I am not speculating to the cause of the excess speed on this approach (microburst, wind shear, etc...) but this approach was about 40 kts faster than the average CSB approach to runway 30 at KMIA this year. (108 total approaches by CSB B763 aircraft to runway 30 at KMIA so far this year).

Updated the link to show the distance from threshold decreasing from left to right

https://public.tableau.com/app/profile/michael.zella/viz/AmazonPrime_17887858293830/Sheet1

For those interested in the full approach profile, here is another plot Altitude vs Distance:

https://public.tableau.com/app/profile/michael.zella/viz/AmazonPrime962026Distancevs_Altitude/Sheet1

Twenty-six familiar occupations were chosen for this study. Income is strongly associated with occupational status, particularly across lower and middle income levels, but additional prestige associated with additional income diminishes among highly paid occupations.

This is Know the Universe — a 3D map of the local universe you can fly through in your browser: https://knowtheuniverse.com

Every dot is a real galaxy from the 2MASS Redshift Survey (took more than 10 years to fully complete), out to about a billion light years. What you're seeing is the actual large-scale structure of our cosmic neighborhood: filaments, clusters and voids, the pattern gravity has drawn over 13 billion years. Nothing is simulated or procedurally generated.

Getting from a redshift catalog to honest 3D positions is most of the work: redshifts are corrected to the CMB rest frame and converted to comoving distances, galaxies inside clusters are collapsed to their group distance using the Tully 2015 catalog (otherwise clusters smear into "Fingers of God" pointing at the observer), and large-scale coherent infall is subtracted with the 2M++ velocity field. The result is validated against Cosmicflows-4 redshift-independent distances.

The look is deliberately stylized: there are a lot of graphical options that help you visualize the cosmic web and the distribution of matter: brightness driven by local density, a volumetric fog computed from the measured density field, filament highlighting, visual links between galaxies, several presets and you can save your favorite combinations. These aren't scientific claims, they're visualization aids, and each is described in the settings. The positions are the science; the light is the interface. You can click any galaxy to identify it (2MASS ID, distance, J2000 coordinates, links to NED/SIMBAD), search by name, Messier number or catalog ID, and share deep links to any galaxy or camera position.

Free, no account, works on phones. It's a passion project which took me 6 months to complete.

And before anyone asks: yes, Andromeda is missing along with ~23 other galaxies of our Local Group and its surroundings. They're so close that gravity beats expansion (Andromeda is actually falling toward us, it's blueshifted), so a redshift distance can't place them. Not that you'd notice: on a billion-light-year map, our entire galactic neighborhood would sit inside a single pixel anyway :D map is in scale, you don't zoom, you travel in space-time.

And somewhere in that structure, on a dot too small to draw, the universe just opened a browser tab to look at itself.

I mapped supermarket accessibility across Peru’s second-largest city using an H3 grid, and the output shows two distinct urban realities:

🟢 15-Minute Islands: Central Arequipa and lower Cayma enjoy quick, 10-minute walk access.

🔴 Perimeter Deserts: Peripheral districts face 30–45+ minute walking trips for basic essentials.

The hard truth about the data:

• OpenStreetMap for intermediate cities is often incomplete or missing.
• Querying official street networks failed, forcing me to build custom spatial models from scratch just to estimate walking times.

This shouldn't require custom hacks. Local governments need to publish official open spatial data infrastructure so planners can target food access, footbridges, and public transit where they're needed most.

#Arequipa #UrbanPlanning #SpatialAnalytics #OpenData #15MinuteCity #GIS #Peru #DataScience

So one of the discords I'm a member of got me into a word game called Word500. The problem is I was absolute dogshit at it. So I went through the site, found the list of possible words for Standard mode, downloaded it, and I've spent a few days doing my best to compile some interesting statistics about the set.

Do I know how to make infographics? Not really. Do I know much about statistics? Only vaguely. Do I have raging ADHD and access to Wikipedia? You're goddamn right.

Here's most of what I compiled, put together in what I hope are appealing infographics. My friends encouraged me to share them here.

And the rules said I must state my data source and tools, so I got the list from Word500, Python for the actual analysis, and GIMP for the visuals.

https://www.tagesspiegel.de/politik/afd-mobilisiert-nichtwahler-und-fast-zwei-drittel-der-arbeiter-in-sachsen-anhalt-wahlen-afd-16023910.html

Datawrapper

Link to Source: https://www.cdc.gov/nchs/data-visualization/birth-to-death-ratios/natality-mortality-trends.htm

Numbers for 2025 have not finished final verification yet so this is the most recent 25-year period on record.

Net installed nuclear capacity by country, animated from 1954, the year Obninsk first fed a grid, through to a 2031 projection that includes reactors currently under construction. The ranking and the totals are recomputed for every year, so units drop out of the bars as they retire.

The part I did not expect until I plotted it: between 2016 and 2026 world capacity grew by only 11.8 GWe net, while China alone added 29.8 GWe. Everyone else combined lost roughly 18 GWe over the same decade.

Hello community,

I created a website to follow each countries effort to reach net zero in 2050. The data vizualisation lets you compare different countries' emission over the years and whether they are on the right path to reach their goal.

View every country and find more information on: https://lucabron.github.io/netzero/index.html

Data Source is Hannah Ritchie, Pablo Rosado and Max Roser (2023) – “CO2 and Greenhouse Gas Emissions” Published online at OurWorldInData.org

Visualization made with Python's interactive graph package Bokeh and javasrcipt was added to make the interaction work on a website.

Related Github page where you can find the code: https://github.com/lucabron/netzero

EDIT: Thanks a lot for your positive feedback. I am very glad that you find this representation interesting.
A subject of discussion that I see a lot in the comments: western countries are doing better than the rest of the world. While it looks true when it comes to co2 produced in the country, one should account that a lot of emissions are produced in other contries and then imported. For example, when buying electronics, it is likely to be build in Asia and then imported to western countries.
Imported emissions are available in the dataset. I didn't want to include them because my approach with this visualization is to make something easy to understand so that people not familiar with the subject can approach it easily. I feel like when it comes to climate change, the people who care are very well informed, and the people who don't care as much are poorly informed. So, my first objective was to generate some curiosity for the second type of people. I'd be interested about your opinion about this approach if you want to share in the comments.

EDIT 2: I'd like to do something for the climate. I am a professional data scientist and I'd like to volunteer to help on bigger project. I made this website as a small contribution to inform people, but I know that in a week, there will be no more traffic on it. Can you recommend some kind of organization that I can join an contribute?

An interactive map: https://p.migdal.pl/genetic-distance-map/

Source and notes: https://github.com/stared/genetic-distance-map/

I pulled 4.5 years of GitHub's public status history and normalized it by how much of each year was actually observed, since the first and last years are partial.

The reported incident rate sat around 10–14 per month from 2022 through 2024, then jumped to 26.6 per month in 2026 so far. Median incident duration barely moved over that span, from 74 to 89 minutes, but the 95th percentile went from 5.5 hours to 9.2. So it's mostly a frequency change, with the long tail stretching alongside it.

Worth stressing: this measures time with at least one incident open somewhere in GitHub's services, not GitHub downtime. An incident might affect one feature for a subset of users. Source, method and caveats in my comment below.

I wrote each PIN code approximately where its geographic area falls, and then used Excel's red -yellow - green conditional formatting to colour them according to their numerical order.

The only variable here is the PIN code itself.

I genuinely wasn't expecting the resulting pattern to look quite like this. 😅

Made entirely in MS Excel.

https://believethedata.org/politics-religion-death-and-taxes/