利用多个数据源和可解释的机器学习,揭示环境特征对绿色视图指数的非线性影响
Cai Chen1,2, Jian Wang1,2, Dong Li3,4
1School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing, 102616, China.
Scientific reports
|December 5, 2024
概括
城市绿化对环境可持续性和福祉产生重大影响. 我们的研究显示,绿色覆盖面提高了绿色视图指数 (GVI),而建筑密度则降低了它,突出了更好的城市规划的非线性关系.
科学领域:
- 城市生态学 城市生态学
- 环境科学环境科学
- 地理信息科学 地理信息科学
背景情况:
- 传统方法与城市环境因素和绿色视图指数 (GVI) 之间的复杂关系作斗争.
- 研究城市绿化中的空间异质性和非线性对于环境可持续性和人类福祉至关重要.
- 解决可解释性和非线性方面的挑战对于有效的城市规划至关重要.
研究的目的:
- 开发一个可解释的空间机器学习框架来分析城市绿化.
- 调查环境因素与北京的GVI之间的非线性和异质关系.
- 为科学城市绿化资源分配提供定量见解.
主要方法:
- 利用了一个新的框架,结合了地理加权随机森林 (GWRF) 和夏普利添加式扩平 (Shapley) 模型.
- 集成的多源大数据,包括百度街景和遥感图像.
- 采用语义细分和地理数据处理技术进行GVI分析.
主要成果:
- 北京的GVI表现出显著的空间聚类,正相关性和明显的空间变化.
- 绿色覆盖率与GVI正相关,而建筑密度显示出强烈的负相关.
- 在预测GVI方面,GWRF模型显著优于对比模型,表现出色.
- 环境和社会经济因素影响GVI非线性,具有显著的值效应.
结论:
- 拟议的可解释空间机器学习框架有效地捕捉了影响GVI的非线性和异质关系.
- 调查结果提供了关于绿色覆盖面和城市密度对GVI的影响的关键定量见解.
- 结果支持城市规划人员基于证据的决策,优化绿色资源分配和改善城市环境.
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