ANALISIS SPASIAL TINGKAT KESEJAHTERAAN DI INDONESIA MENGGUNAKAN GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION
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Date
2022-02
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Elfitra
Abstract
Geographically Weighted Logistic Regression (GWLR) is a method of combining
Geographically Weighted Regression (GWR) with logistic regression which is applied to
spatial data. The purpose of this research to determine model on community welfare using
GWLR and find factors that influence the possibility of increasing the welfare status of
each Province in Indonesia 2019. The variables used are total manpower, rate of GRDP,
PMWand LFPR. In the response variable, the level of welfare as measured by the human
development index (HDI) is in the binary category, namely 0 and 1 following the
Bernoulli distribution. The results showed that the GWLR model with the Adaptive
Gaussian Kernel function was better than the logistic regression model with the smallest
Akaike Information Criterion (AIC) of 37.97.
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Keywords
Human Development Index (HDI), Geographically Weighted LogisticRegression (GWLR), Akaike Information Criterion (AIC)
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