Eigenvector Spatial Filtering for Large Data Sets: Fixed and Random Effects Approaches

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dc.contributor.ORCID0000-0001-5125-6450 (Griffith, DA)
dc.contributor.VIAF14855602 (Griffith, DA)
dc.contributor.authorMurakami, Daisuke
dc.contributor.authorGriffith, Daniel A.
dc.contributor.utdAuthorGriffith, Daniel A.
dc.date.accessioned2020-06-18T20:45:09Z
dc.date.available2020-06-18T20:45:09Z
dc.date.issued2018-03-25
dc.descriptionDue to copyright restrictions and/or publisher's policy full text access from Treasures at UT Dallas is limited to current UTD affiliates (use the provided Link to Article).
dc.description.abstractEigenvector spatial filtering (ESF) is a spatial modeling approach, which has been applied in urban and regional studies, ecological studies, and so on. However, it is computationally demanding, and may not be suitable for large data modeling. The objective of this study is developing fast ESF and random effects ESF (RE-ESF), which are capable of handling very large samples. To achieve it, we accelerate eigen-decomposition and parameter estimation, which make ESF and RE-ESF slow. The former is accelerated by utilizing the Nystrom extension, whereas the latter is by small matrix tricks. The resulting fast ESF and fast RE-ESF are compared with nonapproximated ESF and RE-ESF in Monte Carlo simulation experiments. The result shows that, while ESF and RE-ESF are slow for several thousand samples, fast ESF and RE-ESF require only several seconds for the samples. It is also suggested that the proposed approaches effectively remove positive spatial dependence in the residuals with very small approximation errors when the number of eigenvectors considered is 200 or more. Note that these approaches cannot deal with negative spatial dependence. The proposed approaches are implemented in an R package "spmoran."
dc.description.departmentSchool of Economic, Political and Policy Sciences
dc.description.sponsorshipGrants‐in‐Aid for Scientific Research from JSPS (15H04054 and 17K12974)
dc.identifier.bibliographicCitationMurakami, Daisuke, and Daniel A. Griffith. 2019. "Eigenvector Spatial Filtering for Large Data Sets: Fixed and Random Effects Approaches." Geographical Analysis 51(1): 23-49, doi: 10.1111/gean.12156
dc.identifier.issn0016-7363
dc.identifier.issue1
dc.identifier.urihttp://dx.doi.org/10.1111/gean.12156
dc.identifier.urihttps://hdl.handle.net/10735.1/8675
dc.identifier.volume51
dc.language.isoen
dc.publisherWiley
dc.rights©2018 The Ohio State University
dc.source.journalGeographical Analysis
dc.subjectEigenvectors
dc.subjectAutocorrelation (Statistics)
dc.subjectSpecifications
dc.subjectMatrix analytic methods
dc.subjectModeling
dc.titleEigenvector Spatial Filtering for Large Data Sets: Fixed and Random Effects Approaches
dc.type.genrearticle

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Griffith, Daniel A.