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Working with Geospatial Data in Python

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

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4 Hours16 Videos53 Exercises9,213 Learners4500 XP

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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.
  1. 1

    Introduction to geospatial vector data

    Free

    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.

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    Geospatial data
    50 xp
    Restaurants in Paris
    100 xp
    Adding a background map
    100 xp
    Introduction to GeoPandas
    50 xp
    Explore the Paris districts (I)
    100 xp
    Explore the Paris districts (II)
    100 xp
    The Paris restaurants as a GeoDataFrame
    100 xp
    Exploring and visualizing spatial data
    50 xp
    Visualizing the population density
    100 xp
    Using pandas functionality: groupby
    100 xp
    Plotting multiple layers
    100 xp
  2. 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.

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    Shapely geometries and spatial relationships
    50 xp
    Creating a Point geometry
    100 xp
    Shapely's spatial methods
    100 xp
    Spatial relationships with GeoPandas
    50 xp
    In which district in the Eiffel Tower located?
    100 xp
    How far is the closest restaurant?
    100 xp
    The spatial join operation
    50 xp
    Paris: spatial join of districts and bike stations
    100 xp
    Map of tree density by district (1)
    100 xp
    Map of tree density by district (2)
    100 xp
    Choropleths
    50 xp
    Equal interval choropleth
    100 xp
    Quantiles choropleth
    100 xp
    Compare classification algorithms
    100 xp
  3. 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!

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    Coordinate Reference Systems
    50 xp
    Geographic vs projected coordinates
    100 xp
    Working with coordinate systems in GeoPandas
    50 xp
    Projecting a GeoDataFrame
    100 xp
    Projecting a Point
    100 xp
    Calculating distance in a projected CRS
    100 xp
    Projecting to Web Mercator for using web tiles
    100 xp
    Spatial operations: creating new geometries
    50 xp
    Exploring a Land Use dataset
    100 xp
    Intersection of two polygons
    100 xp
    Intersecting a GeoDataFrame with a Polygon
    100 xp
    Overlaying spatial datasets
    50 xp
    Overlay of two polygon layers
    100 xp
    Inspecting the overlay result
    100 xp
  4. 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.

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    Introduction to the dataset
    50 xp
    Import and explore the data
    100 xp
    Convert to common CRS and save to a file
    100 xp
    Styling a multi-layered plot
    100 xp
    Additional spatial operations
    50 xp
    Buffer around a point
    100 xp
    Mining sites within national parks
    100 xp
    Applying custom spatial operations
    50 xp
    Finding the name of the closest National Park
    100 xp
    Applying a custom operation to each geometry
    100 xp
    Working with raster data
    50 xp
    Import and plot raster data
    100 xp
    Extract information from raster layer
    100 xp
    The end
    50 xp

Datasets

ParisMining

Collaborators

Mari Nazary
Sara Billen

Prerequisites

Data Manipulation with pandas
Joris Van den Bossche Headshot

Joris Van den Bossche

Open Source Software Developer; Core Developer of Pandas, GeoPandas and scikit-learn

Joris is an open source python enthusiast and currently working as a freelance developer and teacher. Joris has an academic background in air quality research at Ghent University and VITO (Belgium), and recently, he worked at the Université Paris-Saclay Center for Data Science (at Inria), working both on data science projects as contributing to Pandas and scikit-learn. He regularly gives Python data analysis workshops and is a core contributor to Pandas and the maintainer of GeoPandas.
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Dani Arribas-Bel Headshot

Dani Arribas-Bel

Senior Lecturer in Geographic Data Science

Dani Arribas-Bel is interested in computers, cities, and data. He is a senior lecturer in Geographic Data Science at the Department of Geography and Planning of the University of Liverpool. He holds honorary positions at the University of Chicago's Center for Spatial Data Science, the Center for Geospatial Sciences of the University of California Riverside, and the Smart Cities Chair of Universitat the Barcelona. Dani's research interests lie at the intersection of urban studies, computational methods and new forms of data. He is member of the development team of PySAL, the Python library for spatial analysis.
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