GIS 4035 Photo Interpretation and Remote Sensing

Week 1 

New assignment for a new class. This week we worked on understanding the basics of photo interpretation. Our lecture and assignments this week provided a great overview of what is to come. The actual lab assignment was made up of three distinct parts. In part one we had to classify features by tone and texture. We were given an aerial photo and had to create feature classes to classify a certain number of features. The second part of our lab required us to identify features based on size/shape, patterns, shadows and associations. Some of the methods we used to identify features were really simple and straightforward however I had never really considered identifying features based on shadow. I was impressed by how something such as a shadow could be used to identify such things as utility poles which are typically very difficult to identify in aerial imagery. Because we were using a black and white image for analysis, special attention had to be paid to labeling to ensure labels were legible. Below is a map I produced showing the features I identified using the various methods. 

The last part of our assignment gave us the opportunity to see how features change based on the electromagnetic energy that was recorded by a sensor. We compared true color and false color image and compared colors of items that we selected. Depending on the wavelength of light we are using different features will be more evident or will form different associations making them either more or less visible to our eyes. 

 GIS 5935 Module 6

This week we have been working exploring the effects of scale and resolution on spatial data. We first completed an exercise that compared vectors at different resolutions. We found that resolution had a great impact on the quality and completeness of data. The lower the resolution of the data used the less detail and less completeness is achieved in data. Higher resolution data results in more detail of vector features and are typically more complete. We compared the lengths of vector data at various resolutions to determine if there were differences in vectors derived at different resolutions. We also compared rasters at different resolutions to determine if there were any differences at varying resolutions. We used rasters to determine slopes and used the values derived from slopes to make comparisons  of resolutions between 2 and 50 meters. The values for slope that were derived varied by over six degrees between 2 and 50 meters. It is evident that when data derived from rasters can be significantly impacted by resolution. 

The second part of this weeks assignment had to do with gerrymandering. The New Times describes gerrymandering as drawing congressional district maps specifically to "tilt political power in favor of one party" (Wines 2019). Geographically gerrymandering creates odd shaped congressional districts that were drawn a particular way to benefit a particular political party. In some occasions congressional districts will have odd shapes or may be composed of a series of land islands that somehow contain political or societal conditions that are favorable to a political party. Because of the odd shapes that are produced we can deduce where gerrymandering is occurring using a mathematical formula that produces a score which involves the area and the perimeter of a congressional district. The score derived in the described formula is the Polsby-Popper score.  The Polsby-Popper score measures compactness of a district. The less compact a district is determined to be the more likely that it has a jagged exterior boundary that has likely been drawn to politically benefit a certain party. 

Below is a screenshot of Congressional District 12 which in my analysis had the lowest Polsby-Popper Score. The odd shape of this district's perimeter is evident. 

Works Cited:

Wines, M. (2019, June 27) What is gerrymandering and how does it work. The New York Times. https://www.nytimes.com/2019/06/27/us/what-is-gerrymandering.html

 Module 5 - Special Topics in Geographic Science

This week we worked on surface interpolations. The problem we were given was to derive Biological Oxygen Demand (BOD) levels within Tampa Bay (Florida). BOD levels are commonly used to measure water pollution. Surface interpolations uses spatially related data to create surfaces in the form of rasters. Interpolations will analyze relationships between points based on the distances from each other to come up with a final product (raster). The resulting product could be used to carry out predictive analysis.

We used three different methods of interpolations to predict the BOD levels within Tampa Bay. We used Thiessen Interpolation, Inverse Distance Weighting (IDW) Interpolation and two forms of Spline Interpolations (Regularized and Tension). Thiessen interpolations also known as Nearest Neighbor Interpolation is the simplest form of interpolation. Thiessen interpolation uses an simple equality function and only considers one nearest point to assign values to areas with no data (Bolstad 2017). IDW interpolations is more complex than Thiessen interpolation in that IDW "estimates values of unknown points by using sampled values and distances to nearby points" (Bolstad 2017). Values at unknown points decrease as the distance to the reference point increases hence the inverse relation in IDW. The final technique we used was Spline Interpolation. Splines use polynomial functions and go through each point to create smooth curves. Splines could be regular meaning there is no weighted values or they could be tensioned meaning results may be coarser as lines must pass directly through the control points (ESRI). 

During this exercise I found the that the IDW interpolation method worked best at predicting the BOD levels within Tampa Bay. I believe that IDW worked the best because it had the lowest standard deviation in the data that was derived from the interpolation and the numerical values produced were similar to other interpolations and they seemed to be in the middle as well. The numerical values produced for BOD landing in the middle when compared to other interpolations led me to believe that the numbers were at least comparable and not outliers. The small standard deviation between data produced told me that there was less of a spread within the data and therefore it is likely the data is more accurate. Below is a screenshot of my IDW interpolation raster. Higher concentrations of BOD are represented by lighter colors. The layer in pink shows land boundaries in the area. 

Works Cited:

Bolstad, P. 2017. GIS Fundamentals. Eider Press, White Bear Lake MN. 

ESRI. (n.d.) Spline (3D Analyst) http://pro.arcgis.com/en/pro-app/latest/tool-reference/3d-analyst/spline.htm