Mapping the world with Python. Buy my book here - locatepress.com/book/pymaps
Planet Earth
- Tweets 2,303
- Following 525
- Followers 77,611
- Likes 7,997
Trying my hand at infographics. Mapping maritime choke points. Unfortunately the publicly available data used to produce this was recorded between 2015 and 2021 - Prior to the Houthis actions in the Red Sea and the war in Iran, which no doubt change this picture.
2
5
26
3,406
Forest Loss. This map shows forest loss since 2000 in Africa. Defined as a stand-replacement disturbance, or a change from a forest to non-forest state.
3
17
78
4,858
Forest Loss. This map shows forest loss since 2000 in South America. Defined as a stand-replacement disturbance, or a change from a forest to non-forest state.
3
20
85
4,764
Forest Loss. This map shows forest loss since 2000 in Asia. Defined as a stand-replacement disturbance, or a change from a forest to non-forest state.
For those who care - projection is EPSG:27703 is WGS 84 / Equi7 Asia
2
6
36
4,840
Forest Loss. This map shows forest loss since 2000 in Oceania. Defined as a stand-replacement disturbance, or a change from a forest to non-forest state.
1
12
27
30,798
Forest Loss. This map shows forest loss since 2000 in North America. Defined as a stand-replacement disturbance, or a change from a forest to non-forest state.
1
12
48
4,653
Forest Loss. This map shows forest loss since 2000 in Europe. Defined as a stand-replacement disturbance, or a change from a forest to non-forest state.
9
22
110
23,282
This bivariate map uses WRI's Aqueduct 4.0 data to show projected gross water demand (white→orange) vs. blue water availability (blue→purple) under a business-as-usual scenario by 2065–2095.
1
23
103
6,671
This bivariate map uses WRI's Aqueduct 4.0 data to show projected gross water demand (white→orange) vs. blue water availability (blue→purple) under a business-as-usual scenario by 2065–2095.
1
1
21
2,755
This bivariate map uses WRI's Aqueduct 4.0 data to show projected gross water demand (white→orange) vs. blue water availability (blue→purple) under a business-as-usual scenario by 2065–2095.
2
19
88
5,963
This bivariate map uses WRI's Aqueduct 4.0 data to show projected gross water demand (white→orange) vs. blue water availability (blue→purple) under a business-as-usual scenario by 2065–2095.
2
14
68
5,431
This bivariate map uses WRI's Aqueduct 4.0 data to show projected gross water demand (white→orange) vs. blue water availability (blue→purple) under a business-as-usual scenario by 2065–2095.
For those who care - projection is EPSG:27703 is WGS 84 / Equi7 Asia
20
90
7,303
This bivariate map uses WRI's Aqueduct 4.0 data to show projected gross water demand (white→orange) vs. blue water availability (blue→purple) under a business-as-usual scenario by 2065–2095.
Inspiration came from this post from @Esri - esri.com/arcgis-blog/product…
1
18
94
6,339
Mercury! We may as well continue the series and explore the rest of the rocky worlds in the Solar System.
5
12
174
5,854
I made this map for Earth / Mars last week and you seemed to like it so here is it for Venus showing the distribution of elevation levels on the surface. Negative values indicate terrain below the Mars areoid — a gravitational reference surface defined where atmospheric pressure equals 610.5 Pa (the triple point of water) — which serves as the zero-elevation datum in place of a sea level.
12
7
148
8,571
I made this map for Earth last week and you seemed to like it so here is it for Mars, the distribution of elevation levels on the earths surface. I have used a blue-red colourmap to give it the illusion of oceans. Negative values indicate terrain below the Mars areoid — a gravitational reference surface defined where atmospheric pressure equals 610.5 Pa (the triple point of water) — which serves as the zero-elevation datum in place of a sea level.
13
26
344
21,837
Finally. Showing the age of oceanic crust alongside volcanoes in East Asia shows the subduction boundary where the oceanic Pacific Plate and Philippine Sea Plate subduct (dive) beneath the continental Eurasian Plate.
1
3
883
The age of oceanic crust is a nice map of the world's divergent plate boundaries. As the plates move apart, magma rises from the mantle, creating new crust.
2
9
1,332
@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
5
1
33
4,165
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
9
66
363
23,697
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
1
20
113
11,004
Day 24 of the #30DayMapChallenge - Places and their names - Here are the World's rivers with labels on some of the major ones.
15
77
5,884
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
17
97
7,101
Day 22 of the #30DayMapChallenge - Natural Earth Data. I used the Ocean Bottom layer to make a Bathymetry map of Northern Europe.
1
15
84
10,955
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.
2
18
4,114
Finally, for funI have included a shipping lanes map using the infamous Spilhaus projection.
1
1
15
2,365
Day 19 of the hashtag#30DayMapChallenge - Projections. Here are maps showing tropical storms using a number of different projections. Starting with the South Polar Stereo
1
6
42
3,738
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.
3
6
65
5,058
Day 17 of the #30DayMapChallenge - New tool. It has been on my radar for a while so I tried out @datashader to visualise population density. These maps usually take minutes to render but with datashader it takes seconds.
3
12
88
6,027
Day 16 of the #30DayMapChallenge - Cell - Here is a map of Cell tower density in Europe. Clearly this is just a population density map but gotta follow the theme.
14
107
6,778
Day 15 of the #30DayMapChallenge - Fire. Wildfire map. Data aggregated for all of 2024.
1
9
39
5,147
Day 13 of the #30DayMapChallenge — 10-minute map.
Once I’ve made a particular type of map once, I can usually recreate it in about 10 minutes. This one’s a bivariate map — the style that probably took me the longest to learn the first time around. Rainfall vs Temperature in South America
21
102
7,925