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Article: Development and deployment of the Computer Assisted Neighborhood Visual Assessment System (CANVAS) to measure health-related neighborhood conditions

TitleDevelopment and deployment of the Computer Assisted Neighborhood Visual Assessment System (CANVAS) to measure health-related neighborhood conditions
Authors
KeywordsGoogle street view
Systematic social observation
Disorder
Neighborhood audit
Walkability
Issue Date2015
Citation
Health and Place, 2015, v. 31, p. 163-172 How to Cite?
Abstract© 2014 Elsevier Ltd. Public health research has shown that neighborhood conditions are associated with health behaviors and outcomes. Systematic neighborhood audits have helped researchers measure neighborhood conditions that they deem theoretically relevant but not available in existing administrative data. Systematic audits, however, are expensive to conduct and rarely comparable across geographic regions. We describe the development of an online application, the Computer Assisted Neighborhood Visual Assessment System (CANVAS), that uses Google Street View to conduct virtual audits of neighborhood environments. We use this system to assess the inter-rater reliability of 187 items related to walkability and physical disorder on a national sample of 150 street segments in the United States. We find that many items are reliably measured across auditors using CANVAS and that agreement between auditors appears to be uncorrelated with neighborhood demographic characteristics. Based on our results we conclude that Google Street View and CANVAS offer opportunities to develop greater comparability across neighborhood audit studies.
Persistent Identifierhttp://hdl.handle.net/10722/277016
ISSN
2021 Impact Factor: 4.931
2020 SCImago Journal Rankings: 1.341
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorBader, Michael D.M.-
dc.contributor.authorMooney, Stephen J.-
dc.contributor.authorLee, Yeon Jin-
dc.contributor.authorSheehan, Daniel-
dc.contributor.authorNeckerman, Kathryn M.-
dc.contributor.authorRundle, Andrew G.-
dc.contributor.authorTeitler, Julien O.-
dc.date.accessioned2019-09-18T08:35:21Z-
dc.date.available2019-09-18T08:35:21Z-
dc.date.issued2015-
dc.identifier.citationHealth and Place, 2015, v. 31, p. 163-172-
dc.identifier.issn1353-8292-
dc.identifier.urihttp://hdl.handle.net/10722/277016-
dc.description.abstract© 2014 Elsevier Ltd. Public health research has shown that neighborhood conditions are associated with health behaviors and outcomes. Systematic neighborhood audits have helped researchers measure neighborhood conditions that they deem theoretically relevant but not available in existing administrative data. Systematic audits, however, are expensive to conduct and rarely comparable across geographic regions. We describe the development of an online application, the Computer Assisted Neighborhood Visual Assessment System (CANVAS), that uses Google Street View to conduct virtual audits of neighborhood environments. We use this system to assess the inter-rater reliability of 187 items related to walkability and physical disorder on a national sample of 150 street segments in the United States. We find that many items are reliably measured across auditors using CANVAS and that agreement between auditors appears to be uncorrelated with neighborhood demographic characteristics. Based on our results we conclude that Google Street View and CANVAS offer opportunities to develop greater comparability across neighborhood audit studies.-
dc.languageeng-
dc.relation.ispartofHealth and Place-
dc.subjectGoogle street view-
dc.subjectSystematic social observation-
dc.subjectDisorder-
dc.subjectNeighborhood audit-
dc.subjectWalkability-
dc.titleDevelopment and deployment of the Computer Assisted Neighborhood Visual Assessment System (CANVAS) to measure health-related neighborhood conditions-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.healthplace.2014.10.012-
dc.identifier.pmid25545769-
dc.identifier.scopuseid_2-s2.0-84920920607-
dc.identifier.volume31-
dc.identifier.spage163-
dc.identifier.epage172-
dc.identifier.eissn1873-2054-
dc.identifier.isiWOS:000348202100021-
dc.identifier.issnl1353-8292-

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