS, volume = "10", number = "1", pages = "5:1--5:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3627160", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:02 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3627160", abstract = "The rapid advancements in sensing techniques, networking, and artificial intelligence (AI) algorithms in recent years have brought autonomous driving vehicles closer to common use in vehicular transportation. One of the fundamental components to enable \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "5", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Almaslukh:2024:SST, author = "Abdulaziz Almaslukh and Yongyi Liu and Amr Magdy", title = "Scalable Spatio-temporal Top-k Interaction Queries on Dynamic Communities", journal = j-TSAS, volume = "10", number = "1", pages = "6:1--6:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3648374", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Apr 30 13:30:02 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", URL = "https://dl.acm.org/doi/10.1145/3648374", abstract = "Social media platforms generate massive amounts of data that reveal valuable insights about users and communities at large. Existing techniques have not fully exploited such data to help practitioners perform a deep analysis of large online communities. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "6", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Song:2024:PTA, author = "Yunting Song and Riccardo Fellegara and Federico Iuricich and Leila {De Floriani}", title = "Parallel Topology-aware Mesh Simplification on Terrain Trees", journal = j-TSAS, volume = "10", number = "2", pages = "7:1--7:39", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3652602", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:33 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "We address the problem of performing a topology-aware simplification algorithm on a compact and distributed data structure for triangle meshes, the Terrain trees. Topology-aware operators have been defined to coarsen a Triangulated Irregular Network (TIN) \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "7", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Alves:2024:RBC, author = "Rodrigo Alves", title = "Regionalization-Based Collaborative Filtering: Harnessing Geographical Information in Recommenders", journal = j-TSAS, volume = "10", number = "2", pages = "8:1--8:23", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3656641", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:33 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Regionalization, also known as spatially constrained clustering, is an unsupervised machine learning technique used to identify and define spatially contiguous regions. In this work, we introduce a methodology to regionalize recommendation systems (RSs) \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "8", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Aref:2024:ISI, author = "Walid Aref and Leila {De Floriani} and Mohamed Mokbel and Cyrus Shahabi", title = "Introduction to the Special Issue on Vision Papers", journal = j-TSAS, volume = "10", number = "2", pages = "9:1--9:2", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3674145", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:33 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "9", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Mokbel:2024:MDS, author = "Mohamed Mokbel and Mahmoud Sakr and Li Xiong and Andreas Z{\"u}fle and Jussara Almeida and Taylor Anderson and Walid Aref and Gennady Andrienko and Natalia Andrienko and Yang Cao and Sanjay Chawla and Reynold Cheng and Panos Chrysanthis and Xiqi Fei and Gabriel Ghinita and Anita Graser and Dimitrios Gunopulos and Christian S. Jensen and Joon-Seok Kim and Kyoung-Sook Kim and Peer Kr{\"o}ger and John Krumm and Johannes Lauer and Amr Magdy and Mario Nascimento and Siva Ravada and Matthias Renz and Dimitris Sacharidis and Flora Salim and Mohamed Sarwat and Maxime Schoemans and Cyrus Shahabi and Bettina Speckmann and Egemen Tanin and Xu Teng and Yannis Theodoridis and Kristian Torp and Goce Trajcevski and Marc van Kreveld and Carola Wenk and Martin Werner and Raymond Wong and Song Wu and Jianqiu Xu and Moustafa Youssef and Demetris Zeinalipour and Mengxuan Zhang and Esteban Zim{\'a}nyi", title = "Mobility Data Science: Perspectives and Challenges", journal = j-TSAS, volume = "10", number = "2", pages = "10:1--10:35", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3652158", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:33 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of Global Positioning System (GPS)-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "10", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Mai:2024:OCF, author = "Gengchen Mai and Weiming Huang and Jin Sun and Suhang Song and Deepak Mishra and Ninghao Liu and Song Gao and Tianming Liu and Gao Cong and Yingjie Hu and Chris Cundy and Ziyuan Li and Rui Zhu and Ni Lao", title = "On the Opportunities and Challenges of Foundation Models for {GeoAI} (Vision Paper)", journal = j-TSAS, volume = "10", number = "2", pages = "11:1--11:46", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3653070", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:33 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Large pre-trained models, also known as foundation models (FMs), are trained in a task-agnostic manner on large-scale data and can be adapted to a wide range of downstream tasks by fine-tuning, few-shot, or even zero-shot learning. Despite their successes \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "11", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Zhang:2024:PHM, author = "Ying Zhang and Zhiwen Yu and Minling Dang and En Xu and Bin Guo and Yuxun Liang and Yifang Yin and Roger Zimmermann", title = "Predictability in Human Mobility: From Individual to Collective (Vision Paper)", journal = j-TSAS, volume = "10", number = "2", pages = "12:1--12:17", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3656640", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:33 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Human mobility is the foundation of urban dynamics and its prediction significantly benefits various downstream location-based services. Nowadays, while deep learning approaches are dominating the mobility prediction field where various model \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "12", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Zufle:2024:SHM, author = "Andreas Z{\"u}fle and Dieter Pfoser and Carola Wenk and Andrew Crooks and Hamdi Kavak and Taylor Anderson and Joon-Seok Kim and Nathan Holt and Andrew Diantonio", title = "In Silico Human Mobility Data Science: Leveraging Massive Simulated Mobility Data (Vision Paper)", journal = j-TSAS, volume = "10", number = "2", pages = "13:1--13:27", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3672557", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:33 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Human mobility data science using trajectories or check-ins of individuals has many applications. Recently, we have seen a plethora of research efforts that tackle these applications. However, research progress in this field is limited by a lack of large \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "13", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Azad:2024:VPV, author = "Fahim Tasneema Azad and K. Sel{\c{c}}uk Candan and Ahmet Kapki{\c{c}} and Mao-Lin Li and Huan Liu and Pratanu Mandal and Paras Sheth and Bilgehan Arslan and Gerardo Chowell-Puente and John Sabo and Rebecca Muenich and Javier Redondo Anton and Maria Luisa Sapino", title = "{(Vision} Paper) {A} Vision for Spatio-Causal Situation Awareness, Forecasting, and Planning", journal = j-TSAS, volume = "10", number = "2", pages = "14:1--14:42", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3672556", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:33 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Successfully tackling many urgent challenges in socio-economically critical domains, such as public health and sustainability, requires a deeper understanding of causal relationships and interactions among a diverse spectrum of spatio-temporally \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "14", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Musleh:2024:LST, author = "Mashaal Musleh and Mohamed F. Mokbel", title = "Let's Speak Trajectories: a Vision to Use {NLP} Models for Trajectory Analysis Tasks", journal = j-TSAS, volume = "10", number = "2", pages = "15:1--15:25", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3656470", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:33 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "The availability of trajectory data combined with various real-life practical applications has sparked the interest of the research community to design a plethora of algorithms for various trajectory analysis techniques. However, there is an apparent lack \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "15", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Winter:2024:CDA, author = "Stephan Winter and Monika Sester and Martin Tomko and Alexandra Millonig", title = "The Challenge of Data Analytics with Climate-neutral Urban Mobility (Vision Paper)", journal = j-TSAS, volume = "10", number = "2", pages = "16:1--16:10", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3649312", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:33 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Urban mobility is a major contributor to human-induced climate change, a challenge that urban and transport planning and spatial computing academic communities have been actively addressing. In this article we argue, however, that the common data \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "16", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Zufle:2024:LSD, author = "Andreas Z{\"u}fle and Flora Salim and Taylor Anderson and Matthew Scotch and Li Xiong and Kacper Sokol and Hao Xue and Ruochen Kong and David Heslop and Hye-Young Paik and C. Raina MacIntyre", title = "Leveraging Simulation Data to Understand Bias in Predictive Models of Infectious Disease Spread", journal = j-TSAS, volume = "10", number = "2", pages = "17:1--17:22", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3660631", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:33 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "The spread of infectious diseases is a highly complex spatiotemporal process, difficult to understand, predict, and effectively respond to. Machine learning and artificial intelligence (AI) have achieved impressive results in other learning and prediction \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "17", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Belal:2024:SFL, author = "Yacine Belal and Sonia {Ben Mokhtar} and Hamed Haddadi and Jaron Wang and Afra Mashhadi", title = "Survey of Federated Learning Models for Spatial-Temporal Mobility Applications", journal = j-TSAS, volume = "10", number = "3", pages = "18:1--18:39", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3666089", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:36 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Federated learning involves training statistical models over edge devices such as mobile phones such that the training data