Well, it’s official, I’m an unrelenting fangirl for Dustin Cable’s Racial Dot Map and everything it stands for. If you’re not yet familiar, it’s one of the coolest data visualization projects to come out of the census data. The map does the following simple thing: every person in the country is represented by a dot on the map, and every dot has a color based on the person’s race. Black, white, asian, hispanic, and other.
The map, a non-trivial feat in data handling, paints a beautiful picture of the racial breakdown of the USA with little commentary beyond what you see in front of you. But it is a rich source for discussion, especially if you are someone who likes to analyze statistical trends, and let’s be real, who doesn’t love a good regression line?
Most recently, Nate Silver on the data science blog FiveThirtyEight, coined the index of dissimilarity, a measure of diversity vs. segregation of American cities with the racial dot map as inspiration. The basic idea is that a city can be simultaneous diverse and segregated. How does this happen? On a city-wide level there can be many people of all different races living in the city, but on a neighborhood level they night be totally distinct.
The beauty of this measure is that it is totally quantifiable, by counting people in cities/neighborhoods and then counting the percentage of their neighbors that are of a different race. If that number is high on both the city and neighborhood level, that means city is both diverse and non-segregated. I always find it satisfying when you can really apply a metric to these types of questions. You can check out the search function in post to see how your city stacks up. Pittsburgh — the Paris of Appalachia that I call home — has a citywide diversity of 50%, which basically means that half of the people in the city are different from you. But the neighborhood diversity is only 35.5%, which means that on the more local scale, things look pretty segregated.
It’s fun to see how different cities and regions of the country look under this metric. As always, a good handling of data and statistics is a great way to start a conversation about the deeper implications that this has for out communities and lives.