Mapping poverty and food security: A spatial correlation analysis in central java

Poverty and food security are two closely interrelated global issues and are top priorities in the Sustainable Development Goals (SDGs) agenda, particularly SDG 1 (no poverty) and SDG 2 (no hunger). This study aims to analyze the spatial correlation between poverty and food security in Central Java in the 2023–2024 period. The research method used is a quantitative descriptive approach with spatial analysis using Moran's I and Local Indicator of Spatial Association (LISA). Secondary data were obtained from the Central Statistics Agency (BPS), the National Food Agency, and administrative maps in shapefile form. The analysis was conducted using GeoDa software, by examining univariate and bivariate spatial autocorrelation patterns, as well as mapping High-High, Low-Low, High-Low, and Low-High clusters. The results show that the distribution of poverty and food security indices in Central Java is not random, but rather forms a clustered pattern. Bivariate analysis shows a negative spatial correlation, where areas with high poverty rates tend to be associated with low food security. The LISA Bivariate Map identifies clusters of High-High areas concentrated in the southern and coastal regions, while urban areas tend to be in the Low-Low category with relatively better socio-economic conditions. The implication of these findings is the importance of spatially based development policies that integrate poverty alleviation programs with improving food security. Therefore, spatially integrated policy interventions are crucial. Local governments are recommended to prioritize targeted programs in High–High areas, including improving rural food logistics and distribution infrastructure, expanding community-based microfinance and agricultural innovation programs.