are kept local. Federated Learning (FL) can serve as an ideal candidate for training spatial temporal models that rely on \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "18", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Liu:2024:UEK, author = "Yaguang Liu and Lisa Singh", title = "Utilizing External Knowledge to Enhance Location Prediction for {Twitter\slash X} Users in Low Resource Settings", journal = j-TSAS, volume = "10", number = "3", pages = "19:1--19:25", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3673899", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:36 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Accurate estimates of user location are important for many online services, including event detection, disaster management, and determining public opinion. Neural network-based techniques have proven to be highly effective in predicting user location. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "19", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Aguilar:2024:GSM, author = "Jordi Aguilar and Kevin Buchin and Maike Buchin and Erfan Hosseini Sereshgi and Rodrigo I. Silveira and Carola Wenk", title = "Graph Sampling for Map Comparison", journal = j-TSAS, volume = "10", number = "3", pages = "20:1--20:24", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3662733", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:36 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Comparing two road maps is a basic operation that arises in a variety of situations. A map comparison method that is commonly used, mainly in the context of comparing reconstructed maps to ground-truth maps, is based on graph sampling. The essential idea \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "20", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Farhadloo:2024:SCO, author = "Majid Farhadloo and Arun Sharma and Shashi Shekhar and Svetomir Markovic", title = "Spatial Computing Opportunities in Biomedical Decision Support: The {Atlas-EHR} Vision", journal = j-TSAS, volume = "10", number = "3", pages = "21:1--21:36", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3679201", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:36 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "We consider the problem of reducing the time that healthcare professionals need to understand the patient's medical history through the next generation of biomedical decision support. This problem is societally important because it has the potential to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "21", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Jiang:2024:ISIa, author = "Renhe Jiang and Flora Salim and Mohamed Mokbel", title = "Introduction to the Special Issue on Machine Learning and Location Data", journal = j-TSAS, volume = "10", number = "3", pages = "22:1--22:2", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3698154", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:36 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "22", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Jin:2024:STD, author = "Guangyin Jin and Huan Yan and Fuxian Li and Jincai Huang and Yong Li", title = "Spatio-temporal Dual Graph Neural Networks for Travel Time Estimation", journal = j-TSAS, volume = "10", number = "3", pages = "23:1--23:22", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3627819", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:36 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Travel time estimation is one of the core tasks for the development of intelligent transportation systems. Most previous works model the road segments or intersections separately by learning their spatio-temporal characteristics to estimate travel time. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "23", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Shen:2024:DGR, author = "Guojiang Shen and Juntao Wang and Xiangjie Kong and Zhanhao Ji and Bing Zhu and Tie Qiu", title = "Deformation Gated Recurrent Network for Lane-level Abnormal Driving Behavior Recognition", journal = j-TSAS, volume = "10", number = "3", pages = "24:1--24:26", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3635141", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:36 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "As a significant part of traffic accident prevention, abnormal driving behavior recognition has been receiving extensive attention. However, the granularity of existing abnormal driving behavior recognition is mostly at road-level, and these methods' high \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "24", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Hou:2024:AST, author = "Mingliang Hou and Feng Xia and Xin Chen and Vidya Saikrishna and Honglong Chen", title = "Adaptive Spatio-temporal Graph Learning for Bus Station Profiling", journal = j-TSAS, volume = "10", number = "3", pages = "25:1--25:23", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3636459", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:36 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Understanding and managing public transportation systems require capturing complex spatio-temporal correlations within datasets. Existing studies often use predefined graphs in graph learning frameworks, neglecting shifted spatial and long-term temporal \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "25", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Wang:2024:AJS, author = "Tianyi Wang and Shu-Ching Chen", title = "Adaptive Joint Spatio-Temporal Graph Learning Network for Traffic Data Forecasting", journal = j-TSAS, volume = "10", number = "3", pages = "26:1--26:20", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3634913", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:36 