Luc Anselin

An Introduction to Spatial Data Science with GeoDa (eBook, PDF)

Volume 1: Exploring Spatial Data

• Format: PDF

Produktbeschreibung

This book is the first in a two-volume series that introduces the field of spatial data science. It offers an accessible overview of the methodology of exploratory spatial data analysis. It also constitutes the definitive user's guide for the widely adopted GeoDa open source software for spatial analysis.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 444
• Erscheinungstermin: 26. April 2024
• Englisch
• ISBN-13: 9781040010877
• Artikelnr.: 70258038

Autorenporträt

Luc Anselin is the Founding Director of the Center for Spatial Data Science at the University of Chicago, where he is also a Stein-Freiler Distinguished Service Professor of Sociology and the College, as well as a member of the Committee on Data Science. He is the creator of the GeoDa software and an active contributor to the PySAL Python open-source software library for spatial analysis. He has written widely on topics dealing with the methodology of spatial data analysis, including his classic 1988 text on Spatial Econometrics. His work has been recognized by many awards, such as his election to the U.S. National Academy of Science and the American Academy of Arts and Science.

Inhaltsangabe

Chapter 1: Introduction. Chapter 2: Basic Data Operations. Chapter 3: GIS
Operations. Chapter 4: Geovisualization. Chapter 5: Statistical Maps.
Chapter 6: Maps for Rates. Chapter 7: Univariate and Bivariate Data
Exploration. Chapter 8: Multivariate Data Exploration. Chapter 9:
Space-Time Exploration. Chapter 10: Contiguity-Based Spatial Weights.
Chapter 11: Distance-Based Spatial Weights. Chapter 12: Special Weights
Operations. Chapter 13: Spatial Autocorrelation. Chapter 14: Advanced
Global Spatial Autocorrelation. Chapter 15: Nonparametric Spatial
Autocorrelation. Chapter 16: LISA and Local Moran. Chapter 17: Other Local
Spatial Autocorrelation Statistics. Chapter 18: Multivariate Local Spatial
Autocorrelation. Chapter 19: LISA for Discrete Variables. Chapter 20:
Density-Based Clustering Methods. Chapter 21: Postscript - The Limits of
Exploration. Appendices, Bibliography