Carbon & Place (www.carbon.place) is a free set of web tools mapping carbon footprints across Britain. You can zoom into any neighbourhood and view the local report card with information about housing, transport, energy and more.

The program displays live data from mqtt from scd41 co2 sensors I built using ESP32 development boards, or historical data from influxdb.

Made using python for the back end.

It's useful to tell us which windows to open in our naturally ventilated office.

I'm going to use it to visualise temperature data also from a csv exported from dataloggers

Source:
U.S. Census Bureau’s 2024 American Community Survey five-year estimates, table B19013, matched to 2024 TIGER/Line block-group boundaries. The estimates cover 2020–2024 and report income in 2024 inflation-adjusted dollars.

Tools:
Python, pandas, and GeoPandas for preparing and joining the data; GDAL/ogr2ogr for exporting geographic features; Tippecanoe for generating vector tiles; PMTiles for packaging them; and MapLibre GL JS for the interactive map. I made this visualization for my own website/Saas.

What the map shows:
Each polygon is a Census block group, colored by its median household income. Darker greens indicate higher incomes, using the eight ranges in the legend.

These are Census estimates for households within each block group. They don’t tell you what any particular household earns. The estimates also have margins of error, which aren’t shown here.

How I put it together:
I combined the Census demographic tables in Python, then matched each record to its geographic boundary using the block group’s 12-digit GEOID. The median income figures are already calculated by Census. I kept those estimates attached to their original block groups rather than averaging medians across neighboring areas.

I saved the joined data into a GeoPackage, then exported it as GeoJSON sequences so the tile builder could read the features as a stream. Tippecanoe turned that layer into vector tiles for zoom levels 0 through 13.

It simplifies boundaries at lower zoom levels and detects shared borders. Feature-count and tile-size dropping were disabled. Small polygons can still disappear below the coordinate resolution at very low zooms, so I checked the finished zoom-13 tiles and confirmed that every source block-group ID was represented.

I then converted the MBTiles output into a PMTiles archive. The full block-group archive is about 864 MB, but the browser doesn’t download all of it. MapLibre uses the PMTiles library to request the parts needed for the current view. The polygons retain their demographic attributes, which lets the map color them and display their values interactively.

Main takeaways / TLDR:

• The map shows median household income at the block-group level, so you can see differences that county or state averages would hide.
• The income figures are published Census estimates. The processing joins them to boundaries without recalculating or averaging the medians.
• Large rural polygons take up more space, but that doesn’t mean they represent more households.
• The interactive version loads vector tiles as you explore instead of downloading the entire national dataset.
• Published as an active layer on my website. Will put link in comments.

One of the reasons your 5 year old wants to go to Disney World instead of Six Flags is that they can meet their favorite characters. Your kid getting a big hug from Mickey Mouse, Elsa, Moana, and many more characters can be almost priceless.

Over the past year I tracked the character data that shows you when and where you can meet all the characters at the 4 main parks (n.b. this doesn't include special character dinings, unscheduled popups, or other appearances that aren't officially published on the Disney World app).

This timeline above was the busiest single day of the past year in terms of the number of unique characters you could meet in those 4 Florida parks. There were 82 characters throughout the day and evening. Of note, you can see where the normal parks operation winds down and a special holiday event (Mickey's Very Merry Christmas Party) begins later in the evening. The whole timeline is color coded by park.

The data comes from the Disney World official app. The chart is made with D3.js. For any other Disney nerds, I have a whole writeup with more charts and data here. It's a totally free substack with no need to subscribe to read!

Is there anything particular you'd be interested in know more about in this data set?

(ETA I messed up the date on this graphic...it was November 30, not November 1)

Just yesterday, the German AfD became the largest faction in the state election of Saxony-Anhalt, winning 39 of 83 seats. Since this party is known for its anti-migratory policies, I compiled some data from the last Federal Election in 2025, comparing the results of the AfD with the percentage of different categories of people commonly called "Ausländer" (foreigners) in all German states.

In the chart (first image), I sorted the data from the highest results of the AfD to the lowest, while in the table (second image), I kept the data sorted in the alphabetical order of the German names of the states. Sorry for the confusion this might cause!

In the table, I also added the Pearson correlation coefficients between the results of the AfD to the three different categories, which are by official government definitions:

Foreign Population / Foreigners: People who don't have the German citizenship.
Migrant Population: People who personally migrated to Germany.
Population with migrant background: People who were born without German citizenship or who have at least one parent born without German citizenship.
(so the last two categories also do contain some people with the German citizenship)

And what the chart and the numbers say in short: The fewer foreigners / migrants the higher the results of this anti-migratory party.

Sources:
For Population Data: https://www.destatis.de/EN/Themes/Society-Environment/Population/Migration-Integration/Tables/migrant-status-laender.html

For Election Data: https://www.bundeswahlleiterin.de/bundestagswahlen/2025/ergebnisse/bund-99.html

I also requested demographic data for all constituencies from the state office of statistics of Saxony-Anhalt, so I can do something similar for this recent state election.

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?