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

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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.

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Notes

  1. Note that MGWR standardizes all the variables for easy interpretation. Therefore, fatality score will be used consistently in the result and discussion section.

  2. The variables that formed Factor 1 include Carpooled with rotated factor loading of 0.624; MBSA = -0.973; Service = 0.61; NCM = 0.74; PTMM = 0.75; WorkHome = -0.56 (% of variance = 26.08). Factor 2 includes DriveAlone = -0.741 and Pub_Walk = 0.871 (% of variance = 13.65). Factor 3 includes Percent Total population employed in 2011 (p_11Emp) = 0.54; Mean travel time to work or MTT = 0.78, Census tract population = 0.61 that joint represent a partial variation of 13.12%. Variables that compose Factor 4 are sales occupation = 0.74 and driving alone which represent a partial variation of 10.6%. Please, refer to Table 2 for full description of the variables.

  3. We tried to compute a Monte Carlo (MC) test of spatial variability for each estimated coefficient surface at 1000 iterations. It allows us to assess the probability of obtaining the observed spatial variation of a given surface by random chance. The test should produce a list of pseudo-p-values that correspond to the model parameter surfaces. A pseudo-p value smaller than 0.05 implies that the observed spatial variability of a coefficient surface is significant at the 95% confidence level and not due to random chances (Oshan et al. 2019). However, we stopped the analysis after 12 days running. The disadvantage of the procedure is that it is computationally time demanding. We suspect that the longtime it took to run the MC with no success was because of the PC capacity (Processor: 2.70, internal RAM: 8 GB, OS: 64-bit). The analysis would run faster on higher capacity machine.

  4. The fatality ratio in Odessa in 2016, 2015, 2014, 2013, 2012, 2011, and 2010 were 788, 918, 954, 951, 1043, 570, 1232 per 100 000 crashes, respectively.

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Acknowledgements

The authors appreciate the two anonymous reviewers and the following people for their assistance in proofreading the final draft of the manuscript: Molly Miranker and Walter Furnace of the department of Geography, Texas State University, San Marcos. We also extend our appreciation to the mgwr software team from the Spatial Analysis Research Center (SPARC), Arizona State University, Tempe, USA, for making the software available for the study.

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  1. Department of Geography, Texas State University, San Marcos, TX, 78666, USA

    Ayodeji E. Iyanda

  2. Department of Geography, University of Ibadan, Ibadan, Oyo State, Nigeria

    Tolulope Osayomi

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  1. Ayodeji E. Iyanda
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  2. Tolulope Osayomi
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Correspondence to Ayodeji E. Iyanda.

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Iyanda, A.E., Osayomi, T. Is there a relationship between economic indicators and road fatalities in Texas? A multiscale geographically weighted regression analysis. GeoJournal 86, 2787–2807 (2021). https://doi.org/10.1007/s10708-020-10232-1

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  • DOI: https://doi.org/10.1007/s10708-020-10232-1

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