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Grid: The Grid module interpolates parameter values at locations were there are no physical data. This is done using various interpolation algorithms (inverse-distance, kriging, trend-surface analysis) based on irregularly spaced data. Sometimes it is of interest to estimate what is occurring between data locations. For other applications, for convenience, or for clarity, irregularly spaced data must be interpolated onto a regular grid to be useful. For example contour, surface, and block require that the data being viewed be gridded with a rectangular pattern. These programs then allow the user to visually view the interpolated estimate of the field data. Grid is used to interpolate values at locations of convenient based on field data.

Within grid there are several gridding algorithms; inverse-distance, simple and ordinary kriging method, and trend-surface analysis. Inverse-distance is a relatively simple method which estimates the value of a location based on the distance and value of surrounding points. Kriging does much the same thing as inverse-distance, except kriging also considers spatial statistics describing how the field data varies directionally. Kriging is often referred to a the best unbiased estimator to estimate a value for a given location. Trend-surface analysis is basically a least-squares regression technique which assumes a data value is a function of a "regional" trend and minor "local" variations. The calculated trend-surface attempts to model the "regional" component.

The grid interface, like all the UNCERT applications in menu driven. Summary results are also displayed and calculated so you can monitor the calculations progress and results.

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Grid supports several gridding techniques and display methods. Inverse-distance is a quick, if basic, estimation method. A contour and surface map are displayed here.

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Kriging is the "best linear un-biased estimator." Statistically it fits the best "surface" possible for the supplied data (in 2D or 3D). In addition to estimating values for unsampled points, it also calculates the estimation variance.

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Instead of mapping the details of a surface, it is othen useful to calculate the regional and local trends. Trend-surface analysis will accomplish this, and statistically quantify the fit with the sample data. Displayed here are 1st, 3rd, and 5th order trend surfaces.

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For more information: info@uncert.com Last Modified: October 2, 2014