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. This book is the second in a two-volume series that introduces the field of spatial data science.
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. This book is the second in a two-volume series that introduces the field of spatial data science.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Luc Anselin is the Founding Director of the Center for Spatial Data Science at the University of Chicago, where he is also 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
Volume 1 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 Volume 2 1. Introduction Part 1: Dimension Reduction 2. Principal Component Analysis (PCA) 3. Multidimensional Scaling (MDS) 4. Stochastic Neighbor Embedding (SNE) Part 2: Classic Clustering 5. Hierarchical Clustering Methods 6. Partioning Clustering Methods 7. Advanced Clustering Methods 8. Spectral Clustering Part 3: Spatial Clustering 9. Spatializing Classic Clustering Methods 10. Spatially Constrained Clustering - Hierarchical Methods 11. Spatially Constrained Clustering - Partitioning Methods Part 4: Assessment 12. Cluster Validation
