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


In this chapter, we defined the differences and commonalities between GIS, data science, and geospatial data science. As we discussed data science, we took a deep dive into the four pillars of data science, which include mathematics, computer science, domain and industry knowledge, and soft skills.

We also briefly discussed the stages involved in the data science process. Parts 2 and 3 of this book will provide you with more hands-on experience in implementing the data science process through exploratory data analysis, hypothesis testing, and in-depth data science use cases, covering a variety of topics and algorithms.

We also discussed how the principles of geospatial data science can be applied more broadly within the domain of spatial data science to solve problems at a smaller, microscopic level, as well as larger, astronomical scales. The power of geospatial data science is only starting to be realized as industries, data storage, and computing methodologies evolve. We’re excited that you’ve decided to embark on this learning journey with us and are even more excited to see what you achieve in your journey to become a geospatial data scientist.

In the next chapter, we’ll dive deeper into the world of geospatial data, which we briefly described in this chapter as being a representation of the real world in vector or raster format. We’ll also spend time in the next chapter discussing the rich sources of open geospatial data.