Predicting Safety Behind the Walls: Machine Learning Insights into Gated Communities in Urban South Africa

Abstract

Across the globe, gated communities have emerged as a dominant trend in residential development, particularly in urban and peri-urban settings. In the South African context, this trend has accelerated in response to rising crime levels and a perceived need for private security solutions. Using triangulated data, the paper employs traditional and machine learning models to test the hypothesis: security measures in gated communities deter property crime rates compared to non-gated neighborhoods in urban South Africa. The results show that only when neighborhoods variables, including public safety resources (i.e., police presence), temporal patterns, and suburb location, are controlled, is when living in a gated community associated with significantly lower property crime. We also find that, while police stations alone have a crime-reducing effect, the results for the interaction term between gating and police presence are counterintuitive; results show that gated communities near police stations experience higher crime than would be expected from either factor alone. Among others, we suggest that this results from the region’s fragmented municipal administrative oversight and police precincts that potentially leads to certain inefficiencies, complicates equitable policing, and exacerbates disparities in crime rates and public safety. This research asserts that policymakers and other stakeholders need to recognise that private investments (i.e., gating) alone may not deter the incidence of property crime; rather, a mix of private and public safety resources could lead to less property crime.