Nov 29, 2021 · The classic GWR is a single-scale model that applies a uniform bandwidth to control the amount of distance-decay in weighting neighbouring data ...
Nov 29, 2021 · Moreover, the MGWR-LL significantly outperformed the MGWR-BF in computational cost, especially for larger-scale data. These results demonstrated ...
ABSTRACT The multiscale geographically weighted regression (MGWR) model is a useful extension of the geographically weighted regression (GWR) model.
The Multiscale Geographically Weighted Regression (MGWR) tool performs an advanced spatial regression technique that is used in geography, urban planning, ...
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Performs Multiscale Geographically Weighted Regression (MGWR), which is a local form of linear regression that models spatially varying relationships.
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A cost-effective algorithm for calibrating multiscale geographically weighted regression models. Article. Nov 2021. Bo Wu · Yan Jinbiao · Hui Lin.
Model calibration and bandwidth vector selection in MGWR are conducted using a back-fitting algorithm. We compare the performance of GWR and MGWR by applying.
This paper reduces computation burden with this advanced GWR model, where significantly faster calibrations are made. This is an important advance, as standard ...
Missing: effective algorithm
A cost-effective algorithm for calibrating multiscale geographically weighted regression models · Bo WuJinbiao YanHui Lin. Geography, Mathematics. Int. J. Geogr ...
We proposed a computationally efficient GWR method, called K-Nearest Neighbors Geographically weighted regression (KNN-GWR).