An Introduction to Spatial Data Science with GeoDa

Autorentext

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.

Klappentext

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.

Inhalt

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