Quantifying the Effect of Socio-Economic Predictors and the Built Environment on Mental Health Events in Little Rock, AR

Law enforcement agencies continue to grow in the use of spatial analysis to assist in identifying patterns of outcomes. Despite the critical nature of proper resource allocation for mental health incidents, there has been little progress in statistical modeling of the geo-spatial nature of mental health events in Little Rock, Arkansas. In this article, we provide insights into the spatial nature of mental health data from Little Rock, Arkansas between 2015 and 2018, under a supervised spatial modeling framework. We provide evidence of spatial clustering and identify the important features influencing such heterogeneity via a spatially informed hierarchy of generalized linear, tree-based, and spatial regression models, viz. the Poisson regression model, the random forest model, the spatial Durbin error model, and the Manski model. The insights obtained from these different models are presented here along with their relative predictive performances. The inferential tools developed here can be used in a broad variety of spatial modeling contexts and have the potential to aid both law enforcement agencies and the city in properly allocating resources. We were able to identify several built-environment and socio-demographic measures related to mental health calls while noting that the results indicated that there are unmeasured factors that contribute to the number of events.

Automated summary

Law enforcement agencies are increasingly using spatial analysis to identify patterns of outcomes. However, there has been little progress in the statistical modeling of mental health events in Little Rock, Arkansas. In this article, insights into the spatial nature of mental health data from 2015 to 2018 in Little Rock, Arkansas are provided. Different models are used and their relative predictive performances are presented. The findings have the potential to assist law enforcement agencies and the city in resource allocation.