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

11 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
Country/TerritoryUnited States
CitySan Francisco
Period16/6/26 → …

Bibliographical note

Funding Information:
D. D. Vu and W.-Y. Shin's work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2014R1A1A2054577) and by the Ministry of Science, ICT & Future Planning (MSIP) (2015R1A2A1A15054-248). H. To and C. Shahabi's work was funded by NSF grants IIS-1320149, CNS-1461963, the USC Integrated Media Systems Center, and unrestricted cash gift from Northrop Grumman. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of any of the sponsors.

Publisher Copyright:
© 2016 ACM.

All Science Journal Classification (ASJC) codes

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

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