@Article{FelgueirasOrtCamNamKör:2021:ExGeMo,
               author = "Felgueiras, Carlos Alberto and Ortiz, Jussara de Oliveira and 
                         Camargo, Eduardo Celso Gerbi and Namikawa, La{\'e}rcio Massaru 
                         and K{\"o}rting, Thales Sehn",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "Exploring Geostatistical Modeling and Visualization Techniques of 
                         Uncertainties for Categorical Spatial Data",
              journal = "Journal of Information and Data Management - JIDM",
                 year = "2021",
               volume = "12",
               number = "4",
                pages = "330--341",
             keywords = "Indicator geostatistics, Spatial Modeling of Categorical 
                         Attributes, Uncertainty visualization.",
             abstract = "This article presents and analyzes the indicator geostatistical 
                         modeling and some visualization techniques of uncertainty models 
                         for categorical spatial attributes. A set of sample points of some 
                         categorical attribute is used as input information. The indicator 
                         approach requires a transformation of sample points on fields of 
                         indicator samples according to the classes of interest. 
                         Experimental and theoretical semivariograms of the indicator 
                         fields are defined representing the spatial variation of the 
                         indicator information. The indicator fields, along with their 
                         semivariograms, are used to determine the uncertainty model, the 
                         conditioned probability distribution function, of the attribute at 
                         any location inside the geographic region delimited by the 
                         samples. The probability functions are considered for producing 
                         prediction and probability maps based on the maximum class 
                         probability criterion. These maps can be visualized using 
                         different techniques. In this article, it is considered individual 
                         visualization of the predicted and probability maps and a 
                         combination of them. The predicted maps can also be visualized 
                         with or without constraints related to the uncertainty 
                         probabilities. The combined visualizations are based on 
                         three-dimensional (3D) planar projection and on the RedGreen-Blue 
                         to Intensity-Hue-Saturation (RGB-IHS) fusion transformation 
                         techniques. The methodology of this article is illustrated by a 
                         case study with real data, a sample set of soil textures observed 
                         in an experimental farm located in the region of S{\~a}o Carlos 
                         city in S{\~a}o Paulo State, Brazil. The resulting maps of the 
                         case study are presented and the advantages and the drawbacks of 
                         the visualization options are analyzed and discussed.",
                  doi = "10.5753/jidm.2021.1786",
                  url = "http://dx.doi.org/10.5753/jidm.2021.1786",
                 issn = "2178-7107",
                label = "lattes: 1649941449641846 2 FelgueirasOrtCamNamKor:2021:ExGeMo",
             language = "en",
           targetfile = "felgueiras_exploring.pdf",
        urlaccessdate = "11 maio 2024"
}