在中国使用多源大数据和机器学习的社区级教育百分位数排名估计
Yanji Zhang1, Zhenyu Pan1, Yongyi You2
1Department of Sociology, School of Humanities and Social Sciences, Fuzhou University, Fuzhou, 350108, China.
Scientific data
|January 26, 2026
概括
本研究介绍了中国教育百分位数排名的开放式访问,社区级数据集,提供了精确的社会地位衡量标准. 这些有价值的社会空间数据有助于政策制定者和研究人员进行详细的社会空间分析.
科学领域:
- 社会空间分析是一种社会空间分析.
- 地理信息系统 (GIS) 是指地理信息系统.
- 机器学习 机器学习
背景情况:
- 中国社会经济数据的有限可用性阻碍了有效的政策制定.
- 传统的衡量标准,比如教育年限,对社会地位的准确指标较少.
- 需要在社区层面建立一个高分辨率的社会地位数据集.
研究的目的:
- 为中国创建一个开放式的,社区级的教育百分位数排名数据集.
- 提供比传统教育指标更准确的社会地位指标.
- 促进精细的社会空间分析,并为政策决策提供信息.
主要方法:
- 使用XGBoost机器学习模型来估计教育百分位数排名.
- 综合多源数据,包括调查,景点,道路网络和夜间照明.
- 在街景图像中使用计算机视觉技术 (语义细分,物体检测,图像回归).
主要成果:
- 开发了涵盖122,126个社区的数据集,代表了97.9%的县级行政单位和81.8%的县级行政单位.
- 在县级,县级和社区级的教育预测中取得了高准确性.
- 数据集捕捉了社会地位和建筑环境特征之间的关系.
结论:
- 新开发的数据集为中国的社会空间研究提供了宝贵的资源.
- 能够对社会差距及其空间分布进行详细分析.
- 通过提供细致的社会经济洞察力,支持基于证据的政策制定.
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