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Selective Daily Mobility Bias in the Community Food Environment: Case Study of Greater Hartford, Connecticut
The community food environment has potential influences on community members’ dietary health outcomes, such as obesity and Type II diabetes. However, most existing studies evaluating such health effects neglect human mobility. In food patrons’ daily travels, certain locations may be preferred and pa...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9867517/ https://www.ncbi.nlm.nih.gov/pubmed/36678275 http://dx.doi.org/10.3390/nu15020404 |
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author | Jin, Ailing Chen, Xiang Huang, Xiao Li, Zhenlong Caspi, Caitlin E. Xu, Ran |
author_facet | Jin, Ailing Chen, Xiang Huang, Xiao Li, Zhenlong Caspi, Caitlin E. Xu, Ran |
author_sort | Jin, Ailing |
collection | PubMed |
description | The community food environment has potential influences on community members’ dietary health outcomes, such as obesity and Type II diabetes. However, most existing studies evaluating such health effects neglect human mobility. In food patrons’ daily travels, certain locations may be preferred and patronized more frequently than others. This behavioral uncertainty, known as the selective daily mobility bias (SDMB), is less explored in community-food-environment research. In this paper, we aim to confirm the existence of the SDMB by systematically exploring the large-scale GPS-based restaurant-visit patterns in the Greater Harford region, Connecticut. Next, we explore the restaurant and neighborhood characteristics that are associated with the restaurant-visit patterns. Our primary results demonstrate that (1) most restaurant customers originate from areas outside of the census tract where the restaurant is located, and (2) restaurants located in socially vulnerable areas attract more customers in total, more customers from local areas, and more customers from other socially vulnerable areas. These results confirm the relevance of the SDMB to the community food environment, and suggest ways that the SDMB can be moderated by an uneven socio-economic landscape. The findings demonstrate the necessity of incorporating human-mobility data into the study of the community food environment. |
format | Online Article Text |
id | pubmed-9867517 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98675172023-01-22 Selective Daily Mobility Bias in the Community Food Environment: Case Study of Greater Hartford, Connecticut Jin, Ailing Chen, Xiang Huang, Xiao Li, Zhenlong Caspi, Caitlin E. Xu, Ran Nutrients Article The community food environment has potential influences on community members’ dietary health outcomes, such as obesity and Type II diabetes. However, most existing studies evaluating such health effects neglect human mobility. In food patrons’ daily travels, certain locations may be preferred and patronized more frequently than others. This behavioral uncertainty, known as the selective daily mobility bias (SDMB), is less explored in community-food-environment research. In this paper, we aim to confirm the existence of the SDMB by systematically exploring the large-scale GPS-based restaurant-visit patterns in the Greater Harford region, Connecticut. Next, we explore the restaurant and neighborhood characteristics that are associated with the restaurant-visit patterns. Our primary results demonstrate that (1) most restaurant customers originate from areas outside of the census tract where the restaurant is located, and (2) restaurants located in socially vulnerable areas attract more customers in total, more customers from local areas, and more customers from other socially vulnerable areas. These results confirm the relevance of the SDMB to the community food environment, and suggest ways that the SDMB can be moderated by an uneven socio-economic landscape. The findings demonstrate the necessity of incorporating human-mobility data into the study of the community food environment. MDPI 2023-01-13 /pmc/articles/PMC9867517/ /pubmed/36678275 http://dx.doi.org/10.3390/nu15020404 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Jin, Ailing Chen, Xiang Huang, Xiao Li, Zhenlong Caspi, Caitlin E. Xu, Ran Selective Daily Mobility Bias in the Community Food Environment: Case Study of Greater Hartford, Connecticut |
title | Selective Daily Mobility Bias in the Community Food Environment: Case Study of Greater Hartford, Connecticut |
title_full | Selective Daily Mobility Bias in the Community Food Environment: Case Study of Greater Hartford, Connecticut |
title_fullStr | Selective Daily Mobility Bias in the Community Food Environment: Case Study of Greater Hartford, Connecticut |
title_full_unstemmed | Selective Daily Mobility Bias in the Community Food Environment: Case Study of Greater Hartford, Connecticut |
title_short | Selective Daily Mobility Bias in the Community Food Environment: Case Study of Greater Hartford, Connecticut |
title_sort | selective daily mobility bias in the community food environment: case study of greater hartford, connecticut |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9867517/ https://www.ncbi.nlm.nih.gov/pubmed/36678275 http://dx.doi.org/10.3390/nu15020404 |
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