Disentangling the impacts of collective mobility of residents and non-residents on burglary levels

Abstract

This study investigates how the collective mobility (including movement and visiting) of residents and non-residents affects neighbourhood burglary levels. While past research has linked mobility to urban crime, this study explores how these relationships vary across population groups and social contexts at the neighbourhood level. Using mobile phone GPS data, we distinguished between residents and non-residents based on daily movement patterns. We then measured their mobility within defined spatial and temporal units. An explainable machine learning method (XGBoost and SHAP) was used to assess how mobility patterns influence burglary in London’s LSOAs from 2020 to 2021. Results show that increased collective mobility is generally associated with higher burglary levels. Specifically, non-resident footfall and residents’ stay-at-home time have a stronger influence than other variables like residents’ travelled distance. The impact also varies across neighbourhoods and shifts during periods of COVID-19 restrictions and relaxations. These findings confirm the dynamic link between mobility and crime, highlighting the value of understanding population-specific patterns to inform more targeted policing strategies.

Keywords

Mobile phone GPS data / Human mobility / Explainable machine learning / Geo big data / Crime analysis

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Funding

U.K. Economic and Social Research Council Consumer Data Research Centre (CDRC)(ES/L011840/1)

Economic and Social Research Council under the U.K. Research and Innovation open call on COVID-19(ES/V00445X/1)

RIGHTS & PERMISSIONS

The Author(s)