Spatial Analysis of Human Development Index in Indonesia
Keywords:
HDI, GWOLR, Adaptive Gaussian Kernel, Spatial Analysis, Cross ValidationAbstract
This study develops a province level HDI classification model for Indonesia by applying the GWOLR approach with an Adaptive Gaussian Kernel weighting scheme. HDI values are grouped into three ordered categories: low, medium, and high. The research data covers 38 provinces in Indonesia in 2024, sourced from the Central Statistics Agency. The predictor variables used include the percentage of the poor population, the open unemployment rate, the school enrollment rate for ages 16-18, the reading interest rate, the number of villages/subdistricts with hospital facilities, and the percentage of households with access to adequate sanitation. The selection of the optimal bandwidth was performed using a k-nearest neighbor based CV method, resulting in an optimal bandwidth of with a CV value of 11.5577. The results of the simultaneous test indicate that, collectively, the predictor variables have a significant effect on the HDI category, with a test statistic value of 71.1677. Based on the results of the partial test, 13 out of 38 provinces have at least one variable with a significant effect. The variable representing the percentage of households with access to adequate sanitation was the most influential variable in 13 provinces, followed by the variable representing school enrollment rates for 16-18 year olds in 12 provinces. Model evaluation using APER shows a classification error rate of 7.89% and an accuracy of 92.11%.
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