Open data and injuries in urban areas-A spatial analytical framework of Toronto using machine learning and spatial regressions

• Published in: PloS one 2021-03, Vol.16 (3), p.e0248285
• Main Authors: Vaz, Eric, Cusimano, Michael D, Bação, Fernando, Damásio, Bruno, Penfound, Elissa
• Format: Article
• Language: English
• Subjects: Ambulatory care; Biology and Life Sciences; Cities; Computer and Information Sciences; Distribution; Drafting software; Earth Sciences; Editing; Environmental studies; Geographic information systems; Geography; Geostatistics; Graduate studies; Information management; Injuries; Injury analysis; Injury prevention; Learning algorithms; Literature reviews; Machine learning; Medical geography; Medical research; Medicine and Health Sciences; Medicine, Experimental; Methodology; Native North Americans; Neighborhoods; Open data; Personal health; Prevention; Public health; Regions; Regression analysis; Remote sensing; Reviews; Social Sciences; Spatial analysis; Spatial distribution; Urban areas; Visualization; Wounds and injuries
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0248285

Injuries have become devastating and often under-recognized public health concerns. In Canada, injuries are the leading cause of potential years of life lost before the age of 65. The geographical patterns of injury, however, are evident both over space and time, suggesting the possibility of spatial optimization of policies at the neighborhood scale to mitigate injury risk, foster prevention, and control within metropolitan regions. In this paper, Canada's National Ambulatory Care Reporting System is used to assess unintentional and intentional injuries for Toronto between 2004 and 2010, exploring the spatial relations of injury throughout the city, together with Wellbeing Toronto data. Corroborating with these findings, spatial autocorrelations at global and local levels are performed for the reported over 1.7 million injuries. The sub-categorization for Toronto's neighborhood further distills the most vulnerable communities throughout the city, registering a robust spatial profile throughout. Individual neighborhoods pave the need for distinct policy profiles for injury prevention. This brings one of the main novelties of this contribution. A comparison of the three regression models is carried out. The findings suggest that the performance of spatial regression models is significantly stronger, showing evidence that spatial regressions should be used for injury research. Wellbeing Toronto data performs reasonably well in assessing unintentional injuries, morbidity, and falls. Less so to understand the dynamics of intentional injuries. The results enable a framework to allow tailor-made injury prevention initiatives at the neighborhood level as a vital source for planning and participatory decision making in the medical field in developed cities such as Toronto.