| In the era of big data,a large amount of attributes information and emergency information occurred on the parcels.The risk index is an important reference to measure the public security level.By analyzing the main influencing factors of public security and using historical emergency data,we determine the weight of each factor and calculate the risk index under different granularity,which provides a new research for the quantitative analysis of the public security level.Based on the 110 data of urban center in a big city in China,urban spatial data and urban social environment data,we started with three aspects:Firstly,a data-driven urban Multi-granularity Risk Index Calculation(MRIC)framework is proposed.Based on ABC ontology,a multi-granularity city risk index ontology model(MRICABC)is constructed.We define data structures such as region,emergency,organizations,etc.,and expand the attribute of the region.The multi-granularity risk index calculation operator,environmental impact factor screening operator,environmental impact factor weight calculation operator,incident extraction operator are defined and formalized.Secondly,the acquisition and cleaning of parcels and event data from a metropolitan area.this part completed the cleaning and preprocessing of urban social environment data such as crawling,emergency extraction,parcel attribute information and spatial mapping of event data.Finally,the risk index of street and community granularity is calculated and compared with risk index.According to the analysis results of the correlation between emergencies and environmental factors,we selected the relevant environmental factors,and then conducted empirical analysis of the street and community granularity risk index,comparative analysis of the results,combined with the risk factors and crime theory to explain the reasons.The theoretical results of this paper provide the decision-making support for the deployment and forecast of the police forces. |
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