GeoAI for Precision Public Health in Agrarian Economies: Multi-Disease Risk Profiling in Rice Belt in East Java

Public health and food security, particularly in the agribusiness sector, are interconnected. As one of the largest rice-producing provinces in Indonesia, East Java faces numerous infectious diseases. To develop a spatial typology of health-agribusiness risks, this study combines epidemiology, agribusiness, and computer science with a Geospatial Artificial Intelligence (GeoAI) approach.The data includes cases of ten infectious diseases (2015–2024), rice harvested area, number of farmers, and district/city population in East Java. Cases were normalized per 100,000 population, and agribusiness indicators were converted to harvested area per farmer ratios. The analysis used internal validation (silhouette score, Davies–Bouldin Index), K-Means clustering, and spatial validation (Moran's I). Results are displayed on OpenStreetMap.Agribusiness can be divided into three main typologies: (1) strong agribusiness with moderate risk; (2) multisector agribusiness with high risk and moderate agribusiness; and (3) moderate agribusiness with a prevalence of lung disease and diarrhea. Moran's I = -0.0263 (p=0.5678), indicating that spatial distribution is not significant. The results suggest that public health does not always correlate with food production intensity. By integrating epidemiology, agribusiness, and GeoAI to support appropriate public health in agricultural areas, this study adds to the international literature.