Geospatial Modeling of Autonomous Vehicle Collision Reports: A Traffic Analysis Zone-Based Study of Spatial Heterogeneity in San Francisco

Autonomous vehicle collision reports provide valuable geospatial information for understanding how emerging mobility systems interact with urban built environments. However, existing studies have mostly examined autonomous vehicle crashes from a global statistical or traffic safety perspective, with less attention paid to the construction of spatial analysis units, multi-source geospatial data integration, and local spatial non-stationarity. This study develops a Traffic Analysis Zone (TAZ)-based geospatial framework to analyze the spatial distribution of autonomous vehicle collision reports in San Francisco. A total of 427 collision reports from 2015 to 2024 were geocoded and integrated with road infrastructure, land-use, and geographic information data within a unified Geographic Information System (GIS) environment. Several global and spatial regression models, including negative binomial regression, generalized linear models, spatial autoregressive modeling, and geographically weighted regression, were compared to examine the association between built-environment variables and collision report counts. The results show that autonomous vehicle collision reports are spatially clustered and that several road infrastructure variables, including bicycle lanes, traffic signs, and intersections, are associated with their spatial distribution. The geographically weighted regression results further indicate that these associations vary across San Francisco, suggesting clear local spatial non-stationarity. This study contributes a GIS-based workflow for integrating autonomous vehicle collision reports with urban spatial data and provides exploratory evidence for spatially targeted safety diagnosis in smart city and urban mobility contexts.