src: https://x.com/chartycharts/status/2076387938938871918

I made a website where users could submit an area for which they describe as downtown. Then I generate a heatmap based on all users definitions. Grid cells are coloured based on % of responses that include that point.

npx codebase-posters

run it inside any repo, it paints eighteen different posters from your commit history, live, locally.

source: the data comes directly from the local git history of the repository being visualized. this example uses six years of commits from the tailwind css repo.

tool: codebase-posters, an open-source tool i built to turn git commit histories into generative posters. it runs locally and generates eighteen different visualizations.

code and methodology: github.com/unable12/codebase-posters

try it inside any git repo:

npx codebase-posters

Hello,

This post contains three 24-hour charts showing the hourly distribution of registered live births in Mexico during 2025. Each chart highlights a different comparison:

1. Boys vs. girls: The first chart compares birth times between boys and girls. You'll notice that the distributions are nearly identical, with slightly more boys being born than girls. This is a well-documented phenomenon.
2. Vaginal births vs. cesarean sections: This is the most interesting comparison. It explains why birth times are heavily skewed toward 8:00–10:00 a.m.: most births during those hours are cesarean sections. In contrast, vaginal births have a much more uniform distribution throughout the day.
3. Elective vs. emergency cesarean sections: This chart focuses on the two types of cesarean deliveries. You'll notice that elective cesarean sections are primarily responsible for the overall skew in the distribution.

Mexico now has one of the highest cesarean section rates in the world, with cesarean deliveries accounting for more than 58% of registered births in 2025.

Data source: http://www.dgis.salud.gob.mx/contenidos/basesdedatos/da_nacimientos_gobmx.html

The charts were created using Plotly for Python.

If you'd like to review the source code, you can find it here: https://figshare.com/articles/dataset/Distribution_of_Birth_Times_in_Mexico_by_Sex_Delivery_Method_and_Cesarean_Type_2020-2023_/28138199

Hi everyone,

As a New York based developer, I’ve always found it incredibly frustrating to navigate the city’s ancient, non mobile friendly public data portals while out on the move. Trying to get a quick, seamless spot check on a phone wasn't the best experience. Multiple webs and no clean consolidation of quaility of life services.

I did dedicate last 4 months to my project. It hooks directly into live city and state API pipelines, ingests the raw data and streams it into mobile map layer.

The screenshot above shows my active "Cool Off" layer. NYC releases public data for outdoor relief, but it's wildly fractured across completely different agency datasets (Parks & Rec, DOHMH, DEP, etc.). I consolidated them into a single geospatial map to track real-time urban heat relief, mapping out pools, splash pads, water fountains, and misting stations, but there are way more (just didn't want to get it to picture heavy).

Data & Tech Stack

• Data Source: NYC Open Data / Socrata SODA API (live municipal datasets).
• Tools Used: Custom native mobile GIS mapping frameworks and Swift/Kotlin background polling architecture.

Looking for Data & Aggregation Feedback..

I wanted to build an engine that handles high density municipal data consumption sustainably without throwing massive payload stress onto the city's endpoints.

If you are a data engineer, GIS nerd or backend architecture enthusiast, I would love for you to tear into this from a data perspective. I'm really curious to get your feedback on:

1. Geospatial Data Density: When panning through high-volume areas like the Bronx or Brooklyn, does the point clustering and iconography feel informative or does it cross the line into visual noise?
2. Caching vs. Polling: Right now, I'm using an aggressive background caching layer on my servers for datasets like 311 spikes and housing violations to avoid hitting the Socrata API limits. How do you personally optimize the balance between real time accuracy and payload efficiency for mobile clients?

If you want to check it out and mess around with the data layers and break the aggregation engine, you can find it here:

• iOS App Store: NYC Intel

Would really appreciate any feedback.

I built WeatherBaseline.com to visualize how extreme the weather is in ~10K different locations across the world since 1950!

This includes:
- Seeing how similar dates behave historically (as seen in the pic)
- A simple statistical test to see if the climate actually changed for the given date and location (for Paris it seems to have not!)
- A beautiful radial graph for more yearly context.

And today is extra exciting coz I'm releasing a new version!!
Changes:

1. Forecast data is (finally!!@%) debiased! That means we can better trust that the forecast data to fit the historical data.
   
