%0 Journal Article
%@holdercode {isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S}
%@versiontype publisher
%@nexthigherunit 8JMKD3MGPCW/3EQCCU5 8JMKD3MGPCW/3ER446E
%@archivingpolicy denypublisher allowfinaldraft36
%@secondarytype PRE PI
%@issn 0198-9715
%@resumeid
%@resumeid
%@resumeid 8JMKD3MGP5W/3C9JGJN
%@usergroup administrator
%@usergroup ariovaldo
%3 ceus finalissimo.pdf
%@dissemination WEBSCI; PORTALCAPES; COMPENDEX.
%X An increasing number of models for predicting land use change in regions of rapid urbanization are being proposed and built using ideas from cellular automata (CA) theory. Calibrating such models to real situations is highly problematic and to date, serious attention has not been focused on the estimation problem. In this paper, we propose a structure for simulating urban change based on estimating land use transitions using elementary probabilistic methods which draw their inspiration from Bayes' theory and the related weights of evidence approach. These land use change probabilities drive a CA model  DINAMICA  conceived at the Center for Remote Sensing of the Federal University of Minas Gerais (CSR-UFMG). This is based on a eight cell Moore neighborhood approach implemented through empirical land use allocation algorithms. The model framework has been applied to a medium-size town in the west of São Paulo State, Bauru. We show how various socio-economic and infrastructural factors can be combined using the weights of evidence approach which enables us to predict the probability of changes between land use types in different cells of the system. Different predictions for the town during the period 1979-1988 were generated, and statistical validation was then conducted using a multiple resolution fitting procedure. These modeling experiments support the essential logic of adopting Bayesian empirical methods which synthesize various information about spatial infrastructure as the driver of urban land use change. This indicates the relevance of the approach for generating forecasts of growth for Brazilian cities particularly and for world-wide cities in general.
%8 September
%N 5
%T Stochastic Cellular Automata Modelling of Urban Land Use Dynamics: Empirical Development and Estimation
%K urban modelling, land use dynamics, cellular automata, geocomputation, town planning.
%@group DSR-INPE-MCT-BR
%@group CASA-INPE-MCT-BR
%@group DPI-INPE-MCT-BR
%@group DPI-INPE-MCT-BR
%@group CSR-INPE-MCT-BR
%@group CSR-INPE-MCT-BR
%@e-mailaddress almeida@dsr.inpe.br
%@secondarykey INPE--PRE/
%2 sid.inpe.br/mtc-m12@80/2006/04.27.19.49.08
%@affiliation Divisão de Sensoriamento Remoto
%@affiliation University College London
%@affiliation Divisão de Processamento de Imagens
%@affiliation Divisão de Processamento de Imagens
%@affiliation Universidade Federal de Minas Gerais
%@affiliation Universidade Federal de Minas Gerais
%@affiliation Inteligenisis
%B Computers, Environment and Urban Systems
%P 481-509
%4 sid.inpe.br/mtc-m12@80/2006/04.27.19.49
%@documentstage ariovaldo
%D 2003
%V 27
%A Almeida, Cláudia Maria,
%A Batty, Michael,
%A Monteiro, Antonio Miguel Vieira,
%A Câmara, Gilberto,
%A Soares Filho, Britaldo Silveira,
%A Cerqueira, Gustavo Coutinho,
%A Pennachin, Cássio Lopes,