Spatial Autocorrelation and Spatial Filtering

By Daniel A. Griffith. 2003

381 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Economics and Econometrics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/9d63dda2f6768de9addd1121e081a15e41ce663d

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Citations in the corpus

The Spatial Durbin Model and the Common Factor Tests (2006, methodology): “To cite only the most recent cases, they are not included in the comprehensive simulation carried out by Anselin & Florax (1995), nor are they mentioned in the meta-analysis of Florax & de Graaff (2004), or in the manuals of Tiefelsdorf (2000) and Griffith (2003).”
Multicollinearity and correlation among local regression coefficients in geographically weighted regression (2005, background): “Details of this approach of generating uncorrelated spatial patterns with a given autocorrelation level and sample maps can be found in Boots and Tiefelsdorf (2000) and Griffith (2003).”
Statistical Modeling of Spatially Stratified Heterogeneous Data (2022, background): “If the underlying spatial variations from the population can be described by a spatial statistical model, then statistical inference becomes possible (see, e.g., Christakos 1992; Griffith 2003; Haining 2003).”
Spatial non-stationarity and scale-dependency of prediction accuracy in the remote estimation of LAI over a tropical rainforest in Sulawesi, Indonesia. (2009, positive): “Regression models with the data considering spatial autocorrelation overestimate the coefficient of determination (R2) and underestimate both the standard error (SE) and the mean square error (RMSE) (Clifford et al., 1989; Griffith, 2003; Tiefelsdorf, 2000).”
Post‐Eocene climate change, niche conservatism, and the latitudinal diversity gradient of New World birds (2006, methodology): “Thus, all significance tests of correlations were performed using a conservative number of degrees of freedom (v*), based on the geographically effective sample size (Clifford et al., 1989; Griffith, 2003), calculated using a modified version of MODTTEST (available from http://www.”
Water links the historical and contemporary components of the Australian bird diversity gradient (2005, methodology): “All significance tests were performed using a reduced degrees of freedom (v*), based on the geographically effective sample size (Griffith, 2003).”
Comparison of Values of Pearson's and Spearman's Correlation Coefficients on the Same Sets of Data (2011, methodology): “The use of Pearson's product-moment correlation coefficient and Spearman's rank correlation coefficient for geographical data (on map data that are spatially correlated) was examined by Haining (1991); see also Griffith (2003).”
A Moran coefficient-based mixed effects approach to investigate spatially varying relationships (2016, methodology): “127 Many studies demonstrate the effectiveness of ESF in estimation and inference 128 for β in the presence of spatial dependence (e.g., Chun, 2014; Griffith and Chun, 2014; 129 4 Another approach includes the model selection procedure based on LASSO (Seya et al., 2015).”
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 .”
Using landscape ecology to understand and manage freshwater mussel populations (2008, background): “Spatial autocorrelation can be addressed by modifying statistical analyses, models, or study designs (Griffith 2003, Fortin and Dale 2005).”