Implementing Moran Eigenvector Spatial Filtering for Massively Large Georeferenced Datasets

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dc.contributor.ORCID0000-0001-5125-6450 (Griffith, DA)
dc.contributor.ORCID0000-0002-4957-1379 (Chun, Y)
dc.contributor.VIAF14855602 (Griffith, DA)
dc.contributor.VIAF297769863 (Chun, Y)
dc.contributor.authorGriffith, Daniel A.
dc.contributor.authorChun, Yongwan
dc.contributor.utdAuthorGriffith, Daniel A.
dc.contributor.utdAuthorChun, Yongwan
dc.date.accessioned2019-11-08T21:36:14Z
dc.date.available2019-11-08T21:36:14Z
dc.date.created2019-04-02
dc.descriptionDue to copyright restrictions full text access from Treasures at UT Dallas is restricted to current UTD affiliates (use the provided Link to Article).
dc.description.abstractMoran eigenvector spatial filtering (MESF) furnishes an alternative method to account for spatial autocorrelation in linear regression specifications describing georeferenced data, although spatial auto-models also are widely used. The utility of this MESF methodology is even more impressive for the non-Gaussian models because its flexible structure enables it to be easily applied to generalized linear models, which include Poisson, binomial, and negative binomial regression. However, the implementation of MESF can be computationally challenging, especially when the number of geographic units, n, is large, or massive, such as with a remotely sensed image. This intensive computation aspect has been a drawback to the use of MESF, particularly for analyzing a remotely sensed image, which can easily contain millions of pixels. Motivated by Curry, this paper proposes an approximation approach to constructing eigenvector spatial filters (ESFs) for a large spatial tessellation. This approximation is based on a divide-and-conquer approach. That is, it constructs ESFs separately for each sub-region, and then combines the resulting ESFs across an entire remotely sensed image. This paper, employing selected specimen remotely sensed images, demonstrates that the proposed technique provides a computationally efficient and successful approach to implement MESF for large or massive spatial tessellations. ©2019 Informa
dc.description.departmentSchool of Economic, Political and Policy Studies
dc.identifier.bibliographicCitationGriffith, D. A., and Y. Chun. 2019. "Implementing Moran eigenvector spatial filtering for massively large georeferenced datasets." International Journal of Geographical Information Science 33(9): 1703-1717, doi: 10.1080/13658816.2019.1593421
dc.identifier.issn1365-8816
dc.identifier.issue9
dc.identifier.urihttps://hdl.handle.net/10735.1/7080
dc.identifier.volume33
dc.language.isoen
dc.publisherTaylor And Francis Ltd.
dc.relation.urihttps://dx.doi.org/10.1080/13658816.2019.1593421
dc.rights©2019 Informa UK Limited, trading as Taylor & Francis Group.
dc.source.journalInternational Journal of Geographical Information Science
dc.subjectBeamforming (Moran eigenvector)
dc.subjectRemote-sensing images
dc.subjectAutocorrelation (Statistics)--Spatial
dc.subjectRegression analysis (Spatial)
dc.titleImplementing Moran Eigenvector Spatial Filtering for Massively Large Georeferenced Datasets
dc.type.genrearticle

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