© 2003

Spatial Autocorrelation and Spatial Filtering

Gaining Understanding Through Theory and Scientific Visualization


  • This book is about spatial statistics and spatial analysis.-

  • The large number of georeferenced data analysis from various parts of the world is of particular interest to the reader.

  • These datasets contain interval/ratio, binary, percentage and counts variables.- Enables the reader to effectively visualize and analyze spatial autocorrelation latent n georeferenced data.


Part of the Advances in Spatial Science book series (ADVSPATIAL)

Table of contents

  1. Front Matter
    Pages I-XIV
  2. Daniel A. Griffith
    Pages 1-32
  3. Daniel A. Griffith
    Pages 91-130
  4. Daniel A. Griffith
    Pages 193-209
  5. Back Matter
    Pages 211-250

About this book


Advances in Spatial Science

This series of books is dedicated to reporting on recent advances in spatial science. It contains scientific studies focusing on spatial phenomena, utilising theoretical frameworks, analytical methods, and empirical procedures specifically designed for spatial analysis. The series brings together innovative spatial research utilising concepts, perspectives, and methods with a relevance to both basic science and policy making. The aim is to present advances in spatial science to an informed readership in universities, research organisations, and policy-making institutions throughout the world.


The type of material considered for publication in the series includes:


- Monographs of theoretical and applied research in spatial science;

- State-of-the-art volumes in areas of basic research;

- Reports of innovative theories and methods in spatial science;

- Tightly edited reports form specially organised research seminars.


Manuscripts must be prepared in accordance with the guidelines for authors and editors that may be obtained from Springer-Verlag. Manuscripts considered for the series will be reviewed by independent experts to ensure their originality, scientific level, and international policy relevance.


Analysis Map Regression analysis Resampling USA geographic data linear optimization

Authors and affiliations

  1. 1.Department of GeographySyracuse UniversitySyracuseUSA

Bibliographic information


From the reviews:

"Daniel Griffith here makes an effort to expand the methodological toolbox of spatial analysis by presenting, analyzing, and meticulously demonstrating with numerous examples, the applications of spatial filtering … . In sum, many readers will find the book an appealing source of geographic and statistical material, richly supplemented by the use of scientific visualization … . Conceivably, spatial researchers will appreciate its invigorating introduction to mathematical-geographical properties of spatial datasets, and the statisticians will enjoy its many witty and challenging examples." (Oleg Smirnov, Journal of Regional Science, Vol. 44 (3), 2004)