High-performance solutions of geographically weighted regression in R

As an established spatial analytical tool, Geographically Weighted Regression (GWR) has been applied across a variety of disciplines. However, its usage can be challenging for large datasets, which are increasingly prevalent in today’s digital world. In this study, we propose two high-performance R...

Täydet tiedot

Tallennettuna:
Bibliografiset tiedot
Päätekijät: Binbin Lu, Yigong Hu, Daisuke Murakami, Chris Brunsdon, Alexis Comber, Martin Charlton, Paul Harris
Aineistotyyppi: Artigo
Kieli:Inglês
Julkaistu: Taylor & Francis Group 2022-05-01
Sarja:Geo-spatial Information Science
Aiheet:
Non-stationarity
big data
parallel computing
Compute Unified Device Architecture (CUDA)
Geographically Weighted models (GWmodel)
Linkit:https://www.tandfonline.com/doi/10.1080/10095020.2022.2064244
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