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

Reviewing foundational data science packages

In this section, we’ll cover a handful of more generalized data science packages, which will be useful within your spatial data science workflows. While these packages are useful for spatial data science, they are not purpose-built for spatial data science workflows, such as the packages covered previously in this chapter.


pandas is the primary Python package for reading, writing, and manipulating tabular data. As you may recall, the GeoPandas package is built on top of pandas and leverages most of pandas’ core functionality. In our coverage of GeoPandas, we did not cover all of the specialized functionality of pandas, which we’ll work to cover in this section. Let’s start with data structures.

Data structures

The primary pandas data structures are Series and DataFrame. As you may recall from the previous chapter, the GeoPandas’ GeoSeries and GeoDataFrame data structures are based on...