A case study on spatio-temporal data mining of urban social management events based on ontology semantic analysis

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.