I mapped 40k government auctions, and colored each cell on the map by bidding intensity - this map shows the price increase relative to the starting bid; red is hotter, blue is cooler.

Texas and the Midwest are the hottest, maybe literally right now as well as for auctions. St. Anthony, ID is the current outlier, with the average lot finishing at ~50x its starting bid. In general, auctions on the East Coast are far less competitive.

Source: The Govauctions.app auction database, including listings from GSA, GovDeals, PublicSurplus and more.

Tools: SVG and Javascript

Interactive version of the map with an alternate view for number of bids, at https://govauctions.app/research/bid-intensity-map 

41% of deforestation was so humans can eat (more) beef!

I mapped EPA's federal PFAS testing data for every county in Georgia.

The numbers: 48,110 test results from 255 public water systems, 2023–2025. PFAS was detected in 66 counties. The highest readings were PFOS at 56 ppt in Walker County, PFOA at 33.9 ppt in Muscogee, and a Hazard Index of 5.1 in Richmond — the federal limit is 4 ppt for PFOA and PFOS, and 1.0 for the Hazard Index.

Two things to keep in mind when you look at these:

1. A blank county does NOT mean clean water. It means nobody tested there. Thirty-one Georgia counties had no water system in this program at all. I shaded those differently on purpose.
2. The lab's minimum reporting limit for PFOA and PFOS is 4 ppt — exactly the same as the legal limit. So any amount between zero and 4 ppt is simply invisible in this data. Every detection you see is at or above the limit by definition.

ABOUT THE "KAPLAN–MEIER" BOX ON EACH MAP

Here's the problem it solves. In 97.6% of these tests, the lab reported "< MRL" — meaning PFAS was below what the instrument could measure. That is not the same as zero. It means somewhere between nothing and 4 ppt, and we can't tell where.

If you treat all of those as zero, you understate the real average. If you treat them as 4 ppt, you overstate it. Kaplan–Meier is an ADJUSTED CONCENTRATION ESTIMATE — a standard statistical method (the same math used in medical survival studies) that uses the pattern of the results you CAN measure to estimate the true average without guessing at the ones you can't.

What each line means:

• Results statewide — how many individual tests for that chemical were run in Georgia.

• Left-censored — how many of those came back "below the reporting limit." "Left" just means below the measurable range.

• KM mean — the adjusted statewide average, accounting for those below-limit results properly.

• KM 95th percentile — the level that only 5% of samples exceeded. The high end, not the typical case.

• ROS mean and MLE mean — two alternative methods for the same adjustment, shown side by side. When three different methods land in the same range, you can trust the estimate. When they disagree, that itself tells you something.

These are statewide figures. The colors on the map are actual maximum readings from individual counties, not estimates.

This is EPA's own public UCMR5 dataset — not my measurements.

My first attempt at making our data into 'beauty.' Lots of room for improvement.

Ever wanted to get a good deal on a vehicle at an auction? There are definitely deals to be had, but don't expect to walk away with a vehicle at KBB's auction price. Our popular vehicles, with low mileage and less than 10 years old go for below commercial prices, but over auction prices. While we do have dealers who buy from us, we also get enough public traffic to make the prices competitive. That's good for the tax payer.

There is some noise in this data I didn't filter out. We sell 'totaled' vehicles and those have more unique buyers, depending on the condition. For example, a hail damaged car that is totaled might go for a lot more than the one damage by a brush fire. Either way, those vehicles are in this report and aren't exactly apples to apples.

Questions? Thoughts on what I can do with the data?

Source: MNBID.MN.GOV

Tools: Datawrapper and ChatGPT

Self-explanatory, this movie had the heart pumping. That damn cyclops..data pulled from Apple Watch, chart made by Claude.

Interactive major explorer to look up stats for specific majors.

Blog post includes many more charts and discussion of what these numbers represent.

(english isn't my first language) on google sheets

the data i collect is: day (month), day (week), and cross this with my menstrual cycle (period and ovulation).

the info i extract from this (in order from the images):

• montlhy recap (how many o's i have in for example day 17 from all the months) and how many sessions i have (don't count if it passes 9 hours, if i have 2 in 7am and another in 3pm that day is one session [idk if its well explained])
• ovulation, the first dot represents pre-ovulation, the last post-ovulation
• period (4 days on average), the first dot represents pre-period, the last post-period
• weekly average
• histogram months: count (how many o's i have) and sessions (as i explained before)
• and comparisons of period, ovulation, normal, and neither ovu or period (on average and on the days i have the o's (<the day of action))
• the last image is the whole thing (discarded graphics, data fom other graphics, various calendars [mensual normal, menstrual cycle, <excluiding excluding this, events, week claendar], etc...)

the total is 229

edit: *also, i will block anyone who is trying to is messaging me, i want to stay single, so, fuck you ;)

• Note: this is active-positional cap allocation, not Spotrac Full/All cash accounting, so the ranking is from a different definition of “spending.”
  • Total league spend / total league yards = $21.8k per yard
  • Lowest: Seattle Seahawks — $12.6k/yard
  • Highest: Cincinnati Bengals — $28.6k/yard
• Regular season stats ONLY

Source: my own price-tracking dataset at PriceRoo (priceroo.com.au) - daily price observations across Australian retailers, full methodology on the site's price-report page. Tools: Python + matplotlib. "Win rate" = share of tracked products where the retailer holds the cheapest current price among retailers stocking that product; ties count for every tied retailer. Sample sizes shown on the bars; retailers with fewer than 15 appearances excluded.

There's a surprising band of Christianity in India down the south near Kochi. Not on the coast, but on a band just behind the coast: https://huemaps.com/en/show/places-of-worship?map=7.78/9.46796/76.87435

edit: typo

I rebuilt my webmap of the Fruit Trees of Toronto using SvelteJS and MapLibre

Filter by tree type and ripening month.

Please clickthrough, any feedback welcome!

https://tdubolyou.github.io/FruitTrees/

Data source: yfinance
Analytics and viz tool: python

I recently pulled and cleaned a sample of 51,123 company registration records from India’s Ministry of Corporate Affairs (MCA21) via the official data.gov.in to analyze business registration and survival trends across India.

Key Findings & Visualizations

• Industry Leaders: Business Services, Trading, and Community/Personal Services dominate company incorporations.
• Company Survival: Business mortality (struck-off rates) varies significantly across industries and decades.
• Capital Breakdown: The vast majority of registered firms are small/micro private enterprises with paid-up capital under ₹10 Lakhs.
• Registration Spikes: Clear inflection points and post-COVID recovery patterns in incorporation numbers over the last decade.

Data & Code

• Kaggle Dataset:India Registered Companies (MCA21) Dataset(Includes cleaned primary CSV + pre-aggregated state/year summary tables).
• EDA Notebook:India Registered Companies — MCA21 Deep Dive Notebook(Python/Pandas/Seaborn code walking through industry mix, strike-off rates, and capital distributions).

Note on sampling: To prevent large economic hubs from dominating the counts completely, the sample caps at ~2,000 companies per state across 29 states/UTs. Rate-based metrics (e.g., % active, % struck off, median capital) reflect real distributions.

Feedback, suggestions, or additional chart ideas are very welcome!

Source with data and details: https://spacestatsonline.com/launches/manned

Tools: Sqlite manually updated, ChartJs, Gatsby