Python Maps

Mapping the world with Python. Buy my book here -

[Python Maps @PythonMaps]

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

[Python Maps @PythonMaps]

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.

[Python Maps @PythonMaps]

I am excited to be representing @SynMaxInt at the #EsriUC2026. If anyone is attending and wants to discuss maps or ships, please get in touch,

[Python Maps @PythonMaps]

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.

[Python Maps @PythonMaps]

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.

[Python Maps @PythonMaps]

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

[Python Maps @PythonMaps]

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.

[Python Maps @PythonMaps]

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.

[Python Maps @PythonMaps]

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.

[Python Maps @PythonMaps]

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.

[Python Maps @PythonMaps]

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.

[Python Maps @PythonMaps]

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.

[Python Maps @PythonMaps]

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.

[Python Maps @PythonMaps]

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

[Python Maps @PythonMaps]

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 - https://t.co/APHofHWuYe

[Python Maps @PythonMaps]

@Nikki_Bea_ Lots of evidence that mars had water in the past but no evidence of grass

[Python Maps @PythonMaps]

Dunno where the green came from. Mars is called the red planet for a reason.

[Fermat's Library @fermatslibrary]

Here's a map of Mars if, like Earth, it were covered by water on 71% of its surface.

[Python Maps @PythonMaps]

Got to have your critics I guess

[☫ Overton Window Defenestrator @BurgerImam]

That's not 70% you fucking fag

[Python Maps @PythonMaps]

@TheWarmChills There’s actually no grass on mars

[Python Maps @PythonMaps]

@Pete_Riot @aji_priyatmoko No AI was used in the generation of this map