Submitted by eqqqxy t3_yhdp6x in dataisbeautiful
Comments
DrTonyTiger t1_iud9tvj wrote
A great example of the danger of extrapolating beyond your data. All the fit lines need to stop a the highest X value or less. That would remove the rearkmable line for Africa, which shoots up beyond Egypt into fictional wealthy polluted countries.
vacri t1_iudbjyb wrote
The richer the country, the better the air quality, except in Africa where a bad line-of-fit makes it look like it's deadly to be wealthy there.
Pyrhan t1_iudcyj4 wrote
Also, what's the quality of those fits anyways?
MisSignal t1_iudh3fl wrote
Giant orange dot
VALMaX1 t1_iudhgo6 wrote
Oh yeah!! Sorry my fault
FreeRadical5 t1_iudj1ge wrote
Us and Canada both not labeled.
Tentoesinmyboots t1_iudjekg wrote
They're they're but unlabeled, in the bottom right corner.
shark_snak t1_iudkh5w wrote
Are you implying causation? That death rate is because of air pollution? How can you possibly point cause of death solely as air pollution, and not some other cause, ex smoking. Or is this just deaths by country plotted against air pollution by country?
FactHole t1_iudl9xv wrote
Exactly. I am skeptical how they determined a death was even related to pollution much less caused by pollution.
sault18 t1_iudn19y wrote
Need to label the USA, China and Russia. I know what bubbles these correspond to, but you absolutely need to label these major countries even if you have to remove other labels to keep the graph from getting too busy.
Kuwait data looks really faulty. They're not doing anything radically different than other Gulf States in reducing pollution deaths. More likely, they're underreporting deaths or they're more effective at shipping foreign workers out of the country once they get sick. Probably both.
Egypt is an outlier because of all the tourism income while also having atrocious air quality.
enakcm t1_iudpd1d wrote
How to understand this: there is no or only a very weak correlation between how rich a country is and how many people die from pollution.
In other words: awareness is more important than wealth to prevent pollution deaths.
enakcm t1_iudpiv8 wrote
Can't imply causation if the graph doesn't event show correlation.
2312family t1_iudpvu0 wrote
How is this beautiful. The sub is data is beautiful
pk10534 t1_iudtdik wrote
I think the data could be interesting were it easier to read. Not sure why Timor and Turkmenistan were deemed necessary to label but the US, China, Russia, Indonesia, etc weren’t…? That’s like what, 25-30% of the global population amongst 4 or so countries and none of them are labeled lol?
Ombrynn t1_iudxrxp wrote
It's not beautiful tho. Labels are misleading (for example Switzerland is the micro red dot but feels like the giant unlabeled blue one)
The fitting is way off. there is no clear correlation between GDP and pollution.
Everything is cluttered and hard to read. The size of dots are not explained...
Pyrhan t1_iue0w5u wrote
India is the second biggest orange dot.
authorPGAusten t1_iue59hx wrote
Africa trend line makes 0 sense.
Fiskefest t1_iuev31v wrote
Day 1 - How to ignore the fitted curves.
ProLibertateCH t1_iuffhxo wrote
Excellent, this really illustrates how Capitalism SAVES LIVES!
Wealthier, classical liberal, capitalist countries are far less polluted than socialist / dictatorial ones.
Take Switzerland - GDP is based to almost 30% on industrial production, yet pollution is minimal.
This destroys the entire WEF narrative: impoverishing people will worsen whatever ecological problems there may be.
sleeper_must_awaken t1_iuflsfa wrote
This chart is atrocious:
- Big countries not labeled (China, US)
- GDP per capita, so why not deaths per capita per year?
- No correction for demographics (age)
- The linear regression is incorrect. It is impossible for a continent to have negative deaths). There are no standard error lines around the curve (like so)
- The chart is too verbose to tell a good story. Remove half of the information, and it improves.
- It should be the "reported death rate", because we don't know the true death rate.
- Is this deaths per year? (probably, but implied).
An informational chart would plot:
- GDP per capita (which is a terrible metric, but hey... you probably read the wikipedia page before publishing a graph, so you know what you're doing)
- vs. Deaths per capita due to outdoor pollution in a specific age-group (60-70 or sth). This is an unreliable metric, because different countries account for deaths differently. If someone smoked in a polluted area and dies of lung cancer, what would you attribute the death to?
- Remove the linear regression lines.
- Remove the bottom 20 percentile of smaller countries.
- Label only the top 20 percentile of countries or don't label at all.
- Remove the average lines.
- Perhaps also remove the size <> population of the scatter plot.
- Make 5 panels for each continent with a shared GDP axis (like so) if you want to disentangle the information.
But, most importantly, what should be the title of the story you want to tell with this chart?
Burnrate t1_iug0ith wrote
Can you point out the US, China, and Russia for me? This graph is so confusing, the trend lines are worthless :/
Grason20 t1_iugfdb9 wrote
Yep, Egypt messed the line up
Creative_Elk_4712 t1_iugvjcu wrote
Considering that Italy has a huge urban population, is really densely inhabitated and has the Padan Plain high pressure smog cloak…this doesn’t even make sense to me, we are good
Creative_Elk_4712 t1_iugvpqi wrote
As gdp increases, in every continent (there are showed the continent curves for the expected deaths by given gdp value) the trend is that country’s citizens are less affected in a serious way by air pollution (here showed by air pollution deaths). You have the position crossed with dotted lines for the average country in the world
DicksB4Chicks t1_iuh32ph wrote
Not to mention the Kuznet's curve OP is referencing turned out to be false
sault18 t1_iuhb6s4 wrote
China is right under Turkmenistan. Russia looks like it's labeled Croatia and the USA is the large teal circle in the bottom right.
[deleted] t1_iuhexm5 wrote
[removed]
eqqqxy OP t1_iud5lkl wrote
Tool: Tableau
Source:
http://ghdx.healthdata.org/gbd-results-tool
https://www.worldbank.org/en/programs/icp
https://www.worldbank.org/en/programs/icp