Graphical interface of geographically weighted negative binomial regression (GWNBR) model using R-Shiny

Abstract

In regression analysis, the spatial aspects need to be considered because each region has different characteristics as well as the Poisson regression model. In this method, some assumptions must be fulfilled, namely that the variance and the mean of the response variable are equal. However, counted data often have a greater variance than the mean, or what is generally called the over-dispersion phenomenon. If over-dispersion occurs, the Poisson regression is not suitable for modeling data, and that will produce biases in the parameter estimates. One method used to overcome over-dispersion in Poisson regression is negative binomial regression. The negative binomial regression model is more flexible than the Poisson regression model because it assumes that the mean and variance are not necessarily equal. Therefore, if the spatial aspect is considered, the Geographically Weighted Negative Binomial Regression (GWNBR) method is used. This study aims to compile a computational application to model spatial data using GWNBR Model using R-Shiny Web Application. And, the GWNBR model will be applied to modeling the number of dengue cases in Central Java province. The GWNBR model with Adaptive Boxcar weight is the best model because it has the smallest AIC. Using this model, two groups of districts/cities are obtained based on significant variables.