Welcome to the end of GIS 3015: Cartography! For the final project we were asked to create a thematic map that had two data sets. I chose to compare average SAT score with participation rates on a per state basis.
To represent the data, a thematic hierarchy of how it should be read was chosen. The SAT scores are a more attractive component to the reader, so a choropleth map with vibrant colors was chosen to attract the eye and for ease of reading. In order to properly distribute the color scheme throughout the states, the data was analyzed and multiple classification methods were tested, but the classification that gave the reader the easiest way to analyze the data quickly was quantile classification with five classes to divide out the data. This was due to the fairly even spread of the average test score across the states; no single classification range is overloaded and the ranges themselves are somewhat equivalent. Natural breaks or equal interval classification methods would work well with the SAT score data set, but quantile gave the best ranges to display the data on a choropleth map.
The participation rates, while very important but not as an attractive component of the data to the reader, were chosen as a secondary thematic tier and graduated symbols were applied with a congruent but very different color scheme so the reader can easily identify the symbology. Again, multiple data classifications where applied to the participation rates, but what made the most sense was the quantile classification with five classes to divide out the data. This is due the bottom-heavy nature of the data where many states had very low participation rates. If the natural breaks or equal intervals data classification methods were used, then there would be very little dispersion of the symbols where the majority of the states would be within the smallest class of data. If this happened, the reader would not be able to extrapolate any proper correlations between the two data sets.
My favorite design technique is the use of negative space. You can see this in my boarders of the different data frames, texts and legends with the white against the gray background.
This class what definitely quite the challenge, and the workload was not too severe, but it really depends on you and how perfect you want your maps to be. Overall, this was a very fun and rewarding class!