Creating Geospatial Heatmaps With Plotly Express MapBox and Folium in Python - Data Visualisation

Heatmaps, also known as Density Maps, are data visualisations that display the spatial distribution of a variable across a geographic area. They can be great tools for visualising and identifying trends, supporting decision-making, detecting outliers, and creating compelling visualisations for presentations.
There are several mapping Python libraries available; however, two very popular and easy-to-use libraries are Folium and Plotly Express.
Folium is a great library that makes it easy to visualise geospatial data. It is powered by Leaflet.js, which is a leading javascript mapping library and is platform-independent. Plotly is a popular library for creating powerful interactive data visualisations with very few lines of code and can be used to create interactive maps with MapBox.
This video will show how we can use these two libraries to visualise acoustic compressional slowness data on the Norwegian Continental Shelf.
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[@gmostafaali]

This is an excellent tutorial. Thank you Mr Andy!

[@chipsoldan7825]

Thanks! I learned something new today!

[@smilefaxxe2557]

thanks! that was helpful!

[@thiagoaamorim3219]

Very good tutorial!

[@issahaddad7071]

Great video Andy, could you please share the csv file so I can follow along?

[@petroali6412]

❤

[@mdaneesau]

Thank you Andy for sharing this. really useful. From this i assume you are taking some mean value of DT for each well to plot this on heatmap. I was wondering if we can just pass whole depth log curve data of each well into it and create the map on different tops and if possible we can get a slice of the distribution of particular property at any particular depth? If you could please give your feedback on this, please.

[@AndyMcDonald42]

Yes, you are correct. I have essentially taken the average of the DT curve within a formation and then plotted it on the map. With a little bit of logic and extra code to calculate the means for the different formations, what you are suggesting should be possible. Especially if you combine it with Streamlit, which I have covered in my latest video. This will allow you to develop the front end and allow you/users to chose the data and the formation

[@JuanCamilo-rn7vy]

Nice video, could you please upload more videos about IA and Machine Learning in geosciences?

[@AndyMcDonald42]

I am planning to. 🙂 Any algorithms in particular or geoscience problems that you would like to see?