Inferring the heterogeneous effect of urban land use on building height with causal machine learning-Reference-Cited by-同舟云学术

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Inferring the heterogeneous effect of urban land use on building height with causal machine learning

Published:2024-02-25 Issue:1 Volume:61 Page:
ISSN:1548-1603
Container-title:GIScience & Remote Sensing
language:en
Short-container-title:GIScience & Remote Sensing

Author:

Chen Yimin¹,Chen Jing¹,Zhao Shuai¹,Xu Xiaocong¹,Liu Xiaoping¹,Zhang Xinchang²,Zhang Honghui³

Affiliation:

1. School of Geography and Planning, and Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou, China

2. School of Geography and Remote Sensing, Guangzhou University, Guangzhou, China

3. Guangdong Engineering Center for Intelligent Spatial Planning, Guangdong Guodi Planning Science Technology Co. Ltd, Guangzhou, China

Funder

National Natural Science Foundation of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/15481603.2024.2321695

Reference61 articles.

1. Athey, S. 2015. “Machine Learning And Causal Inference For Policy Evaluation.” In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney NSW Australia, 5–16.

2. Recursive partitioning for heterogeneous causal effects

3. Generalized random forests

4. Spatial machine-learning model diagnostics: a model-agnostic distance-based approach

5. A novel unsupervised deep learning method for the generalization of urban form

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Building Height Extraction Based on Spatial Clustering and a Random Forest Model;ISPRS International Journal of Geo-Information;2024-07-26

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