Policing and Mental ill-health: Using Big Data to Assess the Scale and Severity of, and the Frontline Resources Committed to, mental ill-health-related calls-for-service

TLDR: In this article, a text mining algorithm was used to estimate the proportion and severity of calls-for-service involving persons with mental ill-health (PMIH) in a study of Greater Manchester, UK.

Abstract: Addressing public safety and welfare, inclusive of responding to incidents involving persons with mental ill-health (PMIH) has become an integral dimension of, and a significant challenge to, contemporary policing. Yet, little is known of the scale and severity of such PMIH-related policing demand, nor of the extent of frontline resource consumed in resolving such incidents. To address this shortfall, we deploy a bespoke text mining algorithm on police incident logs to estimate the proportion and severity of calls-for-service involving PMIH in a study of Greater Manchester, UK. Furthermore, and using Global Positioning System data, we then assess the amount of time spent by frontline officers responding to these calls. Findings suggest that existing police recording practices serve to significantly underestimate the scale and severity of PMIH-related demand. The amount of time spent dealing with PMIH-related incidents is both substantial and disproportionate relative to other forms of police demand.