Spatial Interpolation

This lab uses the process of spatial interpolation and its possible uses. As part of the lab I used two methods of spatial interpolation to help in the comparison and assessment of precipitation level changes between the season to date and the normal precipitation. Spatial interpolation helps us find out the unknown values of rainfall with the already known values. I decided I would use the method of Inverse Distance Weighted (IDW) and Kriging since both follow different paths when interpolating. IDW uses points to calculate weights and distances for the unknown areas of precipitation we are trying to calculate. Kriging has a more statistical approach using relationships in samples to calculate

the weighted average. Although both help us find the unknown values with those that we do, they give us different results due to the different methods they preform.

The first map shown displays the results of the IDW. Here we can see gradual changes from high to low going towards the West. The second map shows us the results of the Kriging method and how the changes are more gradual since it has slight change. When comparing the overall changes, there is more change in the Kriging method than in the IDW method probably because there is more change in the Season to Date map compared to the Normal Precipitation map, thus establishing a greater change when taking the difference between the two. Overall we can see how there tends to be more rain in the eastern part of the county than in the western portion, and there are about equal amounts of rain

going North and South from there. We can also see that there has been less rain this year than average possibly signifying a drought because there are a lot of areas in green. This may, however, change once we reach the wet months closer to the winter.