This is No. 414 of The Process. I’m Nathan Yau, and this week we get into specifics for annotation on line charts to draw attention and communicate patterns more clearly.

When your city doesn’t support a proper infrastructure for bicycles, it can be unsafe to ride among vehicles. BikeNetKit, led by Michael Szell from the IT University of Copenhagen, is an open-source toolkit to help cities fill in the gaps and design a complete and useful network.

Developed over the past eight years, the project links bicycle network research with city planning. The software is available on GitHub and provides tools to grow a network, connect existing pieces, and plan what order to build.

On the BikeNetKit site, the researchers provide analyses for over 400 European cities, but you can use the Python tools with your city’s data.

This seems useful and a nice alternative to cyclists getting hit by cars.

Word Tree got a refresh by Jason Davies. It works more smoothly and better supports non-English languages. The visualization tool lets you load a body of text and see it as a tree network, which is especially useful for text with recurring patterns or themes.

The original version of this tool, by Martin Wattenberg and Fernanda Viégas, came in 2007 on a data exploration and collaborative analysis site called Many Eyes. It was the best of times for visualization on the internet.

Text Time-Lapse by Lev Manovich is a tool that shows the additions, deletions, and edits to a document as a time-lapse. Load a Word document or text files with version numbers, and you get an animation of text.

It is a lot like a generalized tool version of Ben Fry’s piece from 2009 that visualized the evolution of Charles Darwin’s On the Origin of Species.

Apparently inaudible background noise from airplanes in a blurry video clip is enough to reveal your location within a hundred meters.
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For CT Insider, Philip Bump made a racetrack-like graphic to show the timeline of Democrat and Republican senators for New England. Each track represents a state and time swings back and forth, shifting from all red to all blue.

Aatish Bhatia and Arkarup Banerjee use a visual explainer for how a few neurons can orchestrate seemingly complex and surprising behaviors.

If you squint very hard at this, you might be able to see the rudiments of how our brains encode preferences, such as a love for spicy food or an aversion to smelly cheese.

Of course, biology is far more complex than these simplified sparks. We intentionally gave them very simple brains, so that we can understand how every part works.

Their brains are also frozen in time. In reality, brains can learn: They alter their wiring to gain new skills such as learning to walk, ride a bicycle, or play a piano.

This is the first part in a series they call Myriad.

I like the spark characters with different wirings. I got lost in joy for a second making them chase the light.

The New York Times produced a series of charts for the shift in Texas. The multi-line chart above shows the changes over time for each county. All the lines take a sharp left at the bottom.

You’ve seen maps of the results and maps of the shifts. For the Wall Street Journal, Chiqui Esteban and Anthony DeBarros combined results and shifts into a single map using an interesting aesthetic that looks like strands of hair sprouting off the United States.

The base map shows county-by-county results for the predominant party between Democrat and Republican. The hair strands start as spikes pointing straight up, as we’ve grown used to over the years. The height of a spike represents the margin of victory versus the opposing party.

Scroll to the next step. The spikes bend to represent the swing, in comparison to the previous election, like strands of hair that blow in the wind. A deeper bend represents a greater swing for the county.

There’s a lot going on but it works. I approve of this hairy metaphor.

Randy Au on the manual process of exploratory data analysis that is still irreplaceable:

While I’m fine offloading specific tasks to an LLM, I find they’re just really terrible when given any leeway with decision-making. Since there’s no absolute positive/negative feedback in an analysis, typical coding agent harnesses have no way to tell if their analysis has completely flown off the rails. While toying with the idea, I’ve seen Claude get a peculiar idea that obviously takes assumptions that doesn’t apply to the data and attempt to build a giant testing harness around that analysis idea. I watched it run to completion and asked some pointed questions about why it was doing that and it did the “oh! my bad!” routine. You can jump through all sorts of hoops and harnesses to try to get a handle on things, but the only creativity I’ve encountered with this process is how it comes up with new and unimaginable ways to do the wrong thing.

When you know exactly what you want, generative AI is useful for joining that dataset or making that one chart. Exploratory data analysis relies on not having an exact answer and relying on clues during the process to guide you.

This will evolve as the LLMs grow more extensive, but for now, analysis output from the chatbots converge towards generic results without outside guidance.

In this week’s The Process, we look at the ones that highlight the big shifts and see how changing focus can change the story.

Bloomberg used mountain peaks to show the shifts towards Democrats and Republicans. Taller peaks represent greater shifts, and blue and red indicate Democratic and Republican shifts, respectively.

I prefer this map to the NYT map that uses arrows. It’s easier to spot the handful of Republican shifts without zooming in. The transparency helps too.

The New York Times brings back the angled arrows to map the shift in voting margins between parties, compared to the 2024 presidential margins in each county. A larger blue arrow means more Democratic, and a larger red arrow means more Republican.

It was almost all blue arrows across the country, save a few small red ones in Maine and Minnesota.

James Birdle described his experiment using AI to assist in chair design:

What we have here is, quite literally, a chair designed by someone who has read a lot about chairs but never actually sat in one. They know what a chair should look like, what it should do, the mechanics of how one is put together, the physics of holding you up. But they have no embodied experience of the world, no history with chairs or any other object, no physical memory. They don’t know how it feels, not merely to sit in a chair, but to encounter one in the world, to place it among other things in a home, to live with it, to be around it every day as one goes about the mundane business of life.

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For the Washington Post, Dylan Moriarty has a live tracker for the percentage of votes counted. There is still a ways to go.

If feeling uncertain is your thing, the Needle from the New York Times is live to show the chances of each party winning Senate and House control. Views for individual races are also available.

I suggest waiting until later in the evening before you watch the swings. Do it for your heart.

As midterm election results come in tonight, percentages can flip in favor of a candidate in a way that seems artificial if you don’t know what you’re watching. For the New York Times, Christine Zhang describes election night “mirages” that explain the sudden changes.

This is especially important tonight while watching the needle of uncertainty. It’s coming back tonight.

During the World Series, Google ran an ad for Gemini that noted several correlations between home runs and full moons. Marcos Huerta analyzed data on full moons and home runs to debunk each statement.

I had assumed that some Google marketing types asked some MLB data team members8 for some lunar factoids and some poor analyst when probably knew better sent them whatever they could find: error bars be damned.

But maybe they really did use Gemini. The ad included 3 stats that are all a little true – if you ignore the error bars, and the home run stat in particular has some enormous error bars. That feels like what might happen if you ask an LLM to do data analysis without babysitting it carefully. Agentic p-hacking? Yet, not really p-hacking since it still couldn’t find a statistically significant result.

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Evan Reynolds and Christine Zhang, for the New York Times, have quick tabulations for early midterm voting. Turnout is up by a lot compared to the last midterm election.

Elon Musk has donated $446 million for the midterm elections, because sure why not. The Guardian used a Sankey diagram that flows onto a state map to show where the money goes.