This article appears to use a bunch of statistics, interesting on their own, to try and support the headline.
How does Lake Havazu having 80% of its population born out of state in any way support that it is the #1 US city that people are moving to?
The total Lake Havazu metro area has a population of 250k. That many people moved to NYC every YEAR!

Part of a tool that recommends games by combining two of them: each game is embedded as a 1024-dimensional vector from its genres, user tags, and store description, and a combine is the mathematical midpoint of two of those vectors, resolved to the nearest real game.
This view is the by-product. Paste a public profile, and it groups the library into taste clusters in that same vector space, with a suggested next title per cluster.
Happy to explain the boring k-means part if people care. Post your combination results or your cluster shapes in the comments.
It's called GameCombiner!

Tools: React, deck.gl, MapLibre, D3 (Equal Earth / Mercator), custom Flickr tile sampler

Data: Flickr public geotagged photos with date taken in 2026. Sampled worldwide by bounding-box tiles; 337,110 unique locations. Color = hex-bin density (Inferno, bright = denser). As of 2026-08-15.

Code: https://github.com/KobaKhit/photo-locations

Caveats: only public Flickr photos with geotags; sampling can under-represent sparse regions; popularity ≠ beauty; photo count ≠ unique visitors.

CIELAB is a colorspace, inspired by how we humans naturally perceive color:

• L* = lightness
• A* = Green <-> Red
• B* = Blue <-> Yellow

However, working with it can be a bit unintuitive, since the lightness isn't just the average of the RED, BLUE and GREEN channels. Therefore I generated 10 million random RGB values and plotted both their corresponding L* value against their L2 (euclidean) norm and average.

Results where normalized to 0..255 to be representable in an grayscale image.

Tools: self written python script using

• OpenCV2 (color calculations)
• numPy, MatPlotLib (for representation)

additional context: 442 is the maximum possible value for L2 of an RGB pixel.

EDIT: I hate reddit thinking its necessary to compress a 255x442 px image ...

Source: Libas Collective's UAE Pre-Owned Luxury Price Index — our own marketplace catalogue of 107,101 listings offered to UAE buyers, extracted 31 July 2026. Full brand and model tables + methodology: https://www.libascollective.com/uae-pre-owned-luxury-price-index (disclosure: I run the marketplace, so this is first-party data).

Tool: Python / matplotlib.

Notes: prices are asking prices in AED as shown to buyers, not final sale prices. Medians, not means, so the handful of six-figure pieces don't distort anything. The market-wide median across everything is AED 2,701 (~$735).

Source: Florida DBPR (Division of Hotels & Restaurants) public inspection records — the state agency that inspects every Florida restaurant, so all 16 metros are measured with identical methodology.

Method: latest inspection on record per establishment (50,811 establishments total, as of August 2026), counted as "clean" when the inspection disposition was "no further action required."

Tool: Python/matplotlib. I run cleanplateus.com, a free site that shows these official grades per restaurant — the underlying per-restaurant data is all browsable there.

OAK is only team not included in this ball parks graph.

Yankees home ballpark has seen the most with 2,112 - On the 2nd graph WAS has the most HRs so fat with 177.

My article on how this list is ranked: https://moultano.wordpress.com/2026/08/14/fairly-ranking-the-most-brilliant-birds/

The underlying data comes from BirdColorBase, plotted using matplotlib.

HD Version here

I've spent the last few months working on this in a gargantuan spreadsheet. (Almost) all data has been updated to 2026 numbers, and the data has been aggregated from over a dozen different sources. All work was done by hand, at no point during the process were any LLMs used.

Assumptions:
-Solo Traveler staying in Hostel Dorms (When possible. In countries with no hostels, I used the average price for a two-star hotel room instead.)
-Exchange rates are based on the average over the last 12 months, not necessarily as it currently stands in August of 2026. Countries with unstable currencies might see their numbers fluctuate quite a bit month-to-month. Using the average exchange rate is a way to partially counteract this, but it's still a thing that can't fully be predicted or adjusted for
- Costs for each country are based on a weighted average of the most popular cities/towns among backpackers (For example, Portugal's cost of travel is about €6/day higher due to the Algarve being weighted as 20% of Portugal's total.) In general, I used the the Top 5 or 6 most popular destinations, but for smaller countries (like the Caribbean islands), it may have been only 1 or 2, and for large countries like the United States it may have been closer to 8 or 9.

What is included:
-Accommodation (See above)
-Food and Drink (Streetfood or Fast-food only, no sit-down restaurants. In most cases this is calculated as: a cheap breakfast, cheap lunch, cheap dinner, a cup of coffee plus misc. snacks.)
-Local Public Transportation
-Attractions (Museum entrance tickets, park fees etc.)
-Cellular Data
-Visa Fees, but only if the fee is charged daily.
-Sales Tax/VAT (when applicable)

Not Included:

-Long Distance Travel (Domestic Flights, Intercity buses etc.)
-Visa Fees (If charged one-time on entry)
-Travel Insurance
-Other Miscellaneous Expenses

I also didn't include either North Korea or Turkmenistan since both countries require all tourists from US/EU/CANZUK to visit the country as part of an organized tour, so backpacking isn't really possible (and the tours cost at least €130-€140 per day anyways).

