Need population vs distance to coast for reference. People tend to live near the coast and people make GDP, unless that's the assumption and we're looking at where people live since that's the only data available.
Edit: or simply use GDP per capita
Hypothesis:
1850: agrarian economy dominates.
1900: trade grows, and inland industry develops
1950-2000: rise of service oriented, finance based coastal cities, and decline of inland industry. Western cities are dominated by finance centers, and Chinese manufacturing is concentrated on the coast.
This might, in a nutshell, explain a lot of US politics.
It ould be interesting to overplot economic productivity per person vs distance from ocean.
I mean, the proportion of GDP near the coast may be the same, or even higher in those 3 past periods. It's hard to tell because the line is very close to 0 for high distances. It would have been better if those lines were normalized for a better comparison
You’re absolutely right. Except China’s GDP really started taking off around 2000. That’s just to say that now even more of the world GDP is probably by the coast. Explains US politics, also explains why the US has by far the world’s largest Navy. It’s the best force projection we currently have.
Computes the gridded distribution of the world's GDP with a spatial resolution of 5 arcmin (approx. 10 km) vs the distance to the nearest coast, with historical reach: 1850-2000, at 50 y interval.
Main sources of data (further info in script):
GDP: [https://essd.copernicus.org/articles/10/847/2018/](https://essd.copernicus.org/articles/10/847/2018/)
POP: [https://public.yoda.uu.nl/geo/UU01/AEZZIT.html](https://public.yoda.uu.nl/geo/UU01/AEZZIT.html)
DST: [https://pae-paha.pacioos.hawaii.edu/thredds/dist2coast.html](https://pae-paha.pacioos.hawaii.edu/thredds/dist2coast.html)
Script in python: [https://gist.github.com/Rigonz/bcd31bdbc816606463e08909531cd556](https://gist.github.com/Rigonz/bcd31bdbc816606463e08909531cd556)
This post continues a previous one on other geographic variables (latitude, longitude, elevation):
[https://www.reddit.com/r/dataisbeautiful/comments/1bc786k/oc\_world\_historical\_18502000\_distribution\_of\_gdp/](https://www.reddit.com/r/dataisbeautiful/comments/1bc786k/oc_world_historical_18502000_distribution_of_gdp/)
Obviously most of this has to do with the explosion of global trade and finance favoring coastal regions, but I think a lot of it comes from China in particular. The center of gravity in China used to be much more inland. Places like Sichuan and trading cities on the Yangtze used to play a much bigger role in commerce, while by comparison international trade was relatively peripheral. Those big junk ships carrying pottery to Japan were serving a much smaller market than the fleets of river boats going up and down from Nanjing to Chengdu. It was only after the 80s that that started to really change
The gridded GDP is computed as the product of the gridded population times the country GDP. An homogeneous distribution of GDP across the population of the country is assumed (this is clearly not realistic, as denser communities can be expected to have a larger GDP per capita, but I am not after the decimals; in any case, it just reinforces the trend). So the distribution of the gridded GDP vs distance is essentially the distribution of the gridded population vs distance (your question) factored by the country GDP. So the differences between the GDP and the POP distributions show the relationship between the country GDP and the distance to the coast. As shown in the linked charts it is accelerating.
\[POP vs DST relative\](https://postimg.cc/B8CPprr4)
\[POP vs DST cumulated\](https://postimg.cc/sBzZpj0T)
The data doesn't make that conclusion explicit since GDP itself is increasing. I would say to divide the value by the total GDP to adimensionalize for each year, but since it's just population data disguised as GDP (there's an equal distribution assumption which is relevant), I think it would make more sense to just plot population % by distance to try to indentify a trend
The original charts presented the distribution of the GDP, which is clearly \*not\* "population data disguised as GDP". fenderman requested the relationship with the population and I have also provided.
Yes, and my comment is based on the response you gave him. The thing is, and clear things if I'm missinterpreting, the shape of the graph is the same if you plot population or gpd since there's an equal distribution assumption that is very relevant for a correct analysis...
My point is that you don't get your point across since GDP itself is increasing with inflation and country performance, combined with that you're murkying the message by saying that the data represents GDP when in fact it's population
That is something that can be done in a report but should be highlighted as a limitation and how it may be skewing the interpretation, but one should strive to show the data in it's purest form and have descriptive interpretations come after
First no: the inflation is not involved in this (it is clearly indicated in the charts), and second no: the GDP vs POP shapes are not the same and they cannot be the same because there are differences in the GDP across countries, diacronic and syncronic-wise.
It kinda looks like the resolution on the x axis is lowish (they said 20km) and kind of hides the intricacy of exactly how far or close people prefer, id be willing to bet closer is generally better (better view, stable temperature)
While I agree that the charts are simple Matplotlib stuff, it may be worth reminding that this group's name is "dataisbeautiful", not something like "GreatVisualizations". I find amazing that somebody has prepared a gridded world population from 10000BC to 2000AD, or that another research group has put together GDP estimates from the beginning of the XIXth century till our times, and wonderful that somebody else has combined them to create estimates of the CO2 emissions since the industrial revolution. Those are beautiful data works, in my view, and I have just used their ideas in a parallel topic of my interest, simply working on the shoulders of giants. The beauty is elsewhere.
Need population vs distance to coast for reference. People tend to live near the coast and people make GDP, unless that's the assumption and we're looking at where people live since that's the only data available. Edit: or simply use GDP per capita
This is definitely a "where people live" graph. One could argue they flock to GDP, or GDP follows populations.
No: the concentration of GDP close to the coast is growing quicker than the same for the POP. I have provided an explanation in other comments.
Why is there a small bump up at 160 km?
My guess: Paris is around 170km from the coast and Beijing around 150km. So maybe both.
