Romaslia, Susanti2022-12-092022-12-092022-07PerpustakaanElfitrahttps://repository.unri.ac.id/handle/123456789/10776Relationship analysis on spatial data can be done by using the method Geographically Weighted Poisson Regression (GWPR), which is a local form of Poisson regression which is applied to spatial data, where location is considered. The purpose of this study was to determine the GWPR model and the factors that influence the number of cases of Dengue Hemorrhagic Fever (DHF) in Indonesia in 2015. The spatial weighting used is the Gaussian kernel function and the optimum bandwidth. The parameter estimation method for the GWPR model is Maximum Likelihood Estimate (MLE). The results of this study indicate that the maximum likelihood estimator is obtained using the Newton-Raphson iteration method and the factors that affect the number of DHF cases in Indonesia are local. Locally influencing factors are population density, number of health workers, number of health facilities, and amount of rainfall.enGeographically Weighted Poisson RegressionPoisson RegressionDengue Hemorrhagic FeverGaussian Kernel FunctionSpatialMaximum Likelihood EstimationArticle