ANALISIS SPASIAL KETERTINGGALAN DAERAH DI INDONESIA TAHUN 2018 MENGGUNAKAN GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION - Tata Pacu Maulidina; Siskarossa Ika Oktora (Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI), 2020)
Development inequality in Indonesia has led the developed and underdeveloped regions. Regional backwardness caused by high inequality must be handled properly to prevent negative impacts on national stability. But in fact, the handling of underdeveloped regions is only effective in Western Indonesia, while in Eastern Indonesia tends to be not optimal. This study aims to explore regional backwardness in Indonesia and examines the factors that influence it. Based on data, underdeveloped regions tend to cluster in eastern Indonesia, and the independent variables have large variations between regions. This indicates dependence and spatial heterogeneity. Therefore, this study applies spatial analysis using the Geographically Weighted Logistic Regression (GWLR) method. GWLR shows better performance in modeling the regional backwardness in Indonesia compared to its global model (binary logistic regression). This study provides a local model for each district/city that can be used by local governments to implement more effective policies based on factors that do have significant effects on regional backwardness.