LEADER 03682nam 22006135 450 001 9910863165503321 005 20250505002227.0 010 $a3-030-56681-1 024 7 $a10.1007/978-3-030-56681-4 035 $a(CKB)4100000011469621 035 $a(MiAaPQ)EBC6355948 035 $a(DE-He213)978-3-030-56681-4 035 $a(PPN)25022352X 035 $a(EXLCZ)994100000011469621 100 $a20200922d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSpatial Relationships Between Two Georeferenced Variables $eWith Applications in R /$fby Ronny Vallejos, Felipe Osorio, Moreno Bevilacqua 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XII, 194 p. 64 illus., 13 illus. in color.) 311 08$a3-030-56680-3 327 $a1 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. 330 $aThis 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. . 606 $aStatistics 606 $aGeology 606 $aStatistics 606 $aBiometry 606 $aStatistical Theory and Methods 606 $aGeology 606 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 606 $aBiostatistics 615 0$aStatistics. 615 0$aGeology. 615 0$aStatistics. 615 0$aBiometry. 615 14$aStatistical Theory and Methods. 615 24$aGeology. 615 24$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 615 24$aBiostatistics. 676 $a519.5 700 $aVallejos$b Ronny$0917575 702 $aOsorio$b Felipe 702 $aBevilacqua$b Moreno 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910863165503321 996 $aSpatial relationships between two georeferenced variables$92057251 997 $aUNINA