Area Division Method Based on Improved Bisection K-means Clustering
Konferenz: ISCTT 2021 - 6th International Conference on Information Science, Computer Technology and Transportation
26.11.2021 - 28.11.2021 in Xishuangbanna, China
Tagungsband: ISCTT 2021
Seiten: 6Sprache: EnglischTyp: PDFPersönliche VDE-Mitglieder erhalten auf diesen Artikel 10% Rabatt
Liu, Kexin; Zhang, Hongzhou; Wang, Wei (School of Police Information Engineering and Cyber Security, People’s Public Security University of China, Beijing, China)
It is of great significance for controlling the occurrence of urban crime to analyze the urban spatial environmental factors affecting crime and provide reference for the targeted adjustment and defense of urban spatial structure. At present, most relevant studies take administrative regions or grids of the same size as the statistical analysis unit area, which is not conducive to fully reflect the spatial aggregation characteristics of crime, and then affect the accuracy of the analysis of the influencing factors of subsequent crime. To solve this problem, this paper proposes a spatial division method based on improved bisecting K-means clustering, which improves the concentration of crime space in the statistical analysis unit area on the premise that the crime location in the divided statistical area has certain macro characteristics. Through the case analysis of B city data, the rationality and effectiveness of the regional division method proposed in this paper are further verified.