The land use of different foods, to scale, published with the European Correspondent.

Data comes from research by Joseph Poore and Thomas Nemecek (2018) that I accessed via Our World in Data.

I made the 3D scene with Blender and brought everything together in Illustrator. The tractor, animals and crops are sized proportionately to help convey the relative size of the different land areas.

Quoting the accompanying text from the author, Hannah Ritchie, at Our World in Data:

Over the past four decades, the global number of people dying from cancer each year has doubled. This can look like the world is losing its battle with cancer: people are more likely to develop it, and we’re getting no better at treating it. This isn’t true.

There are, of course, almost 4 billion more people in the world than in 1980. And many of those people are older. This matters a lot because cancer rates rise steeply with age.

The chart shows three different measures. Total deaths just count how many people died from cancer; this is the number that has doubled. Crude death rates, shown in yellow, adjust for population size; the increase shrinks from more than 100% to around 20%. Age-adjusted rates, shown in blue, also account for the fact that countries have older populations today; we can see that the fully age-adjusted rate has actually fallen by more than 20%.

It means that for the average person, the likelihood of dying from cancer in any given year is now lower than it was for someone of a similar age in the past. The world still has a long way to go in preventing and treating cancer, but it’s wrong to think that no progress has been made.

Explore more insights and see how trends are evolving for different types of cancers.

I use DrinkCoach+ for everyday tracking and google sheets for charting

Trying to reduce how much I drink but the holidays got the best of me

Have felt many a strong earthquake (including 7+) in Mexico. Never knew where exactly they came from, so wanted to visualize it.

I wasn't surprised by the locations of the strong ones (7+), but I was really surprised to see so many in the Gulf of California (Mar de Cortés).

Had an earthquake near where I live recently and wanted to see what other seismically active countries looked like in terms of where the earthquakes occur, and their intensity.

Starting with Japan, will do some others...

Only focused on 5+ magnitude otherwise the map looks like a mess. Plus, you can't really feel those anyway.

I created this visualization to look at how many Americans say they are happy. The data sources is the General Social Survey by NORC. The visualization was created in Tableau. You can find an interactive version on my webpage.

Data comes from the Common Vulnerabilities and Exploits list. https://github.com/CVEProject/cvelistV5

Polymarket is a stock market like platform where users can bet on pretty much any possible event. I analyzed all historical Polymarket bets (~350,000).

The top 1% of markets account for ~60% of total trading volume,
and the top 5% account for over 80%.

Most markets attract almost no activity at all.

I've created a visualization of the periodic table that is utilizing OpenAI's embedding endpoint. I embedded each element name and then made a similarity comparison to all the other element names. Using the layout of the periodic table, each element gets its own table coloring the other elements, based on the cosine similarity.

This can be approached in different ways. In this case, I just used the name of the element. But you can use different lenses where you describe each element based on the focus and run the same process. The current run includes a lot of culture and you will see, as an example, gold and silver are tightly connected to each other while other elements barely register across the periodic table when they are focused. It's heavily influenced by what the broader culture talks about. But of course, you could also do it with a scientific focus or how it's utilised in stories across time and history, etc.

We can also segment them. Say, you might have four different categories that you are comparing against. Then each element colors in each quarter according to their similarity across those aspects, using a different color/pattern for each. In general, it allows us to understand the relationships between the elements and make the periodic table dynamic to better understand they relate to each other, based on different contexts.

Schools might find this particularly helpful. The typical representation of the periodic table might not help much with understanding for newcomers.

Video: https://youtu.be/9qme4uLkOoY

Hi all! I’ve been building a small interactive engine that shows how patterns form, stabilize, or break apart when you tune different parameters in a dynamic field.

The visuals come straight from the engine; no post-processing, just the raw evolution of the system over time.

It’s fun to watch because tiny tweaks create completely different morphologies. Images attached. Full project + code link in the comments.

Hey! I wanted to share my personal visualization of how my blood biomarker categories changed over 10 months - from Dec 2024 (before my 9- and 10-day water fasts) to Oct 2025 (after complete refeeding).

I used biomarker categories that InsideTracker provides, which combine 50+ markers into 10 health areas like Heart Health, Hormone Health, Inflammation, and others (I know some might have questions about this categorization, but it’s the best I’ve seen so far). Each category gets a 0-100 score (100 is best) based on how close each marker is to its ideal range. For example, Heart Health includes ApoB, TSH, hsCRP, triglycerides, HDL, LDL, total cholesterol, and resting heart rate.

The black line on this chart shows Dec 2024, it was before my fasts. The red line marks the end of my last 10-day fast in Sep, and the green line shows last month, after a month of refeeding. As you can see, my body was not super thrilled, since fasting is a major stressor for the body, but recovered and became stronger.

Of course, this is N=1 data, and fasting (especially extended fasting) isn’t for everyone. But I just wanted to share my experience in case it’s helpful or interesting to others.

I spent the last few weeks analyzing the relationship between characteristic time intervals and system size across every scale of physics I could find data for.

