SNAPScapes: Using Geodemographic Segmentation to Classify the Food Access Landscape
Scholars are in agreement that the local food environment is shaped by a multitude of factors from socioeconomic characteristics to transportation options, as well as the availability and distance to various food establishments. Despite this, most place-based indicators of “food deserts”, including...
Saved in:
Published in | Urban science Vol. 2; no. 3; p. 71 |
---|---|
Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
MDPI AG
01.09.2018
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Scholars are in agreement that the local food environment is shaped by a multitude of factors from socioeconomic characteristics to transportation options, as well as the availability and distance to various food establishments. Despite this, most place-based indicators of “food deserts”, including those identified as so by the US Department of Agriculture (USDA), only include a limited number of factors in their designation. In this article, we adopt a geodemographic approach to classifying the food access landscape that takes a multivariate approach to describing the food access landscape. Our method combines socioeconomic indicators, distance measurements to Supplemental Nutrition Assistance Program (SNAP) participating stores, and neighborhood walkability using a k-means clustering approach and North Carolina as a case study. We identified seven distinct food access types: three rural and four urban. These classes were subsequently prioritized based on their defining characteristics and specific policy recommendations were identified. Overall, compared to the USDA’s food desert calculation, our approach identified a broader swath of high-needs areas and highlights neighborhoods that may be overlooked for intervention when using simple distance-based methods. |
---|---|
AbstractList | Scholars are in agreement that the local food environment is shaped by a multitude of factors from socioeconomic characteristics to transportation options, as well as the availability and distance to various food establishments. Despite this, most place-based indicators of "food deserts", including those identified as so by the US Department of Agriculture (USDA), only include a limited number of factors in their designation. In this article, we adopt a geodemographic approach to classifying the food access landscape that takes a multivariate approach to describing the food access landscape. Our method combines socioeconomic indicators, distance measurements to Supplemental Nutrition Assistance Program (SNAP) participating stores, and neighborhood walkability using a k-means clustering approach and North Carolina as a case study. We identified seven distinct food access types: three rural and four urban. These classes were subsequently prioritized based on their defining characteristics and specific policy recommendations were identified. Overall, compared to the USDA's food desert calculation, our approach identified a broader swath of high-needs areas and highlights neighborhoods that may be overlooked for intervention when using simple distance-based methods. |
Author | Delmelle, Elizabeth Delmelle, Eric Major, Elizabeth |
Author_xml | – sequence: 1 givenname: Elizabeth surname: Major fullname: Major, Elizabeth – sequence: 2 givenname: Elizabeth surname: Delmelle fullname: Delmelle, Elizabeth – sequence: 3 givenname: Eric orcidid: 0000-0002-5117-2238 surname: Delmelle fullname: Delmelle, Eric |
BookMark | eNpd0M1OAjEUBeDGYCIia7d9gZH-zEw77ghRJCFqMrKe3GlvoQSmpB0XvL0gxhhX5-QsvsW5JYMudEjIPWcPUlZs8hlb6JLxgknGFL8iQ5FzmWld8MGffkPGKW0ZY4JppatySOr6dfpeGzhgeqSr5Ls1nWOwuA_rCIeNN7TG9R67HnofOtoHOttBSt4dab9B-hyCpVNjMCW6hM6ms3RHrh3sEo5_ckRWz08fs5ds-TZfzKbLzAil-8yUlTVSMFWA5YU0ZcmddkpzKcHlaJhShXGOyQorax20rea2Egal5ChzK0dkcXFtgG1ziH4P8dgE8M33EOK6gdh7s8MmtxXKE1XkRueqtTp3YHglCyiEQChP1uRimRhSiuh-Pc6a88XNv4vlF3HscrI |
