Analysis and description of crimes in Mexico city using point pattern analysis within networks

Abstract

ABSTRACT The present research work is conducted to analyse spatial distribution and possible spatial association between three types of crimes from January 2018 to December 2019 in the metropolitan area of Mexico City. In this study, we consider treating the data as a realization of spatial point processes precisely on street network and propose an equal split continuous kernel estimator to identify particular street segments with higher crime rates than neighbouring segments. The results identify the location of high-risk areas for different kind of crimes and permit to detect individual street where crime rate is higher than the average rate. Additionally, our analysis reveals the existence of clusters with high crime incidence running eastwest across the central part of the urban study area. In that context, the current study suggests a comprehensive overview of road safety metrices for public security system and has important implications for strategic law enforcement. The methodology can be adapted and applied to other urban locations globally.