Estimation of the spatial heterogeneity in crime incidence across an entire city is an important step towards reducing crime and increasing our understanding of the physical and social functioning of urban environments. This is a difficult modeling endeavor since crime incidence can vary smoothly across space and time but there also exist physical and social barriers that result in discontinuities in crime rates between different regions within a city. A further difficulty is that there are different levels of resolution that can be used for defining regions of a city in order to analyze crime. To address these challenges, we develop a Bayesian non-parametric approach for the clustering of urban areal units at different levels of resolution simultaneously. Our approach is evaluated with an extensive synthetic data study and then applied to the estimation of crime incidence at various levels of resolution in the city of Philadelphia.
翻译:估计整个城市犯罪发生率的空间差异性是朝着减少犯罪和加深我们对城市环境的物理和社会功能的了解迈出的重要一步,这是一项艰难的建模努力,因为犯罪发生率在时间和空间上可以顺利地变化,但也存在有形和社会障碍,造成城市内不同区域犯罪率的不连续。另一个困难是,在确定城市区域以便分析犯罪方面,可以使用不同程度的解决方案。为了应对这些挑战,我们制定了一种巴耶斯非参数性方法,将城市小单位同时组合在不同层次的分辨率上。我们的方法经过广泛的合成数据研究,然后用于评估费城不同层次的分辨率犯罪发生率。