CitedBy_id | crossref_primary_10_3390_ijerph17041263 crossref_primary_10_3390_urbansci3030075 crossref_primary_10_1016_j_annepidem_2021_10_002 crossref_primary_10_1080_00330124_2022_2103720 crossref_primary_10_1007_s44212_022_00021_1 crossref_primary_10_1186_s12942_019_0195_7 crossref_primary_10_1017_S1368980022002476 crossref_primary_10_3390_ijerph16061052 crossref_primary_10_3390_su16031136 crossref_primary_10_3389_fpubh_2020_00071 |
Cites_doi | 10.1016/j.healthplace.2013.01.004 10.1080/19320248.2015.1004221 10.1111/0033-0124.00158 10.1016/j.apgeog.2017.03.018 10.1016/j.socscimed.2015.08.010 10.1016/j.apgeog.2014.07.017 10.1016/j.neuroimage.2009.06.014 10.1016/j.healthplace.2014.08.011 10.1007/s10109-010-0113-9 10.2105/AJPH.2010.192757 10.1016/j.jand.2017.11.004 10.1016/j.apgeog.2014.08.017 10.1007/s10708-016-9716-0 10.1016/j.apgeog.2013.11.002 10.1016/j.socscimed.2014.02.021 10.1016/j.apgeog.2014.06.012 10.1177/0739456X08317461 10.1007/s10460-014-9501-y 10.1016/j.apgeog.2014.03.007 10.1371/journal.pone.0013214 10.1016/j.apgeog.2014.12.002 10.1080/00045608.2015.1052335 10.1177/0038038506067507 10.1362/026725709X429728 10.2105/AJPH.2004.042150 |
ContentType | Journal Article |
DBID | AAYXX CITATION DOA |
DOI | 10.3390/urbansci2030071 |
DatabaseName | CrossRef DOAJ Directory of Open Access Journals |
DatabaseTitle | CrossRef |
DatabaseTitleList | CrossRef |
Database_xml | – sequence: 1 dbid: DOA name: Open Access: DOAJ - Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Sociology & Social History |
EISSN | 2413-8851 |
ExternalDocumentID | oai_doaj_org_article_4d9e3f0354c847bd84fac1935a522ea6 10_3390_urbansci2030071 |
GroupedDBID | AADQD AAFWJ AAYXX ADBBV AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS BCNDV BENPR CCPQU CITATION GROUPED_DOAJ IAO MODMG M~E OK1 PIMPY |
ID | FETCH-LOGICAL-c278t-c69dc32075ad153c661f8f78133af4ec0775cff039e9ddfabb81d92ce331e34d3 |
IEDL.DBID | DOA |
ISSN | 2413-8851 |
IngestDate | Tue Oct 22 15:11:43 EDT 2024 Fri Aug 23 02:58:22 EDT 2024 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 3 |
Language | English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c278t-c69dc32075ad153c661f8f78133af4ec0775cff039e9ddfabb81d92ce331e34d3 |
ORCID | 0000-0002-5117-2238 |
OpenAccessLink | https://doaj.org/article/4d9e3f0354c847bd84fac1935a522ea6 |
ParticipantIDs | doaj_primary_oai_doaj_org_article_4d9e3f0354c847bd84fac1935a522ea6 crossref_primary_10_3390_urbansci2030071 |
PublicationCentury | 2000 |
PublicationDate | 2018-09-01 |
PublicationDateYYYYMMDD | 2018-09-01 |
PublicationDate_xml | – month: 09 year: 2018 text: 2018-09-01 day: 01 |
PublicationDecade | 2010 |
PublicationTitle | Urban science |
PublicationYear | 2018 |
Publisher | MDPI AG |
Publisher_xml | – name: MDPI AG |
References | ref13 ref12 ref34 ref14 ref31 ref30 ref33 ref32 ref2 ref1 ref17 ref19 ref18 Sleight (ref22) 1997 Gallagher (ref5) 2011; 38 Zaki (ref35) 2014 Andrews (ref15) 2012; 10 LeClair (ref11) 2014; 31 ref23 ref26 ref25 ref20 ref21 Shannon (ref10) 2017; 82 ref28 ref27 ref29 ref8 ref7 ref9 ref4 ref3 Kaufman (ref16) 1999; 13 ref6 Harris (ref24) 2005 |
