量化社会经济预测因素和建筑环境对小石城心理健康事件的影响，AR
Quantifying the Effect of Socio-Economic Predictors and Built Environment on Mental Health Events in Little Rock, AR
摘要：
适当分配执法资源仍然是犯罪预测和预防的一个关键问题，其运作方式是对空间聚集的犯罪活动和众多感兴趣的预测变量进行定性。尽管精神健康事件的适当资源分配的重要性，在统计建模的地理空间性质的精神健康事件在小石城，阿肯色州的进展甚微。在这篇文章中，我们提供了对2015年至2018年阿肯色州小石城心理健康数据的空间性质的见解，在监督空间建模框架下，同时扩展了流行的风险地形建模（Caplan等人，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。从这些不同的模型中获得的见解以及它们的相对预测性能在这里呈现。这里开发的推理工具可以用于各种各样的空间建模环境，并有可能帮助执法机构和城市正确分配资源。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
