Module 6- Special Topics- Scale Effect and Spatial Data Aggregation

Our final lab for Special Topics began with exploring the effects of scale on vector data and resolution on raster imagery. We began with a hydrographic vector data set scaled to 1:1,200, 1:24,000, and 1:100,000 and calculated the area and perimeter of the polygon features as well as the count and total length of the line features. The result of scaling the feature classes differently was that as the scale decreased, variables calculated for the water features increased. In addition to the length of the water features being calculated at a higher number, the difference in the scale was visually noticeable as some hydrographic features were only visible at smaller scale.

A close up view of my hydrographic line features with yellow being the largest scale, and blue being the smallest scale. As you can see, the smaller the scale the finer the features that visible.

After exploring scale we resampled a DEM file to compare 6 different resolutions: 1m, 2m, 5m, 10m, 20m, 90m. Raster resolution is correlated with pixel size, so smaller resolution numbers indicate smaller, more detailed pixels, while a raster with larger pixel sizes would be expected to have less detail. We calculated the slope of each raster and examined the results which showed that as the resolution increased, the average slope also decreased.

For the second half of the lab, we explored the Modified Area Unit Problem by using census data to explore how the results of our data analysis can differ based on how we group our data for analysis. In this situation we were analyzing census data to determine how race affected poverty status, and we separately analyzed data by block groups, zip codes, housing voting districts and counties. We saw that the data could vary significantly based on which of the 4 units of analysis were used to analyze it.

Lastly, we explored gerrymandering. Gerrymandering is the process of manipulating the boundaries of voting districts in order to favor a certain political party. Gerrymandered districts tend to have very irregular boundaries and one way to measure this is the Polsby-Popper Test, which uses and equation to measure how compact a district is on a scale of 0 (least compact) to 1 (most compact), with the assumption that the most compact districts are the least gerrymandered. Here is an examples of an extremely gerrymandered district as determined by this method.