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

xxx https://magula10.pythonanywhere.com/incident/ animated time map of incidents

xxx https://magula10.pythonanywhere.com/flight/ live flight tracker

Source and source

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

Hi-res zoomable, color-blind, and absolute-risk versions can be viewed here.

This is an updated model (with better parameters and imputation) and includes another year of data compared to the previous one.

Data are normalized by road type so similar roads are comparable.

Data were compiled using national fatal crash and volume data (FARS 2021-2024, HPMS). Only FHWA functional classes 1-6 are shown (local roads are excluded to make a cleaner visual).

These are my GPS traces over 1 year of living car-free in Vancouver, split into four panels by transport mode:

Top left: Walking. Walking has the finest grain by far. It's a dense tangle around where I live, plus small disconnected patches scattered across the region. Each patch is a place I got to some other way and then explored on foot. (The straight line sticking out into the water near the airport is the Iona Beach jetty, not a GPS glitch.)

Top right: Cycling. Cycling covers much more ground, and unlike walking it's continuous. The routes are big connected loops: the Stanley Park seawall, out to UBC and back, down to Richmond, and a full loop around Burrard Inlet across both bridges.

Bottom left: Bus. The bus is the backbone that links everything. It traces the city's arterial grid in long straight north–south and east–west lines, and it reaches most of the places that show up as isolated walking patches, like West Vancouver, Horseshoe Bay, and the North Shore mountains. The walking map's scattered islands are basically the endpoints of these lines.

Bottom right: Rail and ferry. This is the sparsest panel, but each line covers a lot of distance. There are a few fixed corridors: the SkyTrain toward Burnaby, the Canada Line to Richmond and the airport, the SeaBus across Burrard Inlet, the little False Creek ferry, and a BC Ferries departure off Horseshoe Bay.

Source: GPS data recorded by my own iOS app, Dots of Life, which logs location tracks and tags them by transport mode.

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!

Data: HM Land Registry Price Paid Data, EPC.

Method: cross-sectional regression.

Tools: Python (pandas, numpy, statsmodels) for the model, matplotlib for the chart.

You can find the full analysis with more factors here. It's my own hobby project. I ran the same model for other cities like NYC, Paris, Singapore, Tokyo, etc...

If you consider a London house and a flat, all other things being equal (floor area, location, etc...) the house sells for 27.6% more per m² compared to 2010. The biggest jump was during covid of about 10%. My analysis also shows a sharp reversal after covid, and a trend picking up again.

For comparison, my London index is up only +3.9% since January 2020.

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

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"

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

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! :)

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

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.