MIXED GEOGRAPHICALLY WEIGHTED REGRESSION USING ADAPTIVE BANDWIDTH TO MODELING OF AIR POLLUTER STANDARD INDEX

Abstract

Air pollution is one of the most concerned problems on earth today. It is closely related with and mostly generated from the transportation and industrialization sectors, as well as from the environmentally degrading effect of the urban physical development. Air pollution promotes the lower level of air quality, which in turn promotes the greater risk on health, especially that of the human being. This research aims to aid the government in the policy making process related to air pollution mitigation by developing a standard index model for air polluter (Air Polluter Standard Index - APSI) based on the Mixed Geographically Weighted Regression (MGWR) approach using the adaptive bandwidth. The adaptive bandwidth kernel has different bandwidth value in each observation location. Akaike Information Criterion-corrected (AICc) value is used to choose the most optimum bandwidth. The Monte Carlo Simulation is used to tests for regression coefficient non-stationarity. In this research, we also consider seven variables that are directly related to the air pollution level, which are the traffic velocity, the population density, the business center aspect, the air humidity, the wind velocity, the air temperature, and the area size of the urban forest. Based on AICc and MSE value it is know that the MGWR model with adaptive bisquare kernel is the best bandwidth to analyze this model.