This paper introduces the gstat package for the S language (R, S-PLUS). The package provides multivariable geostatistical modelling, prediction and simulation, as well as several visualisation functions. Gstat (http://www. gstat.org) was started 10 years ago and was released under the GPL in 1996; it is closely linked to several GIS systems. It was not initially written for teaching purposes, b...

This paper explores the potential of infrared thermography and geostatistics in animal production presents results application combination these techniques, contributing significantly to efforts obtain animals’ responses environments which they are located thereby ensuring improvements productivity welfare. The objective was verify variability surface temperature pigs submitted different climat...

Traditional geostatistical space-time geostatistics is not able to take account of the generally strongly non-linear dynamics of multivariate space-time processes. To this effect physico-chemical transport models are in general more suitable. However, as the latter do not fully master the complexity of the processes they attempt to describe, either because of simplifying hypotheses or because t...

Spatial data analysis focuses on both attribute and locational information. Local analyses deal with differences across space whereas global analyses deal with similarities across space. This paper addresses an experimental comparative study to analyse the spatial data by some weighted local regression models. Five local regression models have been developed and their estimation capacities have...

Classical Geostatistics methods have been designed to use mainly statistical knowledge about natural variables and they lack the ability to incorporate important forms of knowledge like physical laws and scientific theories into the mapping process. On the other hand, the powerful and versatile Bayesian maximum entropy (BME) method of Modern Geostatistics can accomplish such a task, rigorously ...

Geostatistics is a distinctive methodology within the field of spatial statistics. In the past, it has been linked to particular problems (e.g., spatial interpolation by kriging) and types of spatial data (attributes defined on continuous space). It has been used more by physical than human geographers because of the nature of their types of data. The approach taken by geostatisticians has seve...

The majority of studies assessing aerosol effects on rainfall use coarse spatial scale (1° latitude/longitude or more) and multi-seasonal or decadal data sets. Here, we present results from a spatial correlation of aerosol size distribution and rain rate for selected stratiform and cumuliform precipitation events. The chemistry transport version of the Weather Research and Forecasting model was...

Geostatistics have become the dominant tool for probabilistic estimation of properties of heterogeneous formations at points where data are not available. Ordinary kriging, the starting point in development of other geostatistical techniques, has a number of serious limitations, chief among which is the intrinsic hypothesis of the (second order) stationarity of the underlying random field. Atte...

Geostatistics has been successfully used to analyse and characterize the spatial variability of environmental properties. Besides providing estimated values at unsampled locations, geostatistics measures the accuracy of the estimate, which is a significant advantage over traditional methods used to assess pollution. This work uses universal block kriging to model and map t...