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

## TL;DR

This study shows how the movement of residents and non-residents affects burglary rates in different areas of London.

## Contribution

The study introduces a method to differentiate mobility impacts of residents and non-residents on burglary using explainable machine learning.

## Key findings

- Increased collective mobility is generally linked to higher burglary levels.
- Non-resident footfall and residents’ stay-at-home time strongly influence burglary.
- Mobility-crime relationships vary across neighborhoods and during the pandemic.

## 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.