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Course

Working with Geospatial Data in Python

IntermediateSkill Level

4.8+

Updated 06/2025

This course will show you how to integrate spatial data into your Python Data Science workflow.

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PythonData Manipulation4 hr16 videos53 Exercises4,500 XP17,489Statement of Accomplishment

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Course Description

A good proportion of the data out there in the real world is inherently spatial. From the population recorded in the national census, to every shop in your neighborhood, the majority of datasets have a location aspect that you can exploit to make the most of what they have to offer. This course will show you how to integrate spatial data into your Python Data Science workflow. You will learn how to interact with, manipulate and augment real-world data using their geographic dimension. You will learn to read tabular spatial data in the most common formats (e.g. GeoJSON, shapefile, geopackage) and visualize them in maps. You will then combine different sources using their location as the bridge that puts them in relation to each other. And, by the end of the course, you will be able to understand what makes geographic data unique, allowing you to transform and repurpose them in different contexts.

Prerequisites

Data Manipulation with pandas

1

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.

Geospatial data

Restaurants in Paris

Adding a background map

Introduction to GeoPandas

Explore the Paris districts (I)

Explore the Paris districts (II)

The Paris restaurants as a GeoDataFrame

Exploring and visualizing spatial data

Visualizing the population density

Using pandas functionality: groupby

Plotting multiple layers

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.

Shapely geometries and spatial relationships

Creating a Point geometry

Shapely's spatial methods

Spatial relationships with GeoPandas

In which district in the Eiffel Tower located?

How far is the closest restaurant?

The spatial join operation

Paris: spatial join of districts and bike stations

Map of tree density by district (1)

Map of tree density by district (2)

Choropleths

Equal interval choropleth

Quantiles choropleth

Compare classification algorithms

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!

Coordinate Reference Systems

Geographic vs projected coordinates

Working with coordinate systems in GeoPandas

Projecting a GeoDataFrame

Projecting a Point

Calculating distance in a projected CRS

Projecting to Web Mercator for using web tiles

Spatial operations: creating new geometries

Exploring a Land Use dataset

Intersection of two polygons

Intersecting a GeoDataFrame with a Polygon

Overlaying spatial datasets

Overlay of two polygon layers

Inspecting the overlay result

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.

Introduction to the dataset

Import and explore the data

Convert to common CRS and save to a file

Styling a multi-layered plot

Additional spatial operations

Buffer around a point

Mining sites within national parks

Applying custom spatial operations

Finding the name of the closest National Park

Applying a custom operation to each geometry

Working with raster data

Import and plot raster data

Extract information from raster layer

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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