关键词: 影子教育, 长三角城市群, 时空演变, 影响因素, 机器学习, 地理探测器

Abstract:

With the global expansion of shadow education, its development exhibits significant spatiotemporal disparities. Based on POI data of shadow education institutions in the region, this study integrates spatial analysis, regression modeling, geographical detectors, and automated machine learning to investigate the evolutionary patterns and driving factors of these institutions from both macro- and micro-level perspectives. The findings reveal that: ① From 2000 to 2020, shadow education institutions experienced rapid growth; however, following the implementation of the "Double Reduction" policy in 2021, growth rates declined sharply and institutional exit rates increased steadily. ② Spatially, the distribution pattern evolved from a "core-periphery" structure to a "point-axis" belt-like configuration, forming four major development corridors. While institutions remain concentrated in core cities, they are steadily diffusing into surrounding smaller cities, indicating a clear trend of spatial spillover. ③ Different types of institutions demonstrate distinct spatial heterogeneity and divergent evolutionary trajectories. ④ At the macro scale, regional economic development is the primary determinant of distributional differences, whereas at the micro scale, the interaction between residential density and school resources serves as the dominant driver. The multi-method approach employed in this study enhances the robustness of mechanism identification. Overall, the research uncovers the dynamic spatial restructuring of shadow education under policy intervention, offering theoretical support and practical guidance for evaluating the impacts of the "Double Reduction" policy and for advancing regional education governance.

Key words: shadow education, Yangtze River Delta Urban Agglomeration, spatio-temporal evolution, influencing factors, machine learning, geographic detectors