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		<citationkey>AlmeidaMoCâSoCePeBa:2002:GIReSe</citationkey>
		<title>GIS and remote sensing as tools  for the simulation of  urban land use change</title>
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		<year>2002</year>
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		<author>Almeida, Cláudia Maria de,</author>
		<author>Monteiro, Antonio Miguel Vieira,</author>
		<author>Câmara, Gilberto,</author>
		<author>Soares-Filho, Britaldo Silveira,</author>
		<author>Cerqueira, Gustavo Coutinho,</author>
		<author>Pennachin, Cássio Lopes,</author>
		<author>Batty, Michael,</author>
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		<affiliation>UFMG, Belo Horizonte. MG // Intelligenesis do Brasil, Belo Horizonte. MG // University College London, UK.,</affiliation>
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		<affiliation></affiliation>
		<conferencename>International Symposium Remote Sensing of Urban Areas, 3.</conferencename>
		<conferencelocation>Istambul</conferencelocation>
		<date>11-13 june 2002</date>
		<booktitle>Anales</booktitle>
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		<contenttype>External Contribution</contenttype>
		<keywords>Urban modelling, land use change, Bayes theorem, weights of evidence, cellular  automata, urban planning.</keywords>
		<abstract>This scientific paper, part of a PhD Thesis currently under execution at the Division for Image Processing of the Brazilian National Institute for Space Research (DPI - INPE), is committed with building up a methodological guideline for modelling urban land use change through GIS, remote sensing imagery and Bayesian probabilistic methods. A medium-sized town in the west of São Paulo State, Bauru, was adopted as case study. Its urban structure was converted into a 100 x 100 (m) resolution grid, and transition probabilities were calculated for each grid cell by means of the "weights of evidence" statistical method and upon basis of the information related to the technical infrastructure and socio-economic aspects of the town. The probabilities therefrom obtained fed a cellular automaton (CA) simulation model - DINAMICA- developed by the Centre for Remote Sensing of the Federal University of Minas Gerais (CSR-UFMG), based on stochastic transition algorithms. Different simulation outputs for the case study town in the period 1979-1988 were generated, and statistical validation tests were then conducted for the best results, employing a multiple resolution fitting procedure.  This modelling experiment revealed the plausibility of adopting Bayesian empirical methods based on the available knowledge of technical infrastructure and socio-economic status to simulate urban land use change, what implies their possible further applicability for generating forecasts of growth trends both for Brazilian and worldwide cities.</abstract>
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		<language>en</language>
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