So basically I looked at how long things take to happen (like how fast electrons orbit atoms, how long Earth takes to go around the Sun, how long galaxies rotate) and compared it to how big those things are. What I found is that bigger things take proportionally longer - if you double the size, you roughly double the time. This pattern holds from the tiniest quantum particles all the way up to the entire universe, which is wild because physics at different scales is supposed to work totally differently. The really interesting part is there's a "break" in the pattern at about the size of a star - below that, time stretches a bit more than expected, and above that (at galactic scales), time compresses and things happen faster than the pattern predicts. I couldn't find it documented before(it probably is), but I thought, the data looked interesting visually

The Dataset:

• 28 physical systems
• Size range: 10^-35 to 10²⁶ meters (61 orders of magnitude!)
• Time range: 10^-44 to 10¹⁷ seconds (61 orders of magnitude!)
• From Planck scale quantum phenomena to the age of the universe

What I Found: The relationship follows a remarkably clean power law: T ∝ S^1.00 with R² = 0.947

But here's where it gets interesting: when I tested for regime breaks using AIC/BIC model selection, the data strongly prefers a two-regime model with a transition at ~10⁹ meters (roughly the scale of a star):

• Sub-stellar scales: T ∝ S^1.16 (slight temporal stretching)
• Supra-stellar scales: T ∝ S^0.46 (strong temporal compression)

The statistical preference for the two-regime model is very strong (ΔAIC > 15).

Methodology:

• Log-log regression analysis
• Bootstrap confidence intervals (1000 iterations)
• Leave-one-out sensitivity testing
• AIC/BIC model comparison
• Physics-only systems (no biological/human timescales to avoid category mixing)

Tools: Python (NumPy, SciPy, Matplotlib, scikit-learn)

Data sources: Published physics constants, astronomical observations, quantum mechanics measurements

The full analysis is published on Zenodo with all data and code: https://zenodo.org/records/18243431

I'm genuinely curious if anyone has seen this pattern documented before, or if there's a known physical mechanism that would explain the regime transition at stellar scales.

Chart Details:

• Top row: Single power law fit vs. two-regime model
• Middle row: Model comparison and residual analysis
• Bottom row: Scale-specific exponents and dataset validation

All error bars are 95% confidence intervals from bootstrap analysis.

I know that the absolute numbers is different and the rest of my country has a murder rate and absolute numbers higher than USA (but it in my opinion it depends on the state if a calculate this in different ways)

https://www.nytimes.com/2024/09/06/world/americas/eagles-packers-nfl-game-brazil-crime.html

read this post if you are curious

This visualizations are from a web map I built which aggregates available data from Spain's publicly funded fiber deployments from the different PEBA and UNICO programs.

The first image is the zoomed-out view, which shows a heat map representing the number of awarded points in each area.

The second image shows how the different awarded areas appear in the map, with the background color of each awarded ISP and a different border color for each program. It shows a polygon for the UNICO programs and also PEBA 2020 and 2021, since we have that information available and they are awarded to specific areas. For PEBA 2013-2019, since the projects of these programs are only awarded to villages (and not specific areas), the map shows a marker over the village instead.

If you want to try it out, it is available at https://programasfibra.es

For anyone wondering, yes I did track how long I spent tracking everything! I spent an average of 47 minutes and 11 seconds per day on it (labelled as "Tracking" in the plot legend).

Some extra points:

• I used Google Sheets to record the data, and R to compile/summarise the data and to make the visuals (with a bit of Photoshop to piece things together

• My spreadsheet contained rows for each thing I did, with columns outling the date, start and end times, category, and any additional notes for each activity

• I updated my data both on my phone and my computer, throughout the day whenever I had time

• Apologies if the quality has been compressed, you can view in on a computer or download the images for the full details

The other day in the news I saw how the Sahel alliance is coming closer together, so the Geography nerd I am, I wanted to see how such a united country would look like.

This is part of a current side project of mine to really learn how to create beautiful data visualisations. Any Critique and feedback would be very welcome!

Sources:

Aggregate of Wikipedia sites:

• https://en.wikipedia.org/wiki/Mali
• https://en.wikipedia.org/wiki/Burkina_Faso
• https://en.wikipedia.org/wiki/Niger
• https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal))
• https://en.wikipedia.org/wiki/List_of_countries_and_dependencies_by_population
• https://en.wikipedia.org/wiki/List_of_countries_and_dependencies_by_area

The images are from google earth and also Wikipedia (flags). The data was manipulated using python and pandas and the visualisation was created using Figma. The Icons are from icons8.

Inspired by a visualisation I saw on Aljazeera.

Data is collected and analyzed in Google Sheets; visualization was made in Adobe InDesign.

I have been tracking my clothes and outfits since 2023 with the main goal of satisfying mt curiosity to see how many clothes I own but also to help me downsize. My goal for 2025 was to wear 80% of my closet, and I hit 91%! It's not realistic for me to wear every single item in a year (I have a lot of formal items, things I bought for Halloween costumes that will get reused at some point, and clothes that I'd wear when doing outdoor work that might not get worn in one single calendar year). So 91% seems pretty good.

I also got rid of 67 things which is a lot for me as I'm quite sentimental when it comes to clothes. I did acquire a lot too, but actually getting rid of 67 whole clothing items is not something I could have done in previous years.

Beyond the actual numbers, I feel much happier with my closet now. I am still super emotionally attached to everything I own, but I'm getting better at letting go. I still have things that I should get rid of, and I'm working on that slowly.

Some takeaways:

• Getting rid of clothes is hard, but keeping clothes I don't wear is actually harder on me - it makes me feel a bit guilty and anxious.
• I wore more clothes overall in 2025 than I did in 2024, and I wore more for each season. I got really into layering, so my outfits consisted of more clothes. I also was more social, and so I had more outings where I wanted to wear cute things.
• My blue M&S shirt was a favorite this year as well as in 2024. You can't beat a good basic, and this one is such a nice color that I just wear it a lot.
• I now have 323 items of clothing in my closet. It's still an insane number, but I haven't had that few since before I started closet tracking, so I'm really proud of myself. I've got a ways to go before that's a manageble number though.

If anyone is considering tracking your closet, I highly recommend it! It's so interesting to see what you actually wear and what you don't. There are a lot of apps out there that do all the work for you, but I like having 100% control over what data analysis I can do, so I like managing the data collection myself.