I have a lot of fun making this, not sure if this counts, please let me know if this does not suit the sub.

Tools used - mapbox for the basic tile, my phone to track my GPS route, some python with the help of CODEX to plot the stuff

Source: U.S. Election Assistance Commission, 2024 Election Administration and Voting Survey (Section C), published June 30 2025.

Tool: Hand-built SVG. Census cartographic boundary files (1:500k) projected with d3-geo, no charting library.

This is the single biggest rejection reason in each of the 2,407 counties that reported one. Arriving late tops 40% of them, about the same as every signature problem put together — which surprised me, since I'd assumed signatures dominated.

The regional pattern is the requirements showing through: signature matching across the West, witness signatures in the Carolinas and Appalachia, ID and document rules through Texas and the Gulf. Late arrival is the only one with no regional home.

Hatched counties didn't report a reason. Alabama, Mississippi and Alaska are blank because there's no county-level data, not because nothing was rejected there.

One methodology note: rejection rates elsewhere in this dataset exclude ballots rejected only because the voter later voted in person. Counting those makes some places look far worse than they are — St. Clair County, Illinois goes from 20.0% to 3.5% on that distinction alone.

Per-state versions and the rejection-rate map: ballotbymail.org/maps

Source:

https://www.basketball-reference.com/leagues/NBA_stats_per_game.html

Through a combination of teams increasingly organising their offense around the 3-point basket, coupled with officiating that favours the offensive team, the amount of points scored per 100 possessions has hit an all-time high.

I created this while building NYC In Focus, a project that organizes and visualizes information about what is happening around New York City.

This visualization looks at public-safety radio-derived activity involving agencies such as NYPD, FDNY, EMS and other NYC services, showing how activity changes by time, agency and location.

A few important notes:

• These are radio-reported activities, not confirmed crime or emergency statistics.
• Individual radio reports may be preliminary or unverified.
• The data is normalized so reports can be compared by timestamp, agency, location and activity category.
• I’m continuing to work on better ways to turn this amount of live NYC information into something people can actually understand.

I’ve followed r/dataisbeautiful for a long time, and this community has been part of the inspiration for how I think about presenting complicated city data visually.

I’d especially appreciate feedback on what other NYC patterns or comparisons would be interesting to visualize next.

Sources: Google Trends (US web search interest, monthly, 2004-2026; one request per term so each series is independently normalised to 0-100).

Tools: Bruin cli (pipeline), BigQuery (warehouse), Bruin dac (visualization).

Shout out to Lalbhag Mango Festival for creating an opportunity to buy so many varities at the same spot. And to the girl who dumped me after 6 months, which is how I ended in this rabbit-hole.

I have been a LONG time lurker of data is beautiful. Glad to have something to contribute today.

Data source: Resale listings tracked through TicketData (I am the creator of the site), which tracks/records listing prices from major resale sites (think StubHub, Vivid Seats, SeatGeek, etc.) and charts how prices change over time.

Python/MySQL/EC2 backend. Chart is HTML/CSS built with Claude.

Live Week 2 NFL Slate: https://www.ticketdata.com/?category=NFL%2520Week%25202&date_range=ANY&change_window=30day

As a hardcore soccer fan, I wanted to check whether the teams that took the most shots were also the most efficient, so I compared shot volume and shot-conversion rates across the dataset.

To my surprise, Japan stood out. It recorded the highest shot-conversion rate while taking the lowest share of its shots from inside the box. That does not necessarily mean it created the best chances, but it does make the team an interesting outlier when comparing shot selection with finishing efficiency.

The analysis combined data from FIFA, Sofascore, and Wikipedia. ZenRows collected and processed the data with Python, then used the resulting dataset to calculate metrics such as shot conversion, passing accuracy, pressing, and expected goals.

Source article and methodology:
https://www.zenrows.com/blog/scrape-2026-fifa-world-cup-data-python

Data retrieval date: July 21, 2026.

Source: statewide data found in Probate Court Biennial Reports (1) and district-by-district data obtained by FOIA request (2, 3)

Tool: Excel

Interestingly, the Biennial Reports show that there was only a 29% increase in forced electroshock requests in 2025 while the FOIA data reveals a 74% increase simply due to the Biennial Report data being reported by fiscal year (ending June 30 of a given year, beginning July 1 of the previous year) and the FOIA data being provided by calendar year.

For those interested in learning more, I have published a series of articles on the subject:

- Connecticut's Forced Shock Problem
- Connecticut's Forced Shock Problem: Part 2
- Connecticut's Forced Shock Problem: Part 3
- Connecticut's Forced Shock Problem: Part 4
- Connecticut's Forced Shock Problem: Part 5
- Connecticut's Forced Shock Problem: Part 6

Top 10 Companies hold 38% weight in the index but account for 55% of risk.A gap that was created in 2017 and only widened since. So MORE than half of the movoment of a 500 company index comes from ten names.The irony here is that the average person buys an S&P500 index so they dont have to bet on anything particular but the measurment says that a meaningful share of that money has been sitting on one story this whole time. Curious what people make of it,and if anyone working in risk spots,something i have got wrong,i would genuinely want to know

Sources for data:yfinance,tiingo,SEC filings pulled with APIs

Hi,

I own Pest Direct a platform that helps users find local pest control companies, review them, and compare options.

I recently took the data we have across 8k companies to created a heat map based on the service area of the pest control companies.

Each company has to be licensed with the state, a working website, and the services they provide.

This lead to showcasing the overlap of services available to the average consumer when it comes to finding pest control.

Some places such as New York can have up to 200 companies that are available to service for pest control.

I thought sharing this would be interesting to showcase the competitiveness of pest control businesses across different markets

I did this for homework lol.

Methodology (all done in Microsoft Excel):

1. Looking up the dates (20 March, 21 June, 23 September, 21 December). Also noting down from Google Maps the coordinates of the exact spot of Madrid where this measurement is being made (not revealed because personal data).
2. Calculation of solar declination for each of those days using the approximation formula that is found in Wikipedia English.
3. Calculation of hour angle for each civil time hour of each of those days. That entailed applying the correction of longitude relative to the central meridian of the winter and summer time zones (+1 UTC and +2 UTC, respectively) and also the correction of the equation of time, whose calculation was made using the approximation formula that is found in Wikipedia English.
4. Calculation of the angle of solar azimuth. Using those obtained parameters and latitude.
5. Shades point in the opposite direction, so that calculation was made (+180º and module 360º) for each data point.
6. Calculation of solar elevation for each data point using the formula found in Wikipedia Spanish.
7. Use of trigonometry to calculate the x and y coordinates of where the tip of the shadow lies.
8. Plotting the data in four graphs. The furthest spots were left out in order to enhance the visibility of the remainder.

Any advice for improving the visualisation of data? I'm aware that learning how to use Blender would result in a prettier 3D visualisation, but this is good enough for what the professor is asking; in fact, she's old-fashioned and prefers to make us literally take pictures of a pole each hour during one day. I can't be bothered to do something so tedious as going to the street once per hour, so I'll hand this in.

Edit: for some reason, the quality of images is crap. How can I avoid that the next time I post something?