Spatial Interpolation

 

     The final lab is spatial interpolation. The LA County has hired Intermediate GIS students to conduct spatial interpolation on precipitation. Precipitation data was gathered from the county's Water Resources homepage. Points for both normal and total were formatted on an excel sheet. Evenly distributed points were taken. Then the points were inputted on ArcMap by adding x and y coordinates of calculated degrees. Spatial interpolation allows better predictions of the area around the points. I chose the IDW and Spline methods. I used these methods because I thought it would be easier to compare with each other. In my personal opinion, I thought that the IDW method was better than the Spline method because the interpolation values were more similar. The lower and higher precipitation values are more similarly represented on the map for the IDW method. The Spline method resulted in a negative value for normal precipitation. I believe that the IDW method allows easier comparing between total and normal precipitation. Spline method makes it more difficult to compare because values are more different between normal and total precipitation. I avoided using Kriging because the result seemed too general. It spread and trends were hard to identify. Both maps show similar distribution of rainfall across LA County. It is evident that rainfall is continuously distributed across the county. The maps show that LA County has received most rainfall in the eastern side of the county. Normal precipitation Spline method shows more white (highest precipitation) than does the Total precipitation Spline method. In contrast, the IDW method proves more similarities on the highest precipitation between total and normal values. Both maps have positive values for precipitation. The Spline method shows differences on the upper parts of the county, as well as the eastern side. Due to these differences in values, IDW proves to be better for spatial interpolation.