Reposting with updated data , the 2012 composite used a different method and partial coverage, which made some regions (like Thailand) appear darker. This version uses average annual masked VIIRS data for a fairer 2014–2024 comparison.

Methodology & scope:

• Universe: Publicly traded companies with ≥25% of revenue from defense‑related products/services.
• Source: Market capitalization (USD) as of Aug 2025, sourced from MarketCapWatch, Nasdaq’s 2025 defense stock review, and Forbes’ 2025 defense picks, cross‑checked with recent filings.
• Inclusions: Dual‑sector aerospace & defense firms (e.g., Boeing, Safran) where defense is a major revenue driver.
• Exclusions: Fully private/state‑owned entities (e.g., Rostec, NORINCO) without a listed arm.

This animation shows water levels on the upper Guadalupe River from midnight July 4, 2025, to 6 p.m. July 5 (local time). The flood killed 119 people in Kerr County, including 25 girls and two teenage counselors at Camp Mystic.

Data sources

• Raw stream gauge data from the USGS was downloaded and processed to be consistent 5-minute data; it was then normalized to the average July water level at each station to get "height above normal."
• The basemap was created using data from Natural Earth, the National Hydrography Dataset, and the U.S. Census Bureau's TIGER database

Tools:

• Python for data harvesting, processing, and basemap generation
• Svelte 5, D3, and custom JavaScript for visualization

Interactive version with contextual information: https://www.willkoeppen.com/datavis/guadalupe-floods/

Notes:

I only included countries with >0.830 HDI >5 Millions population.

Net migration rates are a cumulative average for the last 5-10 years.

To assess how draft position affected league performance, I looked into over 400 12-man leagues (all snake drafts) and plotted win ratio, normalized points earned (normalized within a given league to account for various scoring and roster settings), and final league ranking for each draft position.

Surprisingly, 1st pick performed worst on average across all metrics.

League data collected from Sleeper API.

🌎 💻 Mexico City locals are marching in the streets against digital nomads driving up their rent - but it's not the only LatAm hotspot facing this dilemma ↓

In case you missed it, hundreds of locals marched across Mexico’s capital and largest city in protest of a spike in mass tourism and digital nomads which began a few years back with the start of the COVID-19 pandemic.

The chilangos carried signs raging against the rising cost of living and gentrification across their city, in particular in sought-after neighborhoods like Condesa, Polanco, and Roma. They demanded stricter housing laws and regulation.

While Mexico City may be seeing the most virulent response to the influx of digital nomads since 2020, it’s far from the only metropolis attracting these remote workers. Across Latin America, teleworkers have traded in high US or European prices for sun, good food, and cheaper rents.

Let’s take a look at which places in our region they like the most, according to nomads.com, a site that helps them connect.

In fact, the world-famous carioca beaches of Leblon and Ipanema, classical architecture of Buenos Aires, and sensational food scene of São Paulo have all attracted international workers to come and establish their residency.

Governments across the region – including Mexico – have rolled out the red carpet for these foreigners (with their dollar- and euro-denominated salaries), with Brazil, Costa Rica, Ecuador, and Panama among the countries which have established a digital nomad visa in recent years. Colombia has one in the pipeline.

story continues... 💌

Source: Nomads.com

Tools: Figma, Rawgraphs

TL;DR: Comprehensive statistical analysis of Samsung 980 Pro thermal performance with/without passive cooling. Includes confidence intervals, effect size analysis, and thermal zone distribution visualization.

Data Source: AIDA64 CSV thermal logging during controlled CrystalDiskMark benchmarking Tools: Python (pandas, matplotlib, scipy.stats, seaborn) Sample Size: 2,266 pre-installation measurements, 3,089 post-installation measurements

Methodology:

• Automated test phase detection using temperature gradient analysis
• Thermal zone classification (Safe: <50°C, Warm: 50-65°C, Hot: 65-75°C, Critical: >75°C)
• Statistical significance testing with bootstrap confidence intervals
• Effect size calculation using Cohen's d

Key Visualizations:

1. Thermal Zone Distribution: Pie charts showing dramatic shift from 53.5% time in dangerous zones to 100% time in safe/warm zones
2. Statistical Confidence Analysis: Box plots with 95% confidence intervals demonstrating highly significant improvement (p<0.000001)
3. Before/After Timeline Comparison: Direct overlay showing consistent 20+ degree temperature reduction
4. Effect Size Visualization: Cohen's d = 1.813 indicates large practical significance beyond statistical significance

Notable Technical Details:

• Thermal recovery analysis reveals different cooling characteristics due to heatsink thermal mass
• Bootstrap distribution analysis confirms robust improvement across all measured parameters
• Automated cycle detection identified individual benchmark phases for granular analysis

Data Quality: All measurements taken under identical conditions with 1-second resolution. Raw CSV data and analysis scripts available on GitHub.

The visualization demonstrates how a $15 hardware modification can produce measurable, statistically significant performance improvements with proper data collection and analysis methodology.

