As you brush up on your location intelligence terminology or explore mapping tools for your organization, you may run into something new called a Choropleth. Often referred to as a “heatmaps” in the business intelligence world, Choropleths provide coloring coded visuals of geographic areas / boundaries on a map. Choropleths provide user friendly approach to assign weight to geography using color intensity, which can be valuable for specific scenarios over bar charts. Particularly for more granular geographic levels like postal codes or other sub-administrative areas, you can use this kind of infographic to assess if there is an area containing higher/lower values for a given measure.
Data Visualization Challenges
The challenge of using Choropleths for business analysis is a natural tendency to assign weight not only based on color contrast but also size. The relative size of administrative areas can differ thus causing a visual distortion. For example, lets imagine that we are analyzing a weighted statistic like revenue growth (%). When you look at the following image, where are your eyes drawn to? Without thinking about it, your eyes are drawn to the United States thought the revenue growth is the same in Switzerland which is barely noticeable at this geographic view.
Another challenge with effectively visualizing data with Choropleths is outliers. Outliers that are extremely low or high values for a small number of locations may or may not need to be ignored based on the type of analysis you are building. The global example template shown on the right provides a slider where you can remove a X number of outliers, causing the Choropleth logic to reset.
A Choropleth can be based on a midpoint, median, average, or other values to determine the meaning of color. The colors themselves can use intensity (lightness or darkness) or multiple colors to communicate your desired metrics.
Hopefully this information helps when colleagues start talking about “Choropleths.”