Donald J. Trump’s tweets can be confounding for journalists and his political opponents. Many see them as a master class in diversion, shifting attention to minutiae – ” Hamilton” and flag-burning, to name two recent examples – and away from his conflicts of interest and proposed policies.
This graphic visualizes the China’s year-on-year growth by region over the past two decades. With an original chart format, the graphic provides a new method for understanding the nation’s change in GDP.
There’s good news this week in the monthly jobs report, the latest sign that the economy, however grudgingly, has healed from the financial crisis nine years ago:
The unemployment rate fell to 4.6 percent, the Labor Department said, from 4.9 percent. The last time it was this low was August 2007. That was the month, you may recall, when global money markets first froze up because of losses on United States mortgage-related bonds: early tremors of what would become a recession four months later and a global financial crisis nine months after that.
These things, of course, are cyclical. Here’s how the unemployment rate has changed, by state, during my lifetime:
President-elect Donald Trump’s plan to invest about $550 billion in new infrastructure projects across the country was a central theme in his campaign. “We’re going to rebuild our infrastructure, which will become, by the way, second to none. And we will put millions of our people to work as we rebuild it,” Trump said.
For many Americans, it feels as if the 2016 election split the country in two. To visualize this, we took the election results and created two new imaginary nations by slicing the country along the sharp divide between Republican and Democratic Americas. Trump’s America Geographically, Donald J.
As FiveThirtyEight notes, turnout in the 2016 presidential election isn’t dramatically lower than it was four years ago, according to the latest estimates. And with many mail-in and provision ballots still being counted, the 2016 turnout rate could still change:
Approximately 58.1 percent of eligible voters cast ballots in last week’s presidential election, according to the latest estimates from Michael McDonald, associate professor at the University of Florida, who gathers data at the U.S. Elections Project. That’s down only slightly from 2012, when turnout was 58.6 percent, and well above 2000’s rate of 54.2 percent. Turnout may end up being higher than in any presidential election year between 1972 and 2000….
We won’t have final turnout numbers for weeks or months because some states are still counting ballots; millions remain uncounted. That means estimates based solely on votes counted so far will understate turnout — though already more presidential votes have been counted this year than in 2012 (contrary to reports that fewer voters turned out this year). In the meantime, most news organizations rely on estimates from McDonald.
Here’s a quick look at historic turnout in both midterm and general elections, according to estimates compiled by McDonald:
The students, working journalists and professors, were quite impressive. Working in groups, they created several data-driven projects of their own — a few of which were publishable after just a few days’ work.
For a mapping exercise, students identified several locations by latitude and longitude.
Students demo their project related to Chinese perception of the U.S. presidential election.
I love China, and it was a real honor to be invited to work with such talented group. 谢谢
You can use a pricey tool like SPSS or ArcGIS to do your k-means clustering (or jenks natural breaks) before coding your map. But many people don’t have access to such expensive software. Perhaps this tool will help. Can’t wait to check it out…
D3 scales are an immensely useful tool for data visualization and many other applications. In visualization, one common use case is mapping numeric values to a discrete set of colors, as shown in the map below: D3’s quantile and quantize scales are frequently used for this purpose.
The folks at FiveThirtyEight had a fun data visualization discussion during their regular election chat this week, about whether Hillary Clinton should focus on ensuring victory next month or spending more money in “red” states to expand her Electoral College map.
Back during the Republican primaries, The Upshot published an interesting short post called the Geography of Trumpism. The reporters back then analyzed hundreds of demographic variables, by county, in an effort to determine which ones might be predictive of electoral support for the eventual GOP nominee.
Think: What’s the rate of mobile home ownership? Or what percentage of people in a particular place have college degrees? They found a key variable to explore:
When the Census Bureau asks Americans about their ancestors, some respondents don’t give a standard answer like “English” or “German.” Instead, they simply answer “American.”
The places with high concentrations of these self-described Americans turn out to be the places Donald Trump’s presidential campaign has performed the strongest.
I’ve plotted the percentage of “American” ancestry, by county, on a national map. Keep in mind the data come from a five-year survey by the U.S. Census Bureau, so the accuracy in large counties is relatively safe.
But in smaller counties — say, those with fewer than 10,000 residents — the margins of error can be quite high. The results are even more problematic in the tiniest of counties. Still, this is the best public data we have, and it does produce some interesting geographic trends: