Geographic Data science Best books in 2020
Tags: Geographicdatascience, Geospatial, R, Python, Data Science

There are so many excellent books coming this year or published recently about Geospatial data science. In fact, it is the best time to learn Geospatial data science with the availability of learning resources as well as maturing Geospatial data science libraries.

Some of the readers asked me about the best books in the Geospatial data science resources after I published an article on Geospatial data science Courses.

If you prefer learning through reading books, this article is for you. I am sharing here the best and most recent books available in Geographic data science. Some of these books are work in progress and freely available online, which is an excellent opportunity to learn early and help authors.

1. Geographic Data Science with PySAL and the PyData Stack (Work in Progress)

Geographic Data Science with PySAL and the PyData Stack, is an excellent introductory book to learning Geographic data science and offers an extensive learning resource for both beginners and advanced learners. The book covers both theoretical aspects of geospatial computations as well as practical examples with code. As the authors of the book, are contributors of Python libraries like PySAL, the Python Spatial Analysis Library and Geopandas, the content of the book has tightly integrated with Geospatial data science Python Environment.

This book is a work in progress and can be accessed freely online. The GitHub repository also has Jupyter notebooks that you can experiment, adopt or extend.

2. Geocomputation with R (2019)

R language often amazes me with the ease and elegance of its Geospatial data visualizations. If you want to start learning the R language for Geospatial Data analysis, this is the best book available, and it is freely available online. Even if you are beginning R Language, this book can help you if you have already some background in Geographic Information systems.

3. Mastering Geospatial Analysis with Python (2018)

This book touches many aspects of Geographic data analysis and Python programming that gives the reader what is possible to do within these selected topics. The topics covered in this book include among others cloud computing for Geospatial data, web mapping with GeoDjango and Flask as well as several well known Geographic data science libraries. I find it more of a case study rather than a textbook to learn, nevertheless it provides a well-balanced selection of topics with code implementations.

4. Learning Geospatial Analysis with Python (2019)

Learning Geospatial Analysis with Python is an excellent book with the low-level implementation of Geographic data analysis in Python. In this book, you can learn APIs and generic algorithms for Geospatial tasks. It includes a lot of python code to most of Geospatial data processing tasks, like calculating distances, buffer analysis and working with remote sensing data.

5. Introduction to Python for Geographic Data Analysis (Work in Progress)

The materials of this book are not released yet. However, all materials are developed online before the book comes out next year. I think this will be a considerable addition to Geospatial data analysis books in Python. The content of the book is superbly excellent and includes a well-balanced curriculum. As it is a combination of two courses offered at Helsinki University, the curriculum starts from the fundamentals and progresses to an advanced level. Keep an eye on this book once the materials become available online.

6. Geospatial Data Science Quick Start Guide (2019)

Geospatial Data Science Quick Start Guide is an introduction book and offers a practical geospatial data science with Python. Topics covered in the book include exploratory data analysis, geofencing and machine learning applications with Geospatial data. I should mention here that I co-authored this book, thus being at the last item in the list.

Conclusion

These books are some of my favourite books on learning Geospatial data analysis in Python, and R. Let me know if you have others in your list of reading that you think might be an excellent addition to this list.