@InProceedings{FelgueirasOrtCamNamKor:2017:MoViUn,
               author = "Felgueiras, Carlos Alberto and Ortiz, Jussara de Oliveira and 
                         Camargo, Eduardo Celso Gerbi and Namikawa, La{\'e}rcio Massaru 
                         and Korting, 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 = "Modeling and visualization of uncertainties of categorical spatial 
                         data using geostatistics, 3D planar projections and color fusion 
                         techniques",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Davis Jr., Clodoveu A. (UFMG) and Queiroz, Gilberto R. de (INPE)",
                pages = "152--162",
         organization = "Simp{\'o}sio Brasileiro de Geoinform{\'a}tica, 18. (GEOINFO)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "This article explores the uncertainty modelling and their 
                         different ways of visualizations for categorical spatial 
                         attributes. It shows how to model these attributes using 
                         procedures of indicator geostatistics. The geostatistical 
                         modelling uses as input a set of sample points of the categorical 
                         attribute that are transformed in indicator samples according 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 
                         used for producing prediction and uncertainty maps based on the 
                         maximum class probability criterion. These maps can be visualized 
                         using different techniques. In this work, it is considered 
                         individual visualization of the predicted and uncertainty maps and 
                         of the predictions combined with their uncertainties. The combined 
                         visualizations are based on 3D planar projection and on the 
                         Red-Green-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 
                         Sa\̃o Carlos city in Sa\̃o Paulo State, Brazil. The 
                         resulting maps of this case study are presented and the advantages 
                         and the drawbacks of the visualization options are analyzed and 
                         discussed.",
  conference-location = "Salvador",
      conference-year = "04-06 dez. 2017",
                 issn = "2179-4820",
             language = "pt",
                  ibi = "8JMKD3MGPDW34P/3Q5DM7H",
                  url = "http://urlib.net/ibi/8JMKD3MGPDW34P/3Q5DM7H",
           targetfile = "19felgueiras_korting.pdf",
        urlaccessdate = "27 abr. 2024"
}