GIS 6005 Module 6 - Proportional Symbols and Bivariate Mapping

 The sixth, and final, module of Communicating GIS explored the proportional symbols and the bivariate mapping methodologies employed  throughout the cartographic world; both parts of this assignment also provided opportunities to customize legends in a manner that gives the cartographer complete control over each element within the legend. The first two parts of this assignment used the proportional symbol method to illustrate the magnitude of a provided variable. According to Kimberling, a proportional symbol is 'used to represent an exact data value by scaling the symbol's visual variable to be directly proportional to the value it represents' [Kimberling, 2012, p. 617]. Essentially, the size of the smallest variable is set and the remaining symbols are scaled proportionally. As shown below, the first map was a map displaying cities throughout the country of India, and the magnitude of their population is conveyed by the diameter of that cities point symbol. Due to inadequacies of the human eyes and mind, people tend to visually underestimate the size of the circles as they get bigger, so an appearance compensation algorithm [Flannery] has been applied to these point symbols; more can be read on proportional symbology and the Flannery compensation technique by clicking here.

After the symbology was set, a formatted map was created, and the legend was added. ArcGIS does not currently have a nested legend option [see map above], so the legend had to be converted to graphics to successfully create this type of legend. Once the legend was converted, all elements within the legend are completely editable as graphical elements, giving the cartographer an endless amount of control over the legend's design.

 

The next portion of the lab was also based on proportional symbology, providing more opportunity to explore the capabilities and potential problems of this methodology. The map below is a map of the employment changes from 2007 through 2015 throughout the United States. The difference of data values within this dataset created problematic overlaps throughout the entire map. Once this issue was resolved, the customized legend was created by converting the legend to graphics and manipulating each element individually.

The third portion of the lab explored the bivariate mapping technique; a bivariate map 'displays two variables on a single map by combining two different sets of symbols or colors' [Kimberling, 2012, pg. 456]. The map below is a bivariate map showing the relationship between two individual variables, obesity and inactivity.

 

By examining the map, it is apparent that most U.S. counties fall within the low-low, medium-medium, and high-high classes, thus illustrating a directly proportional relationship with each other. If no correlation between the two classes existed, many of the counties would fall within the low-high or high-low classes. The main challenge of this exercise was to determine the color ramp by choosing colors that were easy to distinguish, were visually complementary, and were also progressively increasing as the values climbed. This took a lot of time and patience, but the final deliverable made the dedication worthwhile. Lastly, a customized legend was created, showing how the qualitative values of a quantitative dataset are represented on the map.

This exercise was a great opportunity to explore some customization options that will be necessary throughout the execution of the final project.

Informational sources:

Environmental Science Resource Institute (2024). Proportional Symbols. 

https://pro.arcgis.com/en/pro-app/latest/help/mapping/layer-properties/proportional-symbology.htm#:~:text=Appearance%20compensation%20uses%20an%20algorithm,and%20no%20Maximum%20size%20specified.

Kimberling, A.J. (2012). Map Use: Reading, Analysis, Interpretation (7th edition). ESRI Press Academic.