Identifying food insecurity in food sharing networks via machine learning

被引:15
|
作者
Nica-Avram, Georgiana [1 ]
Harvey, John [1 ]
Smith, Gavin [1 ]
Smith, Andrew [1 ]
Goulding, James [1 ]
机构
[1] Univ Nottingham, Business Sch, N LAB, Nottingham NG8 1BB, England
基金
英国工程与自然科学研究理事会;
关键词
Food Sharing; Food waste; Food insecurity; Economic deprivation; Machine learning; Sharing economy; BIG DATA; NEIGHBORHOOD DEPRIVATION; FRESH FRUIT; POVERTY; SEGMENTATION; CHALLENGES; PERSPECTIVE; VEGETABLES; AUSTERITY; INDICATOR;
D O I
10.1016/j.jbusres.2020.09.028
中图分类号
F [经济];
学科分类号
02 ;
摘要
Food insecurity in the UK has captured public attention. However, estimates of its prevalence are deeply contentious. The lack of precision on the volume of emergency food assistance currently provided to those in need is made even more ambiguous due to increasing use of peer-to-peer food sharing systems (e.g. OLIO). While these initiatives exist as a solution to food waste rather than food poverty, they are nonetheless carrying a hidden share of the food insecurity burden, with the socio-economic status of technology-assisted food sharing donors, volunteers, and recipients remaining obscure. In this article we examine the relationship between food sharing and deprivation generally, before applying machine learning techniques to develop a predictive model of food insecurity based upon aggregated food sharing behaviours by OLIO users in the UK. We demonstrate that data from food sharing systems can help quantify a previously hidden aspect of deprivation and we make the case for a reformed approach to modelling food insecurity.
引用
收藏
页码:469 / 484
页数:16
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