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Introduction to Spatial Data Science with GeoDa: Volume 2: Clustering Spatial Data [Kõva köide]

(University of Chicago)
  • Formaat: Hardback, 210 pages, kõrgus x laius: 254x178 mm, kaal: 680 g, 132 Line drawings, color; 48 Line drawings, black and white; 14 Halftones, color; 146 Illustrations, color; 48 Illustrations, black and white
  • Ilmumisaeg: 29-May-2024
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 103271302X
  • ISBN-13: 9781032713021
Teised raamatud teemal:
Introduction to Spatial Data Science with GeoDa: Volume 2: Clustering Spatial DataSuurem pilt
  • Formaat: Hardback, 210 pages, kõrgus x laius: 254x178 mm, kaal: 680 g, 132 Line drawings, color; 48 Line drawings, black and white; 14 Halftones, color; 146 Illustrations, color; 48 Illustrations, black and white
  • Ilmumisaeg: 29-May-2024
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 103271302X
  • ISBN-13: 9781032713021
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781032713021.html
Märksõnad:

This book is the second in a two-volume series that introduces the field of spatial data science. It moves beyond pure data exploration to the organization of observations into meaningful groups, i.e., spatial clustering. This constitutes an important component of so-called unsupervised learning, a major aspect of modern machine learning.

The distinctive aspects of the book are both to explore ways to spatialize classic clustering methods through linked maps and graphs, as well as the explicit introduction of spatial contiguity constraints into clustering algorithms. Leveraging a large number of real-world empirical illustrations, readers will gain an understanding of the main concepts and techniques and their relative advantages and disadvantages. The book also constitutes the definitive user’s guide for these methods as implemented in the GeoDa open source software for spatial analysis.

It is organized into three major parts, dealing with dimension reduction (principal components, multi-dimensional scaling, stochastic network embedding), classic clustering methods (hierarchical clustering, k-means, k-medians, k-medoids and spectral clustering), and spatially constrained clustering methods (both hierarchical and partitioning). It closes with an assessment of spatial and non-spatial cluster properties.

The book is intended for readers interested in going beyond simple mapping of geographical data to gain insight into interesting patterns as expressed in spatial clusters of observations. Familiarity with the material in Volume 1 is assumed, especially the analysis of local spatial autocorrelation and the full range of visualization methods.



This book is the second in a two-volume series that introduces the field of spatial data science. It moves beyond pure data exploration to the organization of observations into meaningful groups, i.e., spatial clustering. This constitutes a major aspect of modern machine learning.

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

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.