This week’s lab required us to estimate unknown values with known values, and this process is called spatial interpolation. We were given a website for us to download rainfall data measured by a number of centers that were distributed in Los Angeles County, and the objective was to create maps that display the pattern of rainfall in the region in given criteria; specifically seasonal total, normal rainfall and the difference between these two values. To illustrate these maps, I have used two methods; namely, Inverse Distance Weighted (IDW) and Kriging. The mapping results seem to suggest that there is more rainfall in the eastern region of LA, which is where the mountain ranges are. The reason behind this higher value is the fact that the physical rise in elevation of the mountain forces air to rise and saturate, forming rain clouds; on the other hand, rainfall is particularly lowest in the northern part of LA. Personally, I believe that IDW is a better technique to display this data because it allows one to analyze the data clearly in a visual way; notice how the interpolated lines of classified rainfall regions are smoother than the ones generated by the kriging method. In addition, since we have dense values recorded from those stations, IDW would work best as this density is enough to allow this method to capture the extent of local surface variation needed for analysis.
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