Analisis Autokorelasi Spasial Global dan Lokal Pada Data Kemiskinan Provinsi Bali - Windy Lestari; Adika Setia Brata; Alfian Anhar; Suci Rahmawati (Jambura Journal of Mathematics, 2023)
Poverty is a crucial problem that often occurs in Indonesia, including in the province of Bali. The poverty rate rate in Bali has increased significantly over the last 3 years, so further analysis is needed so that this poverty rate can be reduced. The pattern of poverty between regions is different because it depends on the geographical and socio-cultural conditions in each area. The effect of location on poverty cases can be identified by global and local spatial autocorrelation methods. This study aimed to analyze the spatial autocorrelation of poverty data in Bali Province using the global autocorrelation test with the Moran’s and Geary’s C indices as well as the local spatial autocorrelation test with the Local Indicator of Spatial Association (LISA) and Getis-Ord G to obtain an overview of the spatial distribution of poverty data. Based on the global autocorrelation test, it is concluded that using Moran’s index there is a negative spatial autocorrelation in the 2020-2022 data for a=10%. Similar results were also obtained when using the Geary’s C test. In the local autocorrelation test using LISA, it was found that districts had negative spatial autocorrelation, namely in 2020 Buleleng Regency (high-low) and Klungkung (low-low), for 2021 there is Buleleng Regency (high-low) and Jembrana (low-low), while for 2022 only Buleleng Regency (high-low) has negative spatial autocorrelation. For local autocorrelation testing with the Getis-Ord G test, it was found that there were no districts/cities that showed spatial grouping or that there was no spatial autocorrelation locally.