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Understanding their physical and social structure, and especially their urban land use pattern dynamics throughout time, becomes crucial for successful, effective management of such areas. This study is committed to simulate and predict urban land use change in a pilot city belonging to the S\u00e3o Paulo Metropolitan Region, southeast of Brazil, by means of a cellular automata model associated with the Markov chain. This model is driven by data derived from orbital and airborne remotely sensed images and is parameterized by the Bayesian weights of evidence method. Several layers related to infrastructure and biophysical aspects of the pilot city, S\u00e3o Caetano do Sul, were used as evidence in the simulation process. Alternative non-stationary scenarios were generated for the short-run, and the results obtained from past simulations were statistically validated using a multiresolution \u201cgoodness-of-fit\u201d metric relying on fuzzy logic. The best simulations reached fuzzy similarity indices around 0.25\u20130.58 for small neighborhood windows when an exponential decay approach was employed for the analysis, and approximately 0.65\u20130.95 when a constant decay and larger windows were considered. The adopted Bayesian inference method proved to be a good parameterization approach for simulating processes of urban land use change involving multiple classes and transitions.<\/jats:p>","DOI":"10.3390\/rs15010090","type":"journal-article","created":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T07:31:56Z","timestamp":1672126316000},"page":"90","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Simulation and Prediction of Urban Land Use Change Considering Multiple Classes and Transitions by Means of Random Change Allocation Algorithms"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9232-9043","authenticated-orcid":false,"given":"R\u00f4mulo","family":"Marques-Carvalho","sequence":"first","affiliation":[{"name":"Division for Earth Observation and Geoinformatics, National Institute for Space Research (INPE), Av. dos Astronautas 1758, S\u00e3o Jos\u00e9 dos Campos 12227-010, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6523-3169","authenticated-orcid":false,"given":"Cl\u00e1udia Maria de","family":"Almeida","sequence":"additional","affiliation":[{"name":"Division for Earth Observation and Geoinformatics, National Institute for Space Research (INPE), Av. dos Astronautas 1758, S\u00e3o Jos\u00e9 dos Campos 12227-010, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9437-9351","authenticated-orcid":false,"given":"Elton Vicente","family":"Escobar-Silva","sequence":"additional","affiliation":[{"name":"Division for Earth Observation and Geoinformatics, National Institute for Space Research (INPE), Av. dos Astronautas 1758, S\u00e3o Jos\u00e9 dos Campos 12227-010, Brazil"}]},{"given":"Rayanna Barroso de","family":"Oliveira Alves","sequence":"additional","affiliation":[{"name":"Division for Earth Observation and Geoinformatics, National Institute for Space Research (INPE), Av. dos Astronautas 1758, S\u00e3o Jos\u00e9 dos Campos 12227-010, Brazil"}]},{"given":"Camila Souza dos","family":"Anjos Lacerda","sequence":"additional","affiliation":[{"name":"Surveying and Cartographic Engineering Graduate Program, Federal Institute for Education, Science, and Technology of Sul de Minas (IF SuldeMinas), P\u00e7a Tiradentes 416, Inconfidentes 37576-000, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,24]]},"reference":[{"key":"ref_1","unstructured":"United Nations (2019). 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