Sergio Rey, Dani Arribas-Bel, Levi John Wolf

Geographic Data Science with Python (eBook, PDF)

• Format: PDF

Produktbeschreibung

This book serves as an introduction to a whole new way of thinking systematically about geographic data, using geographical analysis and computation to unlock new insights hidden within data. The book is structured around the excellent data science environment in Python.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 410
• Erscheinungstermin: 14. Juni 2023
• Englisch
• ISBN-13: 9781000885224
• Artikelnr.: 68079816

Autorenporträt

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.

Inhaltsangabe

Part 1. Building Blocks 1. Geographic thinking for data scientists 2.
Computational Tools for Geographic Data Science 3. Spatial Data 4. Spatial
Weights Part 2. Spatial Data Analysis 5. Choropleth Mapping 6. Global
Spatial Autocorrelation 7. Local Spatial Autocorrelation 8. Point Pattern
Analysis Part 3. Advanced Topics 9. Spatial Inequality Dynamics 10.
Clustering & Regionalization 11. Spatial Regression 12. Spatial Feature
Engineering