Urban habitats and food insecurity: Lessons learned throughout a pandemic

被引:0
|
作者
Vaz, Eric [1 ]
Damasio, Bruno [2 ]
Bacao, Fernando [2 ]
Shaker, Richard Ross [1 ]
Penfound, Elissa [3 ]
机构
[1] Toronto Metropolitan Univ, Dept Geog & Environm Studies, Toronto, ON, Canada
[2] Univ Nova Lisboa, Nova Informat Management Sch, NOVA IMS, Lisbon, Portugal
[3] Toronto Metropolitan Univ, Yeates Sch Grad Studies, Toronto, ON, Canada
关键词
LAND-USE; DESERTS; SECURITY; GROWTH; CITIES;
D O I
10.1016/j.habitatint.2023.102779
中图分类号
F0 [经济学]; F1 [世界各国经济概况、经济史、经济地理]; C [社会科学总论];
学科分类号
0201 ; 020105 ; 03 ; 0303 ;
摘要
Background: An increasing amount of literature raises the issue of food deserts and urban heterogeneity in larger metropolitan cores throughout North America. Specific to Canadian cities, the disparity between access to health, education, and affordable food is of growing concern. Recently, these drivers seem to be significantly linked to the propagation of COVID-19. This paper explores the spatially-explicit dynamics of food deserts in Toronto, by integrating Geographic In-formation Systems and machine learning to understand the clusters of food deserts. The integration of spatial analysis with self-organizing maps (SOM) offers insights on the relation between neighborhoods, geodemographic profiles and urban characteristics, and whether one might expect consequences of food insecurity given COVID-19.Methods: The paper starts out with developing a machine learning algorithm based on SOM to define meaningful clusters within the hedonic dataset. Further to this, an exploratory regression was built per cluster as to allow an exploratory spatial analysis to derive an explanatory framework for the key characteristics of socio-economic profiles within the Greater Toronto Area and impacts of SARS-CoV-2.Results: The findings suggest that there are clear spatial profiles within the urban core of Toronto in regards to food deserts, showing a direct relation between socioeconomic characteristics and the results on environmental injustice and livability. These profiles are strongly linked with the areas of COVID-19 occurrence, and share a very similar socio-demographic profile, particularly in regards to young and lower income families.Conclusion: There are several food deserts currently in Toronto, Ontario. The integration of policies that involve public health and spatial decision-support, particularly when linked to machine learning to aggregate characteristics of big data, establish a multi-functional understanding of the complexity of food secu-rity. This has a direct relation with diet, environment, and the opportunity to enhance subjective well-being in city cores.
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页数:11
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