Skip to main content

Color and Choropleths

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 ramp playing around, with no step rate between the steps. I think this is more reader-friendly as it’s easier to understand. 

The last step in the lab was to normalize the population data for Colorado, USA, from 2010 to 2014. I then created a choropleth map. When choosing a coordinate system, I choose not to use a state plane as there are three different zones within Colorado. I looked into using UTM, but the entire state did not reside within a single zone. My last choice was to use Albers Equal Area Conic. Because Colorado does not reside too far east from the prime meridian, I felt this was a viable option and proceeded with USA Contiguous Albers Equal Area Conic projection.
When choosing the design for the legend, I looked at using 5 or 6 classes but decided to use 5 as 6 classes created a huge skew toward negative percentages. When determining which classification to use I looked at each histogram. I ended up using Natural breaks (pic below), as again, the other classification methods inaccurately overemphasized either side of the spectrum. I decided to use a divergent choropleth design to empathize the negative and positive percentages. I used red and orange to represent negative numbers. Yellow as a “neutral” middle for percentages near zero. And lastly, greens for positive percentages.



Comments

Popular posts from this blog

Utilizing ERDAS Imagine to Analyze Map Features

 This week we learned how to utilize histograms and different bands to highlight different features in a map. On the following map that we worked on, dark bodies of water caused high peaks on the left of histograms while snow-peaked mountains were small blips on the far right. These simple distinctions help to quickly identify map features on a graph, that you can then utilize as a stepping stone to finding them on the image. I found it incredibly interesting how the different band layers highlighted different features on the map. Figure 1 below depicts three different features we found on the image.  Figure 1: Distinct features found on an image using ERDAS Imagine. Feature 1: Large body of water. Feature 2: Snow-capped mountains transitioning to thick vegetation. Feature 3: Shallow turbulent body of water near urbanized land, transitioning to deep calm body of water. 

Positional Accuracy: NSSDA

 In this analysis, I compared the street and road intersect data collected for Alburquerque, NM by the City of Alburquerque and the application StreetMaps. I used an orthophoto base layer as the reference for this analysis, to compare and determine the accuracy of both the City and Streetmap layers using NSSDA procedures. The most difficult part of this analysis for me was how to determine what 20% per quadrant looks like. Because the reference map was divided into 208 quadrants, I had to determine how to subdivide all the quadrant's equality into 20%. After multiple trials and error, I decided to subdivide the entire area (208 sub-quadrants) into 4 equal-area subsections. In this way, I could do 5 random right intersection points per subsection or 20% per subsection.  Map 1: City of Albuquerque city map data.  Map 2: City of Alburquerque SteetMap data When selecting a random intersection to place the points within each quadrant, I choose a location that had data f...

Scale Effect and Spatial Data Aggregation

 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 distri...