Neat, but I feel like I want to see a chart with all 50 states.
This is even more dramatic when you account for population density, which by all rights ought to be one of the biggest factors in how quickly a pandemic can spread through a population.
New Jersey has 1,207 residents per square mile and they’re not even on the list by the end of the timeline. North Dakota has just 11 residents per square mile and they’re sitting up there at #7.
It’s a top twenty, all fifty states are included.
My favorite comment made by my great uncle with no irony in his voice: “You liberals always want to politicize everything! now even with the damn virus”
Reminds me of an elementary school associate who was fond of shouting things like: “I’m not being rude! you stink-head!”
No, I got that, but I’d like to still see all 50.
Considering the population of California - they did pretty well so far.
I realize this is off point but…
Florida is only slightly repugnantican? Is that based on the general population? Given that their governor is a repugnantican as well as both the house and senate dominated by the reds?
I get what the chart is showing is, and it’s a great Illustration of policy inaction, but it seems possibly cherry picked? The cumulative cases were of course MUCH higher in states by June 1st, but data from before that date was tossed out and it looks like only new cases from that day forward were counted.
It’s also strange that New York isn’t on there. According to google they had 945 new cases that day which would have started them off at second place on the list.
Unless I’m missing something, I’d love to see a more honest version of this chart… I imagine the results would be similar but with perhaps a few more blue states in the list at then end.
Along the same lines, wouldn’t it be interesting to see the same type of chart with cases as a percentage of total state population? You don’t get the full significance of Idaho beating California when you just look at the raw numbers. 1 in every 100 Idahoans has caught it, vs. 4 in every 10000 Californians. ffs
Coincidentally, my wife knows the first family to have COVID in Idaho. They’re not conservative, so far as I know. They just happened to be travelling the Pacific NW at the wrong time in the wrong place. They quarantined correctly and recovered fine.
That’s basically what this bar graph is showing; cases per 1 million residents. If you adjusted the values to represent “portion of the total population” the bars would be the same positions and relative sizes, just marked with different numbers.
Instead of a bar graph, some kind of rubber sheet map that increases/decreases the size of each state, depending on the cases.
Then you could have the political color and see the geographical spread over time.
That’s a good catch. I am curious why it started at June and not earlier. I still think it’s educational, because you can argue at that point we knew a lot about how it spread and you can definitely see how the states’ actions either promoted or prevented the spread. But I’d be curious to see what it looked like in mid-April or whenever the various states really started trying different methods to close-down. I’d like to see the rational for the June 5th start date there.
I also wondered why NY wasn’t on there. On looking closer, the metric for the graph is “cases per million since June 1” and it starts on June 5. Google’s Covid stats show NY having a cumulative 5,444 new cases during the period June 1-5 (it was 945 on June 1, but over 1,000/day for each of June 2-5), which divided by NY’s 19.45 million population means NY would be at about 280 on this chart as of June 5, which I guess is why NY is not there. That said, I checked the math against NJ and the chart appears to be using slightly different numbers, because I get 328 for NJ using Google’s numbers (2,891 cases for June 1-5 divided by 8.8 million people) while the video shows 472. So I’m not 100% confident that I’m doing it the same way the creators of the chart did.
That said, it sure does look cherry picked.
Also, I’ve never understood why “cases” is the preferred metric for these kinds of things. Is it just because the numbers are bigger so it makes a better news story? Why not use hospitalizations or deaths, which the “if you don’t test, you won’t get cases” assholes can’t manipulate as easily (and can’t dismiss as having been manipulated)?
The cutoff date of June 1st may be somewhat arbitrary, but you have to remember that widespread testing wasn’t available in the early days of the pandemic, so the further you go back the less reliable the data would be. The # of tests done in March and April were a tiny fraction of the number of tests done in later months, and they definitely weren’t equally available in all states.
My mistake, I thought it was total cases. It did seem a little odd that the numbers were so low, but not odd enough for me to think critically on such a beautiful day.
Sad how much worse that makes it all.
This validates my thoughts on the consequences of the binary attitudes to the pandemic, but it’s a bit unfortunate that the creator chose to start the partisan chart in June, instead of March like all the other charts. The claim on the site is that June is when partisanship data was available*. But what are the odds they changed drastically from March? The creator already has no problem spreading numbers to make it look better**. It seems reasonable to do the same for partisan data and extend the charts to include numbers from the start of the pandemic.
Give the full picture. If it’s the same, then there was no reason to exclude it. If it’s different, then there’s definitely a problem.
[Starting in June] also provided an opportunity to contrast the resulting data with states’ political affiliations, using the Cook Partisan Voting Index.
“Normalization” means the abnormalities in the data were evened out. For example, if there were 10 days in a row of a few cases/deaths a day and then one day of 1000… that looks awful and frenetic on a chart like this, even when framed in a per-week display.
I think the point of the graph was to show how policy and or public compliance affects the spread of disease, right. Yeah NY and other high population density states were higher then the midwest (for example) earlier in the year but that is not the point. The point is how they behaved. Actually having them on there would make the point even better, they were worse off but now they are better.
Why cases? Well I think you are right they are more dramatic numbers but also if you want to look at policy, if you have a less restrictive policy you have a more self sorting population. Younger people who are less afraid of the virus go out and take risks, older, or less well, people who are more afraid stay home more. More cases and a lower death ratio. Also cautious people have an easier time isolating in lower density places. You do have a bias for testing but I dont think that is a big a deal as people make it out to be because there are really no places (in the US) that I know of that are doing widespread testing, you feel sick, you get a test. In fact I would think that dense states would be the ones doing more testing, if only because it is easier to do. That is my take at least.
One the few times in my life I can say I’m happy to have lost to Missouri…and sad to have beat Nebraska.