Using mobility data based on GPS locations for the analysis of urban crime - a test of geofencing for filtering transit mobility

Abstract

We use crowdsourced mobility data generated from smartphone GPS locations in order to gain a more comprehensive understanding of spatiotemporal pattern of urban crime. The population at risk of falling victim to crime does not only consists of the resident population but also of the ambient population which can be measured thanks to the increasing availability of large-scale datasets based on mobile phone usage. Using mobility and crime data from Cologne (Germany) in 2019, we describe in detail the data generation process and the filtering out of transit mobility and congested car traffic on major roads applying geofencing. We use spatial regression analysis to show that mobility data are a very powerful predictor of spatial crime patterns, largely mediating the effects of ‘conventional’ predictors representing opportunity theories. Filtering out transit mobility enhances the measurement validity of the ambient population judged by its increased explanatory power in multivariate models