Friday, December 28, 2012

Beautiful Old Maps

Occasionally I'll check out a store on Etsy that has loads of old map prints and I'll become simultaneously inspired and depressed.  Why inspired?  Because these people were creating amazingly beautiful and useful visualizations over a hundred years ago with far fewer resources.  Why depressed? Because these people were creating amazingly beautiful and useful visualizations over a hundred years ago with far fewer resources.

Here are some cool ones I spotted today that I'll share in order to simultaneously inspire and depress you, too.  Feast your eyes!

I play with elevation data, but these dudes dominated it...

A tremendous relief map of Southern Scotland, made in the UK in 1922.

A German 1895 elevation map of a Swiss Glacier.

A Scottish 1862 comparison of mountain heights.

It can be refreshing to see a rendering of the Earth that is not Mercator.  Because of the utility and efficiency of tiling up maps into slippy slappy viewers, Mercator is just overwhelmingly convenient.  And so it is becoming the de-facto Earth portrait in our collective mind's eye.

An 1885 polar projection map by Encyclopedia Britannica.

A 1940 Spanish illustration of various means of map projection.  And the solar system, for some reason.

Partner Components
You know that the people who made these would have killed to have wired up some cross-interaction between the various visual dimensions in these images.

A Rand McNally map of the Panama Canal from 1937.

The good old Encyclopedia Britannica commissioned this map of ocean temperature and density for it's 1885 edition.

Data Maps
Glomming data on top of a geographic framework isn't new.  Ironically, though, when I used to tell people what I did (and earlier, what I was studying), they'd always say some junk like, "Haven't they already mapped everything?" then chuckle coyly, pretty proud of themselves.  But there is always going to be new or old stuff to see and new or old ways to see it.

A German map of Atlantic steamship routes from 1895.

An 1883 map of global ocean currents, made in Chicago.

A German 1895 small-multiple time series.

A better 1878 German map of warm and cold ocean currents.

A minimalist map of ocean salinity from 1887, maybe made in Prague (but printed in English?).

Thursday, December 27, 2012

A National Portrait of Drunk Driving

This map illustrates ten years of traffic fatality incidents by where, how much, and the extent to which intoxication was involved (a big merge of FARS data).  In this manner regions and cities, and the neighborhoods therein, can be visually characterized and compared.  More on the whys and hows of this map below, as well as a load of insets.

Size corresponds to the number of deadly traffic accidents over ten years, and color corresponds to the area's rate of incidents involving intoxication. Navigate this map or click on the image below for the huge image.

Why and How
Traffic fatalities represent an enormous proportion of deaths in the United States each year.  This map is an effort to better understand the geographic characteristics of these events, particularly the regional dimension that that intoxication plays (rather than the temporal characteristics illustrated here).  It turns out, some areas are much more well behaved in this regard than others.

The hexagonal mesh is one way of more fairly visualizing highly overlapping, highly clustered, data (pretty much wringing point data into a polygon choropleth map where ratios can be calculated).  The nature of our population distribution and our transportation infrastructure leads to traffic fatality data points that are highly stacked upon each other.  Each of these incidents represents a horrible event and to paint in the raw data in a way that would obscure them would be unfortunate -and ineffective.  The mesh carves up the country into a baseline set of place-buckets (more uniform than political boundaries) of roughly equal size into which the overall number of fatal crashes is aggregated, and within which a rate of intoxication can be shown.

For a way better description of binning, check out Nate Smith's post over at MapBox.

How to be true to the underlying population?  Once the overall number of events within each zone is added up, the visual for that zone can be scaled accordingly (a way of un-biasing the uneven populations) so that areas with lots of events appear larger and areas with fewer events appear smaller -and areas with no events disappear entirely.

The result is a sort of population density map of the highway infrastructure thick enough to see at a reasonable scale and capable of holding a ratio value (intoxication, in this case).

A dark version of the same map, if you rather.  In this image, dark blue represents low proportions of intoxication, while brighter yellow areas show higher proportions.

The coloration of the zones is tied to the rate of events that involved intoxication.  I was quite surprised by both the general proportion intoxication plays in these events, and also how regionally varied that rate was.  Some areas have impressively low rates (like Memphis and Manhattan, and less surprisingly Salt Lake City) while others demonstrate pretty high rates (like St. Louis and pretty much all of South Carolina).
This map does not answer why some places are more likely to have a problem with drunk driving, but it can help us get a survey of how that terrain looks and get us going on asking more specific questions of places.

Tools: Excel, QGIS, and the Gimp.


Caveats (updated here 1/9/13)
This intoxication flag considers alcohol alone, based on blood alcohol tests provided in the reports.  A BAC of 0.01 or greater by anybody involved in the fatality, not necessarily just the driver, is considered for the 2001-2007 years in this analysis.  2008, 09, and 10 limit that element only to drivers.

Here's the FARS Analytical Reference Guide:
and specifics about the interpretation of the drunk driving data element:

Wednesday, December 19, 2012

Desktop Backgrounds

Here's a batch of images that you can peruse for consideration as your desktop background image(s) or whatever.  So have at these nerd stocking stuffers, and have a Merry Christmas from the team at IDV!

Basemaps are important.  Maybe they are a pretty complex thing or maybe simple simple simple, but usually they serve as an unsung framework to drape other data on top of...which can be a bit of a drag.  Oftentimes a basemap is really beautiful and, while I love data mapping, the basemap can have plenty to say on its own.  Also, the properties of a successful basemap line up well with the properties of a pleasing desktop image...

  • Both are muted context-foundations for the active bits
  • Both ought to be handsome things that you wouldn't mind looking at even with stuff piled atop
  • Both should tempt folks walking past your screen to hesitate for a closer look
So here are some basemaps in the raw.  I'm currently using the South Pole projected basemap (that stirred so much ire when it supported swirling seas of hurricanes) as my background image here at IDV.  It's my favorite one.