DEVELOPING GEOGRAPHICALLY WEIGHTED PANEL REGRESSION MODEL FOR SPATIO-TEMPORAL ANALYSIS OF COVID-19 POSITIVE CASES IN KALIMANTAN, INDONESIA

Abstract

This article describes spatial-temporal analysis using the innovation of developing a Geographically Weighted Panel Regression model with a distance weighting function that includes the interaction between spatial and time aspects (GWPR-st). The method is a local regression technique that provides a parameter model that varies in each location through cross-sectional and time-series data observation units. This study develops a new model in spatial statistics and offers new methodologies in Geographic Models and Geographic Information Systems (GIS). This study aims to determine the factors that influence the increase in positive cases and map the spread of COVID-19 on the Kalimantan Regency/City Scale. The model applied in this study involves geographic weighting functions, including the Gaussian kernel, Bisquare kernel, and tricube kernel, which spatial interactions and time series have modified. This study uses national COVID-19 data from 56 regencies/cities until August 2021. According to the research results, the developed model with the geographic weighting of the Bisquare kernel function was considered the most acceptable method. The developed model, which is deemed capable of information the most substantial influence on the number of COVID-19 cases in Kalimantan, is health services, such as a shortage of doctors, number of hospitals, number of community health centers, and number of tuberculosis cases. The study results provide the local governments with decision-making recommendations for overcoming the COVID-19 problems in their regions.