Spatial regression analysis using eigenvector spatial filtering:
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...
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| Hauptverfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
London
Elsevier, Academic Press
[2019]
|
| Schriftenreihe: | Spatial econometrics and spatial statistics
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| Schlagworte: | |
| Online-Zugang: | UER01 Volltext Inhaltsverzeichnis |
| Zusammenfassung: | 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, (non) linear mixed models, local spatial autocorrelation, space-time models, and spatial interaction models. Spatial Regression Analysis Using Eigenvector Spatial Filtering is accompanied by sample R codes and a Windows application with illustrative datasets so that readers can replicate the examples in the book and apply the methodology to their own application projects. It also includes a Foreword by Pierre Legendre |
| Beschreibung: | Illustrationen, Diagramme |
| ISBN: | 9780128150436 |
| DOI: | 10.1016/C2017-0-01015-7 |
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| id | DE-604.BV046978247 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T15:49:14Z |
| indexdate | 2024-07-10T08:59:11Z |
| institution | BVB |
| isbn | 9780128150436 |
| language | English |
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| oclc_num | 1220920782 |
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| physical | Illustrationen, Diagramme |
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| publishDate | 2019 |
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| publisher | Elsevier, Academic Press |
| record_format | marc |
| series2 | Spatial econometrics and spatial statistics |
| spelling | Griffith, Daniel A. 1948- (DE-588)124843697 aut Spatial regression analysis using eigenvector spatial filtering Daniel A. Griffith, Yongwan Chun, Bin Li London Elsevier, Academic Press [2019] © 2019 Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier Spatial econometrics and spatial statistics 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, (non) linear mixed models, local spatial autocorrelation, space-time models, and spatial interaction models. Spatial Regression Analysis Using Eigenvector Spatial Filtering is accompanied by sample R codes and a Windows application with illustrative datasets so that readers can replicate the examples in the book and apply the methodology to their own application projects. It also includes a Foreword by Pierre Legendre Spatial analysis (Statistics) Regression analysis Eigenvectors Chun, Yongwan (DE-588)1075494974 aut Li, Bin (DE-588)119822942X aut Erscheint auch als Online-Ausgabe 978-0-12-815692-6 Erscheint auch als Druck-Ausgabe 978-0-12-815043-6 https://doi.org/10.1016/C2017-0-01015-7 Verlag URL des Erstveröffentlichers Volltext V:DE-605;X:IDS application/pdf http://digitale-objekte.hbz-nrw.de/storage2/2020/10/14/file_5/8938630.pdf Inhaltsverzeichnis |
| spellingShingle | Griffith, Daniel A. 1948- Chun, Yongwan Li, Bin Spatial regression analysis using eigenvector spatial filtering Spatial analysis (Statistics) Regression analysis Eigenvectors |
| title | Spatial regression analysis using eigenvector spatial filtering |
| title_auth | Spatial regression analysis using eigenvector spatial filtering |
| title_exact_search | Spatial regression analysis using eigenvector spatial filtering |
| title_exact_search_txtP | Spatial regression analysis using eigenvector spatial filtering |
| title_full | Spatial regression analysis using eigenvector spatial filtering Daniel A. Griffith, Yongwan Chun, Bin Li |
| title_fullStr | Spatial regression analysis using eigenvector spatial filtering Daniel A. Griffith, Yongwan Chun, Bin Li |
| title_full_unstemmed | Spatial regression analysis using eigenvector spatial filtering Daniel A. Griffith, Yongwan Chun, Bin Li |
| title_short | Spatial regression analysis using eigenvector spatial filtering |
| title_sort | spatial regression analysis using eigenvector spatial filtering |
| topic | Spatial analysis (Statistics) Regression analysis Eigenvectors |
| topic_facet | Spatial analysis (Statistics) Regression analysis Eigenvectors |
| url | https://doi.org/10.1016/C2017-0-01015-7 http://digitale-objekte.hbz-nrw.de/storage2/2020/10/14/file_5/8938630.pdf |
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