GeoSocialBound

An efficient framework for estimating social POI boundaries using spatio-textual information

Dung D. Vu, Hien To, Won-Yong Shin, Cyrus Shahabi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

In this paper, we present a novel framework for estimating social point-of-interest (POI) boundaries, also termed GeoSocialBound, utilizing spatio-textual information based on geo-tagged tweets. We first start by defining a social POI boundary as one small-scale cluster containing its POI center, geographically formed with a convex polygon. Motivated by an insightful observation with regard to estimation accuracy, we formulate a constrained optimization problem, in which we are interested in finding the radius of a circle such that a newly defined objective function is maximized. To solve this problem, we introduce an efficient optimal estimation algorithm whose runtime complexity is linear in the number of geo-tags in a dataset. In addition, we empirically evaluate the estimation performance of our GeoSocial-Bound algorithm for various environments and validate the complexity analysis. As a result, vital information on how to obtain real-world GeoSocialBounds with a high degree of accuracy is provided.

Original languageEnglish
Title of host publication3rd International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data, GeoRich 2016 - In conjunction with SIGMOD 2016
PublisherAssociation for Computing Machinery, Inc
Pages13-18
Number of pages6
ISBN (Electronic)9781450343091
DOIs
Publication statusPublished - 2016 Jun 26
Event3rd International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data, GeoRich 2016 - San Francisco, United States
Duration: 2016 Jun 26 → …

Publication series

Name3rd International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data, GeoRich 2016 - In conjunction with SIGMOD 2016

Conference

Conference3rd International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data, GeoRich 2016
CountryUnited States
CitySan Francisco
Period16/6/26 → …

Fingerprint

polygon
Constrained optimization
performance
analysis

All Science Journal Classification (ASJC) codes

  • Computers in Earth Sciences
  • Software
  • Geography, Planning and Development
  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition

Cite this

Vu, D. D., To, H., Shin, W-Y., & Shahabi, C. (2016). GeoSocialBound: An efficient framework for estimating social POI boundaries using spatio-textual information. In 3rd International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data, GeoRich 2016 - In conjunction with SIGMOD 2016 (pp. 13-18). [2948652] (3rd International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data, GeoRich 2016 - In conjunction with SIGMOD 2016). Association for Computing Machinery, Inc. https://doi.org/10.1145/2948649.2948652
Vu, Dung D. ; To, Hien ; Shin, Won-Yong ; Shahabi, Cyrus. / GeoSocialBound : An efficient framework for estimating social POI boundaries using spatio-textual information. 3rd International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data, GeoRich 2016 - In conjunction with SIGMOD 2016. Association for Computing Machinery, Inc, 2016. pp. 13-18 (3rd International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data, GeoRich 2016 - In conjunction with SIGMOD 2016).
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abstract = "In this paper, we present a novel framework for estimating social point-of-interest (POI) boundaries, also termed GeoSocialBound, utilizing spatio-textual information based on geo-tagged tweets. We first start by defining a social POI boundary as one small-scale cluster containing its POI center, geographically formed with a convex polygon. Motivated by an insightful observation with regard to estimation accuracy, we formulate a constrained optimization problem, in which we are interested in finding the radius of a circle such that a newly defined objective function is maximized. To solve this problem, we introduce an efficient optimal estimation algorithm whose runtime complexity is linear in the number of geo-tags in a dataset. In addition, we empirically evaluate the estimation performance of our GeoSocial-Bound algorithm for various environments and validate the complexity analysis. As a result, vital information on how to obtain real-world GeoSocialBounds with a high degree of accuracy is provided.",
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Vu, DD, To, H, Shin, W-Y & Shahabi, C 2016, GeoSocialBound: An efficient framework for estimating social POI boundaries using spatio-textual information. in 3rd International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data, GeoRich 2016 - In conjunction with SIGMOD 2016., 2948652, 3rd International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data, GeoRich 2016 - In conjunction with SIGMOD 2016, Association for Computing Machinery, Inc, pp. 13-18, 3rd International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data, GeoRich 2016, San Francisco, United States, 16/6/26. https://doi.org/10.1145/2948649.2948652

GeoSocialBound : An efficient framework for estimating social POI boundaries using spatio-textual information. / Vu, Dung D.; To, Hien; Shin, Won-Yong; Shahabi, Cyrus.

3rd International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data, GeoRich 2016 - In conjunction with SIGMOD 2016. Association for Computing Machinery, Inc, 2016. p. 13-18 2948652 (3rd International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data, GeoRich 2016 - In conjunction with SIGMOD 2016).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Vu DD, To H, Shin W-Y, Shahabi C. GeoSocialBound: An efficient framework for estimating social POI boundaries using spatio-textual information. In 3rd International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data, GeoRich 2016 - In conjunction with SIGMOD 2016. Association for Computing Machinery, Inc. 2016. p. 13-18. 2948652. (3rd International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data, GeoRich 2016 - In conjunction with SIGMOD 2016). https://doi.org/10.1145/2948649.2948652