Track: Data Analytics

Abstract

To prevent crimes, understanding the space structure of society is very important because crime is a social problem. This research analyzes SA and LISA which is the spatial autocorrelation analysis, and they are considered the factor of space based on the five major crime occurrence data of Seoul from 2011 to 2013. The result could identify the spatial dependence and figure out the hot-spot, cold-spot and special outlier. This research shows the flow of result by year interpreted from the result of LISA with group number, type of crime and area. First, group number standard is that the number of hot-spot and cold-spot decreased and spatial dependence decreased either. Second, type of crime could find meaningful characteristics in theft, murder and robbery. Theft shows hot-spot mostly gathered in southeast areas and cold-spot mostly gathered in north areas. Murder were not shown in hot-spot or cold-spot and only shown in special outlier. Robbery mostly made groups in south areas. Third, area pattern is that Songpa-gu showed hot-spot and Nowon-gu showed cold-spot for three years. Also, Seocho-gu showed hot-spot and LH and we could know that the crime rate decreased. Dongjak-gu and Yangcheon-gu mostly showed LH and this means that the crime rate decreased. This result can be used to prevent crimes which are centering hot-spot areas and considering the spatial dependence.