A simulation study to explore inference about global Moran's I with random spatial indexes

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dc.contributor.authorWesterholt, René
dc.date.accessioned2024-01-09T09:26:55Z
dc.date.available2024-01-09T09:26:55Z
dc.date.issued2022-10-17
dc.description.abstractInference procedures for spatial autocorrelation statistics assume that the underlying configurations of spatial units are fixed. However, sometimes this assumption can be disadvantageous, for example, when analyzing social media posts or moving objects. This article examines for the case of point geometries how a change from fixed to random spatial indexes affects inferences about global Moran's I, a popular spatial autocorrelation measure. Homogeneous and inhomogeneous Matérn and Thomas cluster processes are studied and for each of these processes, 10,000 random point patterns are simulated for investigating three aspects that are key in an inferential context: the null distributions of I when the underlying geometries are varied; the effect of the latter on critical values used to reject null hypotheses; and how the presence of point processes affects the statistical power of Moran's I. The results show that point processes affect all three characteristics. Inferences about spatial structure in relevant application contexts may therefore be different from conventional inferences when this additional source of randomness is taken into account.en
dc.identifier.urihttp://hdl.handle.net/2003/42269
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-24106
dc.language.isoende
dc.relation.ispartofseriesGeographical analysis;55(4)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/de
dc.subject.ddc710
dc.titleA simulation study to explore inference about global Moran's I with random spatial indexesen
dc.typeTextde
dc.type.publicationtypeArticlede
dcterms.accessRightsopen access
eldorado.secondarypublicationtruede
eldorado.secondarypublication.primarycitationWesterholt, R. (2023), A Simulation Study to Explore Inference about Global Moran's I with Random Spatial Indexes. Geographical Analysis, 55: 621-650. https://doi.org/10.1111/gean.12349de
eldorado.secondarypublication.primaryidentifierDOI https://doi.org/10.1111/gean.12349de

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