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Spatial Relationships Between Two Georeferenced Variables = With Applications in R /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Spatial Relationships Between Two Georeferenced Variables / by Ronny Vallejos, Felipe Osorio, Moreno Bevilacqua.
Reminder of title:	With Applications in R /
Author:	Vallejos, Ronny.
other author:	Osorio, Felipe.
Description:	XII, 194 p. 64 illus., 13 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Statistics . -
Online resource:	https://doi.org/10.1007/978-3-030-56681-4
ISBN:	9783030566814

Spatial Relationships Between Two Georeferenced Variables = With Applications in R /
Vallejos, Ronny.

Spatial Relationships Between Two Georeferenced Variables With Applications in R /[electronic resource] :by Ronny Vallejos, Felipe Osorio, Moreno Bevilacqua. - 1st ed. 2020. - XII, 194 p. 64 illus., 13 illus. in color.online resource.

1 Introduction -- 2 The Modified t test -- 3 A Parametric Test based on Maximum -- 4 TjØstheim's Coefficient -- 5 The Codispersion Coefficient -- 6 A Nonparametric Coefficient -- 7 Association for More Than Two Processes -- 8 Spatial Association Between Images -- A Proofs -- B Effective Sample Size -- C Solutions to Selected Problems -- Index.

This book offers essential, systematic information on the assessment of the spatial association between two processes from a statistical standpoint. Divided into eight chapters, the book begins with preliminary concepts, mainly concerning spatial statistics. The following seven chapters focus on the methodologies needed to assess the correlation between two or more processes; from theory introduced 35 years ago, to techniques that have only recently been published. Furthermore, each chapter contains a section on R computations to explore how the methodology works with real data. References and a list of exercises are included at the end of each chapter. The assessment of the correlation between two spatial processes has been tackled from several different perspectives in a variety of applications fields. In particular, the problem of testing for the existence of spatial association between two georeferenced variables is relevant for posterior modeling and inference. One evident application in this context is the quantification of the spatial correlation between two images (processes defined on a rectangular grid in a two-dimensional space). From a statistical perspective, this problem can be handled via hypothesis testing, or by using extensions of the correlation coefficient. In an image-processing framework, these extensions can also be used to define similarity indices between images. .

ISBN: 9783030566814

Standard No.: 10.1007/978-3-030-56681-4doiSubjects--Topical Terms:

1253516
Statistics .

LC Class. No.: QA276-280

Dewey Class. No.: 519.5

Spatial Relationships Between Two Georeferenced Variables = With Applications in R /
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520 $a This book offers essential, systematic information on the assessment of the spatial association between two processes from a statistical standpoint. Divided into eight chapters, the book begins with preliminary concepts, mainly concerning spatial statistics. The following seven chapters focus on the methodologies needed to assess the correlation between two or more processes; from theory introduced 35 years ago, to techniques that have only recently been published. Furthermore, each chapter contains a section on R computations to explore how the methodology works with real data. References and a list of exercises are included at the end of each chapter. The assessment of the correlation between two spatial processes has been tackled from several different perspectives in a variety of applications fields. In particular, the problem of testing for the existence of spatial association between two georeferenced variables is relevant for posterior modeling and inference. One evident application in this context is the quantification of the spatial correlation between two images (processes defined on a rectangular grid in a two-dimensional space). From a statistical perspective, this problem can be handled via hypothesis testing, or by using extensions of the correlation coefficient. In an image-processing framework, these extensions can also be used to define similarity indices between images. .
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