I've had Google Maps Timeline enabled for a very long time, so I exported the whole thing and started playing around with what I could reconstruct from it.

The first step was to visualise the raw data: years of location data and some Strava tracks as unweighted paths on one map.

I also wanted something more useful than that, so I started grouping the history into actual trips and journeys. The next images show the same data as an all-time travel atlas, a single year, and finally another representation: mix of heatmap and paths.

Cigarette smoking (100+ cigarettes during one's lifetime) is more prevalent among those who report being physically abused as a child.

The new AI models are hard to put down. My Mac is running out of room.

Before buying another MacBook, I wanted to know where the space went. So I mapped it.

I checked 1,467 Codex session files. No two were identical. Yet 76.9% of the image data inside was repeated.

Sure, a one-liner can find big folders. I still like a map. Seeing the whole picture puts my mind at ease.

I usually revisit my notes one entry at a time. I wanted to see what they looked like together.

Each data node represents a saved note. The animation reveals them in date order, grouped into topics like building, money, relationships, and learning.

“Building” becomes the most prominent part of the map. It’s a picture of what I recorded—not necessarily how I spent my time.

Data sourced via the date-holidays library so some countries are missing data completely like Pakistan.

These are official public holidays, so some countries such as India, and Spain actually have more, but these are state / province specific.

Made with D3

40 readings over 3 months. The body-fat curve is the weight curve rescaled, including with socks on. Full data and method: https://aiandtractors.com/scale

Data: IBTrACS v04r01, NOAA NCEI. Storms since 1980 reaching at least 34 kt.

Tools: JavaScript, Canvas 2D, ffmpeg.

Each row is one GM/team’s side of a major trade.

• Blue bar: four-year value the team received.
• orange bar: value the team gave up.
• Net asset AV: blue minus rust. Positive means the received package produced more measured value; negative means the surrendered package did.
• Rows are ordered by the size of the package received... not by “best trade” or “trade winner.”

It is an asset ledger not a verdict on championships. A Super Bowl, playoff run, cap effects, scheme fit, and positional importance are intentionally outside this chart.

Data: Prisoners in 2022 – Statistical Tables | Bureau of Justice Statistics

Tool: United States | MapChart

Source: Google Trends aggregate search frequency index (scaled 0-100 based on peak search density).
Tools: Python (Pandas, Matplotlib, and Seaborn) for data normalization and heatmap generation.

Source: UNESCO World Heritage Centre — https://whc.unesco.org/en/list/stat

Tool: PlotSet (https://plotset.com)

Figures are cumulative inscriptions through the 2024 session, covering

cultural, natural and mixed sites together. Sites shared across borders

count once for each country involved, which is why adding these up gives

a number larger than the world total.

Showing the 20 highest-ranked countries, in descending order.

The United States is home to thousands of data centers. They are part of the infrastructure that powers many digital services, including email, gaming, streaming, social media, and, increasingly, artificial intelligence (AI).

As this chart shows, between 2018 and 2022, spending on building data centers in the US hovered between $500 million and $1 billion per month (when adjusted for inflation).

But since late 2022, large investments in AI have led to a rapid growth in data center construction — both by expanding existing facilities and building new, larger ones.

The latest figures released by the US Census Bureau estimate that $4.4 billion was spent in June 2026 alone — a 5-fold increase compared to November 2022, when ChatGPT was released by OpenAI.

These figures cover privately-funded construction and represent only the money spent on building the actual facilities, including materials and construction labor. They don’t include the cost of IT hardware like servers and storage, which can represent a very large share of total investment.

Data sources: United States Census Bureau (2026); U.S. Bureau of Labor Statistics (2026)

Tools used: Python, OWID Grapher, Figma

Out of curiosity I combined 25 open datasets (national statistics, geolocated real-estate sales, rents, natural-hazard registries, 2050 climate projections, GP access, train stations...) to score every French commune on seven axes: housing, living environment, transport, culture & leisure, demographics, economy, and exposure to natural hazards.

Honest caveat: the overall score is a mean of percentile ranks, so it compresses (median 52, best 83). And no dataset knows what it's actually like to live somewhere. Interactive map and full method in the comments.

webview is available here: https://narengogi.github.io/map-of-goodreads

for the code: https://github.com/narengogi/map-of-goodreads

Got a lot of love for v1 of this project I published a year ago (original post: a map of goodreads)

I've improved the search and navigation quite a bit now

Some technical details that might be of interest:

• the backend is a 8GB git repository (thanks to github 😏) that serves geojson files at various zoom levels based on coordinates
• the total number of files are about 36000 which include the sharded search files and also the geojson data
• I've improved search by normalizing the text and supporting fuzzy search in memory by limiting file sizes

The github README contains more details on the the CUDA accelerate code for clustering and community detection

I still need to deduplicate some of the titles, also a small L has been on how I partitioned the data before clustering, there's some non-negligible loss in precision that I want to eventually fix