Plate Tectonics. A thread

Claude 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.

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 Claude. 1/4 #Python #GIS #DataViz #Cartography #Forests

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

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.

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

Day 19 of the #30DayMapChallenge - Projections. Here are maps showing tropical storms using a number of different projections. We have the South Polar Stereo, the Robinson, the Lambert Conformal and finally I have included a shipping lanes map using the infamous Spilhaus projection.

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.

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.

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.

Day 15 of the #30DayMapChallenge - Fire. Wildfire map. Data aggregated for all of 2024.

Day 14 of the #30DayMapChallenge Open Street Map - Railways.

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

Day 12 of the #30DayMapChallenge - Map from 2125 - I think Northern Ireland and the Republic of Ireland could merge into a new country. So here is a topography map.

Day 11 of the #30DayMapChallenge - Minimal - Population density of Egypt. This was always my preferred style but recently I caved to academics who wanted labels and keys 🤮. Glad to get back to basics.

Day 10 of the #30DayMapChallenge - Air - Map of the world's airports and airways.

Day 9 of the #30DayMapChallenge - Analog. Create your map using traditional methods. Obviously I am not going to stick to this. Frankly nothing is more traditional that Python so here is another map made with Python. Roads of the Roman Empire.

Day 8 of the #30DayMapChallenge Urban - Roads of the world. Couldn't think of anything more urban than roads.

Capitalism vs communism eh 😉

Day 7 of the #30DayMapChallenge - Accessibility - "Visualize travel time, barriers....." - Here is a map showing nighttime lights in the Korean Peninsula. The border between North and South is visible from space.

Day 6 of the #30DayMapChallenge - Dimensions. A thread of a few maps that cross into the three dimensional world. Here is a 3D representation of the topography and bathymetry around Gibralta.

Day five of the #30DayMapChallenge - Earth. Soil moisture. Data comes from the TerraClimate project. I love this colourmap.

Day four of the #30DayMapChallenge - Data challenge: My Data. I made some historical geojsons of the Roman and Mongol empires. Accuracy is vaguely correct but the Mongol Empire does look a bit like a bear.

Day 7 of the #30DayMapChallenge - Accessibility - "Visualize travel time, barriers....." - Here is a map showing nighttime lights in the Korean Peninsula. The border between North and South is visible from space.

Day three of the #30DayMapChallenge - Polygons. This map shows the earths tectonic plates. I have overlayed all of the earthquakes with a magnitude greater than 4.0 over the last 20 years, coloured according to their magnitude (blue (smallest) - red (largest).

Day 2 of the #30DayMapChallenge - Lines. This map shows the rivers of Africa, coloured according to the minimum river temperature.

Day one of the #30DayMapChallenge - Points. These maps show lighthouses of the British Isles, the Aegean Sea and Italy. I have tried to simulate how they would shine and cast light out to sea.

Croplands. This map shows the croplands of East Asia. Using my new favourite colourmap. Data - www.nature.com/articles/s43...

Soil moisture. Data comes from the TerraClimate project. Is my choice of colourmap appropriate? No. Do I care? No. Do I just like making pretty pictures? Yes

Evapaotranspiration. Data comes from the TerraClimate project.

Wildfires. Data aggregated for all of 2024.

Aridity of Oceania. Aridity is usually expressed as a generalized function of precipitation, temperature and reference evapo-transpiration. Higher values represent more humid conditions and lower values represent higher aridity.

Religions of the world coloured by majority religious affiliation. Deeper colours denote greater percentage of people practicing the corresponding religion.

Aridity of North America. Aridity is usually expressed as a generalized function of precipitation, temperature and reference evapo-transpiration. Higher values represent more humid conditions and lower values represent higher aridity. Data from csidotinfo.wordpress.com/2019/01/24/g...

Bathymetry of the Gulf of America, the Caribbean and parts of the Atlantic and Pacific.

This is a bivariate map showing rainfall vs temperature.

Few people realise the scale of the Himalayas because static maps are a bad way to convey topography. Fortunately, we can use interactive maps to explore topography. Here is an example using Pyvista.

Here is an old favourite. Shipping lanes. This time drawn using the Spilhaus projection, which centers the map on Antartica and presents the worlds oceans as one continuous body. Athelstan F. Spilhaus, a South African-American geophysicist and oceanographer in 1942

I have combined two datasets for fun. Here are the world's shipping lanes (red) and the worlds flight paths (white) on the same map.

Expermineting with different datasets. Plotting a topography map, with contour lines on top creates a really cool hillshading effect. Couple that with rivers and you are getting close to a OS map, all in Python

Roads of London! This map was generated using #Matplotlib #Numpy #Geopandas. #Python #DataScience #Data #DataVisualization #London.

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.

Forest Loss. This map shows forest loss since 2000 in Brazil. Defined as a stand-replacement disturbance, or a change from a forest to non-forest state.

Minimum river temperature. Data comes from Hydrosheds (GLORIC). I think this is the prettiest map that I have made to date.

Minimum river temperatures down under!