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		<issn>2179-4820</issn>
		<citationkey>FelgueirasOrtCamNamKor:2017:MoViUn</citationkey>
		<title>Modeling and visualization of uncertainties of categorical spatial data using geostatistics, 3D planar projections and color fusion techniques</title>
		<format>Pendrive, On-line.</format>
		<year>2017</year>
		<secondarytype>PRE CN</secondarytype>
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		<author>Felgueiras, Carlos Alberto,</author>
		<author>Ortiz, Jussara de Oliveira,</author>
		<author>Camargo, Eduardo Celso Gerbi,</author>
		<author>Namikawa, Laércio Massaru,</author>
		<author>Korting, Thales Sehn,</author>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<editor>Davis Jr., Clodoveu A. (UFMG),</editor>
		<editor>Queiroz, Gilberto R. de (INPE),</editor>
		<e-mailaddress>lubia@dpi.inpe.br</e-mailaddress>
		<conferencename>Simpósio Brasileiro de Geoinformática, 18 (GEOINFO)</conferencename>
		<conferencelocation>Salvador</conferencelocation>
		<date>04-06 dez. 2017</date>
		<publisher>Instituto Nacional de Pesquisas Espaciais (INPE)</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<pages>152-162</pages>
		<booktitle>Anais</booktitle>
		<tertiarytype>Full papers</tertiarytype>
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		<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&#771;o Carlos city in Sa&#771;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.</abstract>
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		<language>pt</language>
		<targetfile>19felgueiras_korting.pdf</targetfile>
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