Open data and injuries in urban areas

Detalhes bibliográficos
Autor(a) principal: Vaz, Eric
Data de Publicação: 2021
Outros Autores: Cusimano, Michael D., Bação, Fernando, Damásio, Bruno, Penfound, Elissa
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/114401
Resumo: Vaz, E., Cusimano, M. D., Bação, F., Damásio, B., & Penfound, E. (2021). Open data and injuries in urban areas: A spatial analytical framework of Toronto using machine learning and spatial regressions. PLoS ONE, 16(March), 1-17. [e0248285]. https://doi.org/10.1371/journal.pone.0248285
id RCAP_d1a5f98f8b82b397dd806f0da78e1a9a
oai_identifier_str oai:run.unl.pt:10362/114401
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Open data and injuries in urban areasA spatial analytical framework of Toronto using machine learning and spatial regressionsinjuriesTorontoCanadaMorbidityWellbeingSpatial analysisBiochemistry, Genetics and Molecular Biology(all)Agricultural and Biological Sciences(all)GeneralSDG 3 - Good Health and Well-beingSDG 10 - Reduced InequalitiesVaz, E., Cusimano, M. D., Bação, F., Damásio, B., & Penfound, E. (2021). Open data and injuries in urban areas: A spatial analytical framework of Toronto using machine learning and spatial regressions. PLoS ONE, 16(March), 1-17. [e0248285]. https://doi.org/10.1371/journal.pone.0248285Injuries 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.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNVaz, EricCusimano, Michael D.Bação, FernandoDamásio, BrunoPenfound, Elissa2021-03-24T23:27:53Z2021-03-112021-03-11T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article17application/pdfhttp://hdl.handle.net/10362/114401eng1932-6203PURE: 28829307https://doi.org/10.1371/journal.pone.0248285info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:57:05Zoai:run.unl.pt:10362/114401Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:42:31.199022Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Open data and injuries in urban areas
A spatial analytical framework of Toronto using machine learning and spatial regressions
title Open data and injuries in urban areas
spellingShingle Open data and injuries in urban areas
Vaz, Eric
injuries
Toronto
Canada
Morbidity
Wellbeing
Spatial analysis
Biochemistry, Genetics and Molecular Biology(all)
Agricultural and Biological Sciences(all)
General
SDG 3 - Good Health and Well-being
SDG 10 - Reduced Inequalities
title_short Open data and injuries in urban areas
title_full Open data and injuries in urban areas
title_fullStr Open data and injuries in urban areas
title_full_unstemmed Open data and injuries in urban areas
title_sort Open data and injuries in urban areas
author Vaz, Eric
author_facet Vaz, Eric
Cusimano, Michael D.
Bação, Fernando
Damásio, Bruno
Penfound, Elissa
author_role author
author2 Cusimano, Michael D.
Bação, Fernando
Damásio, Bruno
Penfound, Elissa
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Vaz, Eric
Cusimano, Michael D.
Bação, Fernando
Damásio, Bruno
Penfound, Elissa
dc.subject.por.fl_str_mv injuries
Toronto
Canada
Morbidity
Wellbeing
Spatial analysis
Biochemistry, Genetics and Molecular Biology(all)
Agricultural and Biological Sciences(all)
General
SDG 3 - Good Health and Well-being
SDG 10 - Reduced Inequalities
topic injuries
Toronto
Canada
Morbidity
Wellbeing
Spatial analysis
Biochemistry, Genetics and Molecular Biology(all)
Agricultural and Biological Sciences(all)
General
SDG 3 - Good Health and Well-being
SDG 10 - Reduced Inequalities
description Vaz, E., Cusimano, M. D., Bação, F., Damásio, B., & Penfound, E. (2021). Open data and injuries in urban areas: A spatial analytical framework of Toronto using machine learning and spatial regressions. PLoS ONE, 16(March), 1-17. [e0248285]. https://doi.org/10.1371/journal.pone.0248285
publishDate 2021
dc.date.none.fl_str_mv 2021-03-24T23:27:53Z
2021-03-11
2021-03-11T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/114401
url http://hdl.handle.net/10362/114401
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1932-6203
PURE: 28829307
https://doi.org/10.1371/journal.pone.0248285
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 17
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
_version_ 1799138036344160256

Registros relacionados