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@InProceedings{FelgueirasMontCamaOrti:2015:ImAcCa,
               author = "Felgueiras, Carlos Alberto and Monteiro, Ant{\^o}nio Miguel 
                         Vieira and Camargo, Eduardo Celso Gerbi and Ortiz, Jussara de 
                         Oliveira",
          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)}",
                title = "Improving accuracy of categorical attribute modeling with 
                         indicator simulation and soft information",
            booktitle = "Proceedings...",
                 year = "2015",
                pages = "25--31",
         organization = "International Conference on GeoComputation Geospatial Information 
                         Sciences, 13. (GeoComputation)",
                 note = "{Setores de Atividade: Pesquisa e desenvolvimento 
                         cient{\'{\i}}fico.}",
             keywords = "Geostatistics, Spatial Modeling of Categorical Attributes, 
                         Indicator Simulations, Uncertainty Assesments, Hard and Soft 
                         Data.",
             abstract = "The objective of this work is to apply an indicator geostatistical 
                         simulation approach to improve the accuracy of spatial modeling of 
                         categorical attributes using hard and soft information. Sample 
                         points of a categorical attribute are considered as the hard, or 
                         primary, information while a categorical map is used for determine 
                         the soft, or the secondary, information. The soft information is 
                         incorporated in the indicator simulation procedure as prior mean 
                         values, taken from a probability distribution function, related to 
                         the hard data. The prior mean values are then updated via 
                         indicator simulation to account for the hard data available in 
                         their neighborhoods. To illustrate the methodology a case study is 
                         presented with samples of soil texture classes, as the hard data, 
                         and with classes of a soil map defining the soft information. 
                         These data are gathered from an experimental farm of agriculture 
                         researches. The results show that the use of soft information, 
                         along with the hard data, improve the accuracy of the final 
                         products and show regions with higher uncertainties that are 
                         candidates to be sampled or resampled in the future.",
  conference-location = "Richardson, Texas",
      conference-year = "20-23 May",
                label = "lattes: 2916855460918534 1 FelgueirasMontCamaOrti:2015:ImAcCa",
             language = "en",
           targetfile = "1_felgueiras.pdf",
                  url = "http://www.geocomputation.org/2015/papers/GC15_06.pdf",
               volume = "1",
        urlaccessdate = "04 dez. 2020"
}


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