Losing ice in the Antarctic

In this chart from The Economist that shows ice extent from 1972 up to present, that falling line for 2023 looks not good.

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Online dating, who filters out what

With online dating apps, you’re able to filter out potential matches based on characteristics like age and height. The Economist charted who’s filtering out what.

The chart took me a second to figure out, but I think I got it. Each bubble represents a demographic group. The x-axis represents the percentage of potential matches the group filters out, and the y-axis represents a characteristic of the searching group. For example, you can see in the above that taller women filter out more people with the height filter, whereas taller men don’t filter out as much.

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Using satellite imagery to assess the damage in Ukraine

The Economist combined two satellite imagery sources, one that estimates fire events and one that estimates building damage, to assess the extent of damage in Ukraine:

Both approaches have weaknesses. NASA’s firms cannot see through cloud cover, a particular problem in winter. sar can pick up damage even through clouds, but is much less sensitive to changes outside of urban areas. But by combining the two datasets, we can form a fuller picture of the war. Our study shows that rather than being limited to a few big offensives and grinding battles, the war has left a brutal mark on large swathes of Ukraine. Fighting has reached 14% of municipalities, and damaged nearly half the built-up area in the hardest-hit cities.

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Bees use social distancing

Research by M. Pusceddu et al. shows that honeybees use social distancing when a parasite is introduced to the hive. In a parasite-free hive, activities are spread throughout the hive, whereas clusters form when parasites are detected. The Economist illustrated the difference with a grids of dot densities.

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Possible cheating seen in a scatterplot

When plotting Russian election results, a structured grid patterns appear. From The Economist:

When Dmitry Kobak and Sergey Shpilkin, two researchers, analysed the results, they found that an unusually high number of turnout and vote-share results were multiples of five (eg, 50%, 55%, 60%), a tell-tale sign of manipulation. According to Messrs Kobak and Shpilkin, there were at least 1,310 polling stations (out of 96,325) with results that were suspiciously tidy, with rounder numbers than you would expect to see by chance.

I’m not familiar with Russian elections, but this seems like lazy cheating. Are they just making up numbers by hand or what?

Check out the full results of Kobak and Shpilkin’s analysis in Python notebook form.

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Comparing correlation in the FiveThirtyEight and Economist election forecasts

FiveThirtyEight and The Economist take similar inputs but output different forecasts. Adam Pearce was curious about how the state-by-state correlations differed between the two models:

Outside of the CA-DC-VT-WA and LA-MS-ND-KY clusters, where the 538 correlation dips below 0, the models are mostly aligned. Glancing over the outliers, it looks like the Economist might not have an equivalent to 538’s regional regression that groups states in the same geographic region together; the Economist has HI at 0.2 correlation with WA & OR while 538 has it around 0.7.

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Coronavirus counts across Europe

Going with the shaded triangle peaks to show case counts and deaths, The Economist visualizes the current status across Europe:

To assess how European countries are coping, and to monitor the danger of “second waves”, The Economist has assembled data on covid-19 cases and deaths for 39 countries, and for 173 sub-national areas for which data are available (see map above). We present the total number of deaths per 100,000 in the population. We also break down the infection and death rates for the past seven days to give a better sense of whether the virus is most active.

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2020 election forecast

The Economist launched their 2020 elections forecast. Right now a part of my brain is telling me to avoid election forecasts this year, but the other part of me is like, don’t fight it, you know you’re going to look.

At least The Economist put their modeling code up on GitHub (implemented in R and Stan) and is publishing their polling data (linked at the bottom of the forecast page as a Google sheet).

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Change in foot traffic in outbreak cities

From The Economist, this chart shows the (mostly) decrease in foot traffic in major cities with coronavirus outbreaks. It’s based on data scraped from that section in Google Maps that shows how busy a location is, which I’m kind of surprised the Google limits allowed for. See James Fransham’s thread for more details on their process.

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Brexit voting divisions

The Economist charted the divisions within political parties using Brexit votes as proxy. I’m here for the bubbles.

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