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Geographically Weighted Regression (GWR) has gained widespread popularity across various disciplines for investigating spatial heterogeneity with respect to data relationships in georeferenced datasets. However, GWR is typically limited to the analysis of continuous dependent variables, which are assumed to follow a symmetric normal distribution. In many fields, nonnegative continuous data are often observed and may contain substantial amounts of zeros followed by a right-skewed distribution of positive values. When dealing with such type of outcomes, GWR may not provide adequate insights into spatially varying regression relationships. This study intends to extend the GWR based on a compound Poisson distribution. Such an extension not only allows for exploration of relationship heterogeneity but also accommodates nonnegative continuous response variables. We provide a detailed specification of the proposed model and discuss related modeling issues. Through simulation experiments, we assess the performance of this novel approach. Finally, we present an empirical case study using a dataset on dengue fever in Tainan, Taiwan, to demonstrate the practical applicability and utility of our proposed methodology.

地理加权回归（Geographically Weighted Regression, GWR）凭借其在地理参考数据集中探究数据关系空间异质性的能力，已在诸多学科中获得广泛应用。然而，传统GWR仅能处理服从对称正态分布的连续因变量分析。在众多研究领域中，常可见非负连续数据，这类数据往往包含大量零值，随后是正值的右偏分布。面对这类响应变量时，传统GWR难以充分揭示空间异质性的回归关系。本研究拟基于复合泊松分布对GWR进行拓展。该拓展方法不仅可探究关系异质性，还能适配非负连续型响应变量。本文详细给出了所提模型的具体设定，并探讨了相关建模问题。通过模拟实验，本文评估了该新方法的性能表现。最后，本文采用中国台湾台南市登革热数据集开展实证案例研究，以展示所提方法论的实际适用性与应用价值。