Geospatial Data Visualization with Python

3 tutorials

Welcome to the world of geospatial data science! Scientists have estimated that more than half of all data is spatial, and knowing how to analyze such data allows us to uncover hidden patterns and gain deeper insights into the world around us.

In this module, we will cover basic geoprocessing operations and geospatial data visualization. You will be introduced to the geopandas library, which builds on the datatypes used by pandas to allow spatial operations to be performed on geometric data (i.e., shapes!). Across three tutorials, we will cover the basics–from what is geospatial data to how we can acquire, manipulate, and visualize such data.

Note: This module assumes some background in Python and basic knowledge of exploratory data analysis using pandas. You may wish to refer to our previous modules, Introduction to Python and EDA in Python, to familiarize yourself with these topics. You will be working with Jupyter Notebook (.ipynb files) in this module.

Helpful prior knowledge


Learning Outcomes

By the end of this module, you will be able to:

  • Describe the key properties and characteristics of geospatial data
  • Perform basic geospatial data processing and manipulation
  • Create simple maps using geospatial data
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