Local Methods: Steps in Local Interpretation (Especially for GATE-Geospatial 2022)

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Local techniques calculate predictions from the measured points within neighborhoods, which are smaller spatial areas within the larger study area. Then a surface is constructed by linking these regional observations together.

Steps in Local Interpretation

Local interpolators apply an algorithm repeatedly to a small portion of the total set of points. On average, values at points closer in space are more likely to be similar than point further apart (spatial autocorrelation) . A change in an input value only affects the result within the window.

  • Because local interpolation uses a sample of known points, it is important to know how to select a sample. The first issue is sampling is the number of points to be used in estimation, GIS packages typically let users specify the number of points or use a default number. One might assume that more points would result in more accurate estimates. But the validity of this assumption depends on the distribution of known points relative to the cell to be estimated, the extent of spatial autocorrelation, and the equality of data.
  • After the number of points is determined, the next task is to search for those known points. A simple option is to use the closest known points to the point to be estimated. An alternative is to select known points within a circle, the size of which depends on the sample size. Some search options may incorporate a quadrant requirement means selecting known points from each of the four quadrants around a cell to be estimated.

Thus, there are two important steps for local interpolation:

  • Define sampling neighborhood
  • Find points in the neighborhood
Diagram Shows Two Important Steps for Local Interpolation

Local Deterministic Methods: Common Elements of Algorithm

  • Define an area around the point
  • Find data point within neighborhood
  • Choose model
  • Evaluate point value
Diagram Shows Local Deterministic Methods

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