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%%% -*-BibTeX-*-
%%% ====================================================================
%%%  BibTeX-file{
%%%     author          = "Nelson H. F. Beebe",
%%%     version         = "1.03",
%%%     date            = "07 April 2020",
%%%     time            = "07:14:55 MDT",
%%%     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",
%%%     FAX             = "+1 801 581 4148",
%%%     URL             = "http://www.math.utah.edu/~beebe",
%%%     checksum        = "33004 4179 24053 222427",
%%%     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 1.03, the COMPLETE journal
%%%                        coverage looked like this:
%%%
%%%                             2015 (   8)    2017 (  10)    2019 (  26)
%%%                             2016 (  16)    2018 (  15)    2020 (   6)
%%%
%%%                             Article:         81
%%%
%%%                             Total entries:   81
%%%
%%%                        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
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%%%  }
%%% ====================================================================
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%%% ====================================================================
%%% 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,
                    FAX: +1 801 581 4148,
                    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",
}