@Article{AlmeidaBaCāMoSoPeCe:2002:EmStCe,
               author = "Almeida, Maria Cl{\'a}udia de and Batty, Michael and C{\^a}mara, 
                         Gilberto and Monteiro, Ant{\^o}nio Miguel Vieira and Soares 
                         Filho, Britaldo Silveira and Pennachin, C{\'a}ssio Lope and 
                         Cerqueira, Gustavo Coutinho",
          affiliation = "Instituto Nacional de Pesquisas Espaciais. Devis{\~a}o de 
                         Sensoriamento remoto (INPE, DSR) and Instituto Nacional de 
                         Pesquisas Espaciais. Devis{\~a}o de Processamento de Imagens 
                         (INPE, DPI) and Department of Cartography, Federal University of 
                         Minas Gerais (UFMG), Belo Horizonte, Minas Gerais and 
                         Intelligenesis do Brasil Ltda, Belo Horizonte, Minas Gerais and 
                         Centre for Advanced Spatial Analysis, University College London, 
                         UK",
                title = "Empiricism and Stochastics in Cellular Automaton Modeling of Urban 
                         Land Use Dynamics",
              journal = "CASA Working Paper Series",
                 year = "2002",
               volume = "42",
                pages = "42",
                month = "2002-02",
                 note = "{Online: http://www.casa.ucl.ac.uk/working_papers/Paper42.pdf}",
             keywords = "land use change, transition probabilities, Bayes theorem, weights 
                         of evidence, cellular automata, urban growth, urban 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.",
           copyholder = "SID/SCD",
                 issn = "1467-1298",
             language = "en",
           targetfile = "CASA_Online.pdf",
        urlaccessdate = "03 maio 2024"
}