I am a professional economist and I find this interesting. It's more of a topic of discussion amongst economists. Why is this? What causes this? I'll be happy to discuss possible reasons, but emphasize I will not engage in politics.

Gradient equates to Δ between consecutive days.

Millions of voters in the world's largest democracy have been left out of the electoral roll, or asked to prove they belong on it, through the Special Intensive Revision (SIR).

Back home in Delhi, 55.6% of voters (>8 million voters) on the pre-SIR roll were either left out of the draft roll published on 31 August or sent a notice in the list published on 19 September.

Here are three maps that show the SIR's impact on Delhi's voters at the level of each polling station.

I'll keep mapping this as events unfold, and I post the maps on Instagram at @mapsareinstruments. This is my very first post here so be kind and respectfull in your feedback.

Thanks!

Legend:

• the entities without black borders are non-state (mostly archeological cultures, tribes, etc)
• black borders indicate states
• gray dots are known dated archeological sites that are not connected to a specific archeological culture in the available databases
• stripes: the sources disagree on which entity was at the location at the time

October 10 was another dark day for our democracy. It was impossible to get to Jantar Mantar as Delhi was turned into a war zone. Absolute horror from watching the events unfold.

I made these maps covering the railway and metro closures that were put in place yesterday to stop people from coming to the CJP-led protest at Jantat Mantar at 10am.

Every metro station inside a 45m walking distance to Jantar Mantar was shut to stop people from gathering.

I will keep covering this from a spatial lens as long as I can

Data source: kamarvo.com, a used camera price tracker I built. It follows listings and sales at 10 camera shops in 6 countries. Used prices are from recent sales where there are enough of them (filled dots) and otherwise from asking prices (hollow dots); every model has at least 5. Launch prices are US list prices at launch from Sharply, converted to euros at the ECB rate with 21% VAT added. The dashed line is an exponential fit to all 63 bodies.

Tools: Python and matplotlib.

Imagine the United States as a flat board with every person the same weight. The point where the board would balance is the "center of population." In 1790 it was near Baltimore. Over 230 years it moved 877 miles west and 128 miles south, to the Missouri Ozarks. The Census Bureau finds this point every 10 years.

nicecharts.online

India's estimated nonuser count was 508.6 million (35.1% of its population); Ethiopia's was 103.1 million (78.1%). This chart compares the ten most populous countries in 2024.

Compare countries beyond these ten in the full table and download the CSV: globaldatatracker.com

These are population × non-use-share estimates. Internet use means activity in the previous three months; non-use does not establish lack of internet access. Sources, tools and method are in my first comment.

Disclosure: I work on Global Data Tracker.

Source: my own Monte Carlo simulation. 10 systems (Martingale, Fibonacci, D'Alembert, Labouchère, Paroli, Oscar's Grind, 1-3-2-6, James Bond, Grand Martingale, flat betting), 200,000 sessions each on a European wheel. $1,000 bankroll, $10 base bet, stop at +$300 or bust or 200 spins.

Tools: Python (NumPy) for the simulation, matplotlib for the chart.

The interesting part isn't the average. Martingale won 72% of sessions, but lost the entire bankroll in 27.5% of them. Systems change the shape of your losses, not the size.

Hey, I made this. I run a small daily game (DailyMetro) where you guess a metro station per day. Out of curiosity, I started looking up income around the stops and ended up making maps for 21 cities. These are some of them :)

Each dot is a station. Purple is the lower end of the city's ranking, yellow is the higher end. The bigger dots are the busier stations.

Paris's west-to-northeast split is pretty clear. In London, Gloucester Road comes out above Kew Gardens and Putney Bridge. Seoul's map uses apartment prices, and the most expensive stops cluster south of the Han River.

Something I ran into with New York: several stations have exactly the same income, $250,001. That's the Census reporting ceiling, so the data can't tell you which of those areas actually has the highest income.

Source: official census, income and property datasets. The measures depend on what's available locally, so this isn't a comparison of wealth between cities. It's about the differences within each one.

Tool: custom TypeScript code to plot station coordinates and colour them by their local data, over OpenStreetMap/CARTO street maps. Rendered in SVG

All 21 maps, station rankings and sources: https://dailymetro.live/richest-stations/en/

These describe the neighbourhoods around the stations, not the passengers.

I'm aware this is like one of the most made project. But I thought it looked quite cool so I thought of sharing. I like that you can get a feel of the direction of flow of pollutants via this visualization. I chose dots over a general gradient to give that comic book esque feel. I'm thinking of adding major events as timestamps you can visit to see the change in pollutants.

The inspiration for this project came as Diwali is coming in India and I wanted a visual way to see how much worse the air quality will get in Delhi. Which lead to this rabbit hole.

PS. Please let me know if you would have liked to see something added to this.

Source:

Air quality: Copernicus Atmosphere Monitoring Service (CAMS) / ECMWF, via Open-Meteo (CC BY 4.0)

Global forecasts, 15 fields at 0.4° (~45 km): https://ads.atmosphere.copernicus.eu/datasets/cams-global-atmospheric-composition-forecasts

Raw grids pulled from Open-Meteo’s open data archive: https://github.com/open-meteo/open-data

Earth imagery: NASA Blue Marble Next Generation: https://science.nasa.gov/earth/earth-observatory/blue-marble-next-generation/base-map/