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A framework for food distribution guided by a spatial index of food insecurity
Dawood, M. F. 2025. A framework for food distribution guided by a spatial index of food insecurity. Unpublished doctoral dissertation. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/d93397a5-89a6-4c34-81d1-f00d50e21cf1
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Stellenbosch : Stellenbosch University
2025
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| _version_ | 1867614091421417472 |
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| access_status_str | Open Access |
| author | Dawood, Mohammud Fuzail |
| author2 | Van Vuuren, J. H. |
| author_browse | Dawood, Mohammud Fuzail Van Vuuren, J. H. |
| author_facet | Van Vuuren, J. H. Dawood, Mohammud Fuzail |
| author_sort | Dawood, Mohammud Fuzail |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Dawood, M. F. 2025. A framework for food distribution guided by
a spatial index of food insecurity. Unpublished doctoral dissertation. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/d93397a5-89a6-4c34-81d1-f00d50e21cf1 |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/132128 |
| institution | Stellenbosch University (South Africa) |
| last_indexed | 2026-06-10T12:46:31.699Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/132128 A framework for food distribution guided by a spatial index of food insecurity Dawood, Mohammud Fuzail Van Vuuren, J. H. Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering. Spatial analysis (Statistics) Food security -- Geographic information systems Nutrition policy -- Planning UCTD Dawood, M. F. 2025. A framework for food distribution guided by a spatial index of food insecurity. Unpublished doctoral dissertation. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/d93397a5-89a6-4c34-81d1-f00d50e21cf1 Thesis (PhD)--Stellenbosch University, 2025. ENGLISH ABSTRACT: Food security is a multifaceted problem that is considered to be one of the most significant concerns of the 21st century. According to the United Nations, nearly one in every three people, or 2.3 billion people worldwide, experienced a moderate to severe degree of food insecurity in 2021. As a result, there is a rising recognition of the critical need to identify, monitor, and improve the degree of food insecurity experienced by vulnerable populations. An effective method of measuring the degree to which a geographic region may be considered to be food insecure is, of course, a prerequisite to launching remedial policies aimed at resolving the problem of food insecurity. A transdisciplinary approach is required to analyse quantitatively and improve the state of food insecurity in developing countries, ideally drawing from the realms of applied mathematics, statistics, machine learning, and economics. The accuracy of local food insecurity solutions greatly depends on the quality of integration between the identification of, and food allocation to, vulnerable populations. The literature on the quantitative modelling of food insecurity is, however, fragmented and scattered across various disciplines — it lacks interdisciplinary cohesion. As such, a framework, based on techniques from the above fields of study, which is capable of assessing the current state of food insecurity and proposing appropriate recommendations as to the implementation of initiatives for vulnerable populations is anticipated to prove useful in the quest to improve the quality of government stakeholder decisions in pursuit of food security and has the potential to influence human livelihoods and the economy of a country significantly. A generic framework is proposed in this dissertation for estimating the degree to which a geographic region may be considered food insecure. The framework facilitates the establishment of a non-algebraic, machine learning-based spatial index of food insecurity drawing on the socio-economic characteristics of a country’s population. The values of this index may be embedded in a mathematical model capable of proposing actionable recommendations with a view to reduce the degree of food insecurity within a specified geographic region. The proposed framework comprises three modelling components — a spatial analysis component, a machine learning component, and a resource allocation component. These components are integrated to provide end-to-end decision support in respect of contributions towards the resolution of food insecurity. The practical application of the framework is demonstrated in the form of a case study involving a foodbank in South Africa. By leveraging the power of analytical tools to guide interventions, the proposed framework may potentially facilitate a significant advancement in the field of food security modelling, providing a robust tool for policymakers and humanitarian organisations aimed at combatting food insecurity more effectively. AFRIKAANSE OPSOMMING: Voedselsekerheid is 'n veelsydige probleem wat as een van die belangrikste bekommernisse van die 21ste eeu beskou word. Volgens die Verenigde Nasies het byna een uit elke drie mense, of 2.3 miljard mense w^ereldwyd, 'n matige tot ernstige graad van voedselonsekerheid in 2021 ervaar. As gevolg hiervan is daar toenemende erkenning van die kritieke behoefte om die mate van voedselonsekerheid wat deur kwesbare bevolkings ervaar word, te identi_seer, te monitor, en te verbeter. 