IDENTIFICATION OF ROAD CRASH ZONES, SPATIAL PATTERNS, AND EMERGING HOT SPOTS OF ROAD TRAFFIC INJURY SEVERITY IN PHUKET, THAILAND

Abstract

Road traffic accidents are a critical global concern. Thailand, a middle-income country, has the highest road traffic mortality rate in Southeast Asia. Phuket, which generates the second-highest tourism revenue in the country after Bangkok, faces road traffic accidents as one of the leading causes of death among both residents and tourists. This study aims to apply space-time cube (STC) analysis to identify high-risk road crash zones in Phuket. Secondary data on road accident locations from 2017 to 2023 were utilized for analysis. The methodological framework included the Severity Index analysis, Spatial Autocorrelation (Moran’s I), Hotspot Analysis (Getis-Ord Gi*), and space-time cube (STC) analysis. The findings indicated an overall increase in road accidents across Phuket's road network, characterized by a rise in minor accidents and a decline in major accidents. This trend resulted in a yearly reduction in the average severity index from 2017 to 2023. Spatial analysis revealed that road accidents in Phuket exhibited a clustered pattern. The distribution of hot and cold spots was predominantly spatially random, accounting for 91.3% of all accident locations. The proportion of hot spots (4.710%) was higher than that of cold spots (3.995%). The spatial analysis of road accidents in Phuket Province, conducted using Getis-Ord statistics, identified Mueang Phuket District as the area with the highest concentration of accident hotspots, particularly in Rawai and Karon Subdistricts. Kathu District ranked second, with the majority of hotspots located in Patong Subdistrict. Thalang District followed, with Pa Khlok Subdistrict exhibiting the highest density of hotspots in that area Furthermore, the spatial distribution of road accidents suggests a strong correlation between high-risk zones and urban zones. The space-time cube (STC) analysis further identified both consecutive hot spots and sporadic hot spots, particularly in urban and built-up areas. These results offer empirical evidence to support spatially targeted traffic safety interventions. The integration of spatial and temporal perspectives highlights the dynamic nature of road crash patterns, enabling local authorities to prioritize areas for immediate and long-term interventions. This study contributes to the growing field of geospatial health by applying advanced spatial-temporal techniques to road safety analysis in a developing tourism context.