Browsing by Author "Cusimano, Michael"
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- A local spatial analysis criterion of post-traumatic brain injury and accessibility to public transportationPublication . Vaz, Eric; Foster, Akeem; Cusimano, MichaelReported cases of traumatic brain injuries are increasing among the Canadian population. With an annual rate of 187,000 reported cases a year and growing, there is an extrapolated growth of 239,000 cases of traumatic brain injuries occurring annually by 2036. As Ontario intends to be a completely accessible province for those with disabilities by 2025, this paper utilizes GIS to visualize and better understand the relationship between post- TBI residents living in Brampton and their accessibility to public transportation. As Brampton is currently the most expensive city to insure a vehicle because of frequent collisions occurring within the city, creating a more accessible, reliable, and efficient public transportation system can integrate those who have experienced a traumatic brain injury back into society while reducing the required use of a personal vehicle. This will contribute to a safer city, as there are fewer vehicles on the road at risk of being involved in a road accident. There are also further benefits to this, as it will also reduce levels of congestion in the foreseeable future.
- New methods for resilient societies: The geographical analysis of injury dataPublication . Vaz, Eric; Miki, Jessica; De Noronha, Maria Teresa; Cusimano, MichaelIn this paper an empirical assessment of injury patterns is supplied as an example of social endurance -resilient societies can be built by means of geographical analysis of injury data, providing better support for decision makers regarding urban safety. Preventing road traffic collisions with vulnerable road users, such as pedestrians, could help mitigate significant loses and improve infrastructure planning. In this sense, the geographical aspects of injury prevention are of clear spatial analog, and should be tested regarding the carrying capacity of urban areas as well as vulnerability for growing urban regions. The application of open source development tool for spatial analysis research in health studies is addressed. The study aims to create a framework of available open source tools through Python that enable better decision making through a systematic review of existing tools for spatial analysis. Methodologically, spatial autocorrelation indices are tested as well as influential variables are brought forward to establish a better understanding of the incremental concern of injuries in rural areas, in general, and in the Greater Toronto Area, in particular. By using Python Library for Spatial Analysis (PySAL), an integrative vision of assessing a growing epidemiological concern of injuries in Toronto, one of North America's fastest growing economic metropolises is offered. In this sense, this study promotes the use of PySAL and open source toolsets for integrating spatial analysis and geographical analysis for health practitioners. The novelty and capabilities of open source tools through methods such as PySAL allow for a cost efficiency as well as give planning an easier methodological toolbox for advances spatial modelling techniques.
- Spatial assessment of road traffic injuries in the greater Toronto area (GTA): Spatial analysis frameworkPublication . Vaz, Eric; Tehranchi, Sina; Cusimano, MichaelThis 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.