Applying a spatial analysis method for epidemiological studies: Advancing public health research with geographically weighted Poisson regression (GWPR) using R
Keywords:
Geographically weighted Poisson regression, Spatial health data analysis, Public health research, Spatial statistical methodAbstract
Public health research increasingly involves spatial analysis to identify disease patterns, assess context-specific risk factors, and inform evidence-based decision-making for specific areas. However, conventional statistical models such as Poisson regression assume spatial stationarity, limiting their ability to compute for geographic heterogeneity in the relationships between health outcomes and explanatory variables. This paper introduces geographically weighted Poisson regression (GWPR) as an
advanced spatial analytical technique that addresses these limitations by allowing regression coefficients to vary across each spatial unit of the study region, enabling the identification of localized patterns of disease occurrence and influencing risk factors. By incorporating spatial weighting based on specified kernel functions and localized parameter estimation, GWPR enables the identification of region-specific associations and enhances the accuracy of epidemiological modeling. This review article elucidates the theoretical foundation of GWPR, its methodological framework, and its application in public health research, along with a comprehensive, step-by-step guide for implementing GWPR in R. The guide includes data preparation, bandwidth optimization, model fitting, diagnostics, and visualization of spatially varying coefficients. By making GWPR more accessible to researchers and public health practitioners, this method can be effectively integrated into real-world spatial health data analysis. Ultimately, it supports the development of targeted, data-driven public health strategies based on robust, location-specific evidence.
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