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A Case Study on Spatio-Temporal Data Mining of Urban Social Management Events Based on Ontology Semantic Analysis

Author

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  • Shaohua Wang

    (School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China)

  • Xianxiong Liu

    (School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China)

  • Haiyin Wang

    (Institute of Qingdao Geotechnical Investigation and Surveying, Qingdao 266071, China
    QingDao Key Laboratory for the Integration and Application of Sea-land Geographical Information, Qingdao 266071, China)

  • Qingwu Hu

    (School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
    QingDao Key Laboratory for the Integration and Application of Sea-land Geographical Information, Qingdao 266071, China)

Abstract

The massive urban social management data with geographical coordinates from the inspectors, volunteers, and citizens of the city are a new source of spatio-temporal data, which can be used for the data mining of city management and the evolution of hot events to improve urban comprehensive governance. This paper proposes spatio-temporal data mining of urban social management events ( USMEs ) based on ontology semantic approach. First, an ontology model for USMEs is presented to accurately extract effective social management events from non-structured UMSEs . Second, an explorer spatial data analysis method based on “event-event” and “event-place” from spatial and time aspects is presented to mine the information from UMSEs for the urban social comprehensive governance. The data mining results are visualized as a thermal chart and a scatter diagram for the optimization of the management resources configuration, which can improve the efficiency of municipal service management and municipal departments for decision-making. Finally, the USMEs of Qingdao City in August 2016 are taken as a case study with the proposed approach. The proposed method can effectively mine the management of social hot events and their spatial distribution patterns, which can guide city governance and enhance the city’s comprehensive management level.

Suggested Citation

  • Shaohua Wang & Xianxiong Liu & Haiyin Wang & Qingwu Hu, 2018. "A Case Study on Spatio-Temporal Data Mining of Urban Social Management Events Based on Ontology Semantic Analysis," Sustainability, MDPI, vol. 10(6), pages 1-24, June.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:6:p:2084-:d:153317
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    References listed on IDEAS

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    1. Ke Wang & Yafei Zhao & Rajan Kumar Gangadhari & Zhixing Li, 2021. "Analyzing the Adoption Challenges of the Internet of Things (IoT) and Artificial Intelligence (AI) for Smart Cities in China," Sustainability, MDPI, vol. 13(19), pages 1-35, October.

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