States are reopening. Some seem ready, and some less so. Lena V. Groeger and Ash Ngu for ProPublica made a reference so that you can quickly see how your state is doing in five important metrics:

To give people context on state reopenings, and what happens afterward, we are tracking metrics derived from a set of guidelines published by the White House for states to achieve before loosening restrictions. Even if these criteria are met, without a vaccine, reopening may cause an increase in cases. What’s more, some states may meet all of the criteria and still have a high infection rate.

There’s a national overview, as shown above, and then it quickly goes to the individual states.

Check out Groeger’s thread for some process.

This straightforward grid map by Danielle Alberti for Axios shows the percentage of adults in a household where someone lost employment income. In all likelihood, you know someone affected in one way or another.

The data comes from the Census Household Pulse Survey, which is an effort to gauge the impact of Covid-19.

What does the coronavirus look like? Rebekah Frumkin for The Paris Review highlights various illustrations and renderings, focusing on why each looks the way it does:

The disease that has put the entire world on pause is easily communicable, capable of stowing silently away in certain hosts and killing others, and, to the human eye, entirely invisible. In media parlance it’s become our “invisible enemy”: a nightmarish, oneiric force that can’t be seen, heard, or touched. But with the use of modeling software, scientists and illustrators have begun to visualize coronavirus, turning it into something that can be seen, understood, and, hopefully, eventually vanquished by science. Many of us imagine the virus as a sphere radiating red spikes—but why? Certain elements of these visualizations are based on the way coronavirus appears under a microscope, and others are choices that were made, an exercise of artistic license.

Rebekah Jones, GIS manager for the Florida Department of Health, was fired a couple of weeks ago, apparently for making the state’s Covid-19 data more accessible and transparent. Florida Today reports on an email that Jones sent to researchers:

She warned that she does not know what the new team’s intentions are for data access, including “what data they are now restricting.”

“I understand, appreciate, and even share your concern about all the dramatic changes that have occurred and those that are yet to come,” she wrote.

“As a word of caution, I would not expect the new team to continue the same level of accessibility and transparency that I made central to the process during the first two months. After all, my commitment to both is largely (arguably entirely) the reason I am no longer managing it.”

Hm.

Thomas Dimson trained a model to generate words that don’t exist in real life and definitions for said imaginary words. If you didn’t tell me the words were machine-generated, I’d believe a lot of them were actual parts of the English dictionary.

Economists at the University of Chicago analyzed unemployment benefits from the CARES act and compared them against median salaries for different occupations and by state. FiveThirtyEight highlighted the differences:

The researchers uncovered other kinds of inequality, too. In some professions, like janitorial work, people who are employed by essential businesses are continuing to show up to their jobs under hazardous conditions. But in doing so, they may be eligible for less money than janitors who have been laid off or furloughed by a nonessential business. In an ideal world, Ganong said, the people who have kept working at hospitals or grocery stores would be receiving some kind of hazard pay. “But that’s generally not the reality, which means there’s a weakness in the current system,” he said. “We’re giving more money to certain workers to stay home than to other workers who are putting themselves at risk by going to work.”

I don’t know the right answers to any of this, but it underscores the difficulties of making decisions based on averages. The system is built for simplicity and basic values. How do we adjust or rebuild the system for real-world variation and distributions?

Let’s just animate all statistical concepts with LEGO from now on:

My daughter loved the @FryRsquared Christmas Lectures so much she made this Lego Statistics Animation https://t.co/Q3X2FUYS2m pic.twitter.com/caavfR8ESD

— Caroline Lear (@CarolineLear) May 15, 2020

Harry Stevens and John Muyskens for The Washington Post put you in the spot of an epidemiologist receiving inquiries from policymakers about what might happen:

Imagine you are an epidemiologist, and one day the governor sends you an email about an emerging new disease that has just arrived in your state. To avoid the complexities of a real disease like covid-19, the illness caused by the novel coronavirus, we have created a fake disease called Simulitis. In the article below, we’ll give you the chance to model some scenarios — and see what epidemiologists are up against as they race to understand a new contagion.

