Abstract
Socio-economic data with fine-grained spatial resolution forms the basis of socio-spatial analysis and policymaking. In response to the limited availability of such data in China, this study provides an open-access, community-level dataset on education percentile rank — a more accurate indicator of social status than years of education. Our dataset comprises 122,126 communities, covering 97.9% of prefecture-level administrative units and 81.8% of county-level administrative units. The data is estimated using an XGBoost machine learning model based on the relationship between mean education percentile rank and the characteristics of the built environment, including functions and facilities, street scene elements, vitality, human perception, physical disorder, and topography at the community level. Multi-source data, including the Chinese General Social Survey, points of interest, road networks, night-time lighting, and street view images processed using computer vision techniques such as semantic segmentation, object detection, and image regression, are used for model training and inference. Our final education predictions are highly accurate at prefecture, county, and community levels. This dataset enables fine-grained socio-spatial analyses across disciplines.
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Data availability
The datasets of community-level education percentile rank estimation in China are openly available on Figshare at https://doi.org/10.6084/m9.figshare.2965459156.
Code availability
The community-level education percentile rank dataset was created using Python 3.9.7 and ArcGIS 10.6 software platform. The code for our extreme gradient boosting (XGBoost) machine learning algorithm, which is used to predict community education percentile ranks, is available at the public repository Figshare (https://doi.org/10.6084/m9.figshare.29648798)57.
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Acknowledgements
The authors would like to thank Guangwen Song of Guangzhou University for providing some of the street view images and for their technical support. We would like to express our gratitude to the editor and the anonymous reviewers for their valuable comments and suggestions.
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Y.J. Zhang conceived the original idea and supervised the research. Z.Y. Pan and B. Qin collected the data and performed data cleaning. Z.Y. Pan and Y.Y. You developed methodology framework, produced this dataset, and analyzed the results. Y.J. Zhang and L. Cai wrote the manuscript.
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Zhang, Y., Pan, Z., You, Y. et al. Community-level education percentile rank estimation in China using multi-source big data and machine learning. Sci Data (2026). https://doi.org/10.1038/s41597-026-06664-y
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DOI: https://doi.org/10.1038/s41597-026-06664-y
