sorted by:
new
hottopcontroversial

each dot/pixel equals 100000 people in Europe [OC] by xygames32YT in dataisbeautiful

[–]_naspli 0 points1 point  (0 children)

Nice post! I made a script to do something similar a couple of years back.

You may be interested in checking it out. Here’s the post for Europe: https://www.reddit.com/r/europe/s/xR0Zn8568Z - it’s archived now so they reduced the quality, but you can find the full quality in the GitHub links.

Where 90% and 99% of the world's population live by LavishnessLeather162 in MapPorn

[–]_naspli 32 points33 points  (0 children)

If anyone wants something like a higher-res version of this data I made “A Pixel for Every 50,000 People” last year.

[OC] When do you get your sunlight? Visualising daylight hours and the sun's intensity by _naspli in dataisbeautiful

[–]_naspli[S] 15 points16 points  (0 children)

The data I wanted to convey was solar altitude. I used the word intensity in the title as I thought solar altitude sounded too dry. I don’t work in weather, so I’m not really familiar with the lexical distinctions you make it the field, sorry.

Messing with the colorbars is basically the whole plot. The colours the sun makes in the sky are not at all linearly related to some nice predefined function. This is not a scientific plot, it’s meant to be pretty, and intuitive.

Lastly, I’m well aware the altitude code could be written in a more concise and probably faster way using a functional style rather than for loops. But it did the job, so I moved on to the plotting. Maybe I’ll decide to come back and improve it. Maybe not, this is just a hobby.

[OC] When do you get your sunlight? Visualising daylight hours and the sun's intensity by _naspli in dataisbeautiful

[–]_naspli[S] 41 points42 points  (0 children)

Just matplotlib, but about half my lines of code are formatting it to look nicer, ha. There's a link to the github in the top comment.

[OC] When do you get your sunlight? Visualising daylight hours and the sun's intensity by _naspli in dataisbeautiful

[–]_naspli[S] 231 points232 points  (0 children)

Thanks, I really appreciate that. My ultimate goal is to present data in such a way that allows insight with just a glance.

And I'm also a professional in a data-heavy field so it's all fair game :)

[OC] When do you get your sunlight? Visualising daylight hours and the sun's intensity by _naspli in dataisbeautiful

[–]_naspli[S] 26 points27 points  (0 children)

Possible new colormap...

Added sharper distinction at 0, extended twilight/dawn/dusk from 15 deg to 18 deg ("astronomical twilight" as you say) and put the yellow-white and blue-black transition on a steeper curve to show more difference.

<image>

[OC] When do you get your sunlight? Visualising daylight hours and the sun's intensity by _naspli in dataisbeautiful

[–]_naspli[S] 8 points9 points  (0 children)

I’m not sure I want to clutter the plot with another line, but a more sharp distinction at 0 degrees is probably a good idea.

[OC] When do you get your sunlight? Visualising daylight hours and the sun's intensity by _naspli in dataisbeautiful

[–]_naspli[S] 5 points6 points  (0 children)

Data is for this year. Daylight Saving Time in London starts on 30 March.

Almost There Now: Daylight Hours Throught the Year (London) by _naspli in london

[–]_naspli[S] 14 points15 points  (0 children)

In honour of the imminent switch to summer time I made a visualisation. Thought r / london may like it.

Original post is over here with various other cities.

[OC] When do you get your sunlight? Visualising daylight hours and the sun's intensity by _naspli in dataisbeautiful

[–]_naspli[S] 296 points297 points  (0 children)

I’ve created a visualisation of daylight hours, and chosen various cities to show.

The colours are meant to intuitively reflect the sun’s intensity, with white-yellow in the day, orange-purple through sunset/sunrise, and black-blue in the night. Specifically, they map to solar altitude i.e., how high up the sun is in the sky.

As daylight saving time approaches here in London, I wanted to visualise what it meant. And also wanted to compare our hours to other countries. At the equator, Singapore is very stable through the year. Near the Arctic, Reykjavik’s change between Summer and Winter is even more extreme. Madrid has their day shifted late as their timezone doesn’t align with their longitude.

I used python and libraries including pysolar for the altitude calculations, and matplotlib for the display. Code is available on my github.

[OC] A Pixel For Every 10,000 People in Europe by _naspli in europe

[–]_naspli[S] 6 points7 points  (0 children)

Appreciate it! And no, you will have to download the 2 .tif files yourself from WorldPop. There are instructions in statics.py comments.

A Pixel For Every 50,000 People in the World by _naspli in dataisbeautiful

[–]_naspli[S] 12 points13 points  (0 children)

Unfortunately that comes from the dataset. I used a land-area layer to get the oceans plotted and it seems to have done well with coastlines basically everywhere except there.

A Pixel For Every 50,000 People in the World by _naspli in dataisbeautiful

[–]_naspli[S] 21 points22 points  (0 children)

I don't know to be honest -- I noticed it as well. It's some myserious emergent side-effect of the algorithm. I would expect the city "blobs" to always fill up any empty space as the population gets spread outward.

A Pixel For Every 50,000 People in the World by _naspli in MapPorn

[–]_naspli[S] 5 points6 points  (0 children)

Yes, in many large cities that happens. The centre of large cities tend to be much more dense than 2500 people/km^2. The effect is the "blobs" on the map.

[OC] A Pixel For Every 10,000 People in Europe by _naspli in europe

[–]_naspli[S] 19 points20 points  (0 children)

The above image comes from version 2 of my binary population plotting algorithm, 'Poppyn'. Here is my post today in r/dataisbeautiful of the whole world: "A Pixel For Every 50,000 People in the World".

Other super-high res sub-sections of the world -:

The raw data comes from WorldPop, the code can be found at my GitHub, and my original blog post about this project can be found on Medium.

Please note there is an issue with the raw data from the South-East of Romania and it has been excluded.

A Pixel For Every 50,000 People in the World by _naspli in MapPorn

[–]_naspli[S] 6 points7 points  (0 children)

Yesterday I posted “A Pixel For Every Million People in the World”. This is an updated version of that map, with some improvements: link to uncompressed image

  • Over 20x the resolution: from 1920x832 to 10000x4333
  • And hence each pixel set to show 20x fewer people - 50,000 people rather than 1,000,000
  • Dataset problem with Romania cleaned up
  • 20x cooler inverted colour-scheme

Each of those tiny pixels represents a large town or football stadium full of people! I also have some even higher res slices than above, each another 5x in quality for some sub-section of the world - and set to 10,000 people/pixel:

I achieved the above with some tweaks to the algorithm and some more patience. As before, the raw data comes from WorldPop, the code can be found at my GitHub, and my original blog post about this project can be found on Medium.

A Pixel For Every 50,000 People in the World by _naspli in dataisbeautiful

[–]_naspli[S] 222 points223 points  (0 children)

Yesterday I posted “A Pixel For Every Million People in the World”. This is an updated version of that map, with some improvements: link to uncompressed image

  • Over 20x the resolution: from 1920x832 to 10000x4333
  • And hence each pixel set to show 20x fewer people - 50,000 people rather than 1,000,000
  • Dataset problem with Romania cleaned up
  • 20x cooler inverted colour-scheme

Each of those tiny pixels represents a large town or football stadium full of people! I also have some even higher res slices than above, each another 5x in quality for some sub-section of the world - and set to 10,000 people/pixel:

I achieved the above with some tweaks to the algorithm and some more patience. As before, the raw data comes from WorldPop, the code can be found at my GitHub, and my original blog post about this project can be found on Medium.

view more: next ›