Fuzzy numbers, meet real-world decisions.

For The Statesider, Andy Murdock wondered how many typefaces are named after American locations. Then he put those typefaces on a map. So how many?

The answer is 222. That’s not actually the answer, it’s just where I had to stop, because the more I looked the more I found. What started as a quirky challenge to make a US font map during COVID-19 quarantine days started to edge into obsessive-compulsive territory. I’d wake up in the middle of the night thinking, “Did I check to see if there’s a Boise font?” (I did. There isn’t.) I finally found the limit to how many fonts I could use in one place.

This issue of The Process is public.

Hi,

Nathan here. This is The Process, the weekly newsletter for FlowingData members where I look at charts closer.

This issue is public, but if you’d like to support FlowingData, becoming a member is the best way to do it. Membership keeps the blog running as a free resource for everyone and gets you bonus access to tutorials, courses, and more.

A new tutorial on animated charts went up yesterday. Maarten Lambrechts explains step-by-step, using the ggplot and gganimate packages in R.

This week, I follow up on the coronavirus visual rundown from two months ago, when there were only 1,190 confirmed cases in the United States. (omg.) A lot has happened since then.

Read More

From Andy Kirk, there’s a new visualization podcast in town:

Explore Explain is a new data visualisation podcast and video series. Each episode is based on a conversation with visualisation designers to explore the design story behind a single visualisation, or series of related works. The conversations provide an opportunity to explain their design process and to share insight on the myriad little decisions that underpin the finished works. It also shines a light on the contextual circumstances that shaped their thinking.

Audiences will gain an appreciation of the what, the why and the how, learning about the hidden problems and challenges, the breakthroughs and the eureka moments, the pressures and frustrations, the things that were done and the things that were not done, as well as the successes and the failures.

My main podcast-listening mode was while driving, so I’m way behind, but this sounds promising. It’s right in line with Kirk’s Little of Visualization Design blog project.

Ed Hawkins, who you might recognize from charts such as spiraling global temperature and the aforementioned temperature grid, encourages you to show your stripes. Select your region, and see how average temperature increased. I saw this last year, but I just realized that people are using this chart to print, knit, and decorate.
Read More

Based on a chart by Ed Hawkins, the shower wall of Gretchen Goldman and Tom Di Liberto transformed into a canvas to show global warming.

Each row represents a country, and each cell — I mean tile — represents the temperature difference compared to the overall average for the time period.

For Bloomberg, Ellen Huet and Lizette Chapman reported on the jolt for Instacart to shift into an essential service. Around the middle of the article is this chart that shows the shifts in what people were shopping for post-coronavirus versus pre-coronavirus.

Bread making and cleaning. These percentage changes are for mid-April, but I’d say that’s about where this household is at.

Is bread-making still a thing, or is that so two weeks ago? If you’re late to the sourdough train or still working out the feeding schedule, the Bread Scheduler by Stuart Thompson makes the timing more obvious. Just enter when you want to start or finish, and the scheduler works out the details.

I haven’t found the patience yet for sourdough. For less time-sensitive breads, try out no-knead bread or this easy focaccia.

I’m just gonna put this right here, from @_daviant: “Another day another stupid Excel chart”.

To show events over time, you can use a timeline, which is often marks on a line that runs from less recent to more recent. But you can vary the shape. Sara Di Bartolomeo and her group researched the effectiveness of different layouts:

Considering the findings of our experiment, we formulated some design recommendations for timelines using one of the data set types we took into account. Here is a list of recommendations regarding timeline readability:

1. Use linear vertical timelines for situations which require fast data lookup.
2. Avoid spiral timelines when the task requires fast lookup.
3. If you use a more creative, expressive shape, such as a spiral timeline, also include a tutorial or visual cues to assist the user in learning and understanding.

Also: it “heavily depends on the context.”