Hyper-local geographically weighted regression: extending GWR through local model selection and local bandwidth optimization

Comber, A orcid.org/0000-0002-3652-7846, Wang, Y, Lü, Y et al. (2 more authors) (2018) Hyper-local geographically weighted regression: extending GWR through local model selection and local bandwidth optimization. Journal of Spatial Information Science (17). pp. 63-84. ISSN 1948-660X

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Authors/Creators:
Copyright, Publisher and Additional Information: This work is licensed under a Creative Commons Attribution 3.0 License.
Keywords: Loess Plateau; geographically weighted regression; GWR; model selection; spatial analysis
Dates:
  • Published: 20 December 2018
  • Accepted: 9 July 2018
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Centre for Spatial Analysis & Policy (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 18 Jul 2018 12:26
Last Modified: 08 Jul 2019 14:44
Status: Published
Publisher: University of Maine
Identification Number: https://doi.org/10.5311/JOSIS.2018.17.422

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