Here’s the good stuff for November.

The University of Oxford’s Blavatnik School of Government defined an index to track containment measures for the coronavirus. For The New York Times, Lauren Leatherby and Rich Harris plotted the index against cases and hospitalizations:

When cases first peaked in the United States in the spring, there was no clear correlation between containment strategies and case counts, because most states enacted similar lockdown policies at the same time. And in New York and some other states, “those lockdowns came too late to prevent a big outbreak, because that’s where the virus hit first,” said Thomas Hale, associate professor of global public policy at the Blavatnik School of Government, who leads the Oxford tracking effort.

A relationship between policies and the outbreak’s severity has become more clear as the pandemic has progressed.

States with more restrictions tend to have lower rates.

From these plots, it seems clear what we need to do. But I think most people have made up their minds already, and the interpretation of the data leads people to different conclusions.

With the holidays coming up, I just hope you lean towards clarity.

For The New York Times, Ford Fessenden, Lazaro Gamio and Rich Harris go with a Dorling cartogram to look at the votes gained per county in the 2020 election, compared against the 2016 election.

As you’d expect, voting overall was up just about everywhere this year. Some counties shifted left. Some shifted right. The key points of interest come about when the the map starts zooming into specific regions.

See also: the election wind map.

Alan McConchie from Stamen recaps the wide array of maps and charts that came out before, during, and after election night:

This year we saw continued refinement of traditional election maps styles, and an exciting (and nerve-wracking) new frontier developed with the visualization of post-election ballot counting. Dataviz practitioners are struggling with challenges of how to show uncertainty and how much uncertainty can be shown while still making our visualizations clean and easy to understand. Election cartographers are dealing with their own dilemma of how much to show the polarization and inequality that currently exists in our electoral system (with the risk of reinforcing it) versus making counterfactual maps of systems that could or should be.

[via Co.Design]

Joseph Cox, reporting for Motherboard:

Some app developers Motherboard spoke to were not aware who their users’ location data ends up with, and even if a user examines an app’s privacy policy, they may not ultimately realize how many different industries, companies, or government agencies are buying some of their most sensitive data. U.S. law enforcement purchase of such information has raised questions about authorities buying their way to location data that may ordinarily require a warrant to access. But the USSOCOM contract and additional reporting is the first evidence that U.S. location data purchases have extended from law enforcement to military agencies.

Oh.

The World Bank tracks global development through a number of indicators. (You can see and download much of the data through their catalog.) With a story-based approach, they published an atlas for 2020 that focuses on 17 development goals, such as end poverty, end hunger, and stop global warming. There’s one story per goal, charting out multiple indicators in each story.

There’s a lot to look at, but one thing you’ll probably notice across all of the topics is progress. It’s not all spikes and waves out there.

Voter turnout this election was higher than it’s been in a long time, but the winner margins were still small. Alyssa Fowers, Atthar Mirza and Armand Emamdjomeh for The Washington Post showed the margins with dots. Each circle represents 3,000 votes, and the blue and red circles represent by how much the candidate won by in a given state.

The dots showing absolute counts are useful to see the scale of each win, which percentages don’t capture.

There’s a video (one of too many I am sure) going around that “shows” election rigging. Statistician Kristian Lum shows, with good ol’ basic math and R plots, why the “evidence” is what happens during a normal election.

[arve url=”https://www.youtube.com/watch?v=fH0Z-8563-E” loop=”no” muted=”no” /]

Coronavirus cases are rising (again), which includes prisoners and prison staff. The Marshall Project has been tracking cases since March and provides a state-by-state rundown:

New infections this week rose sharply to their highest level since the start of the pandemic, far outpacing the previous peak in early August. Iowa, Michigan and the federal prison system each saw more than 1,000 prisoners test positive this week, while Texas prisons surpassed 2,000 new cases.

To combat cheating during online exams, many schools have utilized services that try to detect unusual behavior through webcam video. As with most automated surveillance systems, there are some issues. For The Washington Post, Drew Harwell looks into the social implications of student surveillance:

Fear of setting off the systems’ alarms has led students to contort themselves in unsettling ways. Students with dark skin have shined bright lights at their face, worrying the systems wouldn’t recognize them. Other students have resorted to throwing up in trash cans.

Some law students who took New York’s first online bar exam last month, a 90-minute test proctored by the company ExamSoft, said they had urinated in their chairs because they weren’t allowed to leave their computers, according to a survey by two New York state lawmakers pushing to change the rules for licensing new attorneys during the pandemic.

Oh.

When every day feels like more of the same, here are some of the things I do to shake off the monotony.

For The New York Times, Denise Lu and Karen Yourish looked at the red and blue shifts for the counties that voted red in 2016:

President-elect Joseph R. Biden Jr. won the popular vote by more than five million — and his margin is expected to grow as states finish counting. Still, results so far show that President Trump’s support remained strong in most of the counties that voted for him in 2016. Here’s how.

Always enjoy scrollytelling through spaghetti.

D3.js, a flexible JavaScript library useful for visualization, can feel intimidating at first. It does a lot. So Ian Johnson gave a talk on what the library provides, along with a tour of the essentials.

tixy.land is a minimalist coding environment by Martin Kleppe:

Control the size and color of a 16×16 dot matrix with a single JavaScript function. The input is limited to 32 characters – but no limits to your creativity!

Fun. You can find a tiny bit more info here.

For The Washington Post, Ashlyn Still and Ted Mellnik show the shifts in the 2020 election compared against the 2012 and 2016 elections. Good use of swooping arrows.

The microCOVID Project provides a calculator that lets you put in where you are and various activities to estimate your risk:

This is a project to quantitatively estimate the COVID risk to you from your ordinary daily activities. We trawled the scientific literature for data about the likelihood of getting COVID from different situations, and combined the data into a model that people can use. We estimate COVID risk in units of microCOVIDs, where 1 microCOVID = a one-in-a-million chance of getting COVID.

The Washington Post goes with a wind metaphor to show the change in voting activity between 2016 and 2020. The up and down direction represents change in turnout, and the left and right direction represents change in vote margin.

A fun riff on the classic Viégas and Wattenberg wind map and the Bostock and Carter election map from 2012.

The Post map is based on this and this code.

Sometimes our eyes play tricks on us. Even when we know what is actually happening, our visual system won’t let us see the reality. Michael Bach has an extensive collection of 141 optical illusions, along with explanations of what’s tripping up:

Optical illusion sounds derogative, as if exposing a malfunction of the visual system. Rather, I view these phenomena as highlighting particular good adaptations of our visual system to experience with standard viewing situations. These experiences are based on normal visual conditions, and thus under unusual contexts can lead to inappropriate interpretations of a visual scene (=”Bayesian interpretation of perception”).

As I’m sure you know, it was Election Day on Tuesday here in the United States. I told myself that I wouldn’t watch the results roll in, because the record number of mail-in ballots would throw off regular timelines. Of course I broke.