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      A method for estimating neighborhood characterization in studies of the association with availability of sit-down restaurants and supermarkets

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          Abstract

          Background

          Although neighborhood-level access to food differs by sociodemographic factors, a majority of research on neighborhoods and food access has used a single construct of neighborhood context, such as income or race. Therefore, the many interrelated built environment and sociodemographic characteristics of neighborhoods obscure relationships between neighborhood factors and food access.

          Methods

          The objective of this study was to account for the many interrelated characteristics of food-related neighborhood environments and examine the association between neighborhood type and relative availability of sit-down restaurants and supermarkets. Using cluster analyses with multiple measures of neighborhood characteristics (e.g., population density, mix of land use, and sociodemographic factors) we identified six neighborhood types in 1993 in the Twin Cities Region, Minnesota. We then used mixed effects regression models to estimate differences in the relative availability of sit-down restaurants and supermarkets in 1993, 2001, and 2011 across the six neighborhood types.

          Results

          We defined six types of neighborhoods that existed in 1993, namely, urban core, inner city, urban, aging suburb, high-income suburb, and suburban edge. Between 1993 and 2011, inner city neighborhoods experienced a greater increase in the percent of sit-down restaurants compared with urban core, urban, and aging suburbs. Differences in the percent of sit-down restaurants between inner city and aging suburbs, high-income suburbs and suburban edge neighborhoods increased between 1993 and 2011. Similarly, aging suburb neighborhoods had a greater percent of supermarkets compared with urban and high-income suburb neighborhoods in 2001 and 2011, but not in 1993, suggesting a more varied distribution of food stores across neighborhoods over time. Thus, the classification of neighborhood type based on sociodemographic and built environment characteristics resulted in a complex and increasingly varied distribution of restaurants and food stores.

          Conclusions

          The temporal increase in the relative availability of sit-down restaurants in inner cities after accounting for all restaurants might be partly related to a higher proportion of residents who eat-away-from-home, which is associated with higher calorie and fat intake.

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          Most cited references66

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          Travel demand and the 3Ds: Density, diversity, and design

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            Applied Longitudinal Data Analysis

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              The local food environment and diet: a systematic review.

              Despite growing attention to the problem of obesogenic environments, there has not been a comprehensive review evaluating the food environment-diet relationship. This study aims to evaluate this relationship in the current literature, focusing specifically on the method of exposure assessment (GIS, survey, or store audit). This study also explores 5 dimensions of "food access" (availability, accessibility, affordability, accommodation, acceptability) using a conceptual definition proposed by Penchansky and Thomas (1981). Articles were retrieved through a systematic keyword search in Web of Science and supplemented by the reference lists of included studies. Thirty-eight studies were reviewed and categorized by the exposure assessment method and the conceptual dimensions of access it captured. GIS-based measures were the most common measures, but were less consistently associated with diet than other measures. Few studies examined dimensions of affordability, accommodation, and acceptability. Because GIS-based measures on their own may not capture important non-geographic dimensions of access, a set of recommendations for future researchers is outlined. Copyright © 2012 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                kpeng6@hnu.edu.cn
                Journal
                Int J Health Geogr
                Int J Health Geogr
                International Journal of Health Geographics
                BioMed Central (London )
                1476-072X
                25 March 2021
                25 March 2021
                2021
                : 20
                Affiliations
                [1 ]GRID grid.67293.39, Department of Urban Planning, School of Architecture, , Hunan University, ; Changsha, Hunan China
                [2 ]GRID grid.10698.36, ISNI 0000000122483208, Department of City and Regional Planning, , University of North Carolina at Chapel Hill, ; Chapel Hill, NC USA
                [3 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Department of City and Regional Planning and Institute of Transportation Studies, , University of California, Berkeley, ; Berkeley, CA USA
                [4 ]GRID grid.166341.7, ISNI 0000 0001 2181 3113, Urban Health Collaborative, Department of Epidemiology and Biostatistics, Dornsife School of Public Health, , Drexel University, ; Philadelphia, PA USA
                [5 ]GRID grid.10698.36, ISNI 0000000122483208, Department of Nutrition, Gillings School of Global Public Health, , University of North Carolina at Chapel Hill, ; Chapel Hill, NC USA
                [6 ]GRID grid.10698.36, ISNI 0000000122483208, Carolina Population Center, , University of North Carolina at Chapel Hill, ; Chapel Hill, NC USA
                Article
                257
                10.1186/s12942-020-00257-7
                7995746
                33766045
                d7733af2-37cf-485a-8fc1-f80235088f9e
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000050, National Heart, Lung, and Blood Institute;
                Award ID: HHSN268201300025C
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2021

                Public health
                built environment,sociodemographic,food stores,urbanization
                Public health
                built environment, sociodemographic, food stores, urbanization

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