No 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.

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, United Kingdom. Further, and using Global Positioning System (GPS) 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.

This paper assesses the relevance of social disorganisation and collective efficacy in accounting for neighbourhood inequalities in the exposure to crime. Specifically, it questions the potential of community and voluntary organisations to enhance informal social control and reduce exposure to crime. It utilises calls-for-service (incident) data for Greater Manchester (UK) and a Bayesian spatio-temporal modelling approach. Contrary to expectations, the research finds that measures of social disorganisation (concentrated disadvantage aside) and collective efficacy hold a limited effect on neighbourhood exposure to crime. We discuss the implications of these findings for criminological inquiry and theoretical development, highlighting the necessity of such endeavour to account for the national political-economy and welfare regime of research settings.