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Low-Complexity Detection of POI Boundaries Using Geo-Tagged Tweets: A Geographic Proximity Based Approach

Published: 03 November 2015 Publication History

Abstract

Users tend to check in and post their statuses in location-based social networks (LBSNs) to describe that their interests are related to a point-of-interest (POI). Since the relevance of the data to the POI varies according to the geographic distance between the POI and the locations where the data are generated, it is important to characterize an area-of-interest (AOI) that enables to utilize the location information in a variety of businesses, services, and place advertisements. While previous studies on discovering AOIs were conducted based mostly on density-based clustering methods with the collection of geo-tagged photos from LBSNs, we focus on detecting a POI boundary, which corresponds to only one cluster containing its POI center. Using geo-tagged tweets recorded from Twitter users, this paper introduces a low-complexity two-phase strategy to detect a POI boundary by finding a suitable radius reachable from the POI center. We detect a polygon-type boundary of the POI as the convex hull (i.e., the outermost region) of selected geo-tags through our two-phase approach, where each phase proceeds on with different sizes of radius increment, thus yielding a more precise boundary. It is shown that our approach outperforms the conventional density-based clustering method in terms of runtime complexity.

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Cited By

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  • (2020)A City Adaptive Clustering Framework for Discovering POIs with Different GranularitiesDatabase and Expert Systems Applications10.1007/978-3-030-59003-1_28(425-434)Online publication date: 14-Sep-2020
  • (2018)Zooming in and Out Our Society: Discovering Macro/Micro Events from Social Media2018 International Conference on System Science and Engineering (ICSSE)10.1109/ICSSE.2018.8520111(1-3)Online publication date: Jun-2018
  • (2018) DIR-ST 2 : Delineation of Imprecise Regions Using Spatio–Temporal–Textual Information IEEE Access10.1109/ACCESS.2018.28458436(36364-36375)Online publication date: 2018
  • Show More Cited By

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  1. Low-Complexity Detection of POI Boundaries Using Geo-Tagged Tweets: A Geographic Proximity Based Approach

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    cover image ACM Conferences
    LBSN'15: Proceedings of the 8th ACM SIGSPATIAL International Workshop on Location-Based Social Networks
    November 2015
    47 pages
    ISBN:9781450339759
    DOI:10.1145/2830657
    Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 03 November 2015

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    Author Tags

    1. Area-of-Interest (AOI)
    2. Geo-Tagged Tweet
    3. Geographic Distance
    4. Point-of-Interest (POI) Boundary
    5. Twitter
    6. Two-Phase Approach

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    Overall Acceptance Rate 8 of 15 submissions, 53%

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    Cited By

    View all
    • (2020)A City Adaptive Clustering Framework for Discovering POIs with Different GranularitiesDatabase and Expert Systems Applications10.1007/978-3-030-59003-1_28(425-434)Online publication date: 14-Sep-2020
    • (2018)Zooming in and Out Our Society: Discovering Macro/Micro Events from Social Media2018 International Conference on System Science and Engineering (ICSSE)10.1109/ICSSE.2018.8520111(1-3)Online publication date: Jun-2018
    • (2018) DIR-ST 2 : Delineation of Imprecise Regions Using Spatio–Temporal–Textual Information IEEE Access10.1109/ACCESS.2018.28458436(36364-36375)Online publication date: 2018
    • (2017)A robust noise resistant algorithm for POI identification from flickr dataProceedings of the 26th International Joint Conference on Artificial Intelligence10.5555/3172077.3172349(3294-3300)Online publication date: 19-Aug-2017
    • (2016)GeoSocialBoundProceedings of the Third International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data10.1145/2948649.2948652(1-6)Online publication date: 26-Jun-2016

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