The SLEUTH urban growth model as forecasting and decision-making tool

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dc.contributor.advisorVan der Merwe, J. H.
dc.contributor.authorWatkiss, Brendon Milesen_ZA
dc.contributor.otherStellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies.
dc.date.accessioned2008-05-09en_ZA
dc.date.available2008-05-09en_ZA
dc.date.issued2008-05-09en_ZA
dc.date.submitted2008-03en_ZA
dc.descriptionThesis (MSc (Geography and Environmental Studies))--Stellenbosch University, 2008.
dc.description.abstractAccelerating urban growth places increasing pressure not only on the efficiency of infrastructure and service provision, but also on the natural environment. City managers are delegated the task of identifying problem areas that arise from this phenomenon and planning the strategies with which to alleviate them. It is with this in mind that the research investigates the implementation of an urban growth model, SLEUTH, as a support tool in the planning and decision making process. These investigations are carried out on historical urban data for the region falling under the control of the Cape Metropolitan Authority. The primary aim of the research was to simulate future urban expansion of Cape Town based on past growth patterns by making use of cellular automata methodology in the SLEUTH modeling platform. The following objectives were explored, namely to: a) determine the impact of urbanization on the study area, b) identify strategies for managing urban growth from literature, c) apply cellular automata as a modeling tool and decision-making aid, d) formulate an urban growth policy based on strategies from literature, and e) justify SLEUTH as the desired modeling framework from literature. An extensive data base for the study area was acquired from the product of a joint initiative between the private and public sector, called “Urban Monitoring”. The data base included: a) five historical urban extent images (1977, 1988, 1993, 1996 and 1998); b) an official urban buffer zone or ‘urban edge’, c) a Metropolitan Open Space System (MOSS) database, d) two road networks, and d) a Digital Elevation Model (DEM). Each dataset was converted to raster format in ArcEdit and finally .gif images were created of each data layer for compliance with SLEUTH requirements. SLEUTH processed this historic data to calibrate the growth variables for best fit of observed historic growth. An urban growth forecast was run based on the calibration parameters. Findings suggest SLEUTH can be applied successfully and produce realistic projection of urban expansion. A comparison between modelled and real urban area revealed 76% model accuracy. The research then attempts to mimic urban growth policy in the modeling environment, with mixed results.en_ZA
dc.format.extent1359028 bytesen_ZA
dc.format.mimetypeapplication/pdfen_ZA
dc.identifier.urihttp://hdl.handle.net/10019.1/1654
dc.language.isoenen_ZA
dc.publisherStellenbosch : Stellenbosch University
dc.rights.holderStellenbosch University
dc.subjectSLEUTHen_ZA
dc.subjectUrban Growth Modelen_ZA
dc.subjectForecastingen_ZA
dc.subjectDecision-makingen_ZA
dc.subjectDissertations -- Geography and environmental studiesen
dc.subjectTheses -- Geography and environmental studiesen
dc.subjectCities and towns -- South Africa -- Cape Town -- Growth -- Forecasting -- Mathematical modelsen
dc.subjectCity planning -- South Africa -- Cape Townen
dc.subject.otherGeography and environmental studiesen_ZA
dc.titleThe SLEUTH urban growth model as forecasting and decision-making toolen_ZA
dc.typeThesisen_ZA
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