Accurate mapping of total soil C on the field scale is essential for evaluating efforts to sequester soil C and for providing individual producers with information on C sequestration potentials of their fields. Data on easily measured secondary variables that are strongly related to soil C are believed to be helpful in improving mapping accuracy. The objective of this study was to assess improv...

In statistics it is often assumed that sample observations are independent. But sometimes in practice, observations are somehow dependent on each other. Spatiotemporal data are dependent data which their correlation is due to their spatiotemporal locations.Spatiotemporal models arise whenever data are collected across bothtime and space. Therefore such models have to be analyzed in termsof thei...

Drought monitoring is a fundamental component of drought risk management. It is normally performed usingvarious drought indices that are effectively continuous functions of rainfall and other hydrometeorological variables.In many instances, drought indices are used for monitoring purposes. Geostatistical methods allow the interpolationof spatially referenced data and the prediction of values fo...

BACKGROUND Geostatistical techniques are now available to account for spatially varying population sizes and spatial patterns in the mapping of disease rates. At first glance, Poisson kriging represents an attractive alternative to increasingly popular Bayesian spatial models in that: 1) it is easier to implement and less CPU intensive, and 2) it accounts for the size and shape of geographical ...

In der Arbeit wird die Anwendung von Kriging-Algorithmen zur Einschätzung der Werte jährlicher Regenfälle in ausgewählten Punkten eines Bereiches zur Anfertigung genauer Regenfallkarten dieses Bereiches besprochen. Das Verfahren der Kriging-Approximation besteht aus der Lösung algebraischer Gleichungen, aus mathematischer Modellierung und statischer Optimierung. Es werden ein Approximationsalgo...

Kriging is one of the most often used prediction methods in spatial data analysis. This paper examines which steps of the underlying algorithms can be performed in parallel on a PVM cluster. It will be shown, that some properties of the so called kriging equations can be used to improve the parallelized version of the algorithm. The implementation is based on R and PVM. An example will show the...

Sampling and estimation of geographical attributes that vary across space (e.g., area temperature, urban pollution level, provincial cultivated land, regional population mortality and state agricultural production) are common yet important constituents of many real-world applications. Spatial attribute estimation and the associated accuracy depend on the available sampling design and statistica...

MULTI-OBJECTIVE OPTIMIZATION OF BLAST SIMULATION USING SURROGATE MODEL Toshihiro Tsuga, M.S. George Mason University, 2007 Thesis Director: Dr. Rainald Löhner A multi objective optimization approach using a Kriging model coupled with a Multi Objective Genetic Algorithm (MOGA) is applied to a blast damage maximization problem composed of two objectives, namely number of casualties and damage to ...

Kriging is an interpolation technique that is used to estimate a variable at an unmeasured location from observed values at nearer locations. In this study, it is used to analyze the spatial distributions of the health risk of indoor air pollution. The study case is an air-conditioned office building that has 16 floors, located in Taipei, Taiwan. The Kriging method is used in drawing health ris...

The economic assessment of mining projects includes many factors and resource classification is critical at any stage of mining. The quality of resource classification is a key requirement for accurate economic and environmental risk evaluation. The results of economic assessment are usually reported by companies in order to attract investors. Mineral resource classification standards were crea...