Today I am going to show you a fantastic choropleth map created by Matthew Bloch, Matthew Ericson and Tom Giratikanon from The New York Times. Their graph maps poverty in America.
Now, before we look at the map, let’s discuss what a choropleth map is.
A choropleth map (Greek χώρο– + πλήθ[ος]), (“area/region” + “multitude”) is a thematic map in which areas are shaded or patterned in proportion to the measurement of the statistical variable being displayed on the map, such as population density or per-capita income.
The choropleth map provides an easy way to visualize how a measurement varies across a geographic area or it shows the level of variability within a region.
A special type of choropleth map is a prism map, a three-dimensional map in which a given region’s height on the map is proportional to the statistical variable’s value for that region.
The earliest known choropleth map was created in 1826 by Baron Pierre Charles Dupin. The term “choroplethe map” was introduced 1938 by the geographer John Kirtland Wright in “Problems in Population Mapping”.
Choropleth maps are based on statistical data aggregated over previously defined regions (e.g., counties), in contrast to area-class and isarithmic maps, in which region boundaries are defined by data patterns. Thus, where defined regions are important to a discussion, as in an election map divided by electoral regions, choropleths are preferred.
Where real-world patterns may not conform to the regions discussed, issues such as the ecological fallacy and the modifiable areal unit problem (MAUP) can lead to major misinterpretations, and other techniques are preferable. Choropleth maps are frequently used in inappropriate applications due to the abundance of choropleth data and the ease of design using Geographic Information Systems.
Incorrect (population, left) and correct (population density, right) application of a choropleth to data for Boston, Massachusetts
The dasymetric technique can be thought of as a compromise approach in many situations. Broadly speaking choropleths represent two types of data: Spatially Extensive or Spatially Intensive.
- Spatially Extensive data are things like populations. The population of the UK might be 60 million, but it would not be accurate to arbitrarily cut the UK into two halves of equal area and say that the population of each half of the UK is 30 million.
- Spatially Intensive data are things like rates, densities and proportions, which can be thought of conceptually as field data that is averaged over an area. Though the UK’s 60 million inhabitants occupy an area of about 240,000 km2, and the population density is therefore about 250/km2, arbitrary halves of equal area would not also both have the same population density.
Another common error in choropleths is the use of raw data values to represent magnitude rather than normalized values to produce a map of densities. This is problematic because the eye naturally integrates over areas of the same color, giving undue prominence to larger polygons of moderate magnitude and minimizing the significance of smaller polygons with high magnitudes. Compare the circled features in the maps at right.
When mapping quantitative data, a specific color progression should be used to depict the data properly. There are several different types of color progressions used by cartographers. The following are described in detail in Robinson et al. (1995)
Single-hue progressions fade from a dark shade of the chosen color to a very light or white shade of relatively the same hue. This is a common method used to map magnitude. The darkest hue represents the greatest number in the data set and the lightest shade representing the least number.
Two variables may be shown through the use of two overprinted single color scales. The hues typically used are from red to white for the first data set and blue to white for the second, they are then overprinted to produce varying hues. These type of maps show the magnitude of the values in relation to each other.
Bi-polar progressions are normally used with two opposite hues to show a change in value from negative to positive or on either side of some either central tendency, such as the mean of the variable being mapped or other significant value like room temperature. For example a typical progression when mapping temperatures is from dark blue (for cold) to dark red (for hot) with white in the middle. When one extreme can be considered better than the other (as in this map of life expectancy) then it is common to denote the poor alternative with shades of red, and the good alternative with green.
Complementary hue progressions are a type of bi-polar progression. This can be done with any of the complementary colors and will fade from each of the darker end point hues into a gray shade representing the middle. An example would be using blue and yellow as the two end points.
Blended hue progressions use related hues to blend together the two end point hues. This type of color progression is typically used to show elevation changes. For example from yellow through orange to brown.
Partial spectral hue progressions are used to map mixtures of two distinct sets of data. This type of hue progression will blend two adjacent opponent hues and show the magnitude of the mixing data classes.
Full spectral progression contains hues from blue through red. This is common on relief maps and modern weather maps. This type of progression is not recommended under other circumstances because certain color connotations can confuse the map user.
