Examining spatial heterogeneity in built environment and climate factors affecting motorcycle crashes

Abstract

Motorcycle crashes are a major contributor to road traffic fatalities in Cambodia, where motorcycles represent the dominant mode of transportation. Given the spatial dependence and heterogeneity inherent in crash data, this study examines spatial associations between built environment characteristics, climatic factors, and motorcycle crash frequency across 197 districts in Cambodia in 2019. Global Moran’s Index was used to assess spatial autocorrelation in crash frequency and explanatory variables. After evaluating the distributional properties of crash counts and multicollinearity among predictors, several regression models were estimated and compared, including Ordinary Least Squares regression (OLS), Poisson regression (PR), Negative Binomial regression (NBR), and Geographically Weighted Negative Binomial Regression (GWNBR). The results indicate that the GWNBR model outperforms global models by more effectively capturing spatial heterogeneity in the relationships between environmental factors and motorcycle crash frequency. Several variables exhibit relatively consistent spatial association patterns across districts: road length, road density, residential land use proportion, and precipitation are positively associated with motorcycle crash frequency in many locations, whereas population density, intersection density, and the number of annual rainy days are predominantly negatively associated. By revealing spatially varying association patterns in motorcycle crashes, this study provides evidence to support geographically differentiated approaches to motorcycle safety analysis and planning in Cambodia and other low- and middle-income countries.