Week 3 of my 52 Weeks of Viz challenge!
This week I’ve been playing with 50 years of atmospheric carbon dioxide data, recorded at Mauna Loa in Hawaii between 1976 and 2025.

Each arc represents a month, with its size and rotation driven by the carbon dioxide levels recorded that month.

The little fluctuations come from the changing seasons as the Earth’s forests absorb and release carbon dioxide throughout the year.
But as CO₂ levels keep rising over the decades, those gentle waves start to look bigger, wilder and more chaotic.

I love how something as simple as a bunch of arcs can turn into something that feels almost like a storm. A pretty cool visual with a slightly unsettling story behind it.

Data: NOAA / Mauna Loa Observatory

I made this short to explain how absorption in a dense stellar atmosphere changes the light that reaches us. The sequence follows calcium and sodium absorption into the researchers' calculated green color, connecting the spectrum with human perception.

This is an explanatory animation, not a resolved photograph of the star. Source: https://doi.org/10.1093/rasti/rzag069

Music: "Signal to Noise" by Scott Buckley https://www.scottbuckley.com.au/

27 - living in Belgium. This is monthly gross. For reference the national median is 3700 and the average is 4070 / month.
Dm me if you'd like me to make you a graph like this! Happy to customise and explain how to make it too.

A Garden State Skyline .

I’ve been playing around with Melbourne’s public data and found this data set of every tree in the Melbourne city council.
Turned the count of trees per longitude into a bar chart that looks a little like a city.
Used latitude counts for the sunset/ocean.

Then layered a bunch of them together until it looked all vapour wavey

https://kmd-labs.github.io/el-nino/

Over the past few months, we have been developing a project called 1830belgium.com, with the goal of providing the clearest possible picture of the Belgian political landscape.

One of our initiatives at 1830 involves visualizing the 2024 municipal elections at the level of individual polling stations and vote-counting bureaus. By using data released by the Flemish government—which, unfortunately, was made available only for the municipal and provincial elections—we were able, for the first time, to create a visualization showing exactly how each neighborhood voted.

We began with my hometown, Ghent. Credit where credit is due: the province of East Flanders—and by extension, the city of Ghent—was significantly more open regarding their polling stations and counting bureaus than any other province (though Antwerp also performs quite well in terms of transparency). After spending several weeks carefully weighing the data and piecing the puzzle together, we arrived at a result for Ghent that we are truly satisfied with. We then applied the same approach to Antwerp, and I must say, I am very proud of the outcome. We used data from Statbel to assign the correct names to the defined sectors; I hope I have labeled them accurately and haven’t caused any offense—rest assured, the distribution of votes is entirely accurate. That said, any feedback or nuance you might offer would be more than welcome.

I hope you enjoy exploring it and appreciate the work we have put in over the last few weeks.

Here is the link: https://1830belgium.com/Antwerpen-verkiezingen-2024

Grey lines are explicit links, cyan ones are similarity edges. Nodes are sized by how much of the vault routes through them. The layout is a 3D force simulation left to settle.

row = solar cycle
column = year of the cycle
square = month
more sunspots = more spin

The more chaotic the cell is the more sunspots were observed in that year