Mod 2.2 : Interpolation

 Learning all the different interpolation methods to take the known values to estimate values of other points gave me a different perspective on how to analyze datasets like elevation, rainfall, chemical concentrations, and water quality. A challenge I found between the different interpolation values in which interpolation method would be best to apply to the different datasets if I had data with fewer sample sites and how to correct them so the data can be better represented. Believe elevation data would be easiest to apply the Spline interpolation method due to the simple data values and best representation in elevation change. Data such as data points of chemical values would be better represented with Thiessen interpolation values. 

1.     The difference between two interpolation methods; Spline and IDW (Inverse Distance Weighted) is that Spline estimates values using a mathematical function that minimizes overall surface curvature, resulting in a smooth surface that passes exactly through the input points while IDW assumes that each input point has a local influence that diminishes with distance. As a result, the Spline appearance of the interpolated map is like fabric is pulled over the points and the gaps are naturally filled as opposed to IDW which fills the gaps of all the points surrounding the area. Spline’s range of elevation is based on the space between one point to another such as elevation change. IDW has a convergence of data where they have the same low value.

When implementing DEM or using DEM data, I believe the Spline interpolation method would be the most similar to present the DEM data as advantages to Spline method is estimating the minimum and maximum points and producing a smooth surface data presentation. 

IDW

Spline Regular


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