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

Bivariate Choropleth and Proportional Symbols

In the first part of this lab, we used proportional symbols to represent positive and negative values in job increases/decreases in the USA.  Because there were negative values in this data set, I created a new map to "fix" the data. In this new map, I created a new field and copied the negative job loss data. I then used the Calculate field data and multiplied it by one to make it positive. Lastly, I overlaid both maps on the data and was able to accurately represent the increase and decrease of jobs in the USA by state.   In the second part of this lab, we delved into how to prepare data for a bivariate choropleth map, choose colors for the legend, and create a good layout.  I created three separate fields to analyze the data: Class Obese, Class Inactivity, and Class Final. I used the symbology tool to create 3 Quantile for the Obese and Inactivity classes and used each quantile to set the three classifications in the fields I created using the Select by Attributes tool to

Infographic's

 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