论文标题
量化社会经济预测因素和建立环境对AR Little Rock的心理健康事件的影响
Quantifying the Effect of Socio-Economic Predictors and Built Environment on Mental Health Events in Little Rock, AR
论文作者
论文摘要
执法资源的适当分配仍然是犯罪预测和预防的关键问题，该问题是通过表征空间汇总的犯罪活动和多种感兴趣的预测变量来运作的。尽管对心理健康事件的适当资源分配的至关重要性，但阿肯色州小石城心理健康事件的地理空间性质的统计建模几乎没有进展。在本文中，我们在2015年至2018年之间从阿肯色州小石城的心理健康数据的空间性质提供了见解，在监督的空间建模框架下，同时扩展了流行的风险地形建模（Caplan等，2011，2015; Drawve，2016）方法。我们提供了空间聚类的证据，并通过广义线性模型，空间回归模型和基于树的方法，即Poisson回归，空间Durbin误差模型，Manski模型和随机森林的空间层次结构来确定影响此类异质性的重要特征。从这些不同模型获得的见解以及它们的相对预测性能。这里开发的推论工具可用于各种空间建模环境中，并有可能协助执法机构和城市适当地分配资源。
Proper allocation of law enforcement resources remains a critical issue in crime prediction and prevention that operates by characterizing spatially aggregated crime activities and a multitude of predictor variables of interest. 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 while extending the popular risk terrain modeling (Caplan et al., 2011, 2015; Drawve, 2016) approach. We provide evidence of spatial clustering and identify the important features influencing such heterogeneity via a spatially informed hierarchy of generalized linear models, spatial regression models and a tree based method, viz., Poisson regression, spatial Durbin error model, Manski model and Random Forest. 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.
