Search

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 work, 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 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 São Carlos city in São 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.

This work presents and analyses a geostatistical methodology for spatial modelling of Soil Lime Requirements (SLR) considering punctual samples of Cation Exchange Capacity (CEC) and Base Saturation (BS) soil properties. Geostatistical Sequential Indicator Simulation is used to draw realizations from the joint uncertainty distributions of the CEC and the BS input variables. The joint distributions are accomplished applying the Principal Component Analyses (PCA) approach. The Monte Carlo method for handling error propagations is used to obtain realization values of the SLR model which are considered to compute and store statistics from the output uncertainty model. From these statistics, it is obtained predictions and uncertainty maps that represent the spatial variation of the output variable and the propagated uncertainty respectively. Therefore, the prediction map of the output model is qualified with uncertainty information that should be used on decision making activities related to the planning and management of environmental phenomena. The proposed methodology for SLR modelling presented in this article is illustrated using CEC and BS input sample sets obtained in a farm located in Ponta Grossa city, Parana state, Brazil.

In environmental zoning territorial units are delimited that, for definition of guidelines, they are considered homogeneous. However, it is recognized that there is a variability interns not acted in map, matter to each one of them, be in the composition, arrangement or environmental function. This study has as objective to present a methodological strategy for obtaining of the areas starting from the integration of spacing environmental data with quantitative information about the space relationships among those data, in way to determine the degree of homogeneity of the sampling area. A base of data was used in System of Geographical Information, associated to techniques of analysis of hierarchical grouping of Ward and correspondence analysis. The case study was the hydrographic basin of the high river Taquari (MS/MT). For the applied method, to guarantee the expression of 90% (R2 = 0,90) of the variability of the information of the study area they were determinate 8 areas. In the area most homogeneous variability it interns measured by the inertia was of 4,2, while in the less homogeneous area the inertia was of 10,8. They were also determinate the variables with larger contribution in the definition of the unit, as well as their similarities and the hierarchy among areas.

Geographical information systems (GIS) are tools that have been recently tested for improving our understanding of the spatial distribution of disease. The objective of this paper was to further develop the GIS technology to model and control schistosomiasis using environmental, social, biological and remote-sensing variables. A final regression model (R(2) = 0.39) was established, after a variable selection phase, with a set of spatial variables including the presence or absence of Biomphalaria glabrata, winter enhanced vegetation index, summer minimum temperature and percentage of houses with water coming from a spring or well. A regional model was also developed by splitting the state of Minas Gerais (MG) into four regions and establishing a linear regression model for each of the four regions: 1 (R(2) = 0.97), 2 (R(2) = 0.60), 3 (R(2) = 0.63) and 4 (R(2) = 0.76). Based on these models, a schistosomiasis risk map was built for MG. In this paper, geostatistics was also used to make inferences about the presence of Biomphalaria spp. The result was a map of species and risk areas. The obtained risk map permits the association of uncertainties, which can be used to qualify the inferences and it can be thought of as an auxiliary tool for public health strategies.

Geostatistics is used in this work to make inferences about the presence of the species of Biomphalaria (B. glabrata, B. tenagophila and/or B. straminea), intermediate hosts of Schistosoma mansoni, at the São Francisco River Basin, in Minas Gerais, Brazil. One of these geostatistical procedures, known as indicator kriging, allows the classification of categorical data, in areas where the data are not available, using a punctual sample set. The result is a map of species and risk area definition. More than a single map of the categorical attribute, the procedure also permits the association of uncertainties of the stochastic model, which can be used to qualify the inferences. In order to validate the estimated data of the risk map, a fieldwork in five municipalities was carried out. The obtained results showed that indicator kriging is a rather robust tool since it presented a very good agreement with the field findings. The obtained risk map can be thought as an auxiliary tool to formulate proper public health strategies, and to guide other fieldwork, considering the places with higher occurrence probability of the most important snail species. Also, the risk map will enable better resource distribution and adequate policies for the mollusk control. This methodology will be applied to other river basins to generate a predictive map for Biomphalaria species distribution for the entire state of Minas Gerais.

Geographic Information Systems (GISs) are composed of useful tools to map and to model the spatial distribution of events that have geographic importance as schistosomiasis. This paper is a review of the use the indicator kriging, implemented on the Georeferenced Information Processing System (SPRING) to make inferences about the prevalence of schistosomiasis and the presence of the species ofBiomphalaria, intermediate hosts ofSchistosoma mansoni, in areas without this information, in the Minas Gerais State, Brazil. The results were two maps. The first one was a map ofBiomphalariaspecies, and the second was a new map of estimated prevalence of schistosomiasis. The obtained results showed that the indicator kriging can be used to better allocate resources for study and control of schistosomiasis in areas with transmission or the possibility of disease transmission.

Em zoneamento ambiental delimitam-se unidades territoriais que, para definição de diretrizes, são consideradas homogêneas. No entanto, reconhece-se que há uma variabilidade interna não representada em mapa, particular a cada uma delas, seja na composição, arranjo ou função ambiental. Este estudo tem como objetivo apresentar uma estratégia metodológica para obtenção das zonas a partir da integração de dados ambientais espacializados com informações quantitativas sobre as relações espaciais entre esses dados, de forma a determinar o grau de homogeneidade da área amostra. Foi usada uma base de dados em Sistema de Informações Geográficas, associada a técnicas de análise de agrupamento hierárquico de Ward e análise de correspondência. O estudo de caso foi a bacia hidrográfica do alto rio Taquari (MS/MT). Pelo método aplicado, para garantir a expressão de 90% (R2 = 0,90) da variabilidade das informações da área de estudo foram determinadas 8 zonas. Na zona mais homogênea variabilidade interna medida pela inércia foi de 4,2, enquanto na zona menos homogênea a inércia foi de 10,8. Foram também determinadas as variáveis com maior contribuição na definição da unidade, bem como suas similaridades e a hierarquia entre zonas.