INTERPOLATION

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<strong>INTERPOLATION</strong> Kriging Similar to Inverse Distance Weighting (IDW) Kriging uses the minimum variance method to calculate the weights rather than applying an arbitrary or less precise weighting scheme
Interpolation Kriging Method relies on spatial autocorrelation Higher autocorrelations, points near each other are alike.
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: INTERPOLATION : Splines
Page 37 and 38: Kriging - Lag distance Z i is a var
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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