Lab # 7: Spatial Interpolation

The purpose of this lab was to practice and learn spatial interpolation techniques in ArcGIS. In this lab Los Angeles County wants to compare and analyze its precipitation levels from the current season to the average and has hired me to create a series of maps to present this information. Los Angeles County was nice enough to put its precipitation data from the county’s Water Resource department on the web and thus greatly facilitated the process of retrieving the data.

Spatial interpolation can be used in useful ways when attempting to extend and analyze spatial data. Interpolation is the process of predicting the values of locations that lack sample points. It measures the relationship between objects using spatial autocorrelation and spatial dependence principles. Once the sample point’s data is found it can predict the data in the remaining area. In terms of rainfall, counties will analyze their cities that are experiencing drought and which have a healthy supply of rain. Spatial interpolation facilitates the county’s ability to make good estimations of total precipitation levels from a relatively small set of sample points, which greatly facilitates the decision process.

The maps of Los Angeles County precipitation levels show that the majority of it received similar rainfall as the normal. East Los Angeles County received more rainfall than normal as well as the western tip and the south. Inland to the north western tip showed lower rainfall than normal. To use spatial interpolation on this map I used the IDW and Spline methods. Spline makes estimations of the cell values by using math to minimize the surface curvature. The result of this is a smooth surface. Inverse Distanced Weighted (IDW) is best when the set of points is dense and can capture local surface variation. It determines the values of each cell by using the sample points. IDW worked in this case, but I do not think it was the best method because of the size of Los Angeles County. When looking at the spread of precipitation throughout the county, the spline spatial interpolation method shows more detail. The IDW has large areas that show the same precipitation levels while in the Spline map, the precipitation levels are shown with more variation throughout the county over smaller areas. The more detail found on the spline map is why I believe that it was the best spatial interpolation method to use in this case.