Spatial Modeling in Public Health: A Review of Geographically Weighted Poisson Regression (GWPR) Applications - Raihannabil, Syfriza Davies; Halma, Karini; Hutagalung, Reghita Maria (STATMAT : JURNAL STATISTIKA DAN MATEMATIKA, 2026)
Geographically Weighted Poisson Regression (GWPR) is an important approach in analyzing spatial count data, especially in the field of public health. However, its application in Indonesia still has various methodological weaknesses. This study aims to critically review the suitability and application of the GWPR model, as well as analyze aspects of statistical assumptions, weighting function selection, model evaluation, and methodological innovation. This study uses an article review approach of 10 research articles that apply GWPR in public health. The review results show that most studies have not explicitly articulated research gaps and novelty, and have ignored crucial offset variables in count data. Spatial heterogeneity testing is often performed incorrectly using the BP test instead of visual exploration through thematic maps. The selection of weighting functions and bandwidths is often not based on objective evaluation. Additionally, many studies have not conducted multicollinearity checks and tests of the assumption of equidispersion, which directly impact model validity. Descriptive analysis and visualization of local parameters through maps remain limited, hindering contextual interpretation. Finally, some studies fail to include model goodness-of-fit evaluations such as AIC or pseudo-R², making it impossible to demonstrate the superiority of GWPR over global models objectively. These findings underscore the importance of upholding statistical validation principles and methodological transparency in GWPR modeling to produce accurate and relevant spatial analyses for regional policy-making.