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Spatial Assessment of Road Traffic Injuries in the Greater Toronto Area (GTA): Spatial Analysis Framework

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Abstract

This research presents a Geographic Information Systems (GIS) and spatial analysis approach based on the global spatial autocorrelation of road traffic injuries for identifying spatial patterns. A locational spatial autocorrelation was also used for identifying traffic injury at spatial level. Data for this research study were acquired from Canadian Institute for Health Information (CIHI) based on 2004 and 2011. Moran’s I statistics were used to examine spatial patterns of road traffic injuries in the Greater Toronto Area (GTA). An assessment of Getis-Ord Gi* statistic was followed as to identify hot spots and cold spots within the study area. The results revealed that Peel and Durham have the highest collision rate for other motor vehicle with motor vehicle. Geographic weighted regression (GWR) technique was conducted to test the relationships between the dependent variable, number of road traffic injury incidents and independent variables such as number of seniors, low education, unemployed, vulnerable groups, people smoking and drinking, urban density and average median income. The result of this model suggested that number of seniors and low education have a very strong correlation with the number of road traffic injury incidents.

Suggested Citation

  • Vaz, Eric & Tehranchi, Sina & Cusimano, Michael, 2017. "Spatial Assessment of Road Traffic Injuries in the Greater Toronto Area (GTA): Spatial Analysis Framework," Journal of Tourism, Sustainability and Well-being, Cinturs - Research Centre for Tourism, Sustainability and Well-being, University of Algarve, vol. 5(1), pages 37-55.
    • Handle: RePEc:ris:jspord:0930
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    1. Marco Helbich & Wolfgang Brunauer & Eric Vaz & Peter Nijkamp, 2014. "Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria," Urban Studies, Urban Studies Journal Limited, vol. 51(2), pages 390-411, February.
    2. Morency, P. & Gauvin, L. & Plante, C. & Fournier, M. & Morency, C., 2012. "Neighborhood social inequalities in road traffic injuries: The influence of traffic volume and road design," American Journal of Public Health, American Public Health Association, vol. 102(6), pages 1112-1119.
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    6. Vaz, Eric & Khaper, Monica, 2016. "New Resources For Smart Food Retail Mapping. A Gis And The Open Source Perspective," Journal of Tourism, Sustainability and Well-being, Cinturs - Research Centre for Tourism, Sustainability and Well-being, University of Algarve, vol. 4(4), pages 305-313.
    7. Chisholm, Daniel & Naci, Huseyin & Hyder, Adnan Ali & Tran, Nhan T. & Peden, Margie, 2012. "Cost effectiveness of strategies to combat road traffic injuries in sub-Saharan Africa and South East Asia: mathematical modelling study," LSE Research Online Documents on Economics 55474, London School of Economics and Political Science, LSE Library.
    8. Vaz, Eric, 2013. "The Spatial Business Landscape of India," Journal of Tourism, Sustainability and Well-being, Cinturs - Research Centre for Tourism, Sustainability and Well-being, University of Algarve, vol. 1(4), pages 241-253.
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    10. Mackie, Peter, 2005. "The London congestion charge: A tentative economic appraisal. A comment on the paper by Prud'homme and Bocajero," Transport Policy, Elsevier, vol. 12(3), pages 288-290, May.
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    Cited by:

    1. Teresa de Noronha & Eric Vaz, 2020. "Theoretical Foundations in Support of Small and Medium Towns," Sustainability, MDPI, vol. 12(13), pages 1-15, July.
    2. Eric Vaz, 2021. "COVID-19 in Toronto: A Spatial Exploratory Analysis," Sustainability, MDPI, vol. 13(2), pages 1-15, January.

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    More about this item

    Keywords

    Spatial Analysis; Geographic Information Systems; Injury Analytics; Traffic Injuries; Geographically Weighted Regression;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • I10 - Health, Education, and Welfare - - Health - - - General
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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