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Scalable geographically weighted regression for big data. GeoComputation 2019

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conference contribution
posted on 01.12.2019 by Daisuke Murakami, Narumasa Tsutsumida, Takahiro Yoshida, Tomoki Nakaya, Binbin Lu
This study develops a scalable GWR (ScaGWR) for large samples. Unlike existing GWR algorithms, the ScaGWR achieves a linear time estimation through pre-compression of large matrices and vectors before the leave-one-out cross validation (LOOCV), which is the heaviest part in the standard GWR. Our proposed algorithm enables us to estimate a regularized GWR from one million samples even without parallelization. The R code is available in the R package scgwr. Besides, for faster computation, the code is embedded into C++ code and implemented in the GWmodel package.

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