Modeling the spatial relationship between bike-sharing stations and urban centrality using geographical weight variables
This study explores a new approach for including spatial characteristics in machine learning models based on a kernel function in a station-based bike-sharing (SBBS) dataset. On the basis of existing research on geographically weighted statistical methods, we propose a method for transforming spatia...
Saved in:
Main Authors: | Jianyu Li, Mingxing Hu, Xinyu Zhang, Bing Han, Junheng Qi, Jiemin Zheng, Hui Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2025-08-01
|
Series: | International Journal of Applied Earth Observations and Geoinformation |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225003978 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Spatial Heterogeneity of Bike-Sharing–Conventional Bus Intermodal Trip Distribution
by: Yang Chenyang, et al.
Published: (2025-06-01) -
Nonparametric spatio-temporal modeling: Contruction of a geographically and temporally weighted spline regression
by: Sifriyani, et al.
Published: (2025-06-01) -
A study on influencing factors of port cargo throughput based on multi-scale geographically weighted regression
by: Ruitong Guo, et al.
Published: (2025-07-01) -
Enhancing the computational efficiency of the SGWR model and introducing its software implementation
by: M. Naser Lessani, et al.
Published: (2025-06-01) -
The Spatiotemporal Variations Characteristics of Precipitation in the Ailao Mountain Area during 2000 -2020 based on Geographical Weighted Regression Downscaling Method
by: Hong WANG, et al.
Published: (2025-06-01)