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Kurs

Working with Geospatial Data in Python

Fortgeschrittener Anfänger

Aktualisierte 03.2025

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

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PythonData Manipulation4 Stunden16 Videos53 Übungen4,500 XP14,699Leistungsnachweis

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Kursbeschreibung

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.

Voraussetzungen

Data Manipulation with pandas

1

Introduction to Geospatial Vector Data

Kapitel starten

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

Kapitel starten

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

Kapitel starten

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

Kapitel starten

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

Kurs
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Mach mit 16 Millionen Lernende und starte Working with Geospatial Data in Python heute!

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oder

Durch Klick auf die Schaltfläche akzeptierst du unsere Nutzungsbedingungen, unsere Datenschutzrichtlinie und die Speicherung deiner Daten in den USA.