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A spatio-temporal framework for modelling informal settlement growth
Thesis (PhD)--Stellenbosch University, 2021.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2021
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| _version_ | 1867613865515155456 |
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| access_status_str | Open Access |
| author | Cilliers, Pierre |
| author2 | Van Vuuren, Jan Harm |
| author_browse | Cilliers, Pierre Van Vuuren, Jan Harm |
| author_facet | Van Vuuren, Jan Harm Cilliers, Pierre |
| author_sort | Cilliers, Pierre |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (PhD)--Stellenbosch University, 2021. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/123686 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:42:55.322Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/123686 A spatio-temporal framework for modelling informal settlement growth Cilliers, Pierre Van Vuuren, Jan Harm Van Heerden, Quintin Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering. Geographic information systems UCTD Informal settlements (Squatter settlements) Cellular automata Machine learning Thesis (PhD)--Stellenbosch University, 2021. ENGLISH ABSTRACT: Many developing countries grapple with the problem of rapid informal settlement emergence and expansion. This exacts considerable costs from neighbouring urban areas, largely as a result of environmental, sustainability and health-related problems associated with such settlements, which can threaten the local economy. Hence, there is a need to understand the nature of, and to be able to predict, informal settlement emergence locations as well as the rate and extent of such settlement expansion in developing countries. Although an abundance of research has been dedicated to developing computerised mathematical models for predicting future informal settlement expansion, there are no models in the literature for successfully predicting future informal settlement emergence and expansion which employ the considerable power of machine learning in a temporal setting. In this dissertation, a novel generic framework is proposed for machine learning-inspired prediction of future spatio-temporal informal settlement population growth. This data-driven framework comprises three functional components which facilitate informal settlement emergence and growth modelling within a user- specified area. The framework outputs are based on a computed set of influential spatial feature predictors pertaining to the area in question. The objective of the framework is ultimately to identify those spatial and other factors that in- fluence the location, formation and growth rate of an informal settlement most significantly, by applying a machine learning modelling approach to multiple data sets related to the households and spatial attributes associated with informal settlements. Based on the aforementioned influ- encing factors, a cellular automaton transition rule is developed, enabling the spatio-temporal modelling of the rate and extent of future formations and expansions of informal settlements. Furthermore, the framework facilitates a flexible, exploratory analysis of model results in com- bination with existing structured informal settlement expansion data in order to gain actionable insights into their management. Two separate instantiations of this framework are implemented on a personal computer as concept demonstrations. The first is applied to a real-world case study related to a densely populated informal settlement area of a South African municipality in order to illustrate the practical applicability of the proposed framework. The second implementation is aimed at comparing the model performance of the proposed framework with that of an existing model in the literature on the same real-world case study area. AFRIKAANSE OPSOMMING: Baie ontwikkelende lande worstel met die probleem van snelle informele nedersettingstotstand- koming en -uitbreiding. Hierdie stand van sake veroorsaak noemenswaardige onkostes vir aan- grensende stedelike gebiede, meestal as gevolg van omgewings-, volhoubaarheids- en gesondheids- probleme wat met sulke nedersettings gepaard gaan, wat die plaaslike ekonomie kan bedreig. Daar is dus ’n behoefte om die aard van informele nedersettingstotstandkoming te verstaan en daartoe in staat te wees om liggings vir die ontstaan, sowel as die tempo en bestek van die groei, van sulke nedersettings in ontwikkelende lande te kan voorspel. Alhoewel ’n oorvloed navorsing reeds toegewy is aan die ontwikkeling van gerekenariseerde wiskundige modelle vir die voorspelling van toekomstige uitbreidings van informele nederset- tings, is daar geen modelle in the literatuur vir die suksesvolle voorspelling van die totstand- koming en uitbreiding van informele nedersettings wat die aansienlike krag van masjienleer in ’n temporele konteks benut nie. In hierdie proefskrif word ’n nuwe generiese raamwerk daargestel vir masjienleer-geïnspireerde voorspelling van toekomstige informele nedersettingsbevolkings- groei oor beide ruimte en tyd. Hierdie data-gedrewe raamwerk bestaan uit drie funksionele komponente wat die wiskundige modellering van die ontstaan van informele nedersettings en die bevolkingsgroei in sulke nedersettings in ’n gebruikersgespesifiseerde gebied moontlik maak deur ’n versameling invloedryke ruimtelike kenmerke van die betrokke gebied te bereken en te analiseer. Die doel van die raamwerk is uiteindelik om ruimtelike en ander faktore wat die liggings, vorming en groeitempo van informele nedersettings die meeste beïnvloed, te identifiseer deur ’n masjienleer modelleringsbenadering op verskeie datastelle toe te pas wat met die huishou- dings en ruimtelike eienskappe van informele nedersettings verband hou. Op grond van die bogenoemde invloedryke-faktore word ’n oorgangsreël vir a sellulˆere outomaat ontwikkel wat die ruimtelik-temporele modellering van die tempo en omvang van toekomstige totstandkoming en uitbreiding van informele nedersettings moontlik maak. Verder vergemaklik die raamwerk ’n buigsame, verkennende ontleding van modelresultate in kombinasie met bestaande gestruk- tureerde informele nedersettingsuitbreidingsdata om sodoende ’n insig in die bestuur daarvan te ontwikkel. Twee aparte instansies van hierdie raamwerk word rekenaarmatig as konsepdemonstrasies ge ̈ım- plementeer. Die eerste implementasie word toegepas op ’n werklike gevallestudie wat verband hou met ’n dig-bevolkte informele nedersettingsarea in ’n Suid-Afrikaanse munisipaliteit om sodoende die praktiese toepaslikheid van die voorgestelde raamwerk te illustreer. Die tweede implementasie word toegepas om die modelprestasie van die voorgestelde raamwerk te vergelyk met die van ’n bestaande model in die literatuur wat betrekking het op dieselfde gevallestudie area. Doctoral 2021-10-08T12:40:09Z 2021-12-22T14:15:53Z 2021-10-08T12:40:09Z 2021-12-22T14:15:53Z 2021-12 Thesis http://hdl.handle.net/10019.1/123686 en_ZA Stellenbosch University 449 pages application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Geographic information systems UCTD Informal settlements (Squatter settlements) Cellular automata Machine learning Cilliers, Pierre A spatio-temporal framework for modelling informal settlement growth |
| title | A spatio-temporal framework for modelling informal settlement growth |
| title_full | A spatio-temporal framework for modelling informal settlement growth |
| title_fullStr | A spatio-temporal framework for modelling informal settlement growth |
| title_full_unstemmed | A spatio-temporal framework for modelling informal settlement growth |
| title_short | A spatio-temporal framework for modelling informal settlement growth |
| title_sort | spatio temporal framework for modelling informal settlement growth |
| topic | Geographic information systems UCTD Informal settlements (Squatter settlements) Cellular automata Machine learning |
| url | http://hdl.handle.net/10019.1/123686 |
| work_keys_str_mv | AT cillierspierre aspatiotemporalframeworkformodellinginformalsettlementgrowth AT cillierspierre spatiotemporalframeworkformodellinginformalsettlementgrowth |