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 enabling spatial analysis and modeling

The prior section focused primarily on packages that enable you to work with and perform operations on spatial data. In this next section, we’ll introduce you to packages that allow you to conduct spatial data analysis and modeling.


PySAL, or the Python Spatial Analysis Library, is a collection of open source packages that support geospatial data science. PySAL’s collection of libraries can be broken down into four main categories:

  • Lib: This is the main library of PySAL, which contains the core backbone architecture for creating spatial indices, working with spatial relationships, and creating what is known as a spatial weights matrix
  • Explore: Contains libraries that enable you to conduct an exploratory analysis of both spatial and spatiotemporal data
  • Model: Contains libraries that provide estimations based on spatial relationships present in the data through the use of linear, generalized linear...