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Open data and injuries in urban areas—A spatial analytical framework of Toronto using machine learning and spatial regressions
- Source :
- PLoS ONE, PLoS ONE, Vol 16, Iss 3, p e0248285 (2021)
- Publication Year :
- 2021
- Publisher :
- Public Library of Science, 2021.
-
Abstract
- 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.
- Subjects :
- Geographic information system
Critical Care and Emergency Medicine
Databases, Factual
Epidemiology
Social Sciences
Social Geography
Machine Learning
Geoinformatics
Regional science
Medicine and Health Sciences
Public and Occupational Health
Trauma Medicine
Multidisciplinary
Geography
Traumatic Injury Risk Factors
Spatial Autocorrelation
Open data
Medicine
Neighborhoods
Falls
Traumatic Injury
Research Article
medicine.medical_specialty
Computer and Information Sciences
Canada
Science
Human Geography
Population Metrics
Injury prevention
medicine
Humans
Cities
Spatial analysis
Population Density
Population Biology
business.industry
Public health
Urban Health
Biology and Life Sciences
Metropolitan area
Field (geography)
Health Care
Years of potential life lost
Medical Risk Factors
Earth Sciences
Geographic Information Systems
Wounds and Injuries
Health Statistics
Morbidity
business
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 16
- Issue :
- 3
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....22c07fd1e71fcb2a5e664aadd7001858