dc.contributor.author | Luan, Hui | |
dc.contributor.author | Law, Jane | |
dc.contributor.author | Lysy, Martin | |
dc.date.accessioned | 2018-10-22 12:09:50 (GMT) | |
dc.date.available | 2018-10-22 12:09:50 (GMT) | |
dc.date.issued | 2018-02-01 | |
dc.identifier.uri | https://dx.doi.org/10.1016/j.sste.2017.12.001 | |
dc.identifier.uri | http://hdl.handle.net/10012/14010 | |
dc.description | The final publication is available at Elsevier via https://dx.doi.org/10.1016/j.sste.2017.12.001 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ | en |
dc.description.abstract | Neighborhood restaurant environment (NRE) plays a vital role in shaping residents' eating behaviors. While NRE 'healthfulness' is a multi-facet concept, most studies evaluate it based only on restaurant type, thus largely ignoring variations of in-restaurant features. In the few studies that do account for such features, healthfulness scores are simply averaged over accessible restaurants, thereby concealing any uncertainty that attributed to neighborhoods' size or spatial correlation. To address these limitations, this paper presents a Bayesian Spatial Factor Analysis for assessing NRE healthfulness in the city of Kitchener, Canada. Several in-restaurant characteristics are included. By treating NRE healthfulness as a spatially correlated latent variable, the adopted modeling approach can: (i) identify specific indicators most relevant to NRE healthfulness, (ii) provide healthfulness estimates for neighborhoods without accessible restaurants, and (iii) readily quantify uncertainties in the healthfulness index. Implications of the analysis for intervention program development and community food planning are discussed. (c) 2017 Elsevier Ltd. All rights reserved. | en |
dc.language.iso | en | en |
dc.publisher | Elsevier | en |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Neighborhood restaurant environment | en |
dc.subject | Consumer nutrition environment | en |
dc.subject | Bayesian inference | en |
dc.subject | Spatial modeling | en |
dc.subject | Factor analysis | en |
dc.subject | Community food planning | en |
dc.title | Diving into the consumer nutrition environment: A Bayesian spatial factor analysis of neighborhood restaurant environment | en |
dc.type | Article | en |
dcterms.bibliographicCitation | Luan, H., Law, J., & Lysy, M. (2018). Diving into the consumer nutrition environment: A Bayesian spatial factor analysis of neighborhood restaurant environment. Spatial and Spatio-Temporal Epidemiology, 24, 39–51. doi:10.1016/j.sste.2017.12.001 | en |
uws.contributor.affiliation1 | Faculty of Applied Health Sciences | en |
uws.contributor.affiliation1 | Faculty of Mathematics | en |
uws.contributor.affiliation1 | Faculty of Environment | en |
uws.contributor.affiliation2 | School of Public Health and Health Systems | en |
uws.contributor.affiliation2 | Statistics and Actuarial Science | en |
uws.contributor.affiliation2 | School of Planning | en |
uws.typeOfResource | Text | en |
uws.typeOfResource | Text | en |
uws.peerReviewStatus | Reviewed | en |
uws.scholarLevel | Faculty | en |