Geographically Weighted Regression Analysis

Author:

He Xindong

Publisher

Springer Nature Singapore

Reference17 articles.

1. Andersson, Johanna. 2017. Using Geographically Weighted Regression (GWR) to Explore Spatial Variations in the Relationship Between Public Transport Accessibility and Car Use: A Case Study in Lund and Malmö, Sweden. Student Thesis Series INES.

2. Brunsdon, Chris, A. Stewart Fotheringham, and Martin E. Charlton. 1996. Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity. Geographical Analysis 28 (4): 281–98.

3. Chiou, Yu-Chiun., and Rong-Chang. Jou, and Cheng-Han. Yang. 2015. Factors Affecting Public Transportation Usage Rate: Geographically Weighted Regression. Transportation Research Part A: Policy and Practice 78: 161–77.

4. Gao, Yanhong, Xu. Jianwei, Meng Zhang, and Fengyou Jiang. 2020. Advances in the Study of the 400 Mm Isohyet Migrations and Wetness and Dryness Changes on the Chinese Mainland. Advances in Earth Science 35 (11): 1101.

5. Gu, Kuiying, and Yi. Zhou, Hui Sun, Feng Dong, and Lianming Zhao. 2021. Spatial Distribution and Determinants of PM 2.5 in China’s Cities: Fresh Evidence from IDW and GWR. Environmental Monitoring and Assessment 193: 1–22.

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