Examining the Spatial Concentration of Mental Health Calls for Police Service in a Small City

Abstract

In recent years, police services have begun deploying more robust responses to calls for service involving persons with perceived mental illness (PwPMI), but at times do so in a limited capacity because of various challenges. Drawing from established evidencebased policing practices, a more efficient use of these responses may be to proactively deploy them instead, focusing their efforts on hot spots of PwPMI calls. Unfortunately, little is known about PwPMI call concentrations. Therefore, the current study seeks to contribute to the literature by not only examining the concentration of these calls within a small city, but also by introducing new methods and a new measure of concentration to the literature. Drawing on six years of calls for service data, the results reveal that a high proportion of PwPMI calls are concentrated in few spatial units -more so than in larger jurisdictions. Further analyses also reveal dispersion of these concentrations.

The spatial patterning of emergency demand for police services: a scoping review

“…There were some exceptions. Some studies adjusted the counts according to a denominator, such as the ambient and/or resident population (Andresen & Brantingham, 2007;Andresen, 2006a), used counts to calculate a Location Quotient (Koziarski, 2022;Vaughan et al, 2018), or used clusters generated from a Local Moran's I analysis (Andresen, 2011a). One study calculated dispatched deployment time as a measure of supply to meet call demand, in addition to a count measure (Ellison et al, 2021).…”

“…The operationalisation of these scales varied considerably. Generally speaking, meso-level units were defined according to census units, which given the North American-centric sample, consist primarily of blocks, tracts or dissemination areas (e.g., Koziarski, 2022;Louis Fig. 4 Call types frequency following simplification.…”

“…Nevertheless, studies that used a Location Quotient measure as the outcome in multivariable analysis(Koziarski, 2022;Vaughan et al, 2018) reported very few statistically significant predictors.…”