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		<citationkey>Almeida:2004:SiPrUr</citationkey>
		<title>Simulation and prediction of urban land use change as a tool for better planning</title>
		<project>Sistemas e Métodos de Planejamento e Gestão Territorial</project>
		<year>2004</year>
		<secondarytype>PRE CI</secondarytype>
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		<author>Almeida, Cláudia Maria de,</author>
		<group>DSR-INPE-MCT-BR</group>
		<affiliation>Instituto Nacional de Pesquisas Espaciais, Divisão de Sensoriamento Remoto (INPE, DSR),</affiliation>
		<e-mailaddress>almeida@dsr.inpe.br</e-mailaddress>
		<conferencename>International Symposium on Urbanization Worldwide: Trends and Challenges in the 21st Century.</conferencename>
		<conferencelocation>Stuttgart</conferencelocation>
		<date>2004</date>
		<booktitle>Proceedings</booktitle>
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		<contenttype>External Contribution</contenttype>
		<keywords>PLANEJAMENTO URBANO, gestão ambiental, uso da terra, urban modelling, cellular automata, town planning, land use change, Bayes theorem.</keywords>
		<abstract>This scientific paper 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-size 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 and social infrastructure of the town. The probabilities therefrom obtained fed a cellular automaton (CA) simulation model - DINAMICA- conceived by the Centre for Remote Sensing of the Federal University of Minas Gerais (CSR-UFMG), based on a multiscale vicinity approach and 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 infrastructure knowledge to simulate urban land use change, what implies their possible further applicability for generating forecasts of growth trends both for Brazilian and worldwide.</abstract>
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		<language>en</language>
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