M2.2: Interpolation

 This week I learned that different interpolation techniques can produce varied results depending on the frequency of the data points and the contents of that data. A single instance of coinciding data points can throw off the results of the analysis greatly when using spline interpolation. When working with continuous data with gradual changes spline generates a smooth spatial pattern that more accurately represents the data. Where preserving exact values is necessary IDW shows sharp transitions between data points. Thiessen represents the data as various zones using TINs. 

Spline (regulated)

Spline (tension)

IDW

Thiessen






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