Highlights This study examines the relationships between social vulnerability and crashes. MGWR can depict spatially varying relationships between crashes and SVIs. CDC SVI can help reveal the social inequities of traffic safety in Texas.

    Abstract Understanding the relationship between social vulnerability and traffic crashes is a cornerstone for promoting social justice in transportation planning and policymaking. However, few studies have examined the disparities in traffic crashes by systemically considering the influence of social vulnerability via spatial analysis approaches. This study puts forward a new approach to assess the inequity in transportation safety by spatially examining the relationships between crash risks and the social vulnerability index (SVI) established by the Centers for Disease Control and Prevention (CDC). We performed spatial autocorrelation analyses to identify the clusters of high-risk and high-vulnerable census tracts in Texas. Meanwhile, we innovatively applied the Multiscale Geographically Weighted Regression model (MGWR) to assess the impacts of CDC SVI on crash risks spatially and statistically. The results demonstrate that the crash rate and the social vulnerability are significantly correlated in the highly urbanized regions as well as the southern border along the Rio Grande in Texas. The MGWR results indicate the minority status of census tracts is strongly correlated with overall crashes in north-central and northeastern Texas, and the socioeconomic status is tightly correlated with fatal crashes across Texas. The outcomes from this study have significant implications for transportation planning and policymaking.


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    Title :

    Do underserved and socially vulnerable communities observe more crashes? A spatial examination of social vulnerability and crash risks in Texas


    Contributors:
    Li, Xiao (author) / Yu, Siyu (author) / Huang, Xiao (author) / Dadashova, Bahar (author) / Cui, Wencong (author) / Zhang, Zhe (author)


    Publication date :

    2022-05-23




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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