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. Leveraging a large number of real-world empirical illustrations, readers will gain an understanding of the main concepts and techniques, using dynamic graphics for thematic mapping, statistical graphing, and, most centrally, the analysis of spatial autocorrelation. Key to this analysis is the concept of local indicators of spatial association, pioneered by the author and recently extended to the analysis of multivariate data.

The focus of the book is on intuitive methods to discover interesting patterns in spatial data. It offers a progression from basic data manipulation through description and exploration to the identification of clusters and outliers by means of local spatial autocorrelation analysis. A distinctive approach is to spatialize intrinsically non-spatial methods by means of linking and brushing with a range of map representations, including several that are unique to the GeoDa software. The book also represents the most in-depth treatment of local spatial autocorrelation and its visualization and interpretation by means of GeoDa.

The book is intended for readers interested in going beyond simple mapping of geographical data to gain insight into interesting patterns. Some basic familiarity with statistical concepts is assumed, but no previous knowledge of GIS or mapping is required.

Key Features:

• Includes spatial perspectives on cluster analysis
• Focuses on exploring spatial data
• Supplemented by extensive support with sample data sets and examples on the GeoDaCenter website

This book is both useful as a reference for the software and as a text for students and researchers of spatial data science.

chapter 1|8 pages


part I|52 pages

Spatial Data Wrangling

chapter 102|26 pages

Basic Data Operations

chapter 3|24 pages

GIS Operations

part II|116 pages


chapter 624|24 pages


chapter 5|14 pages

Statistical Maps

chapter 6|14 pages

Maps for Rates

chapter 7|26 pages

Univariate and Bivariate Data Exploration

chapter 8|20 pages

Multivariate Data Exploration

chapter 9|16 pages

Space-Time Exploration

part III|68 pages

Spatial Weights

chapter 17810|24 pages

Contiguity-Based Spatial Weights

chapter 11|22 pages

Distance-Based Spatial Weights

chapter 12|20 pages

Special Weights Operations

part IV|50 pages

Global Spatial Autocorrelation

chapter 24613|20 pages

Spatial Autocorrelation

chapter 14|14 pages

Advanced Global Spatial Autocorrelation

chapter 15|14 pages

Nonparametric Spatial Autocorrelation

part V|94 pages

Local Spatial Autocorrelation

chapter 29616|16 pages

LISA and Local Moran

chapter 18|16 pages

Multivariate Local Spatial Autocorrelation

chapter 19|12 pages

LISA for Discrete Variables

chapter 20|26 pages

Density-Based Clustering Methods

part VI|6 pages


chapter 39021|4 pages

Postscript – The Limits of Exploration