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Daniel Griffith

Spatial Regression Analysis Using Eigenvector Spatial Filtering

Ebook (EPUB Format)

Spatial Regression Analysis Using Eigenvector Spatial Filtering provides theoretical foundations and guides practical implementation of the Moran eigenvector spatial filtering (MESF) technique. MESF is a novel and powerful spatial statistical methodology that allows spatial scientists to account for spatial autocorrelation in their georeferenced data analyses. Its appeal is in its simplicity, yet its implementation drawbacks include serious complexities associated with constructing an eigenvector spatial filter. This book discusses MESF specifications for various intermediate-level topics, including spatially varying coefficients models, (no… Mehr

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Produktdetails


Weitere Autoren: Chun, Yongwan / Li, Bin
  • ISBN: 978-0-12-815692-6
  • EAN: 9780128156926
  • Produktnummer: 35968663
  • Verlag: Elsevier Science & Techn.
  • Sprache: Englisch
  • Erscheinungsjahr: 2019
  • Seitenangabe: 286 S.
  • Plattform: EPUB
  • Masse: 31'621 KB

Über den Autor


Daniel A. Griffith is an Ashbel Smith Professor of Geospatial Information Sciences at the University of Texas at Dallas, affiliated professor in the College of Public Health at the University of South Florida, and adjunct professor in the Department of Resource Economics and Environmental Sociology at the University of Alberta. He holds degrees in Mathematics, Statistics, and Geography, and arguably is the inventor of Moran eigenvector spatial filtering. He is a two-time Fulbright Senior Specialist, an AAG Distinguished Research Honors awardee, and an elected fellow of the Royal Society of Canada, UCGIS, AAG, American Association for the Advancement of Science, American Statistical Association, Regional Science Association International, and Spatial Econometrics Association.

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