Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77752
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dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorZhao, P-
dc.creatorKwan, MP-
dc.creatorZhou, S-
dc.date.accessioned2018-08-28T01:34:33Z-
dc.date.available2018-08-28T01:34:33Z-
dc.identifier.issn1661-7827-
dc.identifier.urihttp://hdl.handle.net/10397/77752-
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rights© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Zhao, P.; Kwan, M.-P.; Zhou, S. The Uncertain Geographic Context Problem in the Analysis of the Relationships between Obesity and the Built Environment in Guangzhou. Int. J. Environ. Res. Public Health 2018, 15, 308 is available at httpsen_US
dc.subjectActivity spaceen_US
dc.subjectBuilt environmenten_US
dc.subjectObesityen_US
dc.subjectRegression analysisen_US
dc.subjectUGCoPen_US
dc.titleThe uncertain geographic context problem in the analysis of the relationships between obesity and the built environment in guangzhouen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume15-
dc.identifier.issue2-
dc.identifier.doi10.3390/ijerph15020308-
dcterms.abstractTraditionally, static units of analysis such as administrative units are used when studying obesity. However, using these fixed contextual units ignores environmental influences experienced by individuals in areas beyond their residential neighborhood and may render the results unreliable. This problem has been articulated as the uncertain geographic context problem (UGCoP). This study investigates the UGCoP through exploring the relationships between the built environment and obesity based on individuals’ activity space. First, a survey was conducted to collect individuals’ daily activity and weight information in Guangzhou in January 2016. Then, the data were used to calculate and compare the values of several built environment variables based on seven activity space delineations, including home buffers, workplace buffers (WPB), fitness place buffers (FPB), the standard deviational ellipse at two standard deviations (SDE2), the weighted standard deviational ellipse at two standard deviations (WSDE2), the minimum convex polygon (MCP), and road network buffers (RNB). Lastly, we conducted comparative analysis and regression analysis based on different activity space measures. The results indicate that significant differences exist between variables obtained with different activity space delineations. Further, regression analyses show that the activity space delineations used in the analysis have a significant influence on the results concerning the relationships between the built environment and obesity. The study sheds light on the UGCoP in analyzing the relationships between obesity and the built environment.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationInternational journal of environmental research and public health, Feb. 2018, v. 15, no. 2, 308, p. 1-20-
dcterms.isPartOfInternational journal of environmental research and public health-
dcterms.issued2018-
dc.identifier.isiWOS:000426721400133-
dc.identifier.scopus2-s2.0-85042040356-
dc.identifier.eissn1660-4601-
dc.identifier.artn308-
dc.description.validate201808 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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