Nonstationary models for exploring and mapping monthly precipitation in the United Kingdom

By Christopher D. Lloyd. 2009

73 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/54bfccb59fee131405c7280d8bb8988669c8f9ea

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In Economics and Econometrics

In Environmental Engineering

Citations in the corpus

A geographically weighted regression model augmented by Geodetector analysis and principal component analysis for the spatial distribution of PM2.5 (2020, background): “…account for spatially non-stationary (or spatially heterogeneous) relationships between the dependent variable and regressors (or predictor variables, independent variables) (Lloyd 2010; Harris et al. 2010a, 2011a; Harris and Juggins 2011; Xu and Huang, 2015; Wang et al., 2019; Yang et al., 2020).”
An extension of geographically weighted regression with flexible bandwidths (2014, methodology): “The effects of various window sizes and the methods to choose an optimal size have been demonstrated by applications such as Lloyd and Shuttleworth (2005) in commuting analysis and Páez et al.”
Links, comparisons and extensions of the geographically weighted regression model when used as a spatial predictor (2011, unknown): “In contrast, GWR has been empirically and unfavourably compared to: (a) alternative regressions (Wheeler and Waller 2009; Salas et al. 2010) or (b) kriging ( Lloyd 2010 ).”
Moving window kriging with geographically weighted variograms (2010, methodology): “A recent example of MWK modelling that caters for both local and global trends in (precipitation) data can be found in Lloyd (2010).”
A Practical Guide to Geostatistical Mapping (2009, methodology): “For example, if we set a search radius to re-estimate the variogram model, 22 then we speak about a local prediction model, also known as the moving window kriging or kriging using 23 local variograms (Haas, 1990; Walter et al., 2001; Lloyd, 2009).”
GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models (2013, methodology): “ 8. GW regression for spatial prediction The use of GW regression as a spatial predictor has attracted much attention (e.g. the empirical evaluations of Gao, Asami, and Chung 2006; Pa´ez et al. 2008; Lloyd 2010), where it has often performed well, relative to a geostatistical (kriging) alterative. M”