Spatial Autocorrelation and Spatial Filtering: Gaining Understanding Through Theory and Scientific Visualization / Edition 1

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Overview

Scientific visualization may be defined as the transformation of numerical scientific data into informative graphical displays. The text introduces a nonverbal model to subdisciplines that until now has mostly employed mathematical or verbal-conceptual models. The focus is on how scientific visualization can help revolutionize the manner in which the tendencies for (dis)similar numerical values to cluster together in location on a map are explored and analyzed. In doing so, the concept known as spatial auorrelation - which characterizes these tendencies - is further demystified.

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Editorial Reviews

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"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)

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Product Details

  • ISBN-13: 9783642056666
  • Publisher: Springer Berlin Heidelberg
  • Publication date: 12/6/2010
  • Series: Advances in Spatial Science Series
  • Edition description: Softcover reprint of hardcover 1st ed. 2003
  • Edition number: 1
  • Pages: 250
  • Product dimensions: 0.56 (w) x 9.21 (h) x 6.14 (d)

Table of Contents

Preface.- Introduction.- Salient Properties of Geographic Connectivity Underlying Spatial Auorrelation.- Sampling Distributions Associated with Spatial Auorrelation.- Spatial Filtering.- Spatial Filtering Applications: Selected Interval/ratio Datasets.- Spatial Filtering Applications: Selected Counts Datasets.- Spatial Filtering Applications: Selected Percentage Datasets.- Concluding Comments.- List of Symbols.- List of Tables.- List of Figures.- References.- Subject Index.- Author Index.- Places Index.

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