Overview

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 Features: ● 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.

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9781032445953

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Reviews

"""The geospatial Python ecosystem is evolving rapidly, and until now there has been no one-stop reference for the geospatial programmer on data I/O, spatial analysis, and geovisualization. I will use this book in my teaching and will also recommend it to students as a book to keep on the shelf and use as a supplement to other courses, for independent projects, and for their future careers. I don't think there is anything quite like it in the market."" -Professor Lee Hachadoorian, Temple University ""Geographic Data Science with Python is an essential resource for data scientists looking to extend their skills into the geographic domain and for geographers looking to add data science skills. The book's approach achieves a highly effective balance between introducing theoretical concepts and applying them to practical examples. The book also serves as a guide to the modern open source spatial Python stack. The accompanying interactive Jupyter notebooks are great resources for running what-if scenarios to extend the concepts introduced in the book and for getting started with new projects. If you want to understand the unique properties of spatial data and how to apply them in creative ways using Python, this book is a must have."" - David C. Folch, Associate Professor, Northern Arizona University"

The geospatial Python ecosystem is evolving rapidly, and until now there has been no one-stop reference for the geospatial programmer on data I/O, spatial analysis, and geovisualization. I will use this book in my teaching and will also recommend it to students as a book to keep on the shelf and use as a supplement to other courses, for independent projects, and for their future careers. I don't think there is anything quite like it in the market. -Professor Lee Hachadoorian, Temple University

Author Information

Sergio Rey is Professor of Geography and Founding Director of the Center for Open Geographical Science at San Diego State University. Rey is the creator and lead developer of the open source package STARS: Space-Time Analysis of Regional Systems as well as co-founder and lead developer of PySAL: A Python Library for Spatial Analysis. He is an elected fellow of the Regional Science Association International, a fellow of the Spatial Econometrics Association, and has served as the Editor of the International Regional Science Review from 1999-2014, editor of Geographical Analysis 2014-2017, and the president of the Western Regional Science Association. Dani Arribas-Bel is a Professor in Geographic Data Science at the Department of Geography and Planning of the University of Liverpool (UK), and Deputy Programme Director for Urban Analytics at the Alan Turing Institute, where he is also ESRC Fellow. At Liverpool, he is a member of the Geographic Data Science Lab, and directs the MSc in Geographic Data Science. Levi John Wolf is a Senior Lecturer/Assistant Professor in Quantitative Human Geography at the University of Bristol’s Quantitative Spatial Science Lab, Fellow at the University of Chicago Center for Spatial Data Science, an Affiliate Faculty at the University of California, Riverside’s Center for Geospatial Sciences, and Fellow at the Alan Turing Institute. He works in spatial data science, building new methods and software to learn new things about social and natural processes.

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