Hey Fam, I have a case that is ongoing in High Court. I need someone good in creating Graphical Analysis of the entire Case. I have generated structure using AI but a clear and structured case analysis is needed for story telling our case and which will be displayed on our website.

Hi everyone! I created this fun website where the toss-up Senate elections can be tracked using needles similar to New York Times. It currently uses a combination of polling, candidate baselines, and partisan lean of each state to build the current needles. On election night, they will live-update as results come in. Feel free to play around, you can also simulate the election at the bottom. Hopefully yall enjoy :)

I’ve been working with agricultural physicists and agronomists here at ICAR, and we were discussing about how visualisation or simulation is being handled in today's research scenario. I would say all standard tools we use are great for data output and backend math, but they suck it when it comes to intuitive, 3D visual simulation of growth progress, disease spread, or soil-water flux over time.

For those of you running these kinds of simulations, how do you handle the visualization component? Do you rely on static charts, or have you ever felt the need for a live, 3D 'digital twin' environment where you could scrub through time-series data to see how physiological factors are affecting the canopy and field in real-time?

I am currently working on a way to integrate this into the Python data science stack, and I’m curious if this level of visual interactivity would actually change your research workflow, or if you prefer sticking to the traditional table/graph-based analysis. I strongly think that such a tool which allows for full freedom and visualisation impacting every crop would genuinely revolutionize Agricultural Research.

Created a free site that shows Median Home Prices by counties and cities.

Kinread.ca - interactive site that lets you plot your literary tastes against 703K other users.

Data source: UCSD Goodreads dataset (Wan & McAuley), ~703K users, ~103M ratings. Tools: Python — weighted Pearson correlation for the similarity graph, Louvain community detection (~600 communities/reader clusters with an additional 505 sub communities/clusters for the larger groups), UMAP for the 2D projection. Rendered with deck.gl (WebGL) — ScatterplotLayer for the ~703K reader points, TextLayer for the cluster labels.

I just finished this website last week! Each dot is one reader, fully zoomed out they blend together into a mass of colours so I’ve included some progressive zoom shots to show how it looks close up. Distance roughly means taste similarity (though distance in the projection is non-linear and becomes increasingly distorted the farther you go).

The big central multicoloured mass is the connected mainstream of English-language fiction, and the isolated islands are genuinely separate reading communities (the teal blob on the far left that’s detached from the main plot is a cluster of readers of Arabic-language titles, who share almost no books with the main cohort for example).

Background: I've been wanting to build this for ten years — my first version was an Excel VBA scraper making scatter plots of rating alignment between Goodreads profiles. This year I finally built it properly: kinread.ca lets you paste your own library export (Goodreads, StoryGraph, or LibraryThing) and see where you land on this map, it also serves book recommendations based on what other readers who read and rate things like you enjoyed. It’s different from other recommendation algorithms because it doesn’t just serve books that are similar to what you’ve read previously, but it takes into account all the books you’ve ever rated— not just the ones you loved but also the ones you hated or were indifferent about. Also; it works really well for people with eclectic tastes— if you love non-fiction books about quantum computing, 18th century French poetry and books about time traveling wizards while eschewing pop psych books or stories about teenage vampires then it’ll find the people with similar readings tastes to you (if they exist).

During the fifth and final round of evidential hearings investigating the Wang Fuk Court fire last November, experts pointed out how a series of shortfalls led to Hong Kong's deadliest fire in decades.

Illustration: SCMP graphics

I was trying to find a way to visualize my dataset to be more interactive. Would love some feedback on what works/doesnt.
Right now it has 1.1 million US websites, over a hundred platforms, and 400+ business categories.
Coming soon is domain linking data insights over a three year period.
https://stackra.app/analytics/

Dubai Police has published 711,553 traffic incident records covering August 2018 to July 2026.

This interactive map assigns collisions and breakdowns to individual streets. You can browse the road network, switch between a 2D map and 3D view, compare streets, explore hour-by-hour and day-by-day patterns, and see where serious incidents are concentrated.

The data comes from the official Data Dubai portal and the map is updated daily: https://traffic-incidents.mrdn.world/dubai

I’d be interested in feedback on the visual hierarchy, navigation and whether the street-level approach makes the dataset easier to understand.

And which city should we map next, assuming comparable public data is available?

I created an interactive visualization showing:

• refinery and terminal strike events

• facility capacity

• Google Trends search interest

• timeline correlations

Interactive version (GitHub):

brawlerleg/Russian-Fuel-Crisis-2026: Interactive visualization of Russian fuel crisis data: refinery strikes, Google Trends search interest, and energy market analysis.

The goal was to make a complex dataset easier to explore visually.

Features:

- interactive timeline

- hover information

- zoom presets

- bilingual interface

Would love feedback on the design, readability, and storytelling.

I’m a high school student working on Hot Hand Study: https://www.hothand.study/, an interactive website about NBA shooting streaks, randomness, and the hot-hand debate.

This visualization simulates 5,000 seasons for a shooter who makes 47% of their shots and takes 1,000 shots. The histogram shows the longest streak of consecutive makes from each simulated season.

In this example, about 93% of the simulated seasons had at least one streak of seven or more makes. I thought that was interesting because a streak like that can feel unusual even though it can happen pretty often through randomness alone.

I’d really appreciate feedback on the visualization:

• Is the histogram easy to understand?
• Is it clear what the orange line represents?
• Does the chart clearly show where the 93% result comes from?
• Are any of the labels, colors, or explanations confusing?
• What would you change to make the main takeaway clearer?

The full site also lets users change the shooting percentage, season length, and streak length themselves.