ABSTRACT- The information on the spatial properties of soil is vital to improve soil management and to increase the crop productivity. Geostatistical analysis technique is one of the most important methods for determining the spatial properties of soil. The aim of this study was to investigate spatial variability of soil chemical and physical attributes for field management in eastern Shiraz, I...

This paper presents the application of Exploratory Spatial Data Analysis (ESDA) and Kriging from GIS (ArcGIS8.3) in disease mapping through the analysis of hepatitis B in China. The research shows that geostatistical analysis techniques such as Kriging and ESDA have a good effect in disease mapping. Kriging methods can express properly the spatial correlation. Furthermore, unlike model-based me...

To obtain fundamental information for assessing water resources and predicting natural hazards caused by heavy rains, rain precipitation data in central Japan have been analyzed statistically and geostatistically using data of AMeDAS (Automated Meteorological Data Acquisition System) established by Meteorological Agency of Japan. The study area is the mountainous Chubu and plain Kanto districts...

The packages geoR and geoRglm are contributed packages to the statistical software system R, implementing methods for geostatistical data analysis. Diggle, Ribeiro Jr. and Christensen (2003) provides an introduction to the modelling and theory behind these two packages. In this paper we focus on the capabilities of the packages, the computational implementation and related issues, and indicate ...

Time-resolved characterization of solar irradiance at the ground level is a critical element in solar energy analysis. Siting of nodes in a network of solar irradiance monitoring stations (MS) is a multi-faceted problem that directly affects the determination of the solar resource and its spatio-temporal variability. The present work proposes an objective framework to optimize the deployment of...

We present a Bayesian inversion method for the joint inference of high-dimensional multiGaussian hydraulic conductivity fields and associated geostatistical parameters from indirect hydrological data. We combine Gaussian process generation via circulant embedding to decouple the variogram from grid cell specific values, with dimensionality reduction by interpolation to enable Markov chain Monte...