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PEMODELAN TINGKAT KEMISKINAN DI PROVINSI JAWA BARAT MENGGUNAKAN SPATIAL AUTOREGRESSIVE MODEL DENGAN PEMBOBOT ROOK CONTIGUITY
Spatial regression of panel data is an analytical approach used to model data with spatial and temporal dimensions while accounting for spatial interdependencies. This study uses panel data from districts and cities in West Java Province for the period 2020-2024, provided by the BPS. The analysis was conducted using SAR and SEM with a Rook’s contiguity weighting matrix to capture spatial effects between regions. The results indicate the presence of spatial dependence in poverty levels, suggesting that poverty conditions in a given region are influenced by conditions in neighboring regions. The SAR-REM model was selected as the best model because it accounts for both spatial effects and random effects in panel data. Model estimates indicate that the poverty line variable has a negative effect on poverty levels, while the Gini ratio and registered job seekers variables have a positive effect. Overall, poverty levels are influenced by socioeconomic factors as well as spatial interdependence between regions, thus requiring policies that take regional aspects into account.
FAKULTAS SAINS DAN TEKNOLOGI, UIN SUNAN KALIJAGA YOGYAKARTA
PEMODELAN TINGKAT KEMISKINAN DI PROVINSI JAWA BARAT MENGGUNAKAN SPATIAL AUTOREGRESSIVE MODEL DENGAN PEMBOBOT ROOK CONTIGUITY (Text)
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