Geographic Data Science with Python (Chapman & Hall/CRC Texts in Statistical Science) - Tapa blanda

Libro 100 de 113: Chapman & Hall/CRC Texts in Statistical Science

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

 
9781032445953: Geographic Data Science with Python (Chapman & Hall/CRC Texts in Statistical Science)
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ISBN 10: 1032445955 ISBN 13: 9781032445953
Editorial: Chapman and Hall/CRC, 2023

Sinopsis

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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Acerca del autor

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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Editorial
Chapman and Hall/CRC
Año de publicación
2023
Idioma
Inglés
ISBN 10 
1032445955
ISBN 13 
9781032445953
Encuadernación
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Número de edición
1
Número de páginas
410
Contacto del fabricante
no disponible
Persona responsable
gpsr@libri.de
gpsr@libri.de

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9780367263119: Geographic Data Science with Python (Chapman & Hall/CRC Texts in Statistical Science)

Edición Destacada

ISBN 10:  0367263114 ISBN 13:  9780367263119
Editorial: Chapman and Hall/CRC, 2023
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