Triple-stack geostatistical modeling for urban injury: Integrating grids, built environment, and Poisson kriging for pedestrian fatalities in Cali, Colombia.

Abstract

We introduce a novel geostatistical framework to map and predict pedestrian fatalities in Cali, Colombia (2008-2010), addressing critical gaps in research on built environment risk factors particularly in low- and middle-income countries. We triple stack: (1) a grid-based global moving window reducing the modifiable areal unit problem, redistributing events and population; (2) a Poisson-based land use regression incorporating built environment data; and (3) Poisson kriging addressing small number issues. Nine built environment variables were significant predictors of pedestrian fatalities. Health centers had the highest risk increase (54%), while parks and population density were protective. The triple-stack model markedly improved risk prediction, sharpening hotspot delineation and reducing background noise; at high population densities, predictive errors were as low as 0.26 deaths. The framework is a transferable tool for other urban contexts; thus, we provide method decision-making support helping users select between a simplified, codeless, GUI based platform or an advanced, custom-coded approach. Our results demonstrate gains in model predictive accuracy may be primarily attributable to the first two layers, the grid and LUR. Although, Poisson kriging is traditionally used to address the "small number problem," the first two layers may substantially stabilize rates, resulting in more realistic, interpretable risk maps.