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Proper allocation of law enforcement resources remains a critical issue incrime prediction and prevention that operates by characterizing spatiallyaggregated crime activities and a multitude of predictor variables of interest.Despite the critical nature of proper resource allocation for mental healthincidents, there has been little progress in statistical modeling of thegeo-spatial nature of mental health events in Little Rock, Arkansas. In thisarticle, we provide insights into the spatial nature of mental health data fromLittle Rock, Arkansas between 2015 and 2018, under a supervised spatialmodeling framework while extending the popular risk terrain modeling (Caplan etal., 2011, 2015; Drawve, 2016) approach. We provide evidence of spatialclustering and identify the important features influencing such heterogeneityvia a spatially informed hierarchy of generalized linear models, spatialregression models and a tree based method, viz., Poisson regression, spatialDurbin error model, Manski model and Random Forest. The insights obtained fromthese different models are presented here along with their relative predictiveperformances. The inferential tools developed here can be used in a broadvariety of spatial modeling contexts and have the potential to aid both lawenforcement agencies and the city in properly allocating resources.

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