Working with Geospatial Data in Python

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.

Datasets

ParisMining

Collaborators

Mari NazarySara Billen

Prerequisites

Data Manipulation with pandas

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

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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