Python Maps

Ending 2023 with my favourite map. South American forests. See you next year!

#A new high-resolution global map details cropland greenhouse gas emissions by crop and source, identifying key hotspots and enabling targeted, efficient mitigation strategies at the local level. @cornell @NatureClimate doi.org/hbpf9p phys.org/news/2026-02-p…

Like a Cotton candy, the elevation tint "irid" of Ireland.

Power lines (OSM and derived) of Romania. #rayshader adventures, an #rstats tale

@warlei_alves @claudeai ecpm.org/en/worldmap/

@PythonMaps @claudeai It failed. As far as I know, the death penalty doesn't exist in any South American country.

@claudeai Did 95% of the work on this. I've been resistant to the AI hype but it does certainly have its uses. Here is a map showing the status of the death penalty around the world

Made a roads map for another project. Thought I would share.

@FernViz @claudeai More AI or more trees?

@PythonMaps @claudeai and we need more :D

I created a global forest map showing how forests vary by climate zone - Added twist, 90% of the code + the following post was generated by @claudeai How it was made: Started with Hansen et al. global forest cover raster (% tree cover per pixel) Loaded climate zone shapefile with boreal, temperate, subtropical & tropical regions Masked the forest raster with each climate zone to extract forest cover for that zone only Assigned distinct colormaps: Blues (boreal), Oranges (temperate), PuRd (subtropical), Greens (tropical) Overlaid all four masked layers to show global forest distribution by climate type The overlapping colors reveal where different forest biomes meet across the planet. Built with Python (GeoPandas, Rasterio, Matplotlib) Data: Hansen et al. Science 2013 #Python #GIS #DataViz #Cartography #Forests

Inspiration here x.com/TheIndexInside…

Day 26 of the #30DayMapChallenge - Transport - Shipping Lanes.

Running a little bit behind. Day 25 of the #30DayMapChallenge - Hexagons - I have used the @KonturInc population density hexagons to generate this population density map of Southern Asia

@researchremora It is literally impossible to get bored of shipping lanes maps

#30DayMapChallenge · Day 26 · Transport. Yet another visualisation of global shipping density. But it's engraved on metal with realistic light and shadow. Because we can do that in R. #rayshader adventures, an #rstats tale

Day 24 of the #30DayMapChallenge - Places and their names - Here are the World's rivers with labels on some of the major ones.

Day 23 of the #30DayMapChallenge - Process - "Show how you make a map" - Well luckily, there is an entire book dedicated to how I make maps - get yours now locatepress.com/book/pymaps

Day 22 of the #30DayMapChallenge - Natural Earth Data. I used the Ocean Bottom layer to make a Bathymetry map of Northern Europe.

Day 21 of the #30DayMapChallenge - Icons - Use icons to highlight points of interest. Here are lighthouses of the Caribbean and Gulf of America. I used a few tricks to make the points look like they are shining out to sea.

This visualization by @pythonmaps (a must follow account) shows global shipping lanes in the gorgeous ocean-centered Spillhaus projection. Extremely pleasing to look at!

Day 20 of the #30DayMapChallenge - Water - Rivers of South America

@Arrrghonaut Oh go on then.

@PythonMaps Now do venus! I've seen it before and 70% ocean venus would be a great fantasy world

Day 18 of the #30DayMapChallenge - Out of this World. Here is a topographical map of Mars. I have added some hill shading and used a colourmap that simulates an ocean, proportionally equal in size to Earths.