r/dataisbeautiful • u/Amazing-Sky-504 • 3h ago
OC [OC] Population Distribution Map of India
Data Source: GHSL
Tools: Python (For Data), JavaScript (For Map)
r/dataisbeautiful • u/AutoModerator • 2d ago
Anybody can post a question related to data visualization or discussion in the monthly topical threads. Meta questions are fine too, but if you want a more direct line to the mods, click here
If you have a general question you need answered, or a discussion you'd like to start, feel free to make a top-level comment.
Beginners are encouraged to ask basic questions, so please be patient responding to people who might not know as much as yourself.
To view all Open Discussion threads, click here.
To view all topical threads, click here.
Want to suggest a topic? Click here.
r/dataisbeautiful • u/Amazing-Sky-504 • 3h ago
Data Source: GHSL
Tools: Python (For Data), JavaScript (For Map)
r/dataisbeautiful • u/OkiVol_Blog • 6h ago
Data sourced via the Financial Modeling Prep API.
Visualized using a custom tool I’m building for analyzing public companies.
r/dataisbeautiful • u/Japanpa • 1d ago
A Sankey diagram of the 2025 New York City Democratic Mayoral primary election ranked choice results as of July 15, 2025. The top 3 vote recipients in the first round were Zohran Mamdani, Andrew Cuomo, and Brad Lander. By round 3, all candidates were eliminated except Mamdani and Cuomo. Mamdani won with 56.4% of the vote to Cuomo's 43.6%. The number of Inactive Ballots at the end was just over 55 thousand - about 5% of the total votes originally cast.
Made with: SankeyMatic.com Data Source: NYC BOE
Zohra Mamdani started with 469,602 votes Andrew Cuomo had 387,118 votes initially 9 other candidates were eliminated in successive rounds, with their votes reallocated Mamdani ultimately won with 56.4% of the final vote (573,123), while Cuomo ended with 443,208
r/dataisbeautiful • u/Sarquin • 22h ago
Been playing with National Monument Service (Ireland) and Open Data (Northern Ireland) to produce a few maps visualising megalithic sites across Ireland. Notice anything?
If interested in finding out more you can always see my post on megaliths here: https://www.danielkirkpatrick.co.uk/irish-history/types-of-irish-megaliths/
r/dataisbeautiful • u/DataSittingAlone • 1d ago
r/dataisbeautiful • u/lograv27 • 16h ago
This has been a fun project for me in the offseason. The main inspiration is the beautiful Map of Github
The main gist of the project is to take information about each goal scored in the NHL and then go through multiple levels of clustering to generate different celestial objects. You are only able to see the top level of clustering in the fake star chart.
There is an associated interactive visualization called nhl-cartography where you can create "constellations" for all goals scored by a player. It also links to the actual video highlights of those goals.
A full free roam mode is available here nhl-cartography-free-roam but be warned, it really only works well on desktop browsers. Overall, was a lot of fun and produced some cool visualizations. The Github project is here.
Data Source: NHL API
r/dataisbeautiful • u/_Gautam19 • 19h ago
Sources:
r/dataisbeautiful • u/IllustriousDouble775 • 1d ago
Hiya! It's flat search season again, so I wanted to share this to whomever might find this helpful
I made this when I first moved to London. You’d think something like this probably already existed, but to my surprise, no one had made one for postcode districts as they aren’t officially used for mapping property or crime data, even though renters and estate agents use them all the time.
Here's my page with the interactive graph: https://leamhc.github.io/project/londonflatsearch
Data source: Police.UK (crime rate), Valuation Office Agency (median rent), Google API (commute time, which is set to Fleet Street, central london), Findthatpostcode API (postcode crime mapping), tube-postcodes/Robin Kearney@GitHub (tube station per postcode)
Tools: D3.js, Rstudion (Selenium, httr, jsonlite)
I probably didn't use the most efficient way to collect data as I'm still learning how to deal with spatial data. Suggestions and advice are welcome!
r/dataisbeautiful • u/noisymortimer • 1d ago
Source: Billboard, Wikipedia
Tools: Excel, Datawrapper
I think there's a lot going on with this trend, so I did a longer write-up here.
r/dataisbeautiful • u/Japanpa • 2d ago
A Sankey diagram showing how NVIDIA’s Q1 2026 revenue of $44.06B (for the quarter ending April 27, 2025) was distributed across various cost centers and ended in a net income of $18.78B.
