Geospatial and Machine Learning Regression Techniques for Analyzing Food Access Impact on Health Issues in Sustainable Communities

Food access is a major key component in food security, as it is every individual’s right to proper access to a nutritious and affordable food supply. Low access to healthy food sources influences people’s diet and activity habits. Guilford County in North Carolina has a high ranking in low food secu...

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Autores principales:	Abrar Almalki, Balakrishna Gokaraju, Nikhil Mehta, Daniel Adrian Doss
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	disaster preparedness smart cities sustainable cities food desert regression analysis Geography (General) G1-922
Acceso en línea:	https://doaj.org/article/f639caf3f25f46ae9483f9201c82b11b
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spelling	oai:doaj.org-article:f639caf3f25f46ae9483f9201c82b11b2021-11-25T17:52:55ZGeospatial and Machine Learning Regression Techniques for Analyzing Food Access Impact on Health Issues in Sustainable Communities10.3390/ijgi101107452220-9964https://doaj.org/article/f639caf3f25f46ae9483f9201c82b11b2021-11-01T00:00:00Zhttps://www.mdpi.com/2220-9964/10/11/745https://doaj.org/toc/2220-9964Food access is a major key component in food security, as it is every individual’s right to proper access to a nutritious and affordable food supply. Low access to healthy food sources influences people’s diet and activity habits. Guilford County in North Carolina has a high ranking in low food security and a high rate of health issues such as high blood pressure, high cholesterol, and obesity. Therefore, the primary objective of this study was to investigate the geospatial correlation between health issues and food access areas. The secondary objective was to quantitatively compare food access areas and heath issues’ descriptive statistics. The tertiary objective was to compare several machine learning techniques and find the best model that fit health issues against various food access variables with the highest performance accuracy. In this study, we adopted a food-access perspective to show that communities that have residents who have equitable access to healthy food options are typically less vulnerable to health-related disasters. We propose a methodology to help policymakers lower the number of health issues in Guilford County by analyzing such issues via correlation with respect to food access. Specifically, we conducted a geographic information system mapping methodology to examine how access to healthy food options influenced health and mortality outcomes in one of the largest counties in the state of North Carolina. We created geospatial maps representing food deserts—areas with scarce access to nutritious food; food swamps—areas with more availability of unhealthy food options compared to healthy food options; and food oases—areas with a relatively higher availability of healthy food options than unhealthy options. Our results presented a positive correlation coefficient of <i>R</i><sup>2</sup> = 0.819 among obesity and the independent variables of transportation access, and population. The correlation coefficient matrix analysis helped to identify a strong negative correlation between obesity and median income. Overall, this study offers valuable insights that can help health authorities develop preemptive preparedness for healthcare disasters.Abrar AlmalkiBalakrishna GokarajuNikhil MehtaDaniel Adrian DossMDPI AGarticledisaster preparednesssmart citiessustainable citiesfood desertregression analysisGeography (General)G1-922ENISPRS International Journal of Geo-Information, Vol 10, Iss 745, p 745 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	disaster preparedness smart cities sustainable cities food desert regression analysis Geography (General) G1-922
spellingShingle	disaster preparedness smart cities sustainable cities food desert regression analysis Geography (General) G1-922 Abrar Almalki Balakrishna Gokaraju Nikhil Mehta Daniel Adrian Doss Geospatial and Machine Learning Regression Techniques for Analyzing Food Access Impact on Health Issues in Sustainable Communities
description	Food access is a major key component in food security, as it is every individual’s right to proper access to a nutritious and affordable food supply. Low access to healthy food sources influences people’s diet and activity habits. Guilford County in North Carolina has a high ranking in low food security and a high rate of health issues such as high blood pressure, high cholesterol, and obesity. Therefore, the primary objective of this study was to investigate the geospatial correlation between health issues and food access areas. The secondary objective was to quantitatively compare food access areas and heath issues’ descriptive statistics. The tertiary objective was to compare several machine learning techniques and find the best model that fit health issues against various food access variables with the highest performance accuracy. In this study, we adopted a food-access perspective to show that communities that have residents who have equitable access to healthy food options are typically less vulnerable to health-related disasters. We propose a methodology to help policymakers lower the number of health issues in Guilford County by analyzing such issues via correlation with respect to food access. Specifically, we conducted a geographic information system mapping methodology to examine how access to healthy food options influenced health and mortality outcomes in one of the largest counties in the state of North Carolina. We created geospatial maps representing food deserts—areas with scarce access to nutritious food; food swamps—areas with more availability of unhealthy food options compared to healthy food options; and food oases—areas with a relatively higher availability of healthy food options than unhealthy options. Our results presented a positive correlation coefficient of <i>R</i><sup>2</sup> = 0.819 among obesity and the independent variables of transportation access, and population. The correlation coefficient matrix analysis helped to identify a strong negative correlation between obesity and median income. Overall, this study offers valuable insights that can help health authorities develop preemptive preparedness for healthcare disasters.
format	article
author	Abrar Almalki Balakrishna Gokaraju Nikhil Mehta Daniel Adrian Doss
author_facet	Abrar Almalki Balakrishna Gokaraju Nikhil Mehta Daniel Adrian Doss
author_sort	Abrar Almalki
title	Geospatial and Machine Learning Regression Techniques for Analyzing Food Access Impact on Health Issues in Sustainable Communities
title_short	Geospatial and Machine Learning Regression Techniques for Analyzing Food Access Impact on Health Issues in Sustainable Communities
title_full	Geospatial and Machine Learning Regression Techniques for Analyzing Food Access Impact on Health Issues in Sustainable Communities
title_fullStr	Geospatial and Machine Learning Regression Techniques for Analyzing Food Access Impact on Health Issues in Sustainable Communities
title_full_unstemmed	Geospatial and Machine Learning Regression Techniques for Analyzing Food Access Impact on Health Issues in Sustainable Communities
title_sort	geospatial and machine learning regression techniques for analyzing food access impact on health issues in sustainable communities
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f639caf3f25f46ae9483f9201c82b11b
work_keys_str_mv	AT abraralmalki geospatialandmachinelearningregressiontechniquesforanalyzingfoodaccessimpactonhealthissuesinsustainablecommunities AT balakrishnagokaraju geospatialandmachinelearningregressiontechniquesforanalyzingfoodaccessimpactonhealthissuesinsustainablecommunities AT nikhilmehta geospatialandmachinelearningregressiontechniquesforanalyzingfoodaccessimpactonhealthissuesinsustainablecommunities AT danieladriandoss geospatialandmachinelearningregressiontechniquesforanalyzingfoodaccessimpactonhealthissuesinsustainablecommunities
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Geospatial and Machine Learning Regression Techniques for Analyzing Food Access Impact on Health Issues in Sustainable Communities

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