Piece of Eden GIS Blog

 This week was a fun and challenging week as we learned about and created infographics. It was fun to create the infographics themselves, but challenging to figure out the best methods and practices in analyzing raw data.  We used 2018 County Health Rankings National Data from countyhealthrankings.org. I chose to move forward with the two values: Unhealthy Mental Days and Premature Dealth.  I   choose these two variables because those that struggle with mental health die before their time due to depression, anxiety, and/or a combination of similar issues. Both variables are normalized by relating their value to all counties within each state in the USA. For example, the poor mental health days is normalized as the average number of reported mentally unhealthy days per month per ctizen. The normalized premature rate is the “age-adjusted years of potential life lost rate per 100,000.”  Below, I created a scatterplot of the normalized data.  I choose to keep the scatterplot in a pretty tr

This lab was very interesting as we dived into color theory.  In the first part of the lab, we created and compared linear and adjusted progression color ramps to themselves as well as a color ramp from the website colorbrewer.org.  I found, the colorbrewer color ramps are not as rhythmic when compared to the other methods, as they don’t step up at set intervals or rates. However, I don’t think that a set rate is needed to go from color to color. I preferred the colorbrewer ramp because each color was distinct from its neighbors. In the linear and adjusted color ramps, the colors looked too similar to each other and were not distinct enough for each step. I think that as long as the color ramp is moving in the opposite direction of the same color hue, the step rate or interval is not as relevant. When I first was completing the linear step I started with the purple hue option but had a difficult time, as each step in the color ramp looked the same. At one point, I created my own color

 In this lab, we explored different types of tools to enhance topography. One tool we focused on is Hillshade. Hillshade utilizes the different light source directions to enhance different topographic features like elevation on a DEM map. While composing the map below, I compared traditional and multidirectional hillshade options. I think that Multidirectional is more beneficial with features that have subtle and sharp changes in topography, that are accentuated with lighter ombre shadow sketching, like a mountain face. The traditional hillside tool tends to shadow in pretty dark, and takes away from find details of a mountain face, but does well with cliffs and flatter topography that doesn’t have huge elevation gains. Therefore, I used the multidirectional option below.  I also tried to keep the area map as big as possible, since there are a lot of different subareas covered in the legend. I centered the North Arrow in the top right corner, and the legend and other map elements on th

 This module was extremely helpful, as we dived deep into projection systems. Through the lab, we analyzed UTM, State Plane, Albers and determined when they are the best fit, as well as how to project them properly.  A handful of tools we utilized are listed below:  Projection Wizard Geodetic Parameter  Search For our final map, we choose a state in the USA, and determined which projection to use; UTM vs State Plan.  I choose the state Utah because I have always wanted to go to Zion! I decided not to use the State Plane because there are three different ones that subdivide the state. Since state plane’s do not work here, I opted for UTM over Albers because we are looking at a relatively smaller area not close to the poles. I tried a couple of different UTM NAD83 Zones and landed on UTM NAD83 Zone 12. In this zone, Utah completely lies within one UTM Zone.  

This module explored the use of typography in map-making. For this assignment, I had to label larger cities/islands, waterways, parks, landmarks, neighborhoods, and topographical features in San Francisco, California.  My thought process for labeling Map 1 below is as follows.  General cities and islands: I used basic large black font, for these main locations' features to stand out. I placed them centrally, in a spot they wouldn’t block other features. I used a blockier font (century gothic) for easier legibility. Water Features: I used italicized blue font to signify water. I used a lighter blue for the Pacific Ocean, as it’s not local information. I used the font commonly used for water features, Bodoni MT Italic, for distinction and legibility. Park Names: I used a green front placed above the area of the parks. I used a blockier font (century gothic) for easier legibility. Landmarks: I used a bright orange to stand out and placed it over the bridge. I used a blockier font (cen

 In this lab we first looked at how the scale at which we analyze data can affect geometric properties. Looking at data at three different scales (1:1200, 1:24000, 1:100000) I was surprised by my final calculations. I thought that there would be a lineal proportional relationship along the scales, but I did not find this. It makes sense that the greatest resolution [1:1200] would have the most detail of geometric properties, but I was surprised that 1:100000 had greater geometric properties than the medium resolution. I resampled the data using the Bilinear technique and found the following effect on DEM resolution.  As DEM resolution increases the average slope in degrees decreases. This makes sense because as you get closer, more in detail by getting closer, you are zooming in as actively seeing less features altogether.  In the last part of the lab, we looked into the Gerrymandering of districts in the USA. Gerrymandering essentially breaks up congressional districts in a manner tha