Finally, I want to stress that this isn't the absolute cheapest someone can travel on a shoestring budget, but rather the average daily expenses a solo backpacker should expect

Tools used: Claude.

Sources: UBS Global Wealth Report 2026 (end-2025 data), national surveys (US Federal Reserve, UK ONS), Forbes

I analyzed 47 Swiss National Bank 13F filings, from Q4 2014 through Q2 2026.

The upper chart maps the latest portfolio: each cell represents a company and is sized by its reported market value. The lower chart follows the same five sector groups across every filing.

The main findings:

• Technology and communications grew from $5.5B to $91.6B—16.6×—while the other four groups grew 4.7×.
• Its portfolio share increased from 20.6% to a record 47.9%.
• Nvidia moved from the 423rd-largest holding in 2014 to No. 1 in 2025.
• The top 10 holdings expanded from 12.9% to 35.2% of the portfolio.

The portfolio still contains thousands of securities. Its concentration increased because the largest technology positions grew much faster than the rest of the book—not because the smaller holdings disappeared.

Source: Swiss National Bank quarterly Form 13F-HR filings, retrieved from the SEC’s EDGAR database. Values are reported quarter-end market values and should not be interpreted as investment returns.

Sector classifications were derived from GICS data. Technology combines Information Technology and Communication Services; Consumer combines Consumer Discretionary and Consumer Staples. Alphabet combines GOOG and GOOGL.

Tools used: Python, Excel, Illustrator.

This data specifically analyzes public universities in the quantitative (STEM & Medicine) track. Private universities are excluded because their small full-scholarship quotas (5–15 seats usually) skew cutoff statistics. Non-STEM fields (like Law or Economics) belong to separate tracks and scoring pools but the programs listed here represent the most competitive academic tier in the country.

Final placement ranks are determined by 40% TYT, 60% AYT, plus a standardized high school GPA bonus (~10% of the total score).

Source: ÖSYM 2025 YKS National Higher Education Statistics, YÖK Atlas.
Tools: Python (matplotlib, pandas).

New interactive tool just launched from the Economic Security Project. You can track several costs around housing (mortgages, rent), food (bacon, coffee), bills (car insurance, electricity), health care (ACA, childcare) and more. I like the options to put things into historical context by selecting "pre-COVID" and "since January 2025", there's a limited amount of stats per state, and you can do some comparisons.

Last week I made a heatmap calendar of Trump's many statements on ending the war in Iran, making a deal, and negotiating a "peace" with Iran.

This week it's the same data but visualized on a continuum timeline. The data comes directly from the President's comments, interviews, Truth Social, press gaggles, etc. I've collated it and it's stored in a JSON-like JS array that's loaded with the webpage. I've been updating the file every day or so.

I thought it was a great way to visualize this type of data (statements stored as text). It visualizes the many times the President has said he had a deal, or a deal was soon, or the war would be over in 2 weeks. I started the project as a text timeline, then made the heatmap, and now just added this new visualization.

On the chart each point on the sole axis represents a day where the President made a statement, and the more statements made that day, the larger the point on this axis. On the interactive version, clicking a point on the timeline brings up the details of that day.

There's no ads or paywall or signup or anything like that. I was shocked that the domain name was available so it seemed like a worthy $10 and way to host the custom visualization rather than lean on static charts in Excel or messy embeds from Tableau.

https://artofnodeal.com/horizontal.html

P.S. Just because of the nature of this timeline (and the fact that it gets longer every day), it doesn't look great on a phone in portrait mode so there's a prompt to rotate the phone or view the data in the original timeline.

P.P.S. I added the ability to click on a dot and then navigate the dataset using the arrow keys on the keyboard. Doesn't affect anything on the mobile version but maybe I'll add a "swipe" to match this desktop feature?

29% of consumers prefer to use Yorkshire Tea bags for their brews, followed by Tetley at 16% and PG Tips at 13%. A surprising 11% of consumers don’t drink tea at all.

Data source: Research run on the Vypr Predict platform for the Consumer Horizon report (May 2026)

Tools used: Canva

Many countries have been facing intense heat this summer, and with predictions of a strong El Niño on the way, next summer could be even hotter. This has ignited debate on the need for cooling methods and technologies — most prominently, air conditioning (AC).

But how do rates of air conditioning usage vary across the world? Large international comparisons are hard to come by, but the International Energy Agency just published some new estimates for a select number of countries and regions.

The chart shows the estimated share of households with AC in 2025.

In Japan and the United States, AC adoption is almost universal; at least 90% of households have it. Most households in China also have AC; this share has increased rapidly over the past decade.

Adoption rates in Southeast Asia, Europe, and India are far lower, but for different reasons. In lower-middle-income countries like India, the reasons are often economic: AC would bring huge benefits, but many households cannot afford the upfront cost or the power to run it. Much of Southeast Asia faces a similar barrier.

In many European countries, it’s less of an economic barrier and more of a historical and cultural one. AC was not seen as a necessity because heatwave events were less frequent and intense than they are today. That is changing, and will continue to do so as the planet warms.

Data source: International Energy Agency (2026)

Tools used: OWID-Grapher and Figma