References_xml | – ident: ref6 doi: 10.1016/j.healthplace.2013.01.004 – ident: ref1 – ident: ref12 doi: 10.1080/19320248.2015.1004221 – ident: ref21 doi: 10.1111/0033-0124.00158 – ident: ref9 doi: 10.1016/j.apgeog.2017.03.018 – ident: ref14 doi: 10.1016/j.socscimed.2015.08.010 – ident: ref20 doi: 10.1016/j.apgeog.2014.07.017 – ident: ref33 doi: 10.1016/j.neuroimage.2009.06.014 – ident: ref3 doi: 10.1016/j.healthplace.2014.08.011 – ident: ref26 doi: 10.1007/s10109-010-0113-9 – ident: ref13 doi: 10.2105/AJPH.2010.192757 – year: 1997 ident: ref22 contributor: fullname: Sleight – ident: ref19 doi: 10.1016/j.jand.2017.11.004 – year: 2014 ident: ref35 contributor: fullname: Zaki – ident: ref34 – ident: ref28 doi: 10.1016/j.apgeog.2014.08.017 – ident: ref30 – ident: ref4 – volume: 82 start-page: 769 year: 2017 ident: ref10 article-title: What Is the Relationship between Food Shopping and Daily Mobility? A Relational Approach to Analysis of Food Access publication-title: GeoJournal doi: 10.1007/s10708-016-9716-0 contributor: fullname: Shannon – ident: ref31 doi: 10.1016/j.apgeog.2013.11.002 – ident: ref18 doi: 10.1016/j.socscimed.2014.02.021 – ident: ref8 doi: 10.1016/j.apgeog.2014.06.012 – ident: ref2 doi: 10.1177/0739456X08317461 – volume: 10 start-page: 1 year: 2012 ident: ref15 article-title: What’s Behind the Rise in SNAP Participation? publication-title: Amber Waves contributor: fullname: Andrews – volume: 31 start-page: 537 year: 2014 ident: ref11 article-title: Redefining the Food Desert: Combining GIS with Direct Observation to Measure Food Access publication-title: Agric. Hum. Values doi: 10.1007/s10460-014-9501-y contributor: fullname: LeClair – volume: 38 year: 2011 ident: ref5 article-title: USDA defines food deserts publication-title: Nutr. Dig. contributor: fullname: Gallagher – ident: ref7 doi: 10.1016/j.apgeog.2014.03.007 – volume: 13 start-page: 19 year: 1999 ident: ref16 article-title: Rural Poor Have Less Access to Supermarkets, Large Grocery Stores publication-title: Rural Dev. Perspect. contributor: fullname: Kaufman – ident: ref27 doi: 10.1371/journal.pone.0013214 – ident: ref29 doi: 10.1016/j.apgeog.2014.12.002 – ident: ref32 doi: 10.1080/00045608.2015.1052335 – ident: ref23 doi: 10.1177/0038038506067507 – year: 2005 ident: ref24 contributor: fullname: Harris – ident: ref25 doi: 10.1362/026725709X429728 – ident: ref17 doi: 10.2105/AJPH.2004.042150 |
SSID | ssj0002087896 |
Score | 2.172048 |
Snippet | Scholars are in agreement that the local food environment is shaped by a multitude of factors from socioeconomic characteristics to transportation options, as... |
SourceID | doaj crossref |
SourceType | Open Website Aggregation Database |
StartPage | 71 |
SubjectTerms | food access food insecurity geodemographics GIS North Carolina SNAP |
Title | SNAPScapes: Using Geodemographic Segmentation to Classify the Food Access Landscape |
URI | https://doaj.org/article/4d9e3f0354c847bd84fac1935a522ea6 |
Volume | 2 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3PS8MwFA4yL17Enzh_jBxEvJRtTdKm3jbZHKJjWAe7lfQlkQlbZW6H_fe-NHVML168lhLK99L3vkfyfY-Q6xAiN7daBGE75wGPdSvIQcmAQ8K5MEJGkdM7Pw-jwZg_TsRka9SXuxPm7YE9cE2uE8NsiwkOmEhzLblVgKxDKGQORnmz7bbYaqbey-M1Gcsk8l4-DPv65mqRY-6HaYi7uhW3f5ShLbf-sqz0D8h-xQdpx3_HIdkx8yNyvpGR0BvqBbTU-3msj0maDjuj1N1b-ryj5Yk_fTCFNjNvPj0Fmpq3WSUpmtNlQcvBl1O7pkj2aL8oNO2UYxLpk9P5upVOyLjfe70fBNVohADCWC4DiBINLMR6rzTmLMAqa6WNJXacynIDztgOLMKWmERrq_IceWkSgmGsbRjX7JTU5sXcnBEKrKUiJa3IgXP84xUDbpB4xU5yariok9tvpLIP74CRYefgQM1-gVonXYfk5jVnXV0-wIBmVUCzvwJ6_h-LXJA9ZDbSXwa7JLXlYmWukD0s8wbZ7faGo5dGuWG-APXxxRc |
link.rule.ids | 315,786,790,870,2115,27955,27956 |
linkProvider | Directory of Open Access Journals |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=SNAPScapes%3A+Using+Geodemographic+Segmentation+to+Classify+the+Food+Access+Landscape&rft.jtitle=Urban+science&rft.au=Elizabeth+Major&rft.au=Elizabeth+C.+Delmelle&rft.au=Eric+Delmelle&rft.date=2018-09-01&rft.pub=MDPI+AG&rft.eissn=2413-8851&rft.volume=2&rft.issue=3&rft.spage=71&rft_id=info:doi/10.3390%2Furbansci2030071&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_4d9e3f0354c847bd84fac1935a522ea6 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2413-8851&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2413-8851&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2413-8851&client=summon |