Here is a short look at how viral maps (“graphic, easy to read, and they make a quick popular point”) are put together by one creator:
When I need to find a particular data set, it’s often as straightforward as a search for the topic with the word “shapefile” or “gis” attached. There’s so much data just sitting on servers that if you can imagine it, it’s probably out there somewhere (often for free). Sometimes though, finding data requires a deeper search. A lot of government-provided data sits inside un-indexed data portals or clearinghouses. Depending on the quality of the portal, these can be tedious to sort through…
Simplicity and ease-of-use: Interactive maps are great, but I want the maps I make to be straightforward to read and understand. I don’t want viewers to have to figure out how to use the map; they should just be able to look at it and figure out what’s going on.
Projections: Typical web maps are limited to the Web Mercator projection. I don’t have any objection to Mercator in principle (in fact it’s brilliant for what it does), but I can’t in good conscience use it for maps at a continental or global scale. Sticking to static maps allows me to choose more appropriate projections for the data and region I’m depicting.
Uniformity: I want everyone who visits my maps to be presented with the same information. I don’t want some algorithm deciding that one visitor is shown a particular view while another visitor gets a different one.
These principles sound similar to what one would expect for any sort of online chart or infographic. There is plenty of data available online but it takes some skill in order to present the data clearly and then market the map to the appropriate audience.
Now that I think about it, it is a little surprising that it took this long for viral maps to catch on. First, the Internet makes a lot of geographic data easily accessible. Two, it is a visual medium and maps are essentially graphics (audio is another story). Third, geographic data seems to feed into a lot of hot-button topics of conversation these days as people of different races (residential segregation), cultural viewpoints (think the American South or the Bible Belt), education (think the Creative Callas looking for exciting urban neighborhoods), and other groupings tend to live in different places.
I wonder if the real story here isn’t the technology that makes mapping on a large-scale relatively easy today. GIS software has been around for a while but it generally pretty expensive and has a learning curve. Now, there are numerous websites that offer access to data and mapping capability (think the Census or Social Explorer). Shapefiles are used by a variety of local governments and researchers and can be downloaded. There are good freeware GIS programs like GeoDa. You need some bandwidth and computing power to get the data and crunch the numbers. All together, the pieces have now come together for more people to access, manipulate, and publish maps in a way that wasn’t possible even just 5 years ago.