Is there a relationship between economic indicators and road fatalities in Texas? A multiscale geographically weighted regression analysis

Abstract

To assess spatial heterogeneity in geographic data, geographically weighted regression (GWR) has been widely used. This study used an advanced version of GWR, multiscale geographically weighted regression (MGWR), which provides a unique extension that allows each predictor to be associated with a distinct bandwidth in predicting traffic fatalities in Texas. Traffic data from fatality analysis reporting system (FARS) between 2010 and 2015, aggregated at the census tract level (N = 5265), were used to examine different scales at which selected economic variables explain the traffic road fatality rate per 100,000 population. Twelve economic variables were initially selected and reduced to four factors (ride-sharing to work, driving alone, mean travel time to work, and work commuting) using the varimax rotation technique. The spatial pattern of the four factors in the GWR model differs significantly from MGWR in spatial patterns, signs, and values relative to the traffic fatality rate. The diagnostic results showed that traditional GWR over fitted the predictors compared to MGWR (max. condition number in GWR = 28.3 versus MGWR = 9.6; adjusted R2 GWR = 61.8% versus MGWR = 44.5%). The application of the MGWR technique is a robust technique in ensuring the correct process scale or bandwidth in modeling spatial data such as road traffic fatality for place and scale-specific intervention purposes. We discussed the three levels of scale identified in the MGWR model for traffic planning intervention and policymaking. Lastly, we concluded with how MGWR mitigates the common problem of aggregated data such as MAUP.