The aim of this endeavour was to make 29 visualisations that are informative but also wouldn't look out of place hanging on your wall. ![]() I want to make it clear from the offset that these visualisations are qualitative, not quantitative. Some of the challenge required some shoehorning to get ideas to fit with themes and hence there are flaws within some of the plots. I will share the maps, cite the data sources, and explain how they fit in with the days theme. The aim of this short article to is to document (and hopefully show off) my efforts over the last 30 days. I posted all of my entries to my twitter account where I have in the past shared my attempts at geospatial data visualisation. Done correctly and you will learn a lot about your chosen technology, explore some insightful datasets and probably gain a deeper understanding of the world around you. It is however a pretty significant time commitment and consumed a large chunk of my evenings at the start of November. As challenges go this is not particularly daunting if you have a love for data visualisation and know your way around the geospatial data sources of the internet. After all, if something is worth doing, it is worth doing in Python. Most entries tend to be GIS based and I was convinced that you could just skip all of that nonsense and do the whole thing in Python, using libraries like numpy, pandas, geopandas, matplotlib, earthpy and rasterio. ![]() These visualisations are then posted on social media under the hashtag #30DayMapChallenge. To those unfamiliar, the 30 day map challenge is a daily mapping/cartography/data visualisation challenge that takes place during November and the challenge is to generate some sort of map or geographical data visualisation each day according to the daily theme.
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