Analisis Autokorelasi Spasial Global dan Lokal pada Data Kemiskinan di Provinsi Gorontalo - Alifa Fitriana Putri Yuaswan; Diska Audian Maharani; Ni Kadek Yuni Antari; Luh Putu Deswinta Dharmariani; Ni Kadek Intan Dwi Pratiwi; Ni Luh Putu Suciptawati (Lembaga Pengembangan Kinerja Dosen, 2026)
This study aims to analyze the spatial pattern of poverty in Gorontalo Province in 2024 using global and local spatial autocorrelation approaches. The data used are the percentages of the poor population in each regency/municipality, obtained from the BPS. The analyses include descriptive statistical analysis, the Moran's Index test for global spatial autocorrelation, and the Local Indicators of Spatial Association (LISA) for local autocorrelation. The results show that poverty in Gorontalo Province tends to be unevenly distributed and exhibits a significant spatial pattern. The Moran's Index indicates positive spatial autocorrelation, where areas with high poverty levels tend to be adjacent to other areas with similarly high poverty levels. The LISA results identify Bone Bolango Regency as a High-Low area, meaning it has a high poverty rate but is surrounded by areas with low poverty rates. These findings highlight the importance of spatial approaches in formulating more targeted poverty alleviation policies.