Eigenvector Spatial Filtering for Large Data Sets: Fixed and Random Effects Approaches

@article{Murakami2017EigenvectorSF,
  title={Eigenvector Spatial Filtering for Large Data Sets: Fixed and Random Effects Approaches},
  author={Daisuke Murakami and Daniel A. Griffith},
  journal={Geographical Analysis},
  year={2017},
  volume={51},
  pages={23-49}
}
Eigenvector spatial filtering (ESF) is a spatial modeling approach, which has been applied in urban and regional studies, ecological studies, and so on. However, it is computationally demanding, and may not be suitable for large data modeling. The objective of this study is developing fast ESF and random effects ESF (RE-ESF), which are capable of handling very large samples. To achieve it, we accelerate eigen-decomposition and parameter estimation, which make ESF and RE-ESF slow. The former is… 
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The main findings of this simulation are that in many cases, parameter estimates of the extended RE-ESF are more accurate than other ESF models; the elimination of the spatial component confounding with explanatory variables results in biased parameter estimates; efficiency of an accuracy maximization-based conventional ESF is comparable to RE- ESF inMany cases.
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