Soil pollution by metal(loid)s resulting from rapid economic development is a major concern. Accurately estimating the spatial distribution of soil metal(loid) pollution has great significance in preventing and controlling soil pollution. In this study, 126 topsoil samples were collected in Kunshan City and the geo-accumulation index was selected as a pollution index. We used Kriging interpolat...

Many spatial interpolation methods perform well for gentle terrains when producing spatially continuous surfaces based on ground point data. However, few interpolation methods perform satisfactorily for complex terrains. Our objective in the present study was to analyze the suitability of several popular interpolation methods for complex terrains and propose an optimal method. A data set of 153...

Interpolating scattered data points is a problem of wide ranging interest. One of the most popular interpolation methods in geostatistics is ordinary kriging. The price for its statistical optimality is that the estimator is computationally very expensive. We demonstrate the space and time efficiency and accuracy of approximating ordinary kriging through the use of covariance tapering combined ...

Spatial information surveyed by photogrammetry, airborne LiDAR and Mobile Measurement System (MMS) above ground level can be analyzed by scientists using standard geostatistical methodologies such as ordinary Kriging and sequential Gaussian simulation to interpolate heterogeneities of profiles from sparse sample data. Proven effective by researchers, the Kriging algorithm model is used by comme...

The spatial pattern of precipitation is known to be highly dependent on meteorological conditions and relief. However, the relationships between precipitation and topography in mountainous areas are not very well known, partly because of the complex topography in these regions, and partly because of the sparsity of information available to study such relationships in high elevation areas. The p...

In this paper, results from three different interpolation techniques based on Geostatistics (ordinary kriging, kriging with external drift and conditional simulation) and one deterministic method (inverse distances) for mapping total monthly rainfall are compared. The study data set comprised total monthly rainfall from 1998 till 2001 corresponding to a maximum of 121 meteorological stations ir...

This paper proposes a new interpolation method based on Kohonen self-organizing networks. This method performs very well, combining an accuracy comparable with usual optimal methods (kriging) with a shorter computing time, and is especially efficient when a great amount of data is available. Under some hypothesis similar to those used for kriging, unbiasness and optimality of neural interpolati...

Atmospheric data is often recorded at scattered station locations. While the data is generally available over a long period of time it cannot be used directly for extracting coherent patterns and mechanistic correlations. The only recourse is to spatially and temporally interpolate the data both to organize the station recording to a regular grid and to query the data for predictions at a parti...

We have developed a domain agnostic ordinary kriging algorithm accessible via a standardsbased service-oriented architecture for sensor networks. We exploit the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) standards. We need on-demand interpolation maps so runtime performance is a major priority. Our sensor data comes from wind in-situ observation stations in an area approximate...