61–70 of 75 posts
Do log scale on both axes, suddenly all the plots will be nearly straight lines and you can start to reason about uncontrolled growth and how well mitigation is working.
Maybe you’re thinking of the double log curve of total cases vs new cases that was on HN recently.
Thanks, those log-scale plots are way more interesting than linear one. Small mistake: Israel isn't in Europe but in Eastern Mediterranean.
the region titles are ... unconventional. greece, clearly in the eastern mediterraneon, is not listed, while iran on the indian ocean is. this group is normally called mena, roughly. likewise, the category called southeast asia contains countries normally considered to be south asian - like india - and omits the larger part of southeast asia. the creation of “western pacific”, grouping australia-new zealand, east asi…
> The countries have been grouped according to regions defined by the World Health Organization.
And, yeah, they’re a bit weird.
Pet peeve - line charts with many lines, yet legend in another place. Even worse, when the order of labels is not the same as the order of values. They take more cognitive power to parse than needed. Compare and contrast with labels next to the lines, vide https://www.ft.com/coronavirus-latest (this example is already quoted in some other thread, and I find it a gold standard of coronavisualizations).
These graphs give a bit of an indication, but you cannot really trust them. Since there is a shortage on tests, at some point countries might decide to only test people coming into the hospital. Another thing about the deaths is also troubling: In the Netherlands doctors were complaining that deaths with symptoms of Corona were not counted as corona deaths, because they were not tested and found positive (again a pro…
It also needs per capita figures, which is dramatically more important than absolute figures, unless everyone happens to know the population figures of each country by memory. You end up missing critical data points like the per 100k population mortality rates (from Friday morning): New York 12, Louisiana 6.6, New Jersey 6, Michigan 4.2, Washington 3.5, Connecticut 3.1, Massachusetts 2.2, Colorado 1.7, Georgia 1.67,…
Why all graphs are cummulative instead of new cases for that day? It's harder to notice how it is growing that way (i.e. more or less new cases than the previous days) and harder to see if it is going exponential, lineal or whatever. And, of course, forces to use log scales because the accumulated number is already high. At least for networking graphs is more meaningful to see difference from the actual from the prev…
https://colab.research.google.com/drive/1dNAVpgRGjEViK-9ULhE...
Pet peeve - line charts with many lines, yet legend in another place. Even worse, when the order of labels is not the same as the order of values. They take more cognitive power to parse than needed. Compare and contrast with labels next to the lines, vide https://www.ft.com/coronavirus-latest (this example is already quoted in some other thread, and I find it a gold standard of coronavisualizations).
https://www.nytimes.com/interactive/2020/03/21/upshot/corona...
If you're still working on it, I'd love to see the second derivative from the data. That's what I'm wondering about most these days: Are we at the inflection point or not?
The turning point is when the second derivative is zero, which would indeed be easy to spot with a second derivative graph. But it’s also very easy to spot with the first derivative graph (as published by the FT now): It’s when the first derivative hits its maximum.
All of these need to be normalized per capita. Otherwise you don't see the true extent of the problem. Also from looking at the stats recently, here's what I find more useful than the raw number of "cases": - Number of deaths per capita - Number of "severe" cases per capita (good indicator of the future number of deaths) - Number of tests per capita (good indicator for whether or not "number of cases" means anything…
> All of these need to be normalized per capita. Otherwise you don't see the true extent of the problem. Normalizing per capita replaces the true extent of the problem with the true relative local impact of the problem; both are significant.
Why all graphs are cummulative instead of new cases for that day? It's harder to notice how it is growing that way (i.e. more or less new cases than the previous days) and harder to see if it is going exponential, lineal or whatever. And, of course, forces to use log scales because the accumulated number is already high. At least for networking graphs is more meaningful to see difference from the actual from the prev…
Earlier quoted context omitted.
It also needs per capita figures, which is dramatically more important than absolute figures, unless everyone happens to know the population figures of each country by memory. You end up missing critical data points like the per 100k population mortality rates (from Friday morning): New York 12, Louisiana 6.6, New Jersey 6, Michigan 4.2, Washington 3.5, Connecticut 3.1, Massachusetts 2.2, Colorado 1.7, Georgia 1.67,…
In this video John Burn-Murdoch (the creator of the FT charts) discusses why they decided against showing numbers per capita. https://mobile.twitter.com/janinegibson/status/1244519429825... There's also this tweet additionally showing how population size of a country has no relationship to pace of disease spread. https://mobile.twitter.com/jburnmurdoch/status/1246185741304...