Value progression maps are monochromatic. Although any color may be used, the archetype is from black to white with intervening shades of gray that represent magnitude. According to Robinson et al. (1995). this is the best way to portray a magnitude message to the map audience. It is clearly understood by the user and easy to produce in print.
When using any of these methods there are two important principles: first is that darker colors are perceived as being higher in magnitude and second is that while there are millions of color variations the human eye is limited to how many colors it can easily distinguish. Generally five to seven color categories is recommended. The map user should be able to easily identify the implied magnitude of the hue and match it with the legend.
Additional considerations include color blindness and various reproduction techniques. For example, the red–green bi-polar progression described in the section above is likely to cause problems for dichromats. A related issue is that color scales which rely primarily on hue with insufficient variation in saturation or intensity may be compromised if reproduced with a black and white device; if a map is legible in black and white, then a prospective user’s perception of color is irrelevant.
Color can greatly enhance the communication between the cartographer and their audience but poor color choice can result in a map that is neither effective nor appealing to the map user; sometimes simpler is better.
Mapping Poverty in America: A View of Philadelphia
Below is a screenshot of the choropleth map from The New York Times Web site. For my example, I focused on Philadelphia (no specific reason; just the one I happened to click on).
To view the actual interactive version of this map, just click on the image below.
 T. Slocum, R. McMaster, F. Kessler, H. Howard (2009). Thematic Cartography and Geovisualization, Third Edn, pages 85-86. Pearson Prentice Hall: Upper Saddle River, NJ.
 Mark Monmonier (1991). How to Lie with Maps. pp. 22-23. University of Chicago Press
 Robinson, A.H., Morrison, J.L., Muehrke, P.C., Kimmerling, A.J. & Guptill, S.C. (1995) Elements of Cartography. (6th Edition), New York: Wiley.
 Patricia Cohen (9 August 2011). “What Digital Maps Can Tell Us About the American Way”. New York Times.
 Light et al. (2004). “The End of the Rainbow? Color Schemes for Improved Data Graphics””. pp. 385. Eos,Vol. 85, No. 40, 5 October 2004.
In the December 28, 2013 digital edition of The Washington Post, Kennedy Elliott and Dan Balz published an interactive data visualization titled Party Control by State. The recent gridlock in Congress has been blamed on political polarity — increasingly antagonistic political ideologies among Democrats and Republicans, with neither party in full control. But states are circumventing this problem by aligning completely with one party: Today, three-quarters of the states are controlled by either Republicans or Democrats, more than at any time in recent memory.
Here is a screenshot of their data visualization. Click on this image to go to The Washington Post Web site and interact with it yourself.
Source: BETSY MASON, 11/20/2013, WIRED
There is a temptation with any kind of data that has a geographical aspect to display it on a map. While maps are by far the best way to convey many of these data, sometimes they are not. This is one of those times.
Even though data on migration between states would seem to cry out to be mapped, this circular visualization by independent data journalist Chris Walker (@cpwalker07) can convey a lot of information far more neatly than a map. Patterns leap out that might have been obscured on a single map, or required many maps to convey the same information (see images below).
“It’s useful to think beyond maps especially for cases where you want to show interconnectedness between regions, which is what I was trying to do,” Walker wrote in an email to WIRED.
[NOTE: The interactive map discussed below can be found on WIRED's site by clicking the link here. I should note that it worked best for me using Google Chrome]
When I first saw Walker’s migration circle (which he built using D3.js), it looked like a jumble that was impossible to untangle, but that’s before I realized it was interactive. If you haven’t already, mouse over the graphic to display information from single states to see where people from that state moved to last year, and where the people who moved into that state came from. More than 7 million Americans moved within the country, so if you’re looking for a time sink, you’ll find it in this circle.
“I think we can learn a lot from migration patterns,” Walker wrote in an email. “In a way, migration flows are one of the oldest forms of crowdsourcing. They tell you which geographies the crowd deems to be low-opportunity, and which the crowd deems to be high-opportunity.”
