Analyzing the Spatial Patterns of Dengue Hemorrhagic Fever in Bandung City: A GIS-Based Approach

Abstract

Dengue Hemorrhagic Fever (DHF) continues to pose a substantial challenge to public health in Indonesia, particularly in urban settings where disease occurrence is unevenly distributed. Within the urban context of Bandung, DHF cases recorded in several administrative districts exhibit clear spatial variability across different urban zones. Accordingly, this study examines spatial patterns of DHF incidence across six selected high incidence districts of Bandung City Antapani, Kiaracondong, Arcamanik, Rancasari, Buah Batu, and Gedebage using ArcGIS as an integrated Geographic Information System (GIS) environment. These districts were selected based on official dengue surveillance records indicating relatively higher case numbers during specific reporting periods. Spatial analysis was performed through point-based mapping, Kernel Density Estimation (KDE), global spatial autocorrelation analysis using Moran’s I, and local cluster detection employing the Getis-Ord Gi* statistic.The analytical results demonstrate that DHF incidence exhibits pronounced spatial heterogeneity characterized by statistically significant clustering. Global Moran’s I confirms positive spatial autocorrelation (I = 0.3396; z = 3.09; p = 0.002), while KDE highlights consistently higher case concentrations in the eastern to southeastern parts of the study area. In parallel, Getis-Ord Gi* analysis identifies multiple high-risk locations with confidence levels reaching up to 99%. These results underscore the applied value of ArcGIS-based spatial analysis in delineating priority areas and supporting geographically targeted dengue surveillance and intervention strategies.