INTERPOLATION

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<strong>INTERPOLATION</strong> Kriging A statistical based estimator of spatial variables Components: •Spatial trend •Autocorrelation •Random variation Creates a mathematical model which is used to estimate values across the surface
Kriging - Lag distance Z i is a variable at a sample point h i is the distance between sample points Every set of pairs Z i ,Z j defines a distance h ij , and is different by the amount Z i –Z j . The distance h ij is the lag distance between point i and j. There is a subset of points in a sample set that are a given lag distance apart
Page 1 and 2: INTERPOLATION Procedure to predict
Page 3 and 4: Random Sampling Select point based
Page 5 and 6: Adaptive sampling More sampling whe
Page 7 and 8: INTERPOLATION Outputs typically: Ra
Page 9 and 10: INTERPOLATION 1 st Method - Thiesse
Page 11 and 12: Thiessen Polygon Draw lines connect
Page 19 and 20: Sampled locations and values Thiess
Page 21 and 22: INTERPOLATION Fixed-Radius - Local
Page 23 and 24: INTERPOLATION Fixed-Radius - Local
Page 25 and 26: INTERPOLATION Inverse Distance Weig
Page 31 and 32: INTERPOLATION Trend Surface Interpo
Page 33 and 34: INTERPOLATION : Splines
Page 35: Interpolation Kriging Method relies
Page 39 and 40: INTERPOLATION Kriging When calculat
Page 41 and 42: INTERPOLATION Kriging When calculat
Page 43 and 44: INTERPOLATION (cont.) Kriging •A
Page 46 and 47: INTERPOLATION (cont.) Exact/Non Exa
Page 48 and 49: Class Vote: Which method works best
Page 50: Interpolation in ArcGIS: Geostatist
Page 56 and 57: What is the Core Area?
Page 58 and 59: Mean Circle
Page 60 and 61: Kernal Mapping
Page 62 and 63: Sum these density functions
Page 64 and 65: How much area should each sample co

INTERPOLATION

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