Topic 3 Module 1: Scale Effect and Spatial Data Aggregation

This week we were involved in scale effects on raster and vector data, gerrymandering. The relationship between scale and geometric properties is that the large-scale maps show fewer properties than the small-scale maps. This is due to the generalization where information is “lost” because fewer vertices are used to represent features. Along with exclusion, where scale matters and can cause a decrease in the level of hydrographic feature detail. After reading the Goodchild, M.F. 2011 article and seeing other Esri documentation on the web I understand that my findings in this lab are as expected and that I have lost detail as the scale changes. The level of detail of features represented by a raster or vector data is often dependent on the cell (pixel) size, or spatial resolution, of the raster/vector. The cell must be small enough to capture the required detail but large enough so computer storage and analysis can be performed efficiently. However, more is not often better especially when considering compuation times and data storage limits. As for gerrymandering, it has a very negative history and is defined by manipulating the boundaries of (an electoral constituency) so as to favor one party or class. Basically it is the redrawing of polygons and can be measured by compactness and community. Below is a screenshot of a district with failing to have district 'compactness'.

Internship Blog Post #3

I chose to update you on my internship. It is going very well, and I am happy to report that I am learning a lot more than I expected. There is so much more going on depending where your internship is, and mine has opened insights into CAD, 911 Operations, Emergency Management, Survey123, and even ArcMap. While ArcMap may be going away one day, I am learning how to use it because that is what my office is using. It is different than Pro, but the same functionality exists. It's just not always in the same spot that we were taught in Pro. All good. I keep the ArcMap help website pinned on my computer just in case. My supervisor sits next to me and we are able to work on things together or separate. She has a few ongoing projects that she hopes we can get more into while I am there. One involves creating a Survey123 to collect data on mile markers down a desolate road, and another project is awaiting data from a local state park, so that we can enter it into 911 CAD, so if someone needs help out there they can find them fast. I have made a few maps already, and that has been fun to interact with the needs of the map and the data they have. I love going to the office there, and everyone is a delight to work with. Hoping good things continue and I learn as much as I can in the little time left.

Module 2.2: Surface Interpolation

This week we covered topics in surface interpolation techniques in GIS, including Theissen, Inverse Distance Weighted (IDW), and Spline. We critically interpreted the results from the techniques to compare and contrast them. The lab consisted of exploring water quality data for Tampa Bay, FL in the Biochemical Oxygen Demand (BOD) in milligrams per liter. Data consisted of 41 sample points in assuming random locations. Determining the best way to accurately represent the data was a bit up to us. Theissen technique uses polygons to define an area of influence around its sample point, so that any location inside the polygon is closer to that point than any of the other sample points. IDW assumes that things that are close to one another are more alike than those that are farther apart. Spline estimates values using a mathematical function that minimizes overall surface curvature, resulting in a smooth surface that passes exactly through the input points. After looking at the statistics of the data for each technique and the overall output for any anomalies I chose IDW interpolation as my image to display below. This is because it is an exact interpolator, good use for water data because of how it works, and there were no adjustments needed to make the data work for the technique. It was the sufficient and accurate way to go for this particular data set in my opinion to show the water quality conditions in Tampa Bay.