Building density and its implications to COVID-19 health risk management: An example from Yogyakarta, Indonesia

Abstract

Abstract Transmission rates of COVID-19 have been associated with the density of buildings where contact among individuals partially contributes to transmission. The research sought to analyze the spatial distribution of building density derived from satellite images and determine its implications to COVID-19 health risk management using Yogyakarta and its surrounding districts as an example. Fine-scale building distribution obtained through remote sensing data transformation was analyzed with GIS. NDBI was applied to Landsat 8 imagery; then, using multiple linear regression analysis, it was correlated to building density’s training samples generated from high-resolution imagery. The derived percent of building density (PBD) was combined with publicly available records of COVID-19 infection to assess risk. This research found that PBD could explain the uneven COVID-19 diffusion at different stages of its development. Instead of dividing regions into zones based on confirmed cases, government and public health officials should observe new cases in high-PBD districts; then, when the cases are decreasing, their attention should shift to low-PBD districts. Remote sensing data allow for moderate-scale PBD mapping and integrating it with confirmed cases produces spatial health risks, determining target areas for interventions and allowing regionally tailored responses to anticipate or prevent the next wave of infections.