Forecasting burglary risk in small areas via network analysis of city streets

Predicting residential burglary can benefit from understanding human movement patterns within an urban area. Typically, these movements occur along street networks. To take the characteristics of such networks into account, one can use two measures in the analysis: betweenness and closeness. The former measures the popularity of a particular street segment, while the latter measures the average shortest path length from one node to every other node in the network. In this paper, we study the influence of the city street network on residential burglary by including these measures in our analysis. We show that the measures of the street network help in predicting residential burglary exposing that there is a relationship between conceptions in urban design and crime