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@InProceedings{FelgueirasOrtiCama:2016:SpPrCa,
               author = "Felgueiras, Carlos Alberto and Ortiz, Jussara de Oliveira and 
                         Camargo, Eduardo Celso Gerbi",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Spatial predictions of categorical attributes constrained to 
                         uncertainty assessments",
            booktitle = "Proceedings...",
                 year = "2016",
         organization = "Simp{\'o}sio Internacional SELPER, 17.",
                 note = "{Informa{\c{c}}{\~o}es Adicionais: Abstract} and This article 
                         explores the use of nonlinear geostatistical procedures, known as 
                         kriging and simulation indicator approaches, for spatial modeling 
                         of categorical attributes. The categorical information is 
                         initially represented by a set of sample points observed within a 
                         spatial region of interest. The original sample set is used to 
                         generate indicator fields take into account the classes of the 
                         categorical data. The indicator fields, or indicator samples, 
                         contain 0 and 1 attribute values according to the class they are 
                         representing. Empirical and theoretical semivariograms are built 
                         from the indicator samples to represent the spatial variation of 
                         each class in relation to the others. The geostatistical 
                         procedures, making use of the samples and the theoretical 
                         semivariograms, allow obtaining an approximation of the stochastic 
                         model, the conditioned probability distribution function (cpdf) of 
                         the categorical attribute at any desired spatial location. From 
                         any cpdf it is possible to assess optimal prediction, or estimate, 
                         and uncertainty values associated to the stochastic model. Optimal 
                         prediction as mean, median or any quantile values can be assessed. 
                         Uncertainty values are obtained by means of the maximum cpdf 
                         probability, Shannon entropy, or another criterion. The 
                         uncertainty values can be used to qualify the predictions and can 
                         also be considered to generate constrained spatial predictions, or 
                         constrained classifications, that are important in decision 
                         makings related to environmental planning activities, for example. 
                         The concepts here presented are applied and tested in a case study 
                         developed for a sample set of soil texture observed in an 
                         experimental farm in the region of S{\~a}o Carlos city in 
                         S{\~a}o Paulo State, Brazil. Four classes of soil texture are 
                         considered, sandy, medium clay, clay and too clay, in order to get 
                         the cpdf values. Some maps derived by constraints are presented 
                         and analyzed considering different probability values from the 
                         attribute stocha.",
             keywords = "Spatial Analyzes, indicator geostatistics, Spatial Modeling of 
                         Categorical Attributes, Uncertainty Assesments, Constrained 
                         Classifications, Decision Making in Environmental Planning.",
  conference-location = "Puerto Iguaz{\'u}, Misiones",
      conference-year = "7-11 nov.",
                label = "lattes: 2916855460918534 1 FelgueirasOrtiCama:2016:SPPRCA",
             language = "en",
           targetfile = "ConstrainedPredictionsv3.pdf",
                  url = "https://selperargentina2016.org/trabajos-aceptados/",
        urlaccessdate = "24 nov. 2020"
}


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