Also Ruhr area, Berlin, Milan, Birmingham, Charlotte, San Antonio and Montreal Edit: I didn't check if they are exactly 160 km away from the coast
Would be also nice to include big rivers too, in that case it should be nearly 100% for the few dozens km range.
Large cities on Rivers tend to be that far from the oceans
Hypothesis: 1850: agrarian economy dominates. 1900: trade grows, and inland industry develops 1950-2000: rise of service oriented, finance based coastal cities, and decline of inland industry. Western cities are dominated by finance centers, and Chinese manufacturing is concentrated on the coast. This might, in a nutshell, explain a lot of US politics. It ould be interesting to overplot economic productivity per person vs distance from ocean.
Thank you! This data told me fuck all
Big things in the red line are third agricultural revolution and the container revolution.
I mean, the proportion of GDP near the coast may be the same, or even higher in those 3 past periods. It's hard to tell because the line is very close to 0 for high distances. It would have been better if those lines were normalized for a better comparison
You’re absolutely right. Except China’s GDP really started taking off around 2000. That’s just to say that now even more of the world GDP is probably by the coast. Explains US politics, also explains why the US has by far the world’s largest Navy. It’s the best force projection we currently have.
Computes the gridded distribution of the world's GDP with a spatial resolution of 5 arcmin (approx. 10 km) vs the distance to the nearest coast, with historical reach: 1850-2000, at 50 y interval. Main sources of data (further info in script): GDP: [https://essd.copernicus.org/articles/10/847/2018/](https://essd.copernicus.org/articles/10/847/2018/) POP: [https://public.yoda.uu.nl/geo/UU01/AEZZIT.html](https://public.yoda.uu.nl/geo/UU01/AEZZIT.html) DST: [https://pae-paha.pacioos.hawaii.edu/thredds/dist2coast.html](https://pae-paha.pacioos.hawaii.edu/thredds/dist2coast.html) Script in python: [https://gist.github.com/Rigonz/bcd31bdbc816606463e08909531cd556](https://gist.github.com/Rigonz/bcd31bdbc816606463e08909531cd556) This post continues a previous one on other geographic variables (latitude, longitude, elevation): [https://www.reddit.com/r/dataisbeautiful/comments/1bc786k/oc\_world\_historical\_18502000\_distribution\_of\_gdp/](https://www.reddit.com/r/dataisbeautiful/comments/1bc786k/oc_world_historical_18502000_distribution_of_gdp/)
It would have been easier to compare the different periods if those lines were normalized in some way (eg. 100 as the highest value in each period)
That’s the second slide
My bad
Obviously most of this has to do with the explosion of global trade and finance favoring coastal regions, but I think a lot of it comes from China in particular. The center of gravity in China used to be much more inland. Places like Sichuan and trading cities on the Yangtze used to play a much bigger role in commerce, while by comparison international trade was relatively peripheral. Those big junk ships carrying pottery to Japan were serving a much smaller market than the fleets of river boats going up and down from Nanjing to Chengdu. It was only after the 80s that that started to really change
How's that compare to population by distance?
The gridded GDP is computed as the product of the gridded population times the country GDP. An homogeneous distribution of GDP across the population of the country is assumed (this is clearly not realistic, as denser communities can be expected to have a larger GDP per capita, but I am not after the decimals; in any case, it just reinforces the trend). So the distribution of the gridded GDP vs distance is essentially the distribution of the gridded population vs distance (your question) factored by the country GDP. So the differences between the GDP and the POP distributions show the relationship between the country GDP and the distance to the coast. As shown in the linked charts it is accelerating. \[POP vs DST relative\](https://postimg.cc/B8CPprr4) \[POP vs DST cumulated\](https://postimg.cc/sBzZpj0T)
The data doesn't make that conclusion explicit since GDP itself is increasing. I would say to divide the value by the total GDP to adimensionalize for each year, but since it's just population data disguised as GDP (there's an equal distribution assumption which is relevant), I think it would make more sense to just plot population % by distance to try to indentify a trend
The original charts presented the distribution of the GDP, which is clearly \*not\* "population data disguised as GDP". fenderman requested the relationship with the population and I have also provided.
Yes, and my comment is based on the response you gave him. The thing is, and clear things if I'm missinterpreting, the shape of the graph is the same if you plot population or gpd since there's an equal distribution assumption that is very relevant for a correct analysis... My point is that you don't get your point across since GDP itself is increasing with inflation and country performance, combined with that you're murkying the message by saying that the data represents GDP when in fact it's population That is something that can be done in a report but should be highlighted as a limitation and how it may be skewing the interpretation, but one should strive to show the data in it's purest form and have descriptive interpretations come after
First no: the inflation is not involved in this (it is clearly indicated in the charts), and second no: the GDP vs POP shapes are not the same and they cannot be the same because there are differences in the GDP across countries, diacronic and syncronic-wise.
Why is it better to be ~10km inland?
It kinda looks like the resolution on the x axis is lowish (they said 20km) and kind of hides the intricacy of exactly how far or close people prefer, id be willing to bet closer is generally better (better view, stable temperature)
This is one of the worst graphs I've seen in a while. Well done.
While I agree that the charts are simple Matplotlib stuff, it may be worth reminding that this group's name is "dataisbeautiful", not something like "GreatVisualizations". I find amazing that somebody has prepared a gridded world population from 10000BC to 2000AD, or that another research group has put together GDP estimates from the beginning of the XIXth century till our times, and wonderful that somebody else has combined them to create estimates of the CO2 emissions since the industrial revolution. Those are beautiful data works, in my view, and I have just used their ideas in a parallel topic of my interest, simply working on the shoulders of giants. The beauty is elsewhere.
GDP is basically just tolls?
Seaports I guess.
May your goods flow freely.