The polling firm YouGov estimated the legislation’s unpopularity by congressional district. The bill itself was quite unpopular, it turns out, even in conservative districts, as FiveThirtyEight’s Nate Silver reported.
Thanks to DailyKos Elections, we can also marry the data with President Donald Trump’s vote share in each district.
I’ve been experimenting with maps in D3.js, and I hadn’t yet tried congressional districts. So this seemed like a perfect opportunity, even if thematic maps aren’t particularly useful in this context (because congressional districts vary in size geographically, such maps can be misleading).
Case in point: The national map of congressional districts, with Republicans in red and Democrats in blue . As we all know, Democratic districts tend to be smaller in terms of area and clustered in more densely populated places. So they don’t get a particularly fair representation on a map:
Consider these two treemaps. This first shows members of the U.S. House by party (with some vacancies in gray). Shapes are sized based on the average population of each congressional district: roughly 710,000 people, give or take five percent. The House has 237 Republicans, 193 Democrats and five vacancies. There’s clearly a red majority, but it’s relatively close:
This treemap, however, shows the geographic area in square miles. Now you see the distortion:
OK, you get it. So let’s see how the health care opposition looks on maps.
It’s Valentine’s Day, a perfect time to note that the marriage rate in the United States has been on a steady decline for decades, save for a brief spike in 2012.
Here’s the rate per 1,000 people since 1997:
You can also view that rate by state. What’s up with you, Hawaii? (I’ve excluded Nevada, which skewed the axes for all the small multiples because of its freewheeling marriage culture). There are some interesting trends here, but most states remain relatively close to the national rate:
Here’s the 2015 marriage rate, by state, on a tile grid map:
South Korea, my adopted home for almost two years, has about 50 million residents as of the last census, in 2015. Most of them are settled in the country’s urban areas. About 22 million residents, for example, live in Seoul, the capital in the country’s northwest corner, and its adjacent province, Gyeonggi.
As an experiment to create a choropleth map with D3 and NPR’s dailygraphics rig, which drives most of the visualizations here, I’ve mapped the total population by municipal districts. In this example, Seoul is outlined with red:
I am, of course, not a citizen of South Korea. I’m a “foreigner” — as we’re referred to here. This is where the 1.3 million foreigners — many of them ethnic Koreans who immigrated from China — have settled across the country. Again, Seoul is outlined with red:
And this map shows the roughly 330,000 foreigners living in Seoul proper. This time I’ve highlighted Yongsan-gu, my home district in the city center:
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:
Last week I published a new heatmap exploring the popularity of American birthdays. The chart, which uses darker shades to represent higher average birth counts on specific days, can give the impression that some birthdays are much more common than others.
In reality, outside of some special occasions, namely major holidays, there isn’t a huge amount of diversity in the data set, which has two decades of births aggregated by day. Most birthdays, including my own, are fairly average — especially in the first six months of the year. For example:
It’s baby season in America, with September the busiest month for births on average in the last two decades. So it seemed like the right time to remix this blog’s most-popular post: How Common is Your Birthday?
That old heatmap, which highlighted specific dates for popularity, has been viewed more than 500,000 times here and published across the web. But it was flawed, namely that it used ordinal data (birthday ranks by date) rather than continuous data (actual births counts by date). This graphic finally addresses that problem:
The previous posts relied on two data sets from the World Health Organization, which calculates consumption (in liters and grams) based on surveys and actual import, exports and sales data. The organization, a reader noted recently, also breaks down the consumption totals proportionally by beverage.
This chart shows each country and its relative tastes for beer, wine, spirits and “other,” which, in South Korea at least, is mostly soju, a fermented rice beverage that’s not easily categorized.
A few weeks ago I posted about gender gaps in alcohol consumption around the world.
In some countries — South Korea, for example — men and women consume quite different amounts of booze, according to the World Health Organization. Fueled by a love for soju, South Korea’s men are among the heaviest drinkers in the world, consuming about 78 grams per day — nearly twice as much as other men on average. Its women drink only slightly more than their counterparts abroad, on average.
But that data only averaged daily consumption, by country, among people who list themselves as “drinkers”. The organization also has estimates about per-capita consumption amounts based on countries’ import, export and sales data, normalized with their adult populations. Depending on your question, that might be more useful information.
Those data, which also offer a breakdown of alcohol types (beer, wine, spirits and “other”), tell a different story. Instead of leading the world, by that measure South Koreans rank farther down a list of 196 countries: 35th.
Earlier this week I posted two scatterplots examining the relationship between a country’s average temperature and its male residents’ average height. The data show some correlation, but there probably are several of other factors affecting height as well.
The earlier plots shaded the country dots by income and region, allowing more context about the groupings of countries (hint: Europe is colder and taller).
This next version, however, proportionally sizes the dots by population, adding another layer of context (or perhaps unnecessary complexity).
I got married in Amsterdam. One thing I remember most about my time in The Netherlands is the obvious height of the locals. Both men and women, generally, are quite tall.
A new study supports my anecdotal observation. Dutch men are the tallest people in the world (women there are second), followed closely by some of their European neighbors. People in Southeast Asian and African countries are, on the other hand, shorter.
I’ve always wondered why the Dutch are so tall. Is it their dairy-rich diet, perhaps? Or could there be a correlation between the lower average temperatures in Northern Europe and its apparent height advantage? Are people taller in colder countries?