Background & Aims of the Study: The aim of this study is spatial distribution of arsenic (As) under the influence of chemical fertilizers using geostatistics in city of Eghlid, Iran. Materials & Methods: In this descriptive study, spatial distribution of arsenic was randomly investigated in 100 soil samples and its content was measured by ICP-OES. Spatial distribution of As and geostatistic...

With the proposition of a Markov chain random field (MCRF) theory and its accompanying spatial measure – the transiogram, Markov chain has been extended into a nonlinear Markov chain-based geostatistical approach for one to multi-dimensional conditional (or unconditional) simulation, called Markov chain geostatistics (MCG). This new approach has nonlinear estimators, considers conditional indep...

Area-class maps are conventionally delineated by human hands based on limited observed (or high-quality) data and expert knowledge. It is recognized that areaclass maps contain spatial uncertainty because the classification of unobserved locations is not completely certain. Spatial uncertainty associated with an area-class map may be quantified by spatial statistical approaches through estimati...

The decline in religious identification and corresponding increase in the unaffiliated has been one of the most important religious changes in the United Kingdom (UK). The emergence of the “religious nones” is the most obvious sign of continuing secularization and the declining social and cultural relevance of religion. Yet while the religiously-unaffiliated often form the plurality — if not so...

One of the most challenging aspects multivariate geostatistics is dealing with complex relationships between variables. Geostatistical co-simulation and spatial decorrelation methods, commonly used for modelling multiple variables, are ineffective in presence complexities. On other hand, multi-Gaussian transforms designed to deal relationships, such as non-linearity, heteroscedasticity geologic...

We describe a prototype approach to flexible modelling for maxima observed at sites in a spatial domain, based on fitting of max-stable processes derived from underlying Gaussian random fields. The models we propose have generalized extreme-value marginal distributions throughout the spatial domain, consistent with statistical theory for maxima in simpler cases, and can incorporate both geostat...

Biography Dr. Dan Cornford is a lecture in Computer Science and works in the Neural Computing Research Group at Aston University. Research interests are in the field of spatial statistics, space-time modelling and data assimilation. Lehel Csato is a post-doc in the same group working on an EPSRC grant (GR/R61857/01) looking at applying sparse sequential Gaussian processes to data assimilation. ...

Geostatistics has become a preferred tool for the identification of lithofacies from sparse data, such as measurements of hydraulic conductivity and porosity. Recently we demonstrated that the support vector machine (SVM), a tool from machine learning, can be readily adapted for this task, and offers significant advantages. On the conceptual side, the SVM avoids the use of untestable assumption...