Applied Geospatial Data Science with Python: Leverage geospatial data analysis and modeling to find unique solutions to environmental problems

Intelligently connect data points and gain a deeper understanding of environmental problems through hands-on Geospatial Data Science case studies written in Python

The book includes colored images of important concepts

Key Features

• Learn how to integrate spatial data and spatial thinking into traditional data science workflows
• Develop a spatial perspective and learn to avoid common pitfalls along the way
• Gain expertise through practical case studies applicable in a variety of industries with code samples that can be reproduced and expanded

Book Description

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.

What you will learn

• Understand the fundamentals needed to work with geospatial data
• Transition from tabular to geo-enabled data in your workflows
• Develop an introductory portfolio of spatial data science work using Python
• Gain hands-on skills with case studies relevant to different industries
• Discover best practices focusing on geospatial data to bring a positive change in your environment
• Explore solving use cases, such as traveling salesperson and vehicle routing problems

Who this book is for

This book is for you if you are a data scientist seeking to incorporate geospatial thinking into your workflows or a GIS professional seeking to incorporate data science methods into yours. You’ll need to have a foundational knowledge of Python for data analysis and/or data science.

Table of Contents

1. Introducing Geographic Information Systems and Geospatial Data Science
2. What Is Geospatial Data and Where Can I Find It?
3. Working with Geographic and Projected Coordinate Systems
4. Exploring Geospatial Data Science Packages
5. Exploratory Data Visualization
6. Hypothesis Testing and Spatial Randomness
7. Spatial Feature Engineering
8. Spatial Clustering and Regionalization
9. Developing Spatial Regression Models
10. Developing Solutions for Spatial Optimization Problems
11. Advanced Topics in Spatial Data Science

David S. Jordan has made a career out of applying spatial thinking to tough problem spaces in the domains of real estate planning, disaster response, social equity, and climate change. He currently leads distribution and geospatial data science at JPMorgan Chase & Co. In addition to leading and building out geospatial data science teams, David is a patented inventor of new geospatial analytics processes, a winner of a Special Achievement in GIS (SAG) Award from Esri, and a conference speaker on topics including banking deserts and how great businesses leverage GIS.