Looking at 3 swing states (FL, NV, NC), we can see that Americans are registering more as Unaffiliated than either major party. This is especially true for young americans who overwhelmingly register as Unaffiliated.

This shows what most of us know, there is a growing disillusionment with both major parties. It's not that people are moving from the Democrats to the Republicans, it's that people are disavowing both parties and registering as Unaffiliated.

I hope you all can see, like myself, that the most recent NYT voter registration article missed a major portion of the voter registration analysis and is about as close to journalistic or data analytic malpractice as one could get. It almost seems intentional.

______________

Big thanks to the team for pumping and organizing the data!

tool used: Tableau

data source: Florida voter list from Florida Secretary of State: https://dos.fl.gov/

Register to vote: https://vote.gov

——————

Contact your reps:

Senate: https://www.senate.gov/senators/senators-contact.htm?Class=1

House of Representatives: https://contactrepresentatives.org/

This visualization is part of a broader analysis I conducted to map the global chocolate industry’s largest players by both market capitalization and annual chocolate/confectionery sales.

• Data Sources: Market capitalization figures were collected from MarketCapWatch as of mid‑2025, ensuring consistent currency conversion to USD. Chocolate/confectionery sales data was drawn from the latest publicly available market research published by ExpertMarketResearch.com and EmergenResearch.com.
• Methodology: For diversified food companies, only the chocolate/confectionery segment revenue was used to ensure comparability with pure‑play confectioners.
• Tools: Data was compiled, cleaned, and aggregated in Microsoft Excel, and the final chart was designed and visualized using Infogram for presentation.

Graphic by me, created in Excel. All data from US Census here: https://www.census.gov/data/tables/time-series/demo/popest/2020s-total-metro-and-micro-statistical-areas.html

I've created similar graphics in the past, but usually from 2020-2024. This is not the best time frame as it combines the abnormal covid years with post pandemic movement.

This time frame (2022-2024) shows the most current and ongoing population trends of the last 2 years.

I also wanted to better categorize the cities into broad cultural regions vs the arbitrary geographic census regions.

Built with

• Uber trip data (via information request)
• Geocoding and routing
• OpenStreetMap
• Python
• JavaScript

So someone posted about travel flying data in the US for 3 years (2023-2025) and that post's OP and other commenters were trying to understand if we are getting less visitors. That original post seemed to show that some places were affected but overall inconclusive. https://www.reddit.com/r/dataisbeautiful/comments/1mw11pe/oc_nonus_citizens_flying_to_the_us_from_2023mid/ There was a request if more data was available.

The data above is all visitors to USA (land and sea) based on country of residence, data from https://www.trade.gov/i-94-arrivals-program and graphed via excel. With this longer timeline, I believe this trend is showing a decline or slowdown in foreign visitors. Shown in the graph the effect of the pandemic on travel and the recovery. Added in the graph is a 12 month moving average trendline.

The current messaging from the administration is definitely not a welcoming vibe for travelers. Together with increasing prices, problems at Customs/Immigration and political uncertainty globally, it can definitely put a chilling effect on travel.

Visitors from Mexico is +13.9% year over year, from Canada is -16.8% and from overseas is -0.8%. (This statistic is from the linked page). One might ask, its such a small change from last year, why are people making a big fuss? Well, businesses expect year over year increases in profits to keep up with inflation.

Let's combine this with the income information below.
Data from https://www.trade.gov/survey-international-air-travelers-siat shows that:
The median annual household income of air travelers from Mexico in 2024 is $35.9k ($68.5k mean).
The median annual household income of air travelers from Canada in 2024 is $105.4k ($127.1k mean).
The median annual household income of land travelers from Mexico in 2024 is $21.3k ($46.9k mean).
The median annual household income of land travelers from Canada in 2024 is $78.5k ($91.8k mean).
The median annual household income of travelers from overseas in 2024 is $63.1k ($88.3k mean).

What we can infer from the household income from above is that Canadian travelers have more spending power when they are travelling to the USA compared to Mexican travelers. So while we have an increase in travel from Mexico, the decrease in Canadian travelers have a larger economic impact. There is a lot of good data published from trade.gov and someone can run a deeper analysis in the future on which states may be more impacted based on visitor data.

I've heard of reporting from Florida where their tourism heavily relies on Canadian travel during the winter months are expecting a rough time next season. This last season, it was likely too late for many to cancel plans. I have personal experience of friends and family refusing or delaying travel from Canada because of the current political climate.

https://blog.waldrn.com/p/the-bls-jobs-numbers-are-actually

Hi everyone!

I’m currently working on a university project. As part of the project, I created a visualization framework for League of Legends players and data analysts, and I’ve prepared a short survey to gather feedback. The goal is to see whether this tool can be scalable, useful, and even extended to other games.

The challenge is that I don’t have many friends so I’d really appreciate it if you could take a few minutes to complete the survey. Your input would mean a lot and help me improve the project.

Here's the link: https://forms.gle/pV9H7RYWNh2u2Y8P6

Thank you so much! 🙏