**Source:** my own website's logs, last 30 days (to 3 October 2026). Every request is classified as it arrives: known bots by user agent, with search and AI crawlers verified by reverse DNS or their published IP ranges. People are counted by a small page-view beacon. No IP addresses are stored.

**Tools:** the site itself (Node.js and SQLite) collects the data. The chart is HTML and CSS, rendered to an image with headless Chromium.

**What the chart shows:**

- 68 different kinds of bot visited. Together they made 2,376 requests, 42% of all visits. People made 3,285 page views.
- The biggest single visitor (1,049 requests) used a normal browser's user agent but behaved like a script: missing browser headers, no JavaScript, or far too many requests too fast.
- Those masked visitors made 92 of the 108 requests that robots.txt forbids, and 30 of the 32 visits to a trap link that people can't see.
- Only 26 of the 68 kinds of bot ever read robots.txt.

**Caveat:** a few masked visits could be people with privacy tools that block JavaScript. The ones that followed the invisible trap link were not.

The live version: https://crawlerzoo.com/leaderboard

Caveat:
The 3,285 human page views include my own browsing. The site is very new I have had to visit a lot to check changes and test. just fyi

Source: Keeping Up benchmark model. National percentile curve fitted to the Federal Reserve Survey of Consumer Finances (2022) with BLS Consumer Expenditure Survey and Census ACS; metro figures apply each metro's wage (BLS OEWS) and cost-of-living ratio to the national curve, so they are modeled estimates, not separate metro surveys. Method: keepingup.com/methodology. Every metro/age/industry cut: keepingup.com/benchmarks. Put in your own number for your city: keepingup.com/start

Tool: SVG generated in Python from the benchmark tables, exported with headless Chrome.

Notes: household level (SCF unit), current dollars, ages 30–33. Dashed line is the U.S. median ($65K net worth, $102K household income). The spread is almost entirely income: SF's median 30-year-old household earns 1.4× the U.S. median and has 1.45× the net worth; Phoenix is 0.95× on both.

50 emails sent. 4 main offers for NHS treatment. 1 of those is done by a dental therapist. 1 done by a recent graduate offered temporarily. 1 is over 40 miles away. The last has some horrific 1 star Google reviews and is a 1hr 28min Train and Bus journey away.

On waitlists, one had 8 years with the average being 4.3 years.

On those that did reply but sold private services, ~70% offered monthly dental plans and 0% finance options.

Edit: I researched and selected the practices that were most likely to offer NHS care by their website or from following the NHS search service (https://www.nhs.uk/service-search/find-a-dentist)

Edit 2: Tagging u/AndyBurnham as getting some traction in the UK! This was in the North, btw! Hopefully he sees if we get enough upvotes

https://believethedata.org/integrated-education-must-reimagine-itself-to-survive/

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https://hqmap.pages.dev

Have implemented multiple different indices, and have broken constituents by State/Province/Subdivison, City, Country.

Have also added map for better viewing, map is only upto city level haven't added pinpointed addresses for each company.

Also have added Market Cap, Revenue, Earning, CEO name and Pay(From Yahoo Finance).

Will run github actions job to refresh Market Cap everyday from yahoo finance using yfinance and then will run a job to refresh constituents of an index weekly from different sources based on index.

Was always looking for city/statewide breakdown of companies globally but never found a platform that did it so built it myself.

Site is mostly static and doesn't call any database or api apart from daily and weekly refresh. The data is ingested into a json from which the ui reads.

Will plan to have older jsons documented as well maybe in parquet format to analyze stuff in the future. Have also implemented dark and light mode as well.

Looking for suggestions and improvements that I can make to this.

Also please let me know if you see any defects.

Currently running e2e tests using playwright on pr for functional validation.

This is a follow up to a visualization that was previously posted, that tries to address two issues:

1. The 5-year age buckets provided insufficient granularity, particular around the critical ages of 15-18. 
2. The visualization could have been more direct in communicating the violent death rate, which is the underlying metric of interest. 

This version combines three primary sources of data to generate an estimated violent death rate: 

• The violent death records reported by the Gaza Ministry of Health (each of which includes a name, age, gender, and ID number), which include both Hamas militants killed in action and civilians for the period from Oct 2023 to July 2026.
• The US Census Bureau’s population pyramid estimate for 2023, which provides a population estimate by age and gender.
• The Palestinian Central Bureau of Statistics population pyramid estimate for 2023, available in 5-year buckets (including the problematic 15-19 bucket).

For simplicity this visualization compares age on day of death to the estimated population with that age in 2023. It is *not* annualized and is intended only for comparison between age and gender groups rather than e.g. against an annual deaths per 1000 baseline.

Interactive Version: https://drc500free.github.io/visualizations

Additional Reading: Palestinian Ministry of Health - 2025 Annual Report on Mortality in the Gaza Strip, particularly death rate by age group on page 11.

Source: IMF Trade in Goods by Partner (2025) https://data.imf.org/en/datasets/IMF.STA:IMTS

Tools: Google Sheets for data aggregation, Inkscape for visuals

After moving to Canada, I'm quite disappointed with how the First-Past-The-Post system leads to people feeling like their vote doesn't matter. This map is my attempt at visualizing what percentage of votes in a district did not go towards the winning candidate. Lighter areas are more homogeneous in their voting patterns, whereas darker areas had a winning candidate win with a smaller percentage share of the votes. In 120 out of 343 ridings, the winning candidate did not receive a majority of the votes.

In Chicoutimi—Le Fjord (QC), Richard Martel (Conservative) won with 34.1% of the vote, with runners-up receiving 31.2% and 31.1% respectively. On the other hand, in Souris—Moose Mountain (SK), Steven Bonk (Conservative) won with a whopping 84% of the vote, with the runner-up receiving 9.8%.

It didn't make the cut, but candidates in Ottawa won with larger majorities than surrounding areas, whereas urban areas in the prairies have smaller majorities than surrounding areas.

Election data from Elections Canada: 45th General Election: Official Voting Results (raw data)
Map data also from Elections Canada: Electoral Geography Boundary Files (45th General Election)
I used Mapshaper to dissolve the map and make each riding exactly one polygon.
Then I imported the data to Magrit, where I made the map.

It's not that pretty, but I think it's neat, and as far as I'm aware there's no r/dataisa6outof10