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

Advanced Topics in Spatial Data Science

Welcome to the final chapter of this book. We’ve covered an incredible amount of content in the prior chapters, from introducing you to the concept of spatial data science to working on numerous challenging case studies applying cutting-edge geospatial machine learning algorithms. Unlike previous chapters, this one will not focus on a single concept but instead will explore a handful of more advanced topics that will further enhance your learnings from previous chapters. We’ll also dive into the topic of ethics in data science and spatial data science, which is becoming a topic of rapidly growing interest due to the improvements in technology and data access, combined with data breaches and other dilemmas.

In this chapter, we’ll cover the following topics:

  • Efficient operations with spatial indexing
  • Estimating unknowns with spatial interpolation
  • Ethical spatial data science