@Article{AlmeidaBaMoCāSoCePe:2003:EmDeEs,
               author = "Almeida, Cl{\'a}udia Maria and Batty, Michael and Monteiro, 
                         Antonio Miguel Vieira and C{\^a}mara, Gilberto and Soares Filho, 
                         Britaldo Silveira and Cerqueira, Gustavo Coutinho and Pennachin, 
                         C{\'a}ssio Lopes",
          affiliation = "{Divis{\~a}o de Sensoriamento Remoto} and {University College 
                         London} and {Divis{\~a}o de Processamento de Imagens} and 
                         {Divis{\~a}o de Processamento de Imagens} and {Universidade 
                         Federal de Minas Gerais} and {Universidade Federal de Minas 
                         Gerais} and Inteligenisis",
                title = "Stochastic Cellular Automata Modelling of Urban Land Use Dynamics: 
                         Empirical Development and Estimation",
              journal = "Computers, Environment and Urban Systems",
                 year = "2003",
               volume = "27",
               number = "5",
                pages = "481--509",
                month = "September",
             keywords = "urban modelling, land use dynamics, cellular automata, 
                         geocomputation, town planning.",
             abstract = "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 S{\~a}o 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.",
                 issn = "0198-9715",
             language = "en",
           targetfile = "ceus finalissimo.pdf",
        urlaccessdate = "01 maio 2024"
}