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Traffic data forecasting has become an integral part of the intelligent traffic system. Great efforts are spent developing tools and techniques to estimate traffic flow patterns. Many existing approaches lack the ability to model the complex and dynamic \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "26", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Chen:2024:GAH, author = "Lei Chen and Jie Cao and Weichao Liang and Qiaolin Ye", title = "Geography-aware Heterogeneous Graph Contrastive Learning for Travel Recommendation", journal = j-TSAS, volume = "10", number = "3", pages = "27:1--27:22", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3641277", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:36 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Recommendation system concentrates on quickly matching products to consumer's needs, which plays a major role in improving user experiences and increase conversion rate. Travel recommendation has become a hot topic in both industry and academia with the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "27", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Wan:2024:CPS, author = "Shixiang Wan and Shikai Luo and Hongtu Zhu", title = "Causal Probabilistic Spatio-Temporal Fusion Transformers in Two-Sided Ride-Hailing Markets", journal = j-TSAS, volume = "10", number = "3", pages = "28:1--28:18", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3643848", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:36 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "In this work, we address the complex problem of multi-objective time series forecasting with a focus on predicting interdependent targets such as supply and demand in ride-hailing services. Traditional machine learning techniques approach the targets \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "28", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Ochiai:2024:DND, author = "Keiichi Ochiai and Masayuki Terada and Makoto Hanashima and Hiroaki Sano and Yuichiro Usuda", title = "Detection of Non-Designated Evacuation Shelters from Real-Time Population Dynamics Using Autoencoder-Based Anomaly Detection", journal = j-TSAS, volume = "10", number = "3", pages = "29:1--29:23", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3643679", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:36 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "In a disaster situation, local and municipal governments need to distribute relief supplies and provide administrative support to evacuees.Although people are supposed to evacuate to evacuation shelters designated by local governments, some people take \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "29", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Jiang:2024:ISIb, author = "Renhe Jiang and Flora Salim and Mohamed Mokbel", title = "Introduction to the Special Issue on Machine Learning and Location Data: {Part II}", journal = j-TSAS, volume = "10", number = "4", pages = "30:1--30:2", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3701218", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:37 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "30", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Schestakov:2024:RTR, author = "Stefan Schestakov and Simon Gottschalk and Thorben Funke and Elena Demidova", title = "{RE-Trace}: Re-identification of Modified {GPS} Trajectories", journal = j-TSAS, volume = "10", number = "4", pages = "31:1--31:28", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3643680", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:37 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "GPS trajectories are a critical asset for building spatio-temporal predictive models in urban regions in the context of road safety monitoring, traffic management, and mobility services. Currently, reliable and efficient data misuse detection methods for \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "31", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Lee:2024:LRL, author = "Ween Jiann Lee and Hady W. Lauw", title = "Latent Representation Learning for Geospatial Entities", journal = j-TSAS, volume = "10", number = "4", pages = "32:1--32:31", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3663474", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:37 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Representation learning has been instrumental in the success of machine learning, offering compact and performant data representations for diverse downstream tasks. In the spatial domain, it has been pivotal in extracting latent patterns from various data \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "32", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Mo:2024:CCA, author = "Zhaobin Mo and Haotian Xiang and Xuan Di", title = "Cross- and Context-Aware Attention Based Spatial-Temporal Graph Convolutional Networks for Human Mobility Prediction", journal = j-TSAS, volume = "10", number = "4", pages = "33:1--33:25", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3673227", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:37 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "The COVID-19 pandemic has dramatically transformed human mobility patterns. Therefore, human mobility prediction for the ``new normal'' is crucial to infrastructure redesign, emergency management, and urban planning post the pandemic. This paper aims to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "33", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Xu:2024:OCF, author = "Xovee Xu and Ting Zhong and Haoyang Yu and Fan Zhou and Goce Trajcevski", title = "Overcoming Catastrophic Forgetting in Continual Fine-Grained Urban Flow Inference", journal = j-TSAS, volume = "10", number = "4", pages = "34:1--34:26", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3660523", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:37 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Citywide fine-grained urban flow inference (FUFI) problem aims to infer the high-resolution flow maps from the coarse-grained ones, which plays an important role in sustainable and economic urban computing and intelligent traffic management. Previous \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "34", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Yi:2024:DST, author = "Peiyu Yi and Feihu Huang and Jian Peng and Zhifeng Bao", title = "Dynamic Spatial-Temporal Embedding via Neural Conditional Random Field for Multivariate Time Series Forecasting", journal = j-TSAS, volume = "10", number = "4", pages = "35:1--35:23", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3675165", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:37 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "How to capture dynamic spatial-temporal dependencies remains an open question in multivariate time series (MTS) forecasting. Although recent advanced spatial-temporal graph neural networks (STGNNs) achieve superior forecasting performance, they either \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "35", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Belussi:2024:GML, author = "Alberto Belussi and Sara Migliorini and Ahmed Eldawy", title = "A Generic Machine Learning Model for Spatial Query Optimization based on Spatial Embeddings", journal = j-TSAS, volume = "10", number = "4", pages = "36:1--36:33", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3657633", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:37 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Machine learning (ML) and deep learning (DL) techniques are increasingly applied to produce efficient query optimizers, in particular in regards to big data systems. The optimization of spatial operations is even more challenging due to the inherent \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "36", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Gong:2024:BIG, author = "Qixu Gong and Huiying Chen and Huiping Cao and Jiefei Liu", title = "Backbone Index and {GNN} Models for Skyline Path Query Evaluation over Multi-cost Road Networks", journal = j-TSAS, volume = "10", number = "4", pages = "37:1--37:45", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3660632", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:37 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Skyline path queries (SPQs) extend skyline queries to multi-dimensional networks, such as multi-cost road networks (MCRNs). Such queries return a set of non-dominated paths between two given network nodes. Despite the existence of extensive works on \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "37", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Michalopoulos:2025:EDQ, author = "Achilleas Michalopoulos and Dimitrios Tsitsigkos and Panagiotis Bouros and Nikos Mamoulis and Manolis Terrovitis", title = "Efficient Distance Queries on Non-point Data", journal = j-TSAS, volume = "11", number = "1", pages = "1:1--1:37", month = mar, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3698194", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:38 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Distance queries, including distance-range queries, k -nearest neighbors search, and distance joins, are very popular in spatial databases. However, they have been studied mainly for point data. Inspired by a recent approach on indexing non-point objects \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "1", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Dadwal:2025:TER, author = "Rajjat Dadwal and Thorben Funke and Michael N{\"u}sken and Elena Demidova", title = "Towards Effective, Robust and Utility-preserving Watermarking of {GPS} Trajectories", journal = j-TSAS, volume = "11", number = "1", pages = "2:1--2:25", month = mar, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3701558", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:38 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Personal GPS trajectory is essential for businesses and emerging data markets due to its relevance in various data-driven methods, including traffic forecasting, accident prediction, and profiling driving behavior. Watermarking is a method that \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "2", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Rottmann:2025:BSH, author = "Peter Rottmann and Anne Driemel and Herman Haverkort and Heiko R{\"o}glin and Jan-Henrik Haunert", title = "Bicriteria Shapes: Hierarchical Grouping and Aggregation of Polygons with an Efficient Graph-Cut Approach", journal = j-TSAS, volume = "11", number = "1", pages = "3:1--3:23", month = mar, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3705001", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:38 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "An important task of pattern recognition and map generalization is to partition a set of disjoint polygons into groups and to aggregate the polygons within each group to a representative output polygon. We introduce a new method for this task called \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "3", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Homburg:2025:QUL, author = "Timo Homburg and Steffen Staab and Frank Boochs", title = "{QPredict}: Using low quality volunteered geospatial data to evaluate high quality authority data", journal = j-TSAS, volume = "11", number = "1", pages = "4:1--4:37", month = mar, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3715910", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:38 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "High-quality, typically administrative, geospatial data should adhere to established measurement and representation practices and be protected from malicious attacks. However, this kind of geospatial data may only be infrequently updated due to its often \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "4", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Guting:2025:ETS, author = "Ralf Hartmut G{\"u}ting and Suvam Kumar Das and Fabio Vald{\'e}s and Suprio Ray", title = "Exact Trajectory Similarity Search With N-tree: an Efficient Metric Index for {kNN} and Range Queries", journal = j-TSAS, volume = "11", number = "1", pages = "5:1--5:54", month = mar, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3716825", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:38 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Similarity search is the problem of finding in a collection of objects those that are similar to a given query object. It is a fundamental problem in modern applications and the objects considered may be as diverse as locations in space, text documents, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "5", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Eldawy:2025:GET, author = "Ahmed Eldawy and Yufei Tao", title = "Guest Editorial: {TSAS} Special Issue on Parallel and Distributed Processing of Spatial Data: Algorithms and Systems", journal = j-TSAS, volume = "11", number = "2", pages = "6:1--6:2", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3721363", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:38 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "6", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Oje:2025:HHG, author = "Olufunso Oje and Tashi Stirewalt and Ofer Amram and Perry Hystad and Solmaz Amiri and Assefaw Gebremedhin", title = "{HierGP}: Hierarchical Grid Partitioning for Scalable Geospatial Data Analytics", journal = j-TSAS, volume = "11", number = "2", pages = "7:1--7:20", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3699511", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:38 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Application domains such as environmental health science, climate science, and geosciences-where the relationship between humans and the environment is studied-are constantly evolving and require innovative approaches in geospatial data analysis. Recent \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "7", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Frye:2025:PSP, author = "Roger Frye and Mark McKenney", title = "Per Segment Plane Sweep Line Segment Intersection on the {GPU}", journal = j-TSAS, volume = "11", number = "2", pages = "8:1--8:23", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3701989", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:38 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Polygon overlay operations are used for various purposes, such as GIS searches and queries, VLSI, and basic geometric operations of intersection, union, and difference. There have been recent research articles presenting algorithms using the GPU to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "8", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Adhikari:2025:STA, author = "Saugat Adhikari and Da Yan and Zhe Jiang and Jiao Han and Zelin Xu and Yupu Zhang and Arpan Sainju and Yang Zhou", title = "Scaling Terrain-Aware Spatial Machine Learning for Flood Mapping on Large Scale {Earth} Imagery Data", journal = j-TSAS, volume = "11", number = "2", pages = "9:1--9:29", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3703157", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:38 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "The accurate and prompt mapping of flood-affected regions is important for effective disaster management, including damage assessment and relief efforts. While high-resolution optical imagery from satellites during disasters presents an opportunity for \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "9", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Bakli:2025:DMS, author = "Mohamed Bakli and Mahmoud Sakr and Esteban Zim{\'a}nyi and Nils Dijk and Marco Slot", title = "Distributed {MobilityDB}: a Scalable Moving Object Database Management System", journal = j-TSAS, volume = "11", number = "2", pages = "10:1--10:39", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3719202", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:38 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "As the volume and complexity of spatiotemporal data continue to expand rapidly across various domains such as urban planning, environmental monitoring, and logistics, the demand for comprehensive data management systems becomes increasingly urgent. