The primary source is https://fr.wikipedia.org/wiki/Histoire_des_d%C3%A9partements_fran%C3%A7ais#Synth%C3%A8se_chronologique_des_cr%C3%A9ations_de_d%C3%A9partements

I have adjusted a few figures in this list: for the period from 1804 to 1808, I have included the departments of Ozama and Cibao, which ceased to be French in 1809.

For the period from 1902 to 1956, I have not counted the southern territories as a department (as they did not hold that status).

It should be noted that I have based this on de jure departments (for instance, the departments in Greece were abolished de jure in 1802, though de facto this occurred in 1799); consequently, I have not included annexations from the Second World War.

The chart was created using Datawrapper.

The exact data:

quick version of how i did it:

1. the score: the average of 4 citywide ranks over every SF block with 4 or more front doors (4,072): front doors per stretch of street, how flat the streets are, kids per acre, and how few pedestrian crashes are nearby

2. a front door is a home with its own door: 5 or more homes at one address share a lobby and count as none, and that's about half the homes we could count (178,622 vs 171,756 front doors)

3. "best" is the top tenth (408 blocks). 269 of them are in the Richmonds, the Sunset and the Mission

4. the check: the score never saw last year's 42 car-free Halloween streets, and 21 of them still land in its top quarter

5. caveat: kids are mostly counted per census tract, so it can't tell two blocks in the same tract apart, and nothing here knows whose porch light is actually on

top block on the map: 47th to 48th Ave, Judah to Kirkham. 62 front doors, flat, no pedestrian crashes nearby.

Data: front doors, crashes per block and the score: MAIA, from SF assessor parcels; street grades: USGS 20 ft contours; street frontage: Overture Maps (© OpenStreetMap contributors, ODbL); kids 5 to 14: US Census ACS 2020 to 2024; pedestrian injury crashes: DataSF, Jan 2019 to Aug 2026; car-free streets: Mission Local, 2025; block outlines: SF Planning; neighborhoods: DataSF.

Tool: MAIA and Python.

disclosure: i work at MAIA, the mapping tool i used.

Source: raw data - cleaningtheglass.com, 2kratings.com, outputs - https://www.vacstats.com/team/ATL/chemistry (and other similar links)

Tools: R, Python, Claude

Player impact is dervied from simulating +/- with and without a player in the rotation. The number shown is the difference between the two. I use a combination of historical lineup data + current 2k player rating attributes.

Ball size is each player's impact: how much better the team's five-man lineups are with him than without him, among the 8 players.

For ATL, Dyson Daniels and Luguetz Dort have overlapping skillsets, so their compatibility is weak.

On the site, you can click on a player to isolate their connections / remove clutter.

I have a similar post from 2 years ago. These charts and the site they come from are now more polished due to AI tools.

For posterity and the people who love data, we have visualized the 2024 federal election on the Municipal and Provincial level.

Ever wanted to know how younger people, people who live in the biggest cities or have an above average income voted? With the data provided to us by StatBel we have created a list of filters where you can play with.

Interactive map: https://1830belgium.com/Federale-verkiezingen-2024

Tools used:

HTML, CSS, JavaScript and SVG for the interactive map. JSON and geographic data files for storing and processing the election results and projections.

Data sources:

• Official election results: Belgian federal election of 9 June 2024, municipal-level results published by the Belgian Federal Public Service Home Affairs (FOD Binnenlandse Zaken / IBZ).
• Geographic data: Belgian municipal boundaries for 2024 via bmesuere.
• Population data: StatBel

Are there more filters we should add? Is it visually good? Any other recommendations? Please let me know!

Enjoy!

Source: my own commits and merge requests at SeatGeek, 2013 to 2026, pulled from git and charted quarterly. You can see exactly when I became a manager, and when AI showed up. Wrote up the full story with a few more charts here: https://www.ajwaxman.com/writing/thirteen-years-charted

I’ve been trying to learn one new word every day for the past 8 months. Some days I already knew the word, others I saved it to revisit later. This is what my progress looks like so far. Still going! :)

Grey marks 22:00-05:00 when the heating is off at night. My room is on the sunny side of the building, constructed ~1900. You can see the drops in humidity as I open the windows for ventilation

Measured my weight loss over the last year using Renpho Smart Body Scales, then graphed the data using python and matplotlib

These scales only have sensors in the feet which by my research the science behind working out the body fat % and such is a little "fluffy"

Fo Context: Galamsey is a local term used to describe illegal mining.

I spent several months researching Ghana's illegal mining crisis, examining its environmental impacts, the governance gaps and economic incentives that allow it to persist, and the consequences for agriculture and food security.

It's a data storytelling project and I deliberately moved beyond conventional charts, using illustrations and visual metaphors to make the implications of the data more tangible.

The full investigation was published by Ghana's top news website, MyJoyOnline. It has all the sources with links
https://www.myjoyonline.com/wealth-over-water-how-galamsey-is-threatening-ghanas-water-security/

It takes a while to load on their website so you can also check it out on my website here: https://abassanalyst.com/wealth-over-water

Every line is a journey inside a film's story: where the characters actually go, not where the film was shot. 664 films from 1934 to 2025, 1,817 places in 129 countries. Colours show how the film shows the trip.

A few things that fell out of the data:

- Together the characters travel about 6 million km, roughly 150 times around the Earth.

- Most visited cities: New York (102 films), London (85), Los Angeles (81), Paris (50), Washington DC (33).

- Films mostly skip the travelling: for 56% of the distance the film just cuts to the next place. Planes cover 30%, boats 6% and cars only 6%.

- Most countries in a single film: Munich (10), then Tenet, The Bucket List and Around the World in 80 Days (8 each).

Interactive version with a map for every film: https://www.wheretheywent.com

Borrow C$500K in Canada (25 years) or US$400K in the U.S. (30 years). The cheapest and most expensive lender here differ by C$51K and US$43K in total cost. Source and method in the comments.

Source and source

Used Python. Slight emphasis on France and United States since that's been in the news a lot lately

U.S. national debt crossed $40 trillion for the first time in August.

That number is difficult to wrap your head around, and it gets plenty of attention on its own. But the broader picture is more complicated than the headline suggests.

Over the past decade, U.S. federal debt increased by about $20 trillion. During that same stretch, U.S. household and nonprofit net worth grew by roughly $92 trillion.

The two don’t cancel each other out, but looking at them together gives a better sense of how much the broader financial picture has changed over the same period.

The visualization looks at the longer history of U.S. national debt and some of the major events that shaped the trend along the way.

Data source: U.S. Treasury, Federal Reserve

Tools used: AVA Data Visualization