Areal interpolation is the procedure of using known attribute values at a set of (source) areal units to predict unknown attribute values at another set of (target) units. Geostatistical areal interpolation employs spatial prediction algorithms, that is, variants of Kriging, which explicitly incorporate spatial autocorrelation and scale differences between source and target units in the interpo...

[1] We compare versions of six interpolation methods for the interpolation of daily precipitation, mean, minimum and maximum temperature, and sea level pressure from station data over Europe from 1961 to 1990. The interpolation methods evaluated are global and local kriging, two versions of angular distance weighting, natural neighbor interpolation, regression, 2D and 3D thin plate splines, and...

18 Tea fields emit large amounts of nitrous oxide (N2O) to the atmosphere. Obtaining accurate 19 estimations of N2O emissions from tea-planted soils is challenging due to strong spatial 20 variability. We examined the spatial variability of N2O emissions from a red-soil tea field in 21 Hunan province, China, on 22 April 2012 (in a wet season) using 147 static mini chambers 22 approximately regu...

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 ...

The objective of this work is to extend kriging, a geostatistical interpolation method, to honor parameter nonnegativity. The new method uses a prior probability distribution based on reflected Brownian motion that enforces this constraint. The work presented in this paper focuses on interpolation problems where the unknown is a function of a single variable (e.g. time), and is developed both f...

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...

Spatial data sets are analysed in many scientific disciplines. Kriging, i.e. minimum mean squared error linear prediction, is probably the most widely used method of spatial prediction. Computation time and memory requirement can be an obstacle for kriging for data sets with many observations. Calculations are accelerated and memory requirements decreased by using a Gaussian Markov random field...

Physical data interpolation is a common issue in Geosciences. For many variable of interest, the measurements are often sparse and irregularly distributed in time and space. Analyzing the data usually requires a numerical model, which samples the data on a regular grid. Mapping irregular measurements on a regular grid is done by interpolation, which aims to generalize, but not to create, inform...

A fundamental building block for supporting better utilization of radio spectrum involves predicting the impact that an emitter will have at different geographic locations. To this end, fixed sensors can be deployed to spatially sample the RF environment over an area of interest, with interpolation methods used to infer received power at locations between sensors. This paper describes a radio m...

Wireless communication networks offer subscribers the possibilities of free mobility and access to information anywhere at any time. Therefore, electromagnetic coverage calculations are important for wireless mobile communication systems, especially in Wireless Local Area Networks (WLANs). Before any propagation computation is performed, modeling of indoor radio wave propagation needs accurate ...