Examining Open Data at the Urban Level: An Exploration of “Wellbeing Toronto”

Barber, Kathryn; MacLellan, Duncan

doi:10.1080/10630732.2018.1558573

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…Wellbeing Toronto (WT) data were used to assess critical variables at the neighborhood level for Toronto. WT corresponds to an integrative and open approach for visualization of Toronto's 140 neighborhoods [20]. As an open data concept, it hosts a significant amount of data over three reference periods (2008, 2011, and 2014), with crucial variables encouraging citizen participation, government accountability, and data transparency ( Figure 3).…”

Section: Socio-economic Datamentioning

confidence: 99%

COVID-19 in Toronto: A Spatial Exploratory Analysis

Vaz

2021

Sustainability

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COVID-19 has had a significant impact on a global scale. Evident signs of spatial-explicit characteristics have been noted. Nevertheless, publicly available data are scarce, impeding a complete picture of the locational impacts of COVID-19. This paper aimed to assess, confirm, and validate several geographical attributes of the geography of the pandemic. A spatial modeling framework defined whether there was a clear spatial profile to COVID-19 and the key socio-economic characteristics of the distribution in Toronto. A stepwise backward regression model was generated within a geographical information systems framework to establish the key variables influencing the spread of COVID-19 in Toronto. Further to this analysis, spatial autocorrelation was performed at the global and local levels, followed by an error and lag spatial regression to understand which explanatory framework best explained disease spread. The findings support that COVID-19 is strongly spatially explicit and that geography matters in preventing spread. Social injustice, infrastructure, and neighborhood cohesion are evident characteristics of the increasing spread and incidence of COVID-19. Mitigation of incidents can be carried out by intertwining local policies with spatial monitoring strategies at the neighborhood level throughout large cities, ensuring open data and adequacy of information management within the knowledge chain.

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Section: Socio-economic Datamentioning

confidence: 99%

COVID-19 in Toronto: A Spatial Exploratory Analysis

Vaz

2021

Sustainability

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“…Wellbeing Toronto (WT) data was used to assess critical variables at the neighborhood level for Toronto ( Fig 1 ). WT corresponds to an integrative and open approach for visualization of Toronto’s 140 neighborhoods [ 46 ]. As an open data concept, it hosts a significant amount of data over three reference periods (2008, 2011, and 2014), that include crucial variables encouraging citizen participation, government accountability, and data transparency.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Socio-economic Datamentioning

confidence: 99%

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

de Noronha Vaz,

Cusimano,

Bação

et al. 2023

Preprint

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<p>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.</p> <p> </p>

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Examining Open Data at the Urban Level: An Exploration of “Wellbeing Toronto”

Cited by 7 publications

References 20 publications

COVID-19 in Toronto: A Spatial Exploratory Analysis

COVID-19 in Toronto: A Spatial Exploratory Analysis

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

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

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