Spatial Interpolation

This lab was about downloading a point dataset with precipitation information, and then create different raster surface models that visually represent the spread and concentration of different values. So areas that are strongly red, for example, mean that they get a lot of rain. There were two datasets - one for the season total this year, and the other for the season normal. The final step was to create two difference maps, one for each method of interpolation.

The first model i chose was inverted distance weighted, which calculates a surface model using sample points. There were only a couple minor differences between the maps i got for the season total and normal - an area in the upper right corner that was drier than expected, and an area in the middle right that was wetter than expected. i'm not sure that inverted distance weighted was the best method to use on this specific lab, because i was only going off of 65 or so points, spread over the entirety of LA county. The lab pdf about surface interpolation said that this works best when there are enough points to create a truly accurate reading off the samples. as you can see, the two maps aren't very detailed.

The second model i used was the spline one. This one uses a model that minimizes surface curvature, thus ending up with a smooth surface that passes through all the input points. This worked a little better i think on the data points, and we ended up with two maps that give a slightly more informative spread that the previous. We can easily see which areas received less/more rain than was expected. 

For the maps measuring the changes in precipitation between expected and actual, it was a simple matter of using the raster calculator to subtract one from the other. I chose to do this using an absolute value function, so that the end result would just show the areas where there was a big change and where there was almost no change at all. Actually this worked pretty well with both models, because both show similar areas of big change.