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dc.contributor.authorYe, Xiang
dc.date.accessioned2016-04-05T19:32:06Z
dc.date.available2016-04-05T19:32:06Z
dc.date.issued2014
dc.identifier.isbn9781321266344
dc.identifier.other1625984782
dc.identifier.urihttp://hdl.handle.net/10477/50804
dc.description.abstractSpatial correlation depicts the pattern of similarity or regularity between two spatially distributed variables. It not only implies the shared information contained in both observed variables of interest from a spatial perspective, but also provides a clue leading to the potential causation that drives the spatial process between them. A statistic by the name of the Moran correlation coefficient (MCC) is proposed to quantify the spatial correlation between a pair of spatially distributed variables. It is inspired by Moran's I and Pearson's r and has a variety of derivations that can be calculated in discrete, continuous, matrix and multivariate scenarios. The characteristics and adaptabilities together with the hypothesis testing of the proposed statistic are discussed. Its broad usability is exemplified by case studies from the arena of econometrics and ecology.
dc.languageEnglish
dc.sourceDissertations & Theses @ SUNY Buffalo,ProQuest Dissertations & Theses Global
dc.subjectPure sciences
dc.subjectSocial sciences
dc.subjectApplied sciences
dc.subjectMoran correlation coefficient
dc.subjectMoran's i
dc.subjectPearson's r
dc.subjectSpatial association
dc.subjectSpatial correlation
dc.titleThe Moran correlation coefficient
dc.typeDissertation/Thesis


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