Environmental data is nearly always multivariate and often spatial–temporal. Thus to interpolate the data in space or to predict in space–time it is necessary to use a multivariate spatial–temporal method. Cokriging is easily extended to spatial–temporal data if there are valid space–time variograms or covariance functions. Various authors have proposed such models. In this paper, a generalized...

This article focuses on the estimation and design aspects of a bivariate collocated cokriging experiment. For large class covariance matrices, linear dependency criterion is identified, which allows best unbiased estimator primary variable in setup to reduce univariate kriging estimator. Exact optimal designs for efficient prediction such simple ordinary reduced models with one-dimensional inpu...

rainfall spatial analysis methods are very helpful since there are not enough rainfall gauge stations and watersheds are scattered in large extent. there are many different methods for estimating average precipitation such as; arithmetic method and thiessen polygon. however, the arrangement and location of data and their correlations are not considered by classic methods. thus, geostatistical t...

Rainfall data is useful in many fields such as urban management, agriculture, and so on. Spatial interpolation is widely used to interpolation continue rainfall data from discrete rainfall gauges. The uncertainty in spatial interpolation is change in different region. Paper focus on urban small area of Beijing city, Xicheng District and analyses uncertainty of spatial interpolation from four as...

Accounting for secondary exhaustive variables (such as elevation) in modelling the spatial distribution of precipitation can improve their estimate accuracy. However, elevation and data are associated with different support sizes it is necessary to define methods combine such data. The paper was aimed compare block ordinary cokriging kriging an external drift estimating annual using covariate. ...

Accurate and efficient analysis of multivariate spatio-temporal data is critical in climatology, geology, and sociology applications. Existing models usually assume simple inter-dependence among variables, space, and time, and are computationally expensive. We propose a unified low rank tensor learning framework for multivariate spatio-temporal analysis, which can conveniently incorporate diffe...

Population information is typically available for analysis in aggregate socioeconomic reporting zones, such as census blocks in the United States and enumeration districts in the United Kingdom. However, such data mask underlying individual population distributions and may be incompatible with other information sources (e.g. school districts, transportation analysis zones, metropolitan statisti...

In this paper, we compare the global accuracy of different strategies to build response surfaces by varying sampling methods and modeling techniques. The final application of the response surfaces being aerodynamic shape optimization, the test cases are issued from CFD simulations of aerodynamic coefficients. For comparisons, a robust strategy for model fit using a new efficient initialization ...

Tea fields emit large amounts of nitrous oxide (N2O) to the atmosphere. Obtaining accurate estimations of N2O emissions from tea-planted soils is challenging due to strong spatial variability. We examined the spatial variability in N2O emissions from a red-soil tea field in Hunan Province, China, on 22 April 2012 (in a wet season) using 147 static mini chambers approximately regular gridded in ...