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "10", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Carniel:2025:FSA, author = "Anderson Chaves Carniel and Markus Schneider", title = "{Fuzzy Spatial Algebra (FUSA)}: Formal Specification of Fuzzy Spatial Data Types and Operations for Databases and {GIS}", journal = j-TSAS, volume = "11", number = "3", pages = "11:1--11:40", month = sep, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3722555", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:39 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Spatial database systems and Geographic Information Systems (GIS) are mainly able to support geographical applications that deal with crisp spatial objects, that is, objects whose extent, shape, and boundary are precisely determined. But geoscientists \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "11", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Nadiri:2025:TTP, author = "Amirhossein Nadiri and Jing Li and Ali Faraji and Ghadeer Abuoda and Manos Papagelis", title = "{TrajLearn}: Trajectory Prediction Learning using Deep Generative Models", journal = j-TSAS, volume = "11", number = "3", pages = "12:1--12:33", month = sep, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3729226", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:39 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Trajectory prediction aims to estimate an entity's future path using its current position and historical movement data, benefiting fields like autonomous navigation, robotics, and human movement analytics. Deep learning approaches have become key in this \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "12", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Hongyang:2025:MSM, author = "Chen Hongyang and Sheng Zhang and Jiajia Xie and Han Liu and Guanlin Chen", title = "{MV-STGCN}: Multi-view Spatial-Temporal Graph Convolutional Networks for On-Street Parking Availability Prediction", journal = j-TSAS, volume = "11", number = "3", pages = "13:1--13:17", month = sep, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3732286", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:39 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Smart cities leverage advancements in big data and artificial intelligence to deliver a multitude of services and information to urban people. Among these services, predicting on-street parking availability is an important application with the potential \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "13", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Aref:2025:SIB, author = "Walid Aref", title = "Special Issue on the Best Papers from the {2022 ACM SIGSPATIAL Conference}", journal = j-TSAS, volume = "11", number = "4", pages = "14:1--14:2", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3756851", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:39 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "14", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Stewart:2025:TDL, author = "Adam J. Stewart and Caleb Robinson and Isaac A. Corley and Anthony Ortiz and Juan M. Lavista Ferres and Arindam Banerjee", title = "{TorchGeo}: Deep Learning With Geospatial Data", journal = j-TSAS, volume = "11", number = "4", pages = "15:1--15:28", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3707459", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:39 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Remotely sensed geospatial data are critical for applications including precision agriculture, urban planning, disaster monitoring and response, and climate change research, among others. Deep learning methods are particularly promising for modeling many \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "15", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Khalil:2025:UTS, author = "Jalal Khalil and Da Yan and Lyuheng Yuan and Mostafa Jafarzadehfadaki and Saugat Adhikari and Jiao Han and Virginia Sisiopiku and Zhe Jiang", title = "Urban Traffic Simulation with Shared Mobility Services: an Approach Using Spatiotemporal Network Kernel Density Estimation and {MATSim}", journal = j-TSAS, volume = "11", number = "4", pages = "16:1--16:31", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3704918", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:39 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "The popularity of smartphones and 4G/5G network have enabled various novel transportation modes based on shared mobility, such as app-based ride-hailing services (e.g., Uber and Lyft) and shared micromobility services (e.g., Veo and Gotcha). However, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "16", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Zhang:2025:EET, author = "Liming Zhang and Jonathan Mbuya and Liang Zhao and Dieter Pfoser and Antonios Anastasopoulos", title = "End-to-end Trajectory Generation --- Contrasting Deep Generative Models and Language Models", journal = j-TSAS, volume = "11", number = "4", pages = "17:1--17:28", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3716892", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:39 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Due to the limited availability of actual large-scale datasets, realistic synthetic trajectory data play a crucial role in various research domains, including spatiotemporal data mining and data management, and domain-driven research related to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "17", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", } @Article{Pan:2025:EST, author = "Yicheng Pan and Haowei Wang and Meng Ma and Ping Wang", title = "Enhancing Spatial-Temporal Prediction Models with Dynamic Causal Graphs", journal = j-TSAS, volume = "11", number = "4", pages = "18:1--18:22", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3777547", ISSN = "2374-0353 (print), 2374-0361 (electronic)", ISSN-L = "2374-0353", bibdate = "Tue Jan 27 17:05:39 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tsas.bib", abstract = "Spatial-temporal prediction has become an important task in many applications, such as traffic forecasting. Due to the spatial-temporal nature of data, most state-of-the-art methods heavily depend on graph neural networks to model the inherent spatial \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Spat. Algorithms Syst.", articleno = "18", fjournal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)", journal-URL = "https://dl.acm.org/loi/tsas", }