@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 = "15 jun. 2024"
}