A Spatial Analysis of Robbery Rate in the City of Detroit using Exploratory Data Analysis Approach

Abstract

Abstract. Many U.S. cities have experienced rising crime rates in recent years. Crime has inherent geographic quality and tend to concentrate in certain places within the city. To prioritize public safety and crime prevention strategies, it is important to identify where crime is occurring and with what severity. Using spatial statistics including the average nearest neighbour index, Moran’s I, Getis-Ord Gi* statistic, and Anselin Cluster and Outlier Analysis, this study investigates robbery locations within the city of Detroit over 5-year period, 2016 to 2020 to identify hot spots, cold spots and spatial patterns across two different spatial scale – block group and census tracts. The study seeks to understand the effect of data aggregation on each spatial scale on the outcome of the analysis to determine the most optimum spatial scale to study robbery rates. The study concludes that, spatial analysis at small scale like block group level is most informative. Policy implications and areas for further research are provided.