Geographic Data Science with Python

by Sergio Rey, Dani Arribas-Bel, Levi John Wolf

This book provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data. In the new world of pervasive, large, frequent, and rapid data, there are new opportunities to understand and analyze the role of geography in everyday life. Geographic Data Science with Python introduces a new way of thinking about analysis, by using geographical and computational reasoning, it shows the reader how to unlock new insights hidden within data. Key ● Showcases the excellent data science environment in Python. ● Provides examples for readers to replicate, adapt, extend, and improve. ● Covers the crucial knowledge needed by geographic data scientists. It presents concepts in a far more geographic way than competing textbooks, covering spatial data, mapping, and spatial statistics whilst covering concepts, such as clusters and outliers, as geographic concepts. Intended for data scientists, GIScientists, and geographers, the material provided in this book is of interest due to the manner in which it presents geospatial data, methods, tools, and practices in this new field.

378 pages, Hardcover

Published June 14, 2023

Reviews

[Harry Lawrence · Rating 3 out of 5]

This book provides a solid foundation in spatial data analysis using Python, with an abundance of real-world examples and clear code snippets throughout.

That said, the 32-page introduction can be skipped entirely - it's just the authors talking about who they are and why they wrote the book. The question sections at the end of some chapters were also a bit of a let down since no answers are provided, making it a bit difficult to check your understanding.

If you can overlook those small drawbacks, I would recommend this for a data scientist looking to get into spatial data.