'n Doeltre_ende metode om te meet tot watter mate 'n geogra_ese streek as voedselonseker beskou kan word, is natuurlik 'n voorvereiste vir die daarstelling van remedierende beleide wat daarop gemik is om die probleem van voedselonsekerheid op te los. 'n Transdissiplin^ere benadering word vereis om die toestand van voedselonsekerheid in ontwikkelende lande kwantitatief te ontleed en te verbeter, wat ideaal-gesproke uit die terreine van toegepaste wiskunde, statistiek, masjienleer en ekonomie onttrek. Die akkuraatheid van oplossings tot plaaslike voedselonsekerheid hang grootliks af van die kwaliteit van integrasie tussen die identi_sering van, en voedseltoewysing aan, kwesbare bevolkings. Die literatuur oor die kwantitatiewe modellering van voedselonsekerheid is egter gefragmenteer en oor verskeie dissiplines versprei | daar is 'n gebrek aan interdissiplin^ere samehang. As sodanig word daar verwag dat 'n raamwerk, gebaseer op tegnieke uit boegenoemde studievelde, wat daartoe in staat is om die huidige stand van voedselonsekerheid te assesseer en toepaslike aanbevelings vir die implementering van inisiatiewe vir kwesbare bevolkings voor te stel, nuttig sal wees in die strewe om die kwaliteit van regeringsbelanghebbende besluite in die nastrewing van voedselsekerheid en die potensiaal het om menslike lewensbestaan en die ekonomie van 'n land aansienlik te be _nvloed. 'n Generiese raamwerk word in hierdie proefskrif voorgestel vir die afskating van die mate waartoe 'n geogra_ese streek as voedselonsekerheid beskou kan word. Die raamwerk fasiliteer die daarstelling van 'n nie-algebra _ese, masjienleer-gebaseerde ruimtelike indeks vir voedselonsekerheid wat op die sosio-ekonomiese kenmerke van 'n land se bevolking gebaseer is. Die waardes van hierdie indeks kan in 'n wiskundige model opgeneem word wat daartoe in staat is om uitvoerbare aanbevelings voor te stel met die oog op die vermindering van die mate van voedselonsekerheid binne 'n gespesi_seerde geogra_ese streek. Die voorgestelde raamwerk bestaan uit drie modelleringskomponente | 'n ruimtelike analisekomponent, 'n masjienleerkomponent en 'n hulpbrontoewysingskomponent. Hierdie komponente word ge _ntegreer om oorkoepelende besluitesteun ten opsigte van bydraes tot die oplossing van voedselonsekerheid te verskaf. Die praktiese toepassing van die raamwerk word in die vorm van 'n gevallestudie oor 'n voedselbank in Suid-Afrika gedemonstreer. Deur gebruik te maak van die krag van analitiese instrumente om intervensies te rig, mag die voorgestelde raamwerk potensieel beduidende vooruitgang op die gebied van voedselsekerheidmodellering fasiliteer, deur 'n robuuste hulpmiddel vir beleidmakers en humanit^ere organisasies te bied wat daarop gemik is om voedselonsekerheid meer doeltre_end te bekamp. Doctoral 2025-05-27T07:50:21Z 2025-05-27T07:50:21Z 2025-03 Thesis https://scholar.sun.ac.za/handle/10019.1/132128 Stellenbosch University xxvi, 244 pages : illustrations application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Spatial analysis (Statistics) Food security -- Geographic information systems Nutrition policy -- Planning UCTD Dawood, Mohammud Fuzail A framework for food distribution guided by a spatial index of food insecurity |
| title | A framework for food distribution guided by a spatial index of food insecurity |
| title_full | A framework for food distribution guided by a spatial index of food insecurity |
| title_fullStr | A framework for food distribution guided by a spatial index of food insecurity |
| title_full_unstemmed | A framework for food distribution guided by a spatial index of food insecurity |
| title_short | A framework for food distribution guided by a spatial index of food insecurity |
| title_sort | framework for food distribution guided by a spatial index of food insecurity |
| topic | Spatial analysis (Statistics) Food security -- Geographic information systems Nutrition policy -- Planning UCTD |
| url | https://scholar.sun.ac.za/handle/10019.1/132128 |
| work_keys_str_mv | AT dawoodmohammudfuzail aframeworkforfooddistributionguidedbyaspatialindexoffoodinsecurity AT dawoodmohammudfuzail frameworkforfooddistributionguidedbyaspatialindexoffoodinsecurity |