![]() These maps of race and religion use a lot of different colors which can be difficult to distinguish if there is a lot of overlap between similar colors. I had no idea there was such an enormous Sikh community in the West of London! Greater London Religious Identity You can again see very clear clustering patterns. Here’s another example dot density map that shows religious identity in greater London. 2010 US Census Data with DataShader, zoomed way in When zoomed out, the colors from these tiny dots blend together in such a way that your eye can perceive the mix of these various colors, and then by comparing these colors with a color legend you can grok these complex geospatial patterns. If you zoom way in, you can see that it’s just a bunch of dots positioned randomly within polygons. 2010 US Census Data with DataShader, zoomed to New York Although there are no color legends with these maps, one can infer by comparing this to the The Racial Dot Map by University of Virginia that pink = Hispanic, green = black, blue = white, red = asian. Here’s the same map zoomed to New York City. This was made with a Python library called DataShader that has many examples of dot density maps of Census data. ![]() You can see clear grouping patterns at this scale as well. Here is another dot density map of similar data, on the scale of the entire US. The Racial Dot Map by University of Virginia You can see here very clear grouping patterns in various parts of the city. This data comes from the US Census American Community Survey. The screenshot below shows census blocks in New York city where color represent ethnic distributions. Perhaps the most impressive of these 1 to 1 dot maps is The Racial Dot Map by the University of Virginia (which uses Stamen Toner for their labels). But with greater computing power and better display capabilities, some newer dot maps use a 1 person = 1 dot ratio which is so much richer and visually compelling. Given the limitations of printing technology and of early computers, until quite recently nearly all dot maps used some aggregation and rounding, where one dot equals 100 or 1000 people, for example. Here is an example of a univariate dot density map printed using only black ink from a World Geography textbook in 1948, with a bit of humorous commentary from Tumblr: Varying densities of dots can successfully communicate subtle variations in the data, when a bucketed choropleth map could not (we will talk more about choropleths below). Dot Distribution map example from Wikipediaĭot density maps have a long history in cartography, especially in the early days of mass-produced print maps when it was difficult or expensive to print with nuanced shades of color. In the map below from Wikipedia, one dot corresponds to 1,000 people from the 2010 Census. If its representing demographic data, for example, one dot might correspond to a certain number of people. One dot generally corresponds to a fixed number of things. Dot density maps, small multiples, multivariate choropleths, oh my!ĭot density maps (sometimes called dot distribution maps) show little dots that are randomly distributed within polygons on a map. This investigation began as a personal curiosity, and expanded to a quest to find the solution to what appears to be an unsolved problem in data visualization - how to calculate the color that one perceives when looking at a particular region of a dot density map. Showing multiple variables on a map is an age old challenge in data visualization and cartography.
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