I have subdivided the globe into 100 sq mile bins and then collected how many tweets were sent with a geolocation within each bin. At first I color coded each bin on the map based off a standardized value of (tweetInBin)/(maxTweetInAnyBin). This produced only one "hot spot" with all the other places being almost uniform in color.
As you can see from the percentile chart, the 100th percentile is so large in population relative to the other bins, that standardizing by the max val destroys any meaningful representation.
My question to you guys is how do I color code my data so that I can have a meaningful heat map. I was toying with the idea of linearizing the data by color coding based off of percentiles instead of based off of population. Basically the 100th percentile would get 100% intensity on the color scale, while the 50th percentile would get 50% intensity on the color scale and so on. My only gripe with this method is that it marginalizes the actual difference in populations much like taking the median marginalizes the outliers.
How do I handle the outliers while still conveying population information?