Lab 8: Spatial Interpolation

Spatial interpolation is an assistive tool that plays an integral role in analyzing surface information from both vector and raster files. The interpolation techniques used in this lab, in conjunction with rainfall data provided by the Los Angeles Department of Public Works, shows the variation and trends in current and past precipitation patterns. Not only can these maps display the difference between the current seasonal rainfall total and the normal seasonal rainfall prediction, but they provide a comparison between distinct types of spatial analysis.

According to the hydrology maps provided by LADPW, the data calculated for precipitation is done between October and March, for both seasonal and normal (expected) seasonal values to provide more accurate comparisons in present and past precipitation patterns in LA. By examining the generated maps, the variance between normal and seasonal values is mostly minimal in the areas of low to moderate annual rainfall, like in the most northern and southern regions of Los Angeles County. Towards the interior and far west, near Cogswell Dam and Agoura, however, the discrepancies in past and current values is more pronounced, where the rainfall data measured for this season is about 8 inches higher tha the normal seasonal values. The inverse can be said for Sanbarg Airways where seasonal rainfall is roughly 10-15 inches shy of the normal range. These insights come from the probative use of spatial analysis to present and compare the datasets using both the IDW and Regularized Spline techniques.

I chose the Inverse Distance Weighted function because of its ability to manage a dense array of points. In doing so, it can extrapolate the surface variation, which is a key geographic factor in rainfall intensity. And because points in close proximity to each other have influence over the others' output values in IDW, it's helpful in analyzing the similarities of rainfall patterns and extents at gauges near one another. The second interpolation tool I used was the Regularized Spline. This method generates a smoother surface through the use of several slope derivative calculations. From these calculations, a more precise interpolation can be done because the spline technique passes directly through the data points. I initially decided on this spline method because it is said to be the best at representing smoothly varying phenomena like temperature, and the continuity of temperature seems much akin to rainfall (Childs, 3). In using both functionalities, I came to the conclusion that spline was much more accurate in its data calculations and led to a more precise comparative result.