G.Hudson Mastering GIS

As seen in the title, this week's module and lab covered two topics. The first topic was scale effect. In a world where we have various interactive maps at the palm of our hand, scale effect may not have the most blatantly obvious issue with viewing data. However, scale effect can have a wide-ranging impact on data we are using and collecting. Vector data, for example, created from imagery or raster data is only as good as the finest detail that can be seen. This means at a small scale the resolution is considered low and small features and intricacies are not visible. Larger features are the only items detectable or discernable. As such, fewer vertices and segments are used to represent a feature, which can lead to less accurate calculations that tend to have a smaller numerical account of the feature. The opposite is true for large scales.  The second topic covered this week was spatial data aggregation. This refers to the combining of data into a boundary, which in turn generali

 During this week's module we were introduced to and learned about interpolation. While we were made aware that there are many interpolation techniques available, we went more in depth when learning about Thiessen, Inverse Distance Weighted and Spline techniques. We were then tasked with performing these techniques.  We were required to use a dataset of water quality samples from the Tampa Bay area to show where there were concentrations of the Biochemical Oxygen Demand (BOD). The Thiessen interpolation method was used first and produced a raster on polygons. This was not deemed suitable based on the abrupt changes that take place at the edge of polygons versus the fluid and continuous nature of water. The IDW and Spline methods (regularized and tension) both produced a continuous surface which was more suitable. Additionally the spline method proved to be helpful because it made an anomalous area noticeable. Ultimately, however, I decided that the IDW was most suitable from my per