TY - BOOK

T1 - Spatial relationships between two georeferenced variables

T2 - With applications in R

AU - Vallejos, Ronny

AU - Osorio, Felipe

AU - Bevilacqua, Moreno

N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020. All rights reserved.

PY - 2020/9/22

Y1 - 2020/9/22

N2 - 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.

AB - 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.

KW - Codispersion map

KW - Coefficients of Spatial Association

KW - Cross-variogram

KW - Georeferenced variables

KW - Image similarity

KW - Image similarity

KW - Maximum likelihood

KW - Multivariate hypothesis testing

KW - Parametric Testing

KW - Quantitative geology

KW - R applications

KW - Relationships between variables

KW - SSIM index

KW - Spatial AR process

KW - Spatial Processes

KW - Spatial autocorrelation

KW - Spatial correlation

KW - Spatial process

KW - T-test

UR - http://www.scopus.com/inward/record.url?scp=85150098688&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-56681-4

DO - 10.1007/978-3-030-56681-4

M3 - Book

AN - SCOPUS:85150098688

SN - 9783030566807

BT - Spatial relationships between two georeferenced variables

PB - Springer International Publishing

ER -