GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION DENGAN FUNGSI KERNEL FIXED GAUSSIAN PADA KEMISKINAN JAWA TENGAH - Wulandari Wulandari (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), 2018)
Poverty alleviation is a problem faced by many countries in the world, included Indonesia. Poverty in Indonesia still relatively high. Poverty is one indicator of welfare. In general, the decline in poverty means that people's welfare increasing. Poverty is a multi-dimensional problem, which involves various microeconomic and macroeconomic factors, including the influence of the surrounding region. Modeling with geographically weighted regression (GWR) accommodates heterogeneous effects of independent variables on the dependent variable and produces a local parameter estimates. Central Java has the second highest poverty rate among provinces in Java. This study will model poverty in Central Java with a model that accommodates the influence of the surrounding region, named Geographically Weighted Logistic Regression (GWLR). Poverty modeling in Central Java with GWLR, in general, literacy rates (AMH), per capita GRDP, and Labor Force Participation Rate (TPAK) significantly affected poverty in Central Java with values that varied between districts / cities.