Something I’ve been meaning to put up for a little while, the New York Times’ coverage of that city’s marathon and changes in the socioeconomic composition of the neighbourhoods through which the course winds.
The piece includes a narrated motion graphic explaining the changes along a map of the course, while a series of charts look at those factors from a static perspective. The horizontal axis being the route of the course.
Brooklyn segment of the NYC Marathon
Credit for the piece goes to Graham Roberts, Alan McLean, Archie Tse, Lisa Waananen, Timothy Wallace, Xaquin G.V., Joe Burgess, and Joe Ward.
The Democratic Republic of the Congo is preparing for elections at the end of the month. For decades since independence from Belgium, the country has been beset by insurrection and civil war. Eastern portions of the country are all lawless and beyond the control of the government in the capital Kinshasa. Yet, DR Congo, which is almost the size of all of Western Europe, holds vast mineral and energy reserves.
Much like with the independence of South Sudan, the BBC has released a small interactive piece detailing DR Congo through maps. While not as extensive and lacking in visualising anything about the warfare and bloodshed, the piece is useful to gain a brief insight into the complexities of the country and the sheer scale of its problems. But that is not wholly surprising as the title of the piece is Failed State: Can DR Congo Recover?.
Forbes released Jon Bruner’s latest map of migration in the United States. It uses IRS figures to show inbound and outbound movement from counties across the United States. The work itself is an improvement from his map from last year, which was a bit more difficult to read. Beneath is the new version, and at the end, for comparison, the old.
This year's migration map
Firstly, the colour palette is far more sophisticated. Secondly, and most crucially, the user can hide the lines on the map, which obscures a key part of the story of migration in urban areas—higher income people moving out of the city and into the suburbs. Thirdly, the map data now includes additional years, which are available by clicking the small chart in the upper right—a welcome addition that allows the data from last year’s map to become accessible this year. Fourthly, and to be fair this may have existed previously but not that I can recall, the new map is accompanied by essays.
These essays use the map and its data to tell stories and explain what one sees going on with the data. It is (relatively) easy for one to put together a piece of data visualisation from a data set. But, without knowing where to look, users may not actually find anything valuable in the visualisation. By pointing to these essays, the map—already much improved from a design perspective—takes on a much more rounded and mature character and becomes more about generating information and knowledge than simply figures and statistics.
In an area very close to me…quite literally…the New York Times published an article about increasing segregation between the rich and the poor via the areas where they live. The study by Stanford University found that the Philadelphia metropolitan area saw the “sharpest rise” in segregation since the 1970s—the study used census data available through 2007. The accompanying graphic highlights the growth of the segregation from 1970, using small multiples of choropleths to compare 1970 to 1990 to 2007.
In 1970, much of the metro area was middle-income neighbourhoods. Certainly, the central core of Philadelphia was depressed. So too was Chester and rural southwestern Chester County. The upper-income neighbourhoods were in the close suburbs, note the townships stretching due west of the city and you see the Main Line, one of the most affluent areas of the United States, while other veins of wealth extend along other old rail lines leaving the city.
Neighbourhoods by Income in 1970
Those such as myself who are familiar with both the area and recent history should note that places like Coatesville and Downingtown are shown as middle-income. In the 1970s, areas like this and in similar places like Falls Township in Bucks County had robust steel and manufacturing sectors that employed a substantial portion of the local population.
But, compare this to 2007 and you will begin to see how many old factory towns of middle-income areas became dense pockets of depression while the city of Philadelphia itself saw a flight of wealth to the rest of the suburbs. The rural parts of Chester, Montgomery, and Bucks have seen high growth by means of new developments of upper-middle- and upper-income homes.
There is a scene in the re-imagined Battlestar Galactica where with the human population almost extinct, one character comments on the romances of two others by saying “they better start having babies”.
The demographics of the United States are changing. Not that they were not changing prior to recent years; Native American populations were reduced by English and Scottish settlers; the English and Scottish populations were diluted by Germans; then came the Irish and the Italians; then the Slavs; then Chinese—simplistic, but you get the idea.
Now, in the Midwest, as the New York Times reports in both an article and its supporting graphic, the long-established relative decline of the United States’ white population is being checked by a surge in Hispanic growth, especially in the rural plains states.
Hispanic Growth in the Plains
I am never so much a fan of the circles as sizes of population—a choropleth would have worked equally well—but it does suffice for this graphic. My larger concern is that the graphic measures growth but does not state growth between what years. Presumably, though the data is sourced from Queens College Department of Sociology, it originates in census figures. That would most likely mean growth between 2000 and 2010.
Humanity is amazing. We have great emotional power for love, sympathy, compassion, &c. We have great intellectual power; we have/are mastering mathematics and science to explore the depths of this ocean and the surfaces of planets not our own.
Yet with these great powers comes a great responsibility. And as we continue to reflect upon the milestone of reaching a population of 7 billion men and women, Bill Marsh at the New York Times, along with Micah Cohen, Matthew Ericson, and Kevin Quealy, reflected Sunday on humanity’s ability to let this responsibility slip from time to time and how at those times the human population of Earth fell.
Timeline of Humanity's Atrocities
The data comes from a book by Matthew White called “The Great Big Book of Horrible Things” that details the worst 100 cases of man killing fellow man. (Although, according to Marsh the account is humourous, though I have never read it.) At the top are no particular surprises: World War II, World War I, and Genghis Khan. The reigns of Chairman Mao, Stalin, and the Kims of North Korea. But a look further down the list, further down the timeline reveals in all its tarnished glory the history of humanity when we not quite so amazing.
We have seven billion living on the planet today. Or at least we think we do. Really, who knows? But for the sake of this blog post and many others like it along with news stories and water cooler conversations, let’s just say we’re at seven billion, okay?
So where do you fit into the giant seven billion-ness of the world?
You enter a few data points such as your birthday and the country in which you live, and you get a customised you-are-a-unique-snowflake report on how special you are.
The graphs are not particularly fancy, but they work. More interesting of the whole set is the world population where you are placed in the context of the global population.
Where You Fit Within the World PopulationWhere Your Country of Residence Fits Within the World
Less interesting are the maps, which serve only to show you where in the world your country is located and then those of the greatest and least population growth or life expectancy. The secondary cases could be useful if the countries were small and relatively unknown. But in terms of life expectancy the highest growth is Japan most people know where Japan is located. The other countries noted, Qatar, Moldova, and the Central African Republic are probably less well-known by some, but could the data be better represented? Probably.
On Halloween, we will welcome the 7 billionth person into this world. That’s a lot of people. And that means a lot of food, water, shelter, comforts, &c. Stress on limited resources could become a defining characteristic of the future.
The Washington Post has an interactive piece with a few graphics out there about the growth of population. This screenshot is from the first tab about consumption. When you press play and watch the highlighted countries move through time and space, you see that the United States has not seen drastic population growth (x-axis) but has, on a per capita level, witnessed a strong growth in consumption (y-axis). Conversely, India and China have seen little growth in personal consumption but have dwarfed all others in population growth. There are very few who countries that have moved greatly in both consumption and population. And that’s probably a good thing.
Population Growth v Consumption Growth
If you check out the Future tab, you will also see that in less than twenty years we will all be having another slice of cake for the 8 billionth person in the world…
Credit for the work goes to Patterson Clark, Dan Keating, Grace Koerber and Bill Webster of the Washington Post.
