Spatial-Filtering-Based Contributions to a Critique of Geographically Weighted Regression (GWR)
By Daniel A. Griffith. 2008
255 citations recorded by the source corpus.
Subfields: Economics and Econometrics
View Paper: https://www.semanticscholar.org/paper/f0bd9551c7c95eb14387b1987e58fd1b8b75bd8d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Stéphane Dray
- Paul Harris
- Martin Charlton
- Binbin Lu
- Daniel A. Griffith
- P. Peres‐Neto
- A. Stewart Fotheringham
- Pierre Legendre
Citations in the corpus
- A Moran coefficient-based mixed effects approach to investigate spatially varying relationships (2016, methodology): “The result is consistent with a suggestion by Griffith (2008) 561 that the ESF-based specification is robust to multicollinearity.”
- GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models (2013, methodology): “This data has been routinely used in a GW regression context for linking educational attainment with various contextual social variables (see also Griffith 2008).”
- mgwr: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale (2018, methodology): “First, is the well-known Georgia dataset that is described in [2] (2002) as well as subsequent publications [7,11].”
- A Simulation-Based Study of Geographically Weighted Regression as a Method for Investigating Spatially Varying Relationships (2011, methodology): “A spatial-filtering formulation of the expansion method (Griffith, 2008) has been shown to control model instability and reduce correlation in estimated GWR coefficients, but the applicability of this approach to larger samples is limited by the ability to compute the eigenvectors of a transformed…”
- A Practical Guide to Geostatistical Mapping (2009, methodology): “html 16Regression coefficients are often mapped with geographically weighted regression (Griffith, 2008).”
- Community ecology in the age of multivariate multiscale spatial analysis (2012, background): “A promising alternative might be to capture this non-stationarity by introducing interaction terms between environmental variables and spatial eigenfunctions in species–environment models (Griffith 2008).”