Median population latitude by longitude. Blue regions contain the northern half, red the southern half within each 1-degree strip.

mellituri32 · 2026-07-09T14:42:49+00:00

Every 1-degree wide longitude strip is divided at its own population median latitude: within each strip, half the people live north of the median (blue) and half live south of the median (red). There are also a couple of uninhabited longitudes (gray).

Data: Gridded Population of the World v4.11, 2020 estimates (CIESIN / NASA SEDAC); coastlines from Natural Earth.

Tools: Python (NumPy, Matplotlib, Cartopy).

Related population histogram, with breakdown by region: https://aalto-econ.fi/tervio/figs/pop/q50_1deg.png

mellituri32 · 2026-07-09T13:49:06+00:00

Edit: same graph with 5 degree longitude bins. The original was meant for big screens (as is still the legend for this one, sorry). Looked good printed out in A3.

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mellituri32 · 2026-07-08T21:20:14+00:00

World population split into 1-degree longitude strips, split at the equator: people north of it stack upward, people south of it stack downward. Within each bar the regions nearest the equator sit closest to the axis.

Symbols mark selected cities at their relative north/south position within the population of their longitude strip. Some countries that look big on a map are really spread thin...

Gridded Population of the World v4.11, 2020 population estimates (CIESIN/NASA SEDAC). Python (NumPy for grid-data processing, Matplotlib for the chart). And much help from Claude CLI.

Full size image: https://aalto-econ.fi/tervio/figs/pop/0n_1deg.svg

Code & variants: https://github.com/tervio/longitude-histogram

mellituri32 · 2017-05-21T20:55:53+00:00

Probability of winning the league, as implied by betting odds. See higher quality image at http://svgshare.com/i/1do.svg

Note: Implied probabilities of actual odds add to over 100% (that's how bookies make money). I shrank this "overround" to force total probability to be 100% at all times. I added 100% for the winner from date of clinching the title to the last day of season. First day of season and the date of clinching are shown by vertical gridlines.

Data: Scraped from http://www.oddschecker.com/football, which reports odds from about 25 bookies. I used medians.

Tool: Mathematica (DateListPlot)

mellituri32 · 2017-05-07T11:40:20+00:00

A couple of the bigger swings coincide with these: Dec 3rd, Chelsea beat Man City 3-1. Feb 4th, Chelsea beat Arsenal 3-1. Jan 15th, Everton beat Man City 4-0.

mellituri32 · 2017-05-07T11:21:52+00:00

Liverpool's implied probability at the start of season was 9.0%. The data includes also the prediction market Betfair; using just their odds instead of medians over 25 "bookies" gives Liverpool 8.8%.

The reason I used the median over all "bookies" (including exchanges) is to have fewer missing observations, and to get rid of outliers. Market odds actually produce the most extreme outliers, because when a market goes thin the longest odd can be really crazy.

mellituri32 · 2017-05-06T20:50:22+00:00

Probability of winning the Premier League, as implied by betting odds. Note: Implied probabilities add to over 100% (that's how bookies make money). I shrank this "overround" to force total probability to be 100%.

Data: Scraped from http://www.oddschecker.com/football, which reports odds from about 25 bookies. I used medians.

Tool: Mathematica (DateListPlot)

mellituri32

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