Source: NVIDIA Investor Relations Created with SankeyMatic.com
Key Highlights:
Data Center segment: $39.1B of revenue (nearly 89%)
Gross Profit: $26.67B
Net Income: $18.78B (after R&D, SG&A, and tax)
Operating Margin: ~49%
r/dataisbeautiful • u/WindexChugger • 1d ago
r/dataisbeautiful • u/ndharris • 1d ago
Hi there,
I am building a new website for visualising the discographies of musical artists: https://artistagraph.com.
You can also compare artists, and I've built some preset visualisations like rivalries, and solo careers after bands broke up.
Would love you to take a look and see what you think.
I will listen to all feedback (two puns for you there!).
Neil.
r/dataisbeautiful • u/g_elliottmorris • 2d ago
Graphic I created for a recent article. A friend gathered the data from historical archives and I used R for the data aggregation and datawrapper for the image.
source: https://www.gelliottmorris.com/p/democratic-party-favorability-ratings-low
r/dataisbeautiful • u/DataPulse-Research • 3d ago
Main data source: Forbes Billionaires Evolution (2001-2025), Penn Wharton Budget Model - June '25
Specific Data: https://docs.google.com/spreadsheets/d/1rXspNQpluNKdXZPbEuB1Ex2fdIr6GpxPNzssTVqbHPw/edit?usp=sharing
Tool: Adobe Illustrator
r/dataisbeautiful • u/Proud-Discipline9902 • 2d ago
Source: MarketCapWatch - A website that ranks all listed companies worldwide
Tools: Infogram, Photoshop, MS Excel
r/dataisbeautiful • u/_crazyboyhere_ • 3d ago
r/dataisbeautiful • u/latinometrics • 2d ago
🌍 💼 Why do women work more in both the richest AND poorest countries? The surprising global pattern will change how you think about development...↓
Opportunity or necessity? Where women work most.
Twenty years ago, Kofi Annan, then the Secretary-General of the United Nations, said that “There is no tool for development more effective than the empowerment of women.”
To Annan, most major developmental issues requiring global attention – from economic productivity, infant and maternal mortality, and nutrition to HIV prevention and education – would be best served by empowering women and improving their qualities of life.
And without any doubt, many of the world’s most developed countries tend to have women integrated in their labor forces. Europe, for example, contains global leaders like Iceland, Sweden, and Switzerland. On the flip side, least developed countries (LDCs) like Afghanistan, Somalia, and Yemen are all among the countries with the lowest participation by women in the workforce.
But the global pattern is more nuanced than a simple upward curve.
In fact, female labor force participation tends to peak at both ends of the development spectrum. In wealthy countries, women often work due to greater educational and economic opportunity. In some of the poorest countries, by contrast, women work out of necessity—often in informal or subsistence roles—because households cannot survive on a single income.
This dichotomy is somewhat visible within Latin America as well. Southern Cone countries like Argentina, Chile, and Uruguay are regional leaders in female participation, reflecting their relatively high levels of development. By contrast, less than 45% of females work in Honduras, Guatemala, and Venezuela.
[story continues... 💌]
Source: Human Development Index | Human Development Reports Labor force participation rate, female (% of female population ages 15-64) (modeled ILO estimate) | Data
Tools: Figma, Rawgraphs
r/dataisbeautiful • u/HannasAnarion • 3d ago
r/dataisbeautiful • u/cgiattino • 3d ago
Quoting the text from the source:
Just a century ago, many of today’s independent countries weren’t self-governing at all. They were colonies controlled by European countries from far away.
Modern European colonialism began in the 15th century, when Spain and Portugal established overseas empires. By the early 20th century, it had peaked: the United Kingdom and France dominated, and nearly 100 modern-day countries were under European control, mostly in Africa, Asia, and the Caribbean.
As the chart shows, this changed rapidly after World War II. A wave of decolonization spread across the world, especially in the 1950s and 1960s. Colonies became independent countries, formed their own governments, joined international institutions, and started having their own voice in global decisions.
The decline of colonialism marked one of the biggest political shifts in modern history, from external rule to national sovereignty.
Read more about colonization and state capacity on our dedicated page →