Course
Working with Geospatial Data in Python
IntermediateSkill Level
Updated 06/2025
PythonData Manipulation4 hr16 videos53 Exercises4,500 XP17,690Statement of Accomplishment
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Prerequisites
Data Manipulation with pandas1
Introduction to Geospatial Vector Data
In this chapter, you will be introduced to the concepts of geospatial data, and more specifically of vector data. You will then learn how to represent such data in Python using the GeoPandas library, and the basics to read, explore and visualize such data. And you will exercise all this with some datasets about the city of Paris.
2
Spatial Relationships
One of the key aspects of geospatial data is how they relate to each other in space. In this chapter, you will learn the different spatial relationships, and how to use them in Python to query the data or to perform spatial joins. Finally, you will also learn in more detail about choropleth visualizations.
3
Projecting and Transforming Geometries
In this chapter, we will take a deeper look into how the coordinates of the geometries are expressed based on their Coordinate Reference System (CRS). You will learn the importance of those reference systems and how to handle it in practice with GeoPandas. Further, you will also learn how to create new geometries based on the spatial relationships, which will allow you to overlay spatial datasets. And you will further practice this all with Paris datasets!
4
Putting It All Together – Artisanal Mining Sites Case Study
In this final chapter, we leave the Paris data behind us, and apply everything we have learnt up to now on a brand new dataset about artisanal mining sites in Eastern Congo. Further, you will still learn some new spatial operations, how to apply custom spatial operations, and you will get a sneak preview into raster data.
Working with Geospatial Data in Python
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FAQs
Which Python library is used for geospatial data in this course?
You will use GeoPandas, which extends pandas to handle spatial data, for reading, exploring, manipulating, and visualizing geospatial vector data in Python.
What geospatial file formats are covered?
You will learn to read tabular spatial data in common formats including GeoJSON, shapefiles, and geopackage files.
What datasets and locations are used in the exercises?
Most of the course uses datasets about the city of Paris, and the final chapter switches to a case study on artisanal mining sites in Eastern Congo.
Does the course cover Coordinate Reference Systems?
Yes. Chapter 3 explains how coordinates are expressed based on their CRS, why reference systems matter, and how to handle projections and transformations in GeoPandas.
What spatial operations will I learn?
You will learn spatial relationships, spatial joins, choropleth visualizations, geometry transformations, overlaying datasets, and custom spatial operations for real-world analysis.
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