A New Methodological Framework for Crime Spatial Analysis Using Local Entropy Map

The highest crime rate in major cities has been always a challenge for managers and urban planners. In order to control and reduce the crime rate, different methods have been proposed in recent decades. Considering the relationship between land use and crime, in this study, potential spatial dependency between commercial land uses, banks, bus and subway stations and pickpocketing was investigated in Tehran. To analyze the spatial dependency, local entropy models and nonparametric approach were used. Using ArcGIS ESRI product we created the local entropy maps to show the significance level of each local region, which allows interactive examination of significant local multivariate relationships. The results show a high spatial autocorrelation between mentioned land uses and specified crimes. The parameters indicate a significant cluster distribution. Furthermore, pickpocketing density at the bus stops is at the high Bonferroni level. The results specify that spatial patterns of pickpocketing are related to land use in the study area.

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