Data-driven Zone Classification
Data-Driven Zone Classification is a zone classification method that derives zoning boundaries and categories through systematic analysis of empirical data, distinguished from rule-based or expert-driven approaches by its reliance on quantitative patterns and statistical or machine learning models to define spatially coherent zones.
5
Publications
143
Citations
20
Authors
12
Institutions
Publications per year
2017–2026
4
Parents
5
A New Urban Functional Zone-Based Climate Zoning System for Urban Temperature Study
Zhaowu Yu, Yongcai Jing, Gaoyuan Yang et al. · Remote Sensing · 2021 · 57 citations · Full text
Amna M. Handhal, Shaymaa M. Jawad, Alaa M. Al-Abadi · Journal of Petroleum Science and Engineering · 2019 · 21 citations
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Pacific Coastal and Marine Science Center
Santa Cruz, United States
1 h-index
4 publications
68 citations
The University of Tokyo
Tokyo, Japan
1 h-index
3 publications
75 citations

Chinese Academy of Sciences
Beijing, China
1 h-index
2 publications
114 citations

University of Shahrood
Shahrud, Iran
1 h-index
3 publications
69 citations
United States Geological Survey
Reston, United States
1 h-index
5 publications
85 citations

Pacific Science Center
Seattle, United States
1 h-index
4 publications
68 citations
Research Center for Eco-Environmental Sciences
Beijing, China
1 h-index
2 publications
114 citations
Fudan University
Shanghai, China
1 h-index
1 publications
57 citations
1–8 of 12
1 publications · 57 citations
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1 publications · 23 citations
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1 publications · 21 citations
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1 publications · 25 citations
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1 publications · 17 citations
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