Spatiotemporal Characterization of Variations in Road Traffic Accident Severity in Cole County Using Multiple GIS Techniques
DOI:
https://doi.org/10.31185/wjes.Vol14.Iss3.975Keywords:
Spatial Autocorrelation, Hotspot Analysis, Accident Risk Assessment, Spatial ClusteringAbstract
Whereas the recognition of the pattern of hotspot clusters is critical, this study fills the gap of understanding the spatiotemporal evolution of Road Traffic Accidents (RTAs) severity by selecting Cole County, which consists state’s capital, Jefferson City, between 2020 and 2023. Leveraging an effective GIS-based framework, the research assesses "endurance efficiency" in a three-phased methodology that involved systematic data integration, calculation of an Accident Severity Index (ASI) and sophisticated spatial statistics. In order to ensure statistical significance in the detection of clusters, the research uses Global Moran's I for the spatial autocorrelation, Getis-Ord Gi* for the localized hot/cold spot detection and Kernel Density Estimation (KDE) for the high-resolution density mapping. The results show a steep fall in the number of accidents in April 2020, which is a direct byproduct of the containment measures against the coronavirus. Temporal analysis shows that Saturdays are the peak for RTAs in 2022 with adults (ages 25-64) making up 53% of all occurrences. Critically, high-severity incidents continued to cluster in the downtown of Jefferson City using multiple methods in the GIS analysis. These results deliver a granular, evidence-based foundation for urban planners and law enforcement to implement targeted safety interventions and optimize emergency response allocation.
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