Spatial Autocorrelation and Spatial Filtering: Gaining Understanding Through Theory and Scientific Visualization

By Daniel A. Griffith. 2010

481 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/68798f4b5b0781a50b2974aef7d5a7ffb7a0274f

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Citations in the corpus

Spatio-temporal pattern of China's rural development: A rurality index perspective (2015, background): “2003; Gülümser et al., 2008, 2009; Madu, 2010).”
About predictions in spatial autoregressive models: optimal and almost optimal strategies (2017, methodology): “Y, where Wo. is row o of matrix W. Griffith (2010) proposes an EM procedure by combining the estimation of spatial parameters and the imputation of missing values in the framework of the spatial filtering method (Griffith, 2003).”
Design of computer experiments: space filling and beyond (2011, unknown): “More accurate bounds are given in (Griﬃth, 2003) when the points in ξ follow a regular pattern.”
Stochastic multi-site generation of daily weather data (2009, methodology): “A more useful spatial autocorrelation statistic is Moran’s I (Moran 1950; Odland 1988; Griffith 2003), presented as: I ¼ Pn i¼1 xi xð Þ Pn j¼1 wij xj x .”
A two-step estimator for a spatial lag model of counts: Theory, small sample performance and an application☆ (2010, methodology): “Griffith (2003) extended their model using spatial filters. cross-sectional data with spatial dependence.”
Disentangling good from bad practices in the selection of spatial or phylogenetic eigenvectors (2018, methodology): “Specifically, a variable selection is required to avoid model overfitting and a loss of statistical power to detect the environmental contribution to the variability of the response data (Griffith 2003, Dray et al.”
Spatial Autocorrelation (2020, unknown): “More specifically, spatial autocorrelation is the correlation between the values of a single variable that is strictly due to the proximity of these values in geographical space by introducing a deviation from the assumption of independent observations of classical statistics (Griffith, 2003).”
Modeling Network Autocorrelation in Space–Time Migration Flow Data: An Eigenvector Spatial Filtering Approach (2011, methodology): “Griffith (2000), extended by Tiefelsdorf and Griffith (2007), described how a spatial filter, which is a linear combination of selected eigenvectors, can be constructed in a stepwise manner, can account for spatial components, and results in unbiased parameter estimates.”
A Moran coefficient-based mixed effects approach to investigate spatially varying relationships (2016, methodology): “Thus, Efull ={e1, ..., eN}, provides all the possible 106 distinct map pattern descriptions of latent spatial dependence, with each magnitude being 107 indexed by its corresponding eigenvalue in {λ1, ..., λN} (Griffith, 2003).”
Dimension reduction and alleviation of confounding for spatial generalized linear mixed models (2010, methodology): “In an attempt to reveal the structure of missing spatial covariates, Griffith (2003) augments a generalized linear model with selected eigenvectors of (I − 11′/n)A(I − 11′/n),”
Geospatial analysis requires a different way of thinking: the problem of spatial heterogeneity (2014, background): “s unlikely to be the right means to characterize complex geographic features. Spatial dependence and spatial heterogeneity are unique properties to spatial data and geospatial analysis (Anselin 1989, Griffith 2003), and probably the two most important principles of geographic information science (GI”