research-article

Point of interest to region of interest conversion

Authors:

Victor de Graaff,

Rolf A. de By,

Maurice van Keulen,

Jan FlokstraAuthors Info & Claims

SIGSPATIAL'13: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 388 - 391

https://doi.org/10.1145/2525314.2525442

Published: 05 November 2013 Publication History

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Abstract

Trajectories of people contain a vast amount of information on users' interests and popularity of locations. To obtain this information, the places visited by the owner of the device on such a trajectory need to be recognized. However, the location information on a point of interest (POI) in a database is normally limited to an address and a coordinate pair, rather than a polygon describing its boundaries. A region of interest can be used to intersect trajectories to match trajectories with objects of interest. In the absence of expensive and often not publicly available detailed spatial data like cadastral data, we need to approximate this ROI. In this paper, we present several approaches to approximate the size and shape of ROIs, by integrating data from multiple public sources, a validation technique, and a validation of these approaches against the cadastral data of the city of Enschede, The Netherlands.

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Cited By

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Psyllidis AGao SHu YKim EMcKenzie GPurves RYuan MAndris C(2022)Points of Interest (POI): a commentary on the state of the art, challenges, and prospects for the futureComputational Urban Science10.1007/s43762-022-00047-w2:1Online publication date: 28-Jun-2022
https://doi.org/10.1007/s43762-022-00047-w
Ganesan AGupta AMathew J(2021)Mining points of interest via address embeddingsProceedings of the 5th ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising10.1145/3486183.3491002(1-10)Online publication date: 2-Nov-2021
https://dl.acm.org/doi/10.1145/3486183.3491002
Belcastro LFalcone AGarro AMarozzo FCalafate CDe Rango F(2021)Evaluation of large scale RoI mining applications in edge computing environmentsProceedings of the 2021 IEEE/ACM 25th International Symposium on Distributed Simulation and Real Time Applications10.1109/DS-RT52167.2021.9576131(1-8)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1109/DS-RT52167.2021.9576131
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Index Terms

Point of interest to region of interest conversion
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Geographic visualization
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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Information & Contributors

Information

Published In

SIGSPATIAL'13: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2013

598 pages

ISBN:9781450325219

DOI:10.1145/2525314

General Chairs:
Craig Knoblock
University of Southern California
,
Markus Schneider
University of Florida
,
Program Chairs:
Peer Kröger
Ludwig-Maximilians-Universität München, Germany
,
John Krumm
Microsoft Research
,
Peter Widmayer
ETH Zürich, Switzerland

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2013

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SIGSPATIAL'13

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SIGSPATIAL

SIGSPATIAL'13: 21st SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 5 - 8, 2013

Florida, Orlando

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Overall Acceptance Rate 220 of 1,116 submissions, 20%

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Psyllidis AGao SHu YKim EMcKenzie GPurves RYuan MAndris C(2022)Points of Interest (POI): a commentary on the state of the art, challenges, and prospects for the futureComputational Urban Science10.1007/s43762-022-00047-w2:1Online publication date: 28-Jun-2022
https://doi.org/10.1007/s43762-022-00047-w
Ganesan AGupta AMathew J(2021)Mining points of interest via address embeddingsProceedings of the 5th ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising10.1145/3486183.3491002(1-10)Online publication date: 2-Nov-2021
https://dl.acm.org/doi/10.1145/3486183.3491002
Belcastro LFalcone AGarro AMarozzo FCalafate CDe Rango F(2021)Evaluation of large scale RoI mining applications in edge computing environmentsProceedings of the 2021 IEEE/ACM 25th International Symposium on Distributed Simulation and Real Time Applications10.1109/DS-RT52167.2021.9576131(1-8)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1109/DS-RT52167.2021.9576131
Belcastro LMarozzo FPerrella E(2021)Automatic detection of user trajectories from social media postsExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.115733186:COnline publication date: 30-Dec-2021
https://dl.acm.org/doi/10.1016/j.eswa.2021.115733
Belcastro LMarozzo FTalia DTrunfio P(2021)Cloud Computing for Enabling Big Data AnalysisCloud Computing and Services Science10.1007/978-3-030-72369-9_4(84-109)Online publication date: 23-Mar-2021
https://doi.org/10.1007/978-3-030-72369-9_4
Li LChen XLiu QBao Z(2020)A Data-Driven Approach for GPS Trajectory Data CleaningDatabase Systems for Advanced Applications10.1007/978-3-030-59410-7_1(3-19)Online publication date: 18-Sep-2020
https://doi.org/10.1007/978-3-030-59410-7_1
Belcastro LKechadi MMarozzo FPastore LTalia DTrunfio P(2020)Parallel extraction of Regions‐of‐Interest from social media dataConcurrency and Computation: Practice and Experience10.1002/cpe.563833:8Online publication date: 2-Jan-2020
https://doi.org/10.1002/cpe.5638
Belcastro LMarozzo FTalia DTrunfio P(2018)G-RoIACM Transactions on Knowledge Discovery from Data10.1145/315441112:3(1-22)Online publication date: 23-Jan-2018
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de Graaff Vde By Rvan Keulen MOssowski S(2016)Automated semantic trajectory annotation with indoor point-of-interest visits in urban areasProceedings of the 31st Annual ACM Symposium on Applied Computing10.1145/2851613.2851709(552-559)Online publication date: 4-Apr-2016
https://dl.acm.org/doi/10.1145/2851613.2851709
de Graaff VPfoser Dvan Keulen MDe By R(2015)Spatiotemporal Behavior Profiling: A Treasure Hunt Case StudyWeb and Wireless Geographical Information Systems10.1007/978-3-319-18251-3_9(143-158)Online publication date: 23-Apr-2015
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