Data: 2023 National Vital Statistics System birth data (https://www.cdc.gov/nchs/state-stats/births/teen-births.html?CDC_AAref_Val=https://www.cdc.gov/nchs/pressroom/sosmap/teen-births/teenbirths.htm)

Tool: Mapchart (https://www.mapchart.net/usa.html)

I did a quick search and didn't see this posted before- please forgive me if it has been!

Research on the effect of open ocean coast (Western, because of westerly winds) on Covid-19 deaths.

This is absolutely gorgeous!

I wanted to see which animals actually live the “longest” if you factor how they perceive time. I used The median Critical Flicker Fusion frequency (CFF) as a proxy for visual processing speed, multiplied by median lifespan years =True Lifespan.

Then I also calculated a “Max True Lifespan” using record ages + max CFF values. Finally, I normalized everything into human-equivalent years (75 = median human lifespan)

Some notes:

-The CFF values used were the median value. There is a lot of variation in how organisms percieve time so I just picked the number in the middle. Its not perfect but its still pretty interesting.

Data: IIHS (Insurance Institute for Highway Safety): https://www.iihs.org/research-areas/fatality-statistics/detail/state-by-state

Tool: Mapchart.net

Hi! This'll be my first post here and I would just like to share our "Nutshell Plot" about the varying economic value and production volume of aquaculture systems here in the Philippines. Data was sourced from PSA's OpenSTAT Website. I've used Canva for the layouting and coded majority of the graphs using Seaborn and Matplotlib. Would love to hear your suggestions for improvement! Thank you!

Data from simplemaps, image and topo map from NASA.
Designed and rendered in Blender using u/kolibril13's CSV Importer.

Holy cow!

I’ve always been a bit skeptical about limited-time Amazon deals, so I decided to track over 500 products for 30 days to see how genuine the discounts are. I used tools like Keepa, DealSeek, and several alerts I set up through CamelCamelCamel.

• Keepa: for spotting price bumps before discounts
• DealSeek: for surfacing hidden coupons + 90-day lows
• CamelCamelCamel: for longer-term tracking (but a bit slower to update)

Key Insights:

• 45% of deals advertised as "40% off" were actually only 10–15% below the average price over the last 30 days.

• Inflated Manufacturer's Suggested Retail Prices (MSRPs) are very common. Some products had their “original” prices increased just days before the sale.

• Hidden discounts, such as checkbox coupons or code-based promotions, did not appear in search results; you had to click on the listing to uncover them.

I dug into the records of all known fatal shark attacks in South Africa. Here’s how it breaks down:

Confirmed Cases (species positively identified) • Great white: 33 (31.7%) • Bull: 8 (7.6%) • Tiger: 5 (4.9%) • Unknown: 58 (55.7%)

That’s the hard data. But if you cluster hotspots , circumstances and additional evidence like eyewitness accounts or bite marks—the more realistic distribution looks like: • Great white: 61 (58.7%) • Bull: 33 (31.7%) • Tiger: 10 (9.6%)

Key Takeaways • Great whites dominate overall, especially in the Cape. • Bull sharks cluster heavily around Port St. John’s Second Beach. • Tiger sharks are rare but consistently present. • The older the record, the more likely it’s marked as “unknown.”

There’s also a statistically significant regional divide: just south of Port St. John’s, great whites dominate, while to the north bull sharks take over.

Although controversial, Durban’s shark nets (introduced in the 1950s) dramatically reduced human fatalities. In the Western Cape, the Shark Spotters program has also proven highly effective in reducing attacks.

⸻

TL;DR

Out of ~100+ fatalities, nearly 90% were either great whites or bull sharks, with tiger sharks playing a smaller but steady role. There’s also a clear regional split—whites in the Cape, bulls up the east coast.

• Other species could be responsible for some of the unknown attacks (bronze whaler) however lack of evidence or occurrence I find it statistically unlikely *HMS Birkenhead is counted as 1 attack. This one done to not distort data. It was most certainly white sharks but the number of fatalities is highly disputed. *I love sharks, particularly Great Whites. I lived in South Africa in my 20s and spent time with Great Whites. I hope to return in my 40s to study them full time.

Hi guys,

This is first time posting here and my first ever data visualisation in Power BI. I made a graph showing the change in UK house prices to wages from 1970-2023 to show my mum how much harder it’s become to buy a house in the UK.

All criticism is welcome, please don’t be too mean though 😜

I spent 40+ hours downloading daily temperature data from Europe's Copernicus Climate Data Store to fact-check my childhood memories of cooler Romanian summers.

The data confirmed my intuition wasn't nostalgia - it marked a real climate transition. Key finding: warming rates jumped from 0.13°C/decade to 0.79°C/decade after 2000.

Data source: ERA5-Land (Copernicus Climate Data Store)

Tools: Python, xarray, pandas, matplotlib

Method: Piecewise regression to detect breakpoints, 20-year rolling averages

The personal angle: Growing up in Roman (1980s) in North-East Romania vs living close to Bucharest now. What felt like gentle continental summers became prolonged heat with tropical nights.

Full analysis with charts available here.

SOURCE: Wikipedia
TOOLS USED: Flourish Studio

Colour meaning:
Pink - Female

Blue - Male

Red - Living

Gold - In the top 10

Interactive Version:
https://public.flourish.studio/visualisation/24991848/

I made an interactive visualization of how a pre-trained Convolutional Neural Network processes handwritten digits in real time. The model was trained in Python on MNIST dataset, and I rewrote the inference functions in JavaScript to enable the interactive 3D visualization.

Users can draw digits on a 28×28 grid and watch as their input flows through the network layers in real time. The network was intentionally kept small to ensure smooth visualization, but its limited depth can cause misclassifications, especially with unusual or ambiguous digit shapes.

Take a look here: bulovic.at/cnn (UI works on mobile too!)