The Potential of 3-D Building Height Data to Characterize Socioeconomic Activities: A Case Study from 38 Cities in China
Abstract
:1. Introduction
2. Materials
3. Methodologies
3.1. Data Preprocess
3.2. The Correlation between Urban Form and NTL
3.3. The Relationship between Road Density and Building Height
3.3.1. Quantile-Based Approach to Building Masking
3.3.2. Road Distribution Pattern Extraction
4. Results and Discussion
4.1. Correlation with NTL Data
4.2. The Extracted Urban Cores
4.3. Identification of Road Distribution Patterns
4.3.1. Classification of Road Distribution Patterns
4.3.2. Association with City Size
4.3.3. Differences among Road Distribution Patterns
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Yu, G.; Xie, Z.; Li, X.; Wang, Y.; Huang, J.; Yao, X. The Potential of 3-D Building Height Data to Characterize Socioeconomic Activities: A Case Study from 38 Cities in China. Remote Sens. 2022, 14, 2087. https://doi.org/10.3390/rs14092087
Yu G, Xie Z, Li X, Wang Y, Huang J, Yao X. The Potential of 3-D Building Height Data to Characterize Socioeconomic Activities: A Case Study from 38 Cities in China. Remote Sensing. 2022; 14(9):2087. https://doi.org/10.3390/rs14092087
Chicago/Turabian StyleYu, Guojiang, Zixuan Xie, Xuecao Li, Yixuan Wang, Jianxi Huang, and Xiaochuang Yao. 2022. "The Potential of 3-D Building Height Data to Characterize Socioeconomic Activities: A Case Study from 38 Cities in China" Remote Sensing 14, no. 9: 2087. https://doi.org/10.3390/rs14092087
APA StyleYu, G., Xie, Z., Li, X., Wang, Y., Huang, J., & Yao, X. (2022). The Potential of 3-D Building Height Data to Characterize Socioeconomic Activities: A Case Study from 38 Cities in China. Remote Sensing, 14(9), 2087. https://doi.org/10.3390/rs14092087