New methods for resilient societies: The geographical analysis of injury data
In 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.