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!

Roads in Northern Ireland and the Rupublic of Ireland.

Another ACLED map. This map shows violence and social unrest in the Middle East between 2021-2024.

Minimum river temperatures in South East Asia!

Minimum river temperatures in Africa!

Minimum river temperatures in the Sub Continent.

Here is a map showing the minimum temperature of rivers in Europe. Always tricky to know where to draw the line on countries to include. Should Turkey and Iceland be in this map?

The print copy is officially here. Available via Locatepress or on Amazon. locatepress.com/book/pymaps

Topography in contour form of Northern Ireland and the Republic of Ireland. Northern Ireland has the most beautiful mountains in the world. The data is from "Global Multi-resolution Terrain Elevation Data".

Print copy is nearly ready, stay tuned.

Here is a map of South American rivers overlaid on top of forest density.

A first attempt at using ACLED data. This map shows violence and social unrest in South America between 2021-2024.

If you don't care about my book - Please enjoy some road maps. If you want to know how to make these maps, there is a whole chapter in my book that teaches the all of the concepts of lines, with these road maps as the end goal. 1/n

My book starts with simple concepts and builds up into the complex stuff. In one of the early chapters I discuss the basics of raster plotting. We produce some interesting livestock maps like these. 1/n

In the final chapter of my book, I show you how to use GDAL to transform digital elevation models into contour line maps (and vice versa). Here's a sneak peek at the maps 1/n

In my new book, I show you how to use Rasterio to transform ultra-high-resolution population polygons into stunning raster maps. Here's a sneak peek at maps made with KonturInc population data! 1/n

Sorry for the bombardment of book adverts but I am genuinely proud of it. I reckon it is one of the most complete guides to the world for geopandas, rasterio, gdal etc in existence. Here is a thread of river maps generated using the lessons in the book. 1/n blog.locatepress.com/new-release-...

Geospatial visualisation is such a powerful tool for communicating big concepts and Adam does a great job in demonstrating what can be achieved with open source tools with relative ease, unbound to platforms like Google Maps, Mapbox, ESRI etc.

How do you display data from a sphere (😉) in a 2D image. There are many different types of projection, cylindrical, conic etc. Depending on the projection you choose, you will get wildly different maps. Here are some examples, using tropical storms as our data. 1/n

Does anyone have any historical geospatial data lying around that they would like to share. I have a few geojsons for the Roman/Mongolian empires and have commited them to this repository. It would be great if the community could build this resource up. github.com/symmy596/his...