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SpatialInterpolation
Chapter 16 - Spatial Interpolation Triangulation Inverse-distance Kriging (optimal interpolation) What is “Interpolation”? Predicting the value of attributes at “unsampled” sites from measurements made at point locations within the same area or region Predicting the value outside the area - “extrapolation” Creating continuous surfaces from point data - the main procedures Types of Spatial Interpolation Global or Local Global-use every known points to estimate unknown value. Local – use a sample of known points to estimate unknown value. Exact or inexact interpolation Exact – predict a value at the point location that is the same as its known value. Inexact (approximate) – predicts a value at the point location that differs from its known value. Deterministic or stochastic interpolation Deterministic – provides no assessment of errors with predicted values Stochastic interpolation – offers assessment of prediction errros with estimated variances. Classification of Spatial Interpolation Methods Global Interpolation Global use all available data to provide predictions for the whole area of interest, while local interpolations operate within a small zone around the point being interpolated to ensure that estimates are made only with data from locations in the immediate neighborhood. Two types of global: Trend surface and regression methods Trend Surface Analysis Approximate points with known values with a polynomial equation. See Box 16.1 Local polynomial interpolation – uses a sample of known points, such as convert TIN to DEM Local, deterministic methods Thiessen Polygon (nearest neighbor) Any point within a polygon is closer to the polygon’s known point than any other known points. One observation per cell, if the data lie on a regular square grid, then Thiessen polygons are all equal, if irregular then irregular lattice of polygons are formed Delauney triangulation - lines joining the data points (same as TIN - triangular irregular network) Example data set soil dat
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