

Lab 7 introduces us to spatial interpolation techniques. These techniques take us through the process of predicting values of unknown observations and creating a surface, using preexisting data. The methods of interpolation we used are IDW (Inverse Distance Weighting), Kriging, and Spline. All three methods are unique and can be used effectively in various scenarios.
For this Lab, we used Los Angeles precipitation data from the county’s Water Resources homepage to determine the current amount of rainfall in LA County, as well as the normal rainfall values and the difference between these two values. Both values were interpolated using the IDW and Spline methods. To do this, I loaded the Los Angeles County shapefile to Arcmap, as well as an excel sheet with the rainfall data. I also marked the locations of the rain gauge stations on the map using display X-Y values. Spatial analysis tools let me calculate the values which were shaded into the map.
Based on my maps, I feel the IDW maps are better suited for this project. This method uses linear data points to find unknown values. This works in this lab because we have limited amounts of point on the maps. Because IDW is a deterministic process, it better shows us the rainfall values than Spline. Looking at my Spline maps, one can see a significant difference between the normal and total maps. This is another sign that the IDW interpolation technique works better for our data.


