Spatial Analysis of School-Related Pedestrian Crashes Near Urban Schools: Comparing Area-Based and Segment-Based Models in Developing Countries

Abstract

Ensuring pedestrian safety, especially for children near urban schools in developing countries, has always been critical in transportation and urban planning. Most prior research has employed area-based approaches with global statistical methods, which overlook spatial heterogeneity. This study explored how the built environment near schools affects pedestrian crashes, using both area-based and segment-based approaches. Eight models—global and local—were developed to analyze factors influencing pedestrian crashes near schools. Global models included Poisson regression (PR1) and negative binomial regression (NB2) for area-based analysis and binary logistic regression (LR1 and LR2) for segment-based analysis. Local models included geographically weighted Poisson (GP1 and GP2) and logistic (GL1 and GL2) regression for area-based and segment-based analyses, respectively. These models were applied using 5 years of crash data across two strategies: (1) school-age and (2) all-age. High-crash locations were identified using kernel density estimation based on significant variables. The results revealed that local area-based models (GP1 and GP2) have better accuracy than their corresponding global models. These local models showed that some variables (such as transit stop’ density and land use entropy) had location-specific effects. In global models, the all-age area-based model (NB2) demonstrated that some variables positively associate (arterial roads, transit stops’ density, average betweenness, and land use entropy) with crash counts during school start and end times. The segment-based models (LR1 and LR2) showed that some variables (arterial roads, average betweenness, one-way streets, and commercial land use) increase crash likelihood, while some others (residential land use, medians and pedestrian overpasses) reduce it.