Geographic data science with Python / by Sergio Rey, Dani Arribas-Bel and Levi John Wolf.

Includes bibliographical references and index.

Geographic thinking for data scientists -- Computational tools for geographic data science -- Spatial data -- Spatial weights -- Choropleth mapping -- Global spatial autocorrelation -- Local spatial autocorrelation -- Point pattern analysis -- Spatial inequality dynamics -- Clustering & regionalization -- Spatial regression -- Spatial feature engineering.

"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. Using geographical and computational reasoning, it shows the reader how to unlock new insights hidden within data. The book is structured around the excellent data science environment available in Python, providing examples and worked analyses for the reader to replicate, adapt, extend, and improve. This book codifies what a geographic data scientist does, and covers the crucial knowledge that these scientists need. 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. This book intends to show that these concepts are fundamental to both data science and geographic data science, and any differences in language and framing is superficial. Intended for data scientists, GIScientists and geographers, the material provided in this book will be of interest due to the manner in which it presents geospatial data, methods, tools and practices that this new field presents, and its intention to create collaboration between the communities of data science and geography"-- Provided by publisher.

Description based on print version record and CIP data provided by publisher.