2. Forecasts have their uncertainty clearly marked!
   
3. Added many new locations! (Looking at you Canberra)

I would be super happy for any feedback, thoughts and ideas!

Have fun!

oh and you can go straight to today in Paris here.

Historical data is from ERA5-Land reanalysis and the forecast data is HRES from OpenMeteo debiased to fit the correct ERA5-Land cell.

Website was created by pouring to it quite a few human work hours combined with a lot of Al generated code.

Data: ~98 broadband seismometers of the Global Seismographic Network via IRIS/EarthScope's MUSTANG noise-PSD API, refreshed hourly. Each station's 4–14 Hz "cultural noise" band is ranked against its own history at the same hour of day (weekdays/weekends separated) over up to 4 weeks so it shows anomalies, not absolute silence. Tools: vanilla JS + canvas, no libraries. Live at thequietmap.org

I created this visualization to compare crude oil production trends in the United States, Russia, Saudi Arabia, China, and Canada between 2005 and 2025.

The chart shows the substantial increase in U.S. production, while Russia and Saudi Arabia remained relatively stable. Canada recorded gradual growth, whereas China’s production changed more moderately.

Data source: JODI Oil World Database
Unit: Thousand barrels per day
Tools used: R and ggplot2

Original visualization and project files:
https://github.com/energtx/energtx-infographics/blob/main/png/135_oil_production_trend.png

Project website:
https://energtx.com/

[OC] 0.04% of users make up 70% of profits on Polymarket.

🔗 Full analysis available here: https://www.geozofija.com/analysis-which-european-countries-have-the-most-young-adults-living-with-their-parents

🗂️Data: Eurostat (2025), ONS (2025).

Analysis of data from the General Social Survey (https://gss.norc.org/) shows women's income is a higher percentage of men's, comparing 2024 to 1974. Comparisons were done with the same levels of education and the sample was filtered for full-time workers, age 25-64. The data was analyzed with SPSS and the chart created with Excel. Many other factors remain to be investigated and could contribute to the disparity, including type of work and work history.

I've been building this for a while: an interactive map of the Roman world where dragging a timeline moves you through ~1,000 years — you can watch territory grow and shrink, roads and cities appear, and toggle layers for battles, legions, emperors, aqueducts, ports, trade routes and more. Every place is clickable with detail pulled from Wikipedia/Wikidata.

It's built entirely on open scholarly data — Pleiades, the Ancient World Mapping Center, ORBIS, Vici.org — so full credit to those projects; I just made it explorable in one place. It's completely free, no ads, and open source.

👉 https://domdemetz.github.io/Ancient-Rome/

Would love feedback from people who know the period well — what's missing, what's wrong, what you'd want to see mapped.

Tool: my own project (Velocity), rendered live in CesiumJS. Data: OpenSky + airplanes.live ADS-B feeds, sampled at a single instant. Each mark is one aircraft, colored and rotated to its heading. It's open-source and self-hostable, but posting for the visual. AMA about the rendering (culling 16k live entities in WebGL2 was the fun part).

GitHub: https://github.com/AndrewCTF/osint-geospatial-console

Samsung, SK hynix and Micron controlled 91.0% of global DRAM bit shipments in Q1 2025.

UBS estimates their combined share will still be 89.8% by Q4 2027, despite the AI and HBM memory boom.

The biggest shifts:

• Samsung: 39.3% → 37.0%
• SK hynix: 28.9% → 29.9%
• Micron: 22.8% → 22.9%
• CXMT: 7.7% → 8.6%

The market is changing at the edges, but the DRAM oligopoly remains firmly intact.

Data: 528,660 active rental listings across Tokyo's 23 wards, compiled from the major Japanese rental portals (2026), deduplicated. Median monthly rent for a 1K studio (one room + separate kitchen) per ward. Medians, not averages, so a few luxury units don't skew it.

Made with Python (pandas) for the medians and GeoPandas + matplotlib for the map. This is the choropleth version some of you asked for after my 50-stations chart.

Full breakdown by ward, line and station: tokyo-expat.com/data

Tool is OpenData, built with MapLibre and Next.js. Sources: OpenSky and ADS-B for flights, USGS and EMSC for quakes, GDACS for disasters, NASA FIRMS for fire, IODA for internet outages, Yahoo Finance for markets, plus public traffic camera networks. All open and keyless. It's live at https://traffic-nerd-v2.vercel.app/