A neural network model to optimize the measure of spatial proximity in geographically weighted regression approach: a case study on house price in Wuhan-Reference-Cited by-同舟云学术

A neural network model to optimize the measure of spatial proximity in geographically weighted regression approach: a case study on house price in Wuhan

Published:2024-04-25 Issue:7 Volume:38 Page:1315-1335
ISSN:1365-8816
Container-title:International Journal of Geographical Information Science
language:en
Short-container-title:International Journal of Geographical Information Science

Author:

Ding Jiale¹^ORCID,Cen Wenying¹,Wu Sensen¹²^ORCID,Chen Yijun¹²,Qi Jin¹²,Huang Bo³^ORCID,Du Zhenhong¹²^ORCID

Affiliation:

1. School of Earth Sciences, Zhejiang University, Hangzhou, China

2. Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou, China

3. Department of Geography, The University of Hong Kong, Hong Kong, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Provincial Key R&D Program of Zhejiang

Fundamental Research Funds for the Central Universities

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/13658816.2024.2343771

Reference31 articles.

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3. A Big Data–Based Geographically Weighted Regression Model for Public Housing Prices: A Case Study in Singapore

4. Neural networks for nonlinear dynamic system modelling and identification

5. Distance metric choice can both reduce and induce collinearity in geographically weighted regression