The impacts of specific place visitations on theft patterns: a case study in Greater London, UK

Abstract

Exploring the impacts of population place visitation on crime patterns is crucial for understanding crime mechanisms and optimising resource allocation in crime prevention. While recent studies have broadly examined dynamic population activities at specific places from geo big data, limited crime-related studies have utilised this measurement to disentangle the impact of specific place visitation on urban crime patterns. This study aims to investigate the impact of population activities at different urban functional places on theft levels across different urban areas and distinctive social changing contexts. We utilised geo big data (mobile phone GPS trajectory records) collected from millions of anonymous users to measure footfalls (counts of visitations) attached to place types on weekdays and weekends. An explainable machine learning approach was applied to analyse the impacts of place visitations on theft levels: the ‘XGBoost’ algorithm trained a high-performance regression model and ‘SHapley Additive exPlanations’ (SHAP) values were measured to identify the contributions of different visitation variables to theft levels at specific spatial and temporal scales. Using the police records and geo big data in Greater London from 2020 to 2021, the optimised model revealed that visitation to ‘Accommodation, eating and drinking’ services during weekdays had the most significant impact compared to 17 other types of place visitations. Further, the influence of place visitations on theft varied across different local urban areas corresponding with changes in social restrictions during the pandemic. Specifically, the urban areas where theft was most impacted by visitation at specific types of places (e.g., accommodation, eating and drinking services) shifted to outer London during the first national lockdown compared to normal times. The findings provide further evidence from direct micro-level analysis and contribute to tailoring policing strategies in places with different contexts and urban visitation patterns.

Keywords

Mobile phone GPS data / Human mobility / Ambient population / Explainable machine learning / Urban vitality / Geo big data / Studies in Human Society / Human Geography

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)