The following publications are possibly variants of this publication:
- Geographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationshipsSensen Wu, Zhongyi Wang, Zhenhong Du, Bo Huang 0001, Feng Zhang 0009, Renyi Liu. gis, 35(3):582-608, 2021. [doi]
- Modeling spatially non-stationary land use/cover change in the lower Connecticut River Basin by combining geographically weighted logistic regression and the CA-Markov modelHui Wang, Scott R. Stephenson, Shijin Qu. gis, 33(7):1313-1334, 2019. [doi]
- Geographically convolutional neural network weighted regression: a method for modeling spatially non-stationary relationships based on a global spatial proximity gridZhen Dai, Sensen Wu, Yuanyuan Wang, Hongye Zhou, Feng Zhang 0009, Bo Huang 0001, Zhenhong Du. gis, 36(11):2248-2269, 2022. [doi]
- Exploring the Influences of Point-of-Interest on Traffic Crashes during Weekdays and Weekends via Multi-Scale Geographically Weighted RegressionXinyu Qu, Xinyan Zhu, Xiongwu Xiao, Huayi Wu, Bingxuan Guo, Deren Li. ijgi, 10(11):791, 2021. [doi]
- Spatial Non-Stationarity of Influencing Factors of China's County Economic Development Base on a Multiscale Geographically Weighted Regression ModelZiwei Huang, Shaoying Li, Yihuan Peng, Feng Gao. ijgi, 12(3):109, 2023. [doi]
- An New Algorithm for Modeling Regression CurveJiSheng Hao, LeRong Ma, Wendong Wang. ifip12 2009: 86-91 [doi]
- Investigating spatial non-stationary and scale-dependent relationships between urban surface temperature and environmental factors using geographically weighted regressionShuangcheng Li, Zhiqiang Zhao, Xie Miaomiao, Yanglin Wang. envsoft, 25(12):1789-1800, 2010. [doi]