Libraries used: pandas | geopandas | shapely | keplergl
For the first day I wanted a quick win (spoiler: didn't work out) to stay motivated which didn't work out as intended. I wanted to show the difference of the centroid of France when considering the overseas areas in comparison to when considering only the mainland. The same applies to Hamburg to some degree due to an exclave called "Neuwerk" close to the North Sea.
Data source: Natural Earth Data (https://www.naturalearthdata.com/)
Libraries used: pandas | geopandas | altair
Inspired by the US ZIPScribble Map I've looked for a way to replicate the same process to Germany. Mainly I wanted to see if regions will be as neatly divideable by ZIP codes in Germany as well.
Data source: Launix (https://launix.de/launix/launix-gibt-plz-datenbank-frei/)
Libraries used: geopandas | pandas | keplergl
A while back I've read an article about the Gender Pay Gap in Germany and how it's still a problem in most places. I also got some data for Germany but wanted to look at how Germany compares to other countries in the EU/Europe. I could get my hands on some data from the European Comission (Eurostat), combined the data with the corresponding areas and created a simple plot. Maybe I'll revisit this when doing interactive plots to show the difference of the Gender Pay Gap over time!
Data source: Eurostat (https://ec.europa.eu/eurostat/databrowser/view/sdg_05_20/default/table?lang=en) and GeoJSON Maps (https://geojson-maps.ash.ms/)
Libraries used: osmnx | matplotlib
I was looking for the "greenest" city in Europe (how ever that might be determined) and stumbled upon the website of the European Commission which, each year, awards a city with the "European Green Capital Award". Next years winner is Tallinn, the capital of Estonia. Looking up Tallinn, I've seen quite some greenery in Mustamäe. Mustamäe translates to "Black Hill" which, in fact, looked pretty green from above which is why I decided to show it in todays map!
Data source: OpenStreetMap (https://www.openstreetmap.de/)
Libraries used: osmnx | matplotlib
For Day 05: Ukraine I was really undecided what to do without doing something obvious and might have ended up doing something obvious: Snake Island. Since I'm trying to solve all 30 days with Python, I thought it might be appropriate to do Snake Island or Zmiinyi Island.
Data source: OpenStreetMap (https://www.openstreetmap.de/), Icon8 (https://icon8.com)
Libraries used: geopandas | pandas | osmnx | shapely | keplergl
I first wanted to compare "metro network length" to "city area" but found out that plotting water bodies with OSMnx isn't trivial so I've switched over to look at the destinations in Europe of Turkish Airlines flights from Istanbul. Just to find out that that's not as trivial as well. Anyways, here is the map for Day 6: Networks!
Data source: OpenStreetMap (https://openstreetmap.de/), FlightConnections (https://www.flightconnections.com/flights-from-istanbul-ist)
Libraries used: pillow (PIL fork)
Well, I guess this was my first five minute map for this challenge, there will be another one on another day. I didn't really have time so I went with a downsampling function I've written with pillow that gives your image a more "cubic" or obvious "raster"-like look. It's a simple function and in theory a good way to learn how down- and upsampling works.
Data source: NASA Blue Marble (https://visibleearth.nasa.gov/images/73909/december-blue-marble-next-generation-w-topography-and-bathymetry/73911l)
Libraries used: osmnx | matplotlib
I've been using Open Street Map Data all the time accessing it with OSMnx so let's not change that. Todays map is showing St. Pauli (Sankt Pauli). St. Pauli is a quarter of the city of Hamburg and contains the red-light district around the Reeperbahn area. Besides that there are lots of bars and pubs which we are looking at today!
Data source: OpenStreetMap (https://openstreetmap.org/)
Libraries used: geopandas, pandas, keplergl, osmnx
I had literally no idea what to do for today since I don't know any way to plot things in a coordinate system that's not on our planet. Which is why I decided not to look at space directly but to people who went to space. To be more precise I took a look at the countries of origin and gender of people who went to space according to the International Astronaut Database.
Data source: International Astronaut Database (https://aerospace.csis.org/data/international-astronaut-database/), OpenStreetMap (https://osm.org/)
Libraries used: geopandas | pandas | keplergl
Today we are revisiting Day 3. What we can see there is a pretty basic map showing the Gender Pay Gap in percent for 2020 in Europe. The first difference is the range that's being used to color the corresponding countries. The second difference is choice of color. Next to misleading color ranges (as inverting red and green for example), are easy to steer around.
Data source: Eurostat (https://ec.europa.eu/eurostat/databrowser/view/sdg_05_20/default/table?lang=en) and GeoJSON Maps (https://geojson-maps.ash.ms/)
Libraries used: geopandas, pandas, keplergl
Most of the time things that aren't optimal will be visualised in red and today we're following that trend by taking a look at the deforestation on our planet. The data is taken from OurWorldInData which in itself is a great ressource for data for your projects. They also list all the sources, map the data and also show different kinds of statistical analyses. I've taken a look at the annual deforestation volume from 2010 to 2015.
Data source: OurWorldInData (https://ourworldindata.org/deforestation) and GeoJSON Maps (https://geojson-maps.ash.ms/)
Apparently I've created 11 maps in 2022 which is 11 more than in 2021.