This is the map place

  In the above image, you can my work done for the LULC and Ground Truthing lab. My residential points came back very accurate, while I mistakenly labeled some industrial areas as commercial and mistook a bus lot for a potential farm. Overall, my areas were pretty accurate though I could've spent more time and drilled down into the third level of classification.

 There are no instructions in the assignment to create a blog, but it is in the rubric so I'll briefly cover this week. We looked at aerial imagery to ascertain: tone, texture, color, and to identify features within the imagery. Below you can find two jpegs I created for the exercises:

As I stated in my deliverables word document, I did not find an effect from scale on vector data. Of course, scale affects the visual representation of the data but the assignment had us testing numeric values within the data. The resolution affect on raster data was more apparent, the diminished resolution had a direct affect on the statistical analysis done on the data. Gerrymandering is the intentional drawing of voting districts to nullify certain populations voting power, whether that be done on a political or racial bias. It can be measured by searching for odd segments that seem to oddly cutout nearby populations or connect distant ones. See below for an example of an offender failing to have district 'compactness'.

 This week we did an analysis of different methods for surface interpolation. The methods being: IDW, Thiessen, and two different Spline methods. The Thiessen did a better job of keeping the numerical values closer to the raw data, while the other three methods were much more visually appealing. Below is the method I found to be the best blend of numerical accuracy and visual impact, which was the Spline (Tension):