In a sea of endless possibilities, narrow down your visualization options by looking for what works instead of looking for what is the best.

For Bloomberg Green, Jin Wu, Laura Millan and Hayley Warren, on the challenges ski resorts face with rising temperatures:

Artificial snowmaking has become more efficient, so it uses less water and electricity. But even with advanced technology, fake snow can’t always be deployed — and climate change is creating a more difficult environment, making water more scarce and temperatures too high for it to freeze. This year, skyrocketing energy prices forced some resorts in Japan to shut down their snow cannons and wait for natural flakes to fall.

The piece starts with a horizontal scroll through the mountains and then transitions to the chart above. There’s a nice flow between the photo into the abstract view, so they don’t seem like two separate things.

Based on estimates from CarbonCounter by the MIT Trancik Lab, electric vehicles typically produce less emissions than gas vehicles when you account for battery production and charging. However, when the batteries get bigger, like they do with large electric trucks, you start to see some overlap. Elena Shao, for The New York Times, used a beeswarm chart to compare the vehicle groups.

The Economist combined two satellite imagery sources, one that estimates fire events and one that estimates building damage, to assess the extent of damage in Ukraine:

Both approaches have weaknesses. NASA’s firms cannot see through cloud cover, a particular problem in winter. sar can pick up damage even through clouds, but is much less sensitive to changes outside of urban areas. But by combining the two datasets, we can form a fuller picture of the war. Our study shows that rather than being limited to a few big offensives and grinding battles, the war has left a brutal mark on large swathes of Ukraine. Fighting has reached 14% of municipalities, and damaged nearly half the built-up area in the hardest-hit cities.

The structure of a dataset can help you pick a visualization method or chart type, but it only takes you part of the way there. To demonstrate, Ferdio started with a simple dataset with six data points and made 100 charts with it:

Every time we turn a set of data into a visual depiction, hundreds of design choices have to be made to make the data tell the best story possible. Many of the choices are unconscious, often resulting in similar solutions. The obvious and uninspired. This project goes beyond common solutions and best practice. It demonstrates how even the simplest dataset can be turned into 100 proper data visualizations telling different stories, using very limited visual properties and assets.

This is a fun one from Russell Samora and Reshad Malekzai for The Pudding. When watching basketball, one of the best things is when a player has an unexpectedly great game, so Samora looked for the greatest and most unexpected game based on data.

The video is also a nice example of how distributions and outliers can be applied in an analysis. Check out the data yourself on GitHub.

Here’s the good stuff for February.

The New York Times shows how the west tried to isolate Russia and how things haven’t gone as expected. A series of packed bubbles, cartograms, and flowcharts provide a visual timeline for each country’s reactions.

Bloomberg compared retirement years in the context of life expectancy and healthy life expectancy. The latter represents how long the average person stays healthy, which is shorter than the former.

The data are clear on one thing though: it’s pensioners in Western Europe who enjoy the longest, healthy retirement periods. The Americas, by contrast, have some of the shortest.

In the above, the yellow dots represent retirement age, the green dots represent healthy life expectancy, and the purple dots represent regular life expectancy. Starting at the top and going clockwise, countries are ordered by the difference between healthy life expectancy and retirement age.

I like circles, but I think I would’ve gone with a more list-like layout here. The patterns and reference points get lost in all the dots and spokes.

For The Washington Post, Emmanuel Martinez, Kevin Schaul and Hamza Shaban mapped the share of houses bought with all cash in 2022. It was about a third of all homes, which was an 8% increase from 2021, meaning owning a house continues a trend towards the rich.

For The New York Times, Eleanor Lutz illustrated things in the sky, because there are other objects up there other than spy balloons and UFOs. A long vertical scale is used to represent altitude. Bonus points for moving the objects around to give a floating effect.

Marking the third anniversary of the first Covid deaths in the United States, Ally J. Levine, for Reuters, used cyanotype to talk about the grief of those who lost a loved one. Levine explained the process behind the piece here.

Tie in the real bits among the abstract shapes, colors, and geometries of charts to make the latter easier to understand.

Grocery stores with loyalty programs collect data on what and when you buy at their stores. Then they sell that data, because of course they do. For The Markup, Jon Keegan delves into why that matters when two big companies, Kroger and Albertsons, plan to get together:

In October 2022, Kroger and another top supermarket chain, Albertsons, announced plans for a $24.6 billion merger that would combine the top two supermarket chains in the U.S., creating stiff competition for Walmart, the overall top seller of groceries. U.S. regulators and members of Congress are scrutinizing the deal, including by examining its potential to erode privacy: Kroger has carefully grown two “alternative profit business” units that monetize customer information, expected by Kroger to yield more than $1 billion in “profits opportunity.” Folding Albertsons into Kroger will potentially add tens of millions of additional households to this data pool, netting half the households in America as customers.

As part of the Knowing Machines research project, A Critical Field Guide for Working with Machine Learning Datasets, by Sarah Ciston, offers advice for working through the life cycle of complex and large datasets:

Machine learning datasets are powerful but unwieldy. They are often far too large to check all the data manually, to look for inaccurate labels, dehumanizing images, or other widespread issues. Despite the fact that datasets commonly contain problematic material — whether from a technical, legal, or ethical perspective — datasets are also valuable resources when handled carefully and critically. This guide offers questions, suggestions, strategies, and resources to help people work with existing machine learning datasets at every phase of their lifecycle. Equipped with this understanding, researchers and developers will be more capable of avoiding the problems unique to datasets. They will also be able to construct more reliable, robust solutions, or even explore promising new ways of thinking with machine learning datasets that are more critical and conscientious.

Plus points for framing the guide in a spreadsheet layout.

For Tedium, Chris Dalla Riva examined why the number of credited songwriters per song appears to have increased so much over the past decade:

Between 1960 and 1980, 48 percent of number ones had at least one common person get both a songwriting and production credit. Between 2010 and 2020, that percentage had risen to over 99 percent. Songwriting just isn’t what it used to be. And I don’t mean that in a condescending way. I mean that we are using the same word to describe two very different things.

It’s not about a decline in skills and instead about a change in process.

In celebration of the most romantic day of the year that is sometimes comedic, Sam Hart, with illustrations by Catherine Tai, for Reuters, tours the genres within the genres of romantic comedy. You had me at analysis.