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

Dung D. Vu; Hien To; Won Yong Shin; Cyrus Shahabi

doi:10.1145/2948649.2948652

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

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

Department of Computational Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

15 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 language	English
Title of host publication	3rd International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data, GeoRich 2016 - In conjunction with SIGMOD 2016
Publisher	Association for Computing Machinery, Inc
Pages	13-18
Number of pages	6
ISBN (Electronic)	9781450343091
DOIs	https://doi.org/10.1145/2948649.2948652
Publication status	Published - 2016 Jun 26
Event	3rd International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data, GeoRich 2016 - San Francisco, United States Duration: 2016 Jun 26 → …

Publication series

Name	3rd International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data, GeoRich 2016 - In conjunction with SIGMOD 2016

Conference

Conference	3rd International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data, GeoRich 2016
Country/Territory	United States
City	San Francisco
Period	16/6/26 → …

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

Access to Document

10.1145/2948649.2948652

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 et al. / 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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title = "GeoSocialBound: An efficient framework for estimating social POI boundaries using spatio-textual information",

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.",

author = "Vu, {Dung D.} and Hien To and Shin, {Won Yong} and Cyrus Shahabi",

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: {\textcopyright} 2016 ACM.; 3rd International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data, GeoRich 2016 ; Conference date: 26-06-2016",

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Vu, DD, To, H, Shin, WY & 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 et al.

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 proceeding › Conference contribution

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PY - 2016/6/26

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N2 - 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 WY, 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). doi: 10.1145/2948649.2948652

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

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