My Third Week of the #30DayMapChallange
Since 2019, the Geographic Information System (GIS) and spatial analytics community have been quite busy each November – thanks to a fun challenge called the #30DayMapChallange. Each year, this challenge has a thematic schedule, proposing a topic that should be the primary directive for map visualisation to be posted on that particular day. While the pre-defined daily topics certainly mean a constraint for the creative mind, they also help participants to find mutual interest, share data sources, and express individual styles visually and technologically.
Here, I would like to briefly overview my third week of this challenge, detailing and showing the different maps I created – usually in Python using various tools of spatial analytics and Geospatial data.
In this article, all images were created by the author.
Day 15 – OSM
OpenStreetMap if one of the first go-to sources for map data. While quite a few libraries are building on it, my favourite discovery from last year was PrettyMaps, which I used to I visualise some of the top global tourist sights curated by PlanetWare, such as the Eiffel Tower in Paris. See the rest of the collection here, with the Colosseum (Rome), Statue of Liberty (New York City), Château de Versailles (France), Central Park (New York City), Forbidden City (China), Prague Castle (Czech Republic), and the Sydney Opera House .
Day 16 – Oceania
To visualise Australia and the great many islands of Oceania, I used a 10m resolution physical vector data from Natural Earth. Namely, I imagined the Coastline of Australia and Oceania here. For this line plot, I used an older trick of mine – to create these ‘ lightsaber' looking, glowing, Star Wars-inspired lines – which is created 100% in Python using Matplotlib. Besides, finding the right bounding box was a bit tricky, so I just followed a few online Maps as a reference.
Day 17 – Flow
When I was trying to figure this map out, with the topic "Flow", I was stuck for a bit – and then I was like, okay, let's get some calories in and get it going. tl;dr I decided to visualise the export flow network of chocolate (HS 1806 when it comes to its standard code in the Harmonized System) between the largest exporting countries using international trade data from Comtrade. This dataset contains information on which country exported to which, in what quantity, and in what value. I used this information to create a non-geographical but topological map, a network visualisation. In this network, each country is a node, while country A is linked to country B if A exports chocolate products to country B, where the link is proportional to the total value of the traded goods in 2022. Node colours correspond to network communities – clusters of countries that seem to trade much more internally than the rest of the world, while node size measures the total value each country makes by exporting such sweets. Countries not exporting are marked by dark grey. Fun fact: I created this map while having a coffee at #Flow Specialty Coffee Bar & Bistro, Budapest.
Day 18 – Atmosphere
In this visual, I created actual heatmaps showing global monthly maximum temperature values using World Climate data:
"This is WorldClim version 2.1 climate data for 1970–2000. This version was released in January 2020. There are monthly climate data for minimum, mean, and maximum temperature, precipitation, solar radiation, wind speed, water vapor pressure, and for total precipitation. There are also 19 "bioclimatic" variables."
So, in my map, each frame corresponds to one month, using the max temperature of the WorldClim 2.1 raster dataset.
Day 19–5-minutes map
That is right – we only had 5 minutes to put a map viz together. Not much time, so I decided to go for something I do very often – combine networks and geospatial data and visualise another road network, this time, about Manhattan and its beautiful, absolutely artificial square grid-like road system. Instead of rerunning a previous piece of code, I wrote this notebook from scratch and made it in just about 5 minutes, without much googling (except finding the right hex code).
Day 20 – Outdoors
To venture outdoors – while creating a map – I decided to look around beautiful landmarks online, making me feel like I was there – when I was sitting at my Python terminal. So in my outdoor map I combined the effort of visualising climate change and the grandiose ESA's Sentinel data availability new update by creating this animation of Upsala Glacier, Argentina. The footage is built based on querying the least cloudy true-colour image of each half-year from 2016 up until today. Due to file-size issues, here comes a static snapshot, will you can find the animation here.
Day 21 – Raster
After the previous day's satellite footage, I am getting back to rasterised remote sensing data. In particular, I am reposting this older piece of mine where I used the old Sentinel API to compute the NDVI index for the city of Zurich, showing a basic but very handy transformation of satellite image bands. Just a bit on its theory, according to #Wikipedia is:
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