A Generalized Cross-Entropy Approach for Modeling Spatially Correlated Counts
应用广义交叉熵方法，在建模空间相关计数数据时融入空间结构信息，处理未观测异质性和空间聚类，并通过芝加哥343个社区凶杀案数据验证了该方法。
This article discusses and applies an information-theoretic framework for incorporating knowledge of the spatial structure in a sample while extracting from it information about processes resulting in count outcomes. The framework, an application of the Generalized Cross-Entropy (GCE) method of estimating count outcome models, allows researchers to incorporate such real-world features as unobserved heterogeneity—with or without spatial clustering—when modeling spatially correlated counts. The information-recovering potential of the approach is investigated using a limited set of simulations. It is then used to study the determinants of counts of homicides recorded in 343 neighborhoods in Chicago, Illinois.