Book Image

Applied Geospatial Data Science with Python

By : David S. Jordan
3 (1)
Book Image

Applied Geospatial Data Science with Python

3 (1)
By: David S. Jordan

Overview of this book

Data scientists, when presented with a myriad of data, can often lose sight of how to present geospatial analyses in a meaningful way so that it makes sense to everyone. Using Python to visualize data helps stakeholders in less technical roles to understand the problem and seek solutions. The goal of this book is to help data scientists and GIS professionals learn and implement geospatial data science workflows using Python. Throughout this book, you’ll uncover numerous geospatial Python libraries with which you can develop end-to-end spatial data science workflows. You’ll learn how to read, process, and manipulate spatial data effectively. With data in hand, you’ll move on to crafting spatial data visualizations to better understand and tell the story of your data through static and dynamic mapping applications. As you progress through the book, you’ll find yourself developing geospatial AI and ML models focused on clustering, regression, and optimization. The use cases can be leveraged as building blocks for more advanced work in a variety of industries. By the end of the book, you’ll be able to tackle random data, find meaningful correlations, and make geospatial data models.
Table of Contents (17 chapters)
Part 1:The Essentials of Geospatial Data Science
Free Chapter
Chapter 1: Introducing Geographic Information Systems and Geospatial Data Science
Part 2: Exploratory Spatial Data Analysis
Part 3: Geospatial Modeling Case Studies

Packages for working with geospatial data

There are many packages that enable you to work with geospatial data in Python. In this section, we’ll discuss some of the most common packages that you’ll interact with during the course of common geospatial data science workflows.


As we mentioned in the prior chapter, GeoPandas is an extension of pandas, which adds support for additional data types necessary for working with spatial data. It also includes additional methods not found in pandas, which enable you to perform spatial operations and produce spatial data visualizations. We’ll discuss pandas later on in this chapter in the Reviewing foundational data science packages section. pandas is a foundational package required for most general data science workflows.

The core functionality of GeoPandas includes the following:

  • Reading and writing spatial data
  • Spatial data structures
  • Projection management
  • Spatial data visualization...