Uso de simulação estocástica não linear para inferências de atributos espaciais numéricos

O presente artigo explora o uso de uma ferramenta geoestatística conhecida por simulação Sequencial por Indicação para inferir atributos numéricos a partir de um conjunto pontual de amostras. No trabalho utilizou-se um conjunto amostral de elevações, obtidos em uma fazenda experimental do Brasil para geração de modelos numéricos, representados por grades regulares, em ambiente de Sistemas de Informação Geográfica. Os métodos geoestatísticos assumem que o atributo umérico se comporta como uma variável aleatória em cada localização da superfície terrestre. Assim, os dados de altimetria, são inferidos a partir de um conjunto de realizações das variáveis aleatórias definidas para cada nó da grade. Além disso, essa técnica possibilita a obtenção de mapas de incertezas relacionados com as inferências de altimetria. Por isso, o trabalho explora, também, a definição de métricas de incerteza a partir dos conjuntos de realizações de mapas de altimetria, representados como grades regulares. ABSTRACT: This work explores the use of a geostatistical tool named Indicator sequential Simulation to infer numerical attributes from a sample point set. Elevation samples from a Brazilian experimental farm are used as input for creation of regular grids to be used as numerical models in Geographical Information Systems environment. The geostatistical procedures consider the numerical attribute as a random variable for each location of the earth surface. In this way, the elevation values are estimated from a set of random variable realizations simulated for each grid location. Furthermore, the presented simulation technique allows the generation of uncertainty maps related to the elevation inferences. On that account, this work also explores metrics for uncertainty definitions from a set of realizations of elevation grid maps.Pages: 437-44

Integrating geostatistical tools in geographical information systems

Geostatistical analysis can be used for spatial modelling in a diversity of geographic applications. In particular, geostatistical methods and tools are of great utility when searching for surface models for fitting a data set sampled as points. By exploring the spatial structure of the data being modeled, the geostatistics yields procedures for surface estimation that contemplates the spatial variability inherent to, for instance, a large set of environmental data. At the same time these developments can generate a spatial estimate of the uncertainty of the information conveyed by those surface representing the attribute information. When developing a geographical application on a GIS platform that has no geostatistical tools, these analysis are performed by exchanging the data to be modeled between the GIS and the geostatistical package where the analysis will take place. These back and forth procedure can be very cumbersome, and in fact , can take a lot of time out that should be used in the task of being thinking on the modelling for that data. These are some reasons for the current trend to incorporate geostatistical facilities in the most popular GIS packages. Initial efforts has been done in the direction to integrate a geostatistical module in the GIS software. Unfortunately these is not enough to guarantee that the geostatistical power is fully incorporated in geographic analysis using GIS packages. Geostatistical procedures must be implemented as operators to be used in spatial modelling languages. Also, the uncertainty related to the modeled data should be considered and propagated to the results of a spatial model that contains non-deterministic parameters and data. This paper addresses the central aspects involved in the incorporation of geostatistical facilities in a GIS modelling environment. The requirements for a geostatistical module to be aggregated into a GIS package and the issues related to geostatistical and error propagation operators in GIS modeling languages are presented and discussed