I live in California, so I started there. I love this state, and it seems to me people are moving here all the time. But it turns out, more people are leaving (73,345 more). As you can see in the snapshot to the right, Californians don’t usually stray too far though, tending to migrate to other western states. Or Texas. A likely explanation for the outflux is that you could buy a mansion in some parts of the country for the same amount as a 2 bed, 1 bath house in the Bay Area.
Despite the initial jumble, a couple of things jump out before you even begin interacting with the graphic. A lot of people are moving out of New York (135,793 net loss) and ending up all over the place, including the other side of the country. The same is true for the Midwest, with people mostly landing in the Southeast, Southwest and California. These trends are clearer when you look at individual states, but the broader trends would be easier to grasp if you could mouse over the region names to see where, say, everyone in the Northeast moved.
One thing to note as you look at what’s happening with New York, for example, is that only exchanges of at least 10,000 people are depicted. This, Walker says in his blog post, is to keep the graphic from becoming to messy. So, while many people are moving into New York, most of them aren’t shown because they are coming from many different places in smaller numbers.
Some of the other things Walker noticed include that fact that a lot of people are moving to Florida, many of them likely retirees. “Interestingly the state contributing the most migrants to Florida is neighboring Georgia,” Walker wrote in his blog. “Texas, New York and North Carolina are the next largest contributors.”
The second largest draw for migrants was Texas. “Over 500,000 people moved to Texas in 2012,” Walker wrote. “People tend to come from the Southeast, Southwest and the West, with the biggest contributor being California. 62,702 Californians packed up and moved to the Lone Star state in 2012.”
People who leave D.C. don’t really leave, generally moving next door to Virginia or Maryland. In contrast, people moving from Maine and Alaska are chasing the sun all the way to California and Florida. Check out his blog post for more insight.
In contrast to Walker’s circular visualization, old census atlases used maps to show the migration data. In August, we visited the Prelinger Library here in San Francisco and took a look at some of these atlases in their collection. In the images below, you can see how the data was displayed. On the right is a map showing where New York natives lived in 1890. New York has the most natives, unsurprisingly, and is the darkest. But the migration pattern is similar to 2012 with New Yorkers heading all over the place. These maps highlight some of the limitations of maps for displaying this kind of data.
Maps from the 1890 Census Atlas at the Prelinger Library in San Francisco. (Ariel Zambelich/WIRED)
I though this interactive infographic from The International Consortium of Investigative Journalists (ICIJ) was interesting in that it shows you visually how to “follow the money.”
The International Consortium of Investigative Journalists is an active global network of 160 reporters in more than 60 countries who collaborate on in-depth investigative stories.
Founded in 1997, ICIJ was launched as a project of the Center for Public Integrity to extend the Center’s style of watchdog journalism, focusing on issues that do not stop at national frontiers: cross-border crime, corruption, and the accountability of power. Backed by the Center and its computer-assisted reporting specialists, public records experts, fact-checkers and lawyers, ICIJ reporters and editors provide real-time resources and state-of-the-art tools and techniques to journalists around the world.
Why ICIJ exists
The need for such an organization has never been greater. Globalization and development have placed extraordinary pressures on human societies, posing unprecedented threats from polluting industries, transnational crime networks, rogue states, and the actions of powerful figures in business and government.
The news media, hobbled by short attention spans and lack of resources, are even less of a match for those who would harm the public interest. Broadcast networks and major newspapers have closed foreign bureaus, cut travel budgets, and disbanded investigative teams. We are losing our eyes and ears around the world precisely when we need them most.
Meanwhile, in many developing countries, investigative reporters are killed, threatened, or imprisoned with alarming regularity. Amazingly unbowed by these life-and-death realities, journalists are in dire need of help from colleagues abroad, many of whom do similar work and can offer support.
This infographics shows us the distribution of wealth in America, highlighting both the inequality and the difference between our perception of inequality and the actual numbers. The reality is often not what we think it is.
I actually remember seeing this when I was a little boy.
Michael Bentine explains why it would be madness for Britain to join the Common Market, while all hell breaks loose behind him thanks to a bit of Biographic genius. Keen-eyed Bob Godfrey connoisseurs may recognise the cutout figures and vehicles from the ‘chase’ section of 1959’s ‘The Do It Yourself Cartoon Kit’, which was of course narrated by Bentine.
The funnest 2 minutes you will spend today.