For the final lab, we were given a hypothetical situation where the county of Los Angeles has hired us to conduct a spatial interpolation on precipitation. Many points were gathers and formatted on an excel sheet. In an attempt to cover the entire county, various points from all over Los Angeles were taken. Some discrepancies that I experienced were the total and normal precipitation of the Rolling Hills rain gauge indicator. It showed a discrepancy between the data given from the absent normal precipitation versus the given total levels. The data was then not added from the LADWP.org website hence the missing data close to the bottom of the county. I chose only the data that I felt could be best represented on the map. In addition, the LA Country gauge station map lacks many points in the northern rural portion of the county, just like how the Rolling Hills gauge was not appearing on the normal map on LADWP.org.
Spatial interpolation is a useful tool for extending spatial data and is helpful when funding and time is limited. Through spatial interpolation, sample points are used in order to make a better prediction of the area around the point. When looking at precipitation, spatial interpolation allows the county to make a good estimate on precipitation levels based on a relatively small set of points. The need to gather data from every single point in the county is not necessary; rather the collection of several points that cover the county is adequate. According to Dr. Barnali Dixon from University of South Florida St. Petersburg, her paper “Spatial Interpolation of Rainfall Data Using ArcGIS: A Comparative Study” stated that IDW and Kriging are the most commonly used spatial interpolation methods for estimating rainfall.”
The interpolation maps of Los Angeles County precipitation reveal that most of the county’s levels did not fluctuate too much from the norm. The spatial interpolation processes that I used were IDW and Spline. I felt that the statement from the research of Dr. Barnali Dixon is correct in stating that IDW was a good method for interpolating precipitation. I felt that the IDW was better than the Spline technique that I used. For some odd reason the Spline data showed negative values of the precipitation. At first I thought it had been an error on my behalf, but I soon realized that most people that implemented the Spline methods observed the same results. Also, the concentration of land with below average rainfall is not pronounced in the Spline map and an overall deficiency in rainfall looks as if it is more prominent but I began to think of the negative values and how that would affect the overall map. Honestly, at first I could not really tell the differences between IDW and Spline and why an ArcGIS user would use one over another. But upon further analysis, I feel that the IDW is easier for the map user to interpret the difference. The recent versus normal in IDW shows differences and the maps do not seem to be too different while showing the necessary info. While the Spline method seems to have drastically different maps from normal versus recent. Overall, I felt that IDW does a better job over Spline in precipitation interpolation analysis.
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