When analysing data from computationally expensive simulation codes or process measurements, surrogate modelling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualisation and optimisation. Kriging is a popular surrogate modelling technique for data based on deterministic computer experiments. There exist several types of Kriging, mostly dif...

This article reviews Kriging (also called spatial correlation modeling). It presents the basic Kriging assumptions and formulas—contrasting Kriging and classic linear regression metamodels. Furthermore, it extends Kriging to random simulation, and discusses bootstrapping to estimate the variance of the Kriging predictor. Besides classic one-shot statistical designs such as Latin Hypercube Sampl...

accurate knowledge of spatial distribution of soil physical and chemical properties is needed for suitable management and proper use of rangelands in masileh plain, qom, iran. in present study, for the spatial modeling of chemical and physical parameters such as sodium (na), calcium (ca), soluble potassium (k), magnesium (mg), electrical conductivity (ec), saturation percentage (sp%), silt, cla...

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...

Spatial estimations are increasingly used to estimate geocoded ambient particulate matter (PM) concentrations in epidemiologic studies because measures of daily PM concentrations are unavailable in most U.S. locations. This study was conducted to a) assess the feasibility of large-scale kriging estimations of daily residential-level ambient PM concentrations, b) perform and compare cross-valida...

Many different algorithms can be used to optimize spatial network designs. For spatial interpolation of environmental variables in routine and emergency situations, computation time and interpolation accuracy are important criteria. The objective of this work is to compare the performance of different optimization algorithms for both criteria. Both adding to and deleting measurements from an ex...

The best unbiased linear predictor for a stochastic process is the best unbiased predictor (i.e., the linearity constraint is removed) if the process is Gaussian. This provides a stronger justi cation for the universal kriging predictor than is generally o ered. For log-Gaussian processes, we show that the standard predictor (obtained by correcting the bias of the exponential of the best unbias...

Maps are useful scientific tools for presenting environmental information, but the statistical techniques necessary to prepare scientifically rigorous maps have primarily focused on terrestrial habitats. This study compares three popular techniques (triangulation, kriging, and co-kriging) to map sediment grain size in Santa Monica Bay, California. Two grain size data sets, one collected in 1994...

This paper investigates the use of Kriging in random simulation when the simulation output variances are not constant. Kriging gives a response surface or metamodel that can be used for interpolation. Because Ordinary Kriging assumes constant variances, this paper also applies Detrended Kriging to estimate a non-constant signal function, and then standardizes the residual noise through the hete...