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 exposed you to a handful of more advanced topics that weren’t covered in prior chapters of the book, including spatial indexing and spatial interpolation. Within the section on spatial indexing, we discussed how spatial indexes can be used to improve the efficiencies of spatial queries and spatial operations. In this section, you were introduced to the R-tree index as well as Uber’s H3 spatial index, which was used to filter and summarize Airbnb locations in Manhattan. In the section on spatial interpolation, you learned how to infer missing values using sampled points through the application of IDW interpolation and Kriging. In our discussion on Kriging, you were also exposed to variography and semivariograms.

In the last section of this book, we briefly discussed the topic of ethics in spatial data science. We walked you through numerous examples where spatial data has been used in potentially unethical ways, including an altered hurricane...