I bill myself as a data scientist. After all, 50% of any GIS or cartography project, in general, involves data wrangling. Knowledge of statistics and geo-specific analytics is imperative to getting complex maps right. Of course, as with many tech fields, tools are always changing and there always seems to be something new to learn.
However, I take issue with this little snippet in Sunday’s NY Times from David J. Hand. When speaking about geographic clusters* he wags his finger at us and pontificates, “…if you do see such a cluster, then you should work out the chance that you would see such a cluster purely randomly, purely by chance, and if it’s very low odds, then you should investigate it carefully.” See the short article here.
Granted, he’s probably reacting to the surfeit of maps that have been circulating the internet claiming to prove this, that or the other, when in fact they are mostly bogus. For example, Kenneth Fields tweeted this abomination this morning:
— Kenneth Field (@kennethfield) February 24, 2014
Jonah Atkins has created a github location for sharing remedies to bad maps like the above called Amazing-Er-Maps (this is itself in reaction to the name “Amazing Maps,” which has been given to a twitter account that showcases maps of questionable quality at times.)
Amazing-Er-Maps, as I understand it, is a place for you to upload a folder that contains the link to a bad map and a new map that is similar but does a better job. You include the data and the map as well as any code that goes with it. It’s a fabulous idea. Don’t just complain about bad maps, seek to make them better in a way that the whole community can gain inspiration from and learn from. Check it out, Jonah’s already got it going with several fun examples. Super warm-fuzzies.
Circling back to Mr. Hand, he has a point: we need to apply sound statistical and mathematical reasoning to our datasets and the maps we make from them. For example, when I was helping the Hood Canal Coordinating Council map septic system points, I didn’t just provide maps for them to visually inspect for clusters of too-old septics, I produced a map of statistically significant clusters of the too-old septics using hierarchical nearest neighbor clustering, which provides a confidence level for the chance that the cluster could be random.
The point is, those who are already practicing sound data mapping practices don’t like to be lumped in with the creators of maps that are produced–let’s face it–as sensational products. Our little map community is challenging those bad maps out there, creating great ones for our clients and bosses, and continuing to learn to make them better. Give us a bit more credit here and check out some of the really amazing things we’ve done.
*On an exciting note, “geographic clusters” makes main-stream news media!