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<p>Urbanization often results in unequal outcomes in social well-being, particularly in rapidly growing megacities within developing countries. This study investigates spatial inequalities in urban public service accessibility across communities in Beijing, with a focus on migrant populations who often face systemic disadvantages. Using fine-scale spatial and census data, we identify a significant negative correlation between facility accessibility and the proportion of migrants. Among the lowest-income communities, those with higher migrant shares experienced accessibility distances 2.09 times greater than others. In high-migrant areas, inequality levels surpassed the city average by 14.57%, reflecting entrenched spatial disparities. Interpretable machine learning models reveal key threshold effects: when housing prices fall below 80,590 yuan/m² and migrant ratios exceed 32%, inequalities rise sharply. Furthermore, more than 18.98% of communities with high migrant population proportions exhibited cumulative inequality, where multiple disadvantage factors overlap and reinforce each other. These findings highlight how spatial, economic, and demographic vulnerabilities intersect, underscoring the urgent need for data-informed, equity-oriented urban planning to foster more inclusive, resilient, and sustainable cities.</p>

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Spatially compounding effects of cumulation and thresholds amplify urban inequality in megacities

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月7日

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  • MingXing Chen,
  • Wenming Song,
  • Yuanxuan Yang,
  • Huiping Jiang,
  • Liangkan Chen

摘要

Urbanization often results in unequal outcomes in social well-being, particularly in rapidly growing megacities within developing countries. This study investigates spatial inequalities in urban public service accessibility across communities in Beijing, with a focus on migrant populations who often face systemic disadvantages. Using fine-scale spatial and census data, we identify a significant negative correlation between facility accessibility and the proportion of migrants. Among the lowest-income communities, those with higher migrant shares experienced accessibility distances 2.09 times greater than others. In high-migrant areas, inequality levels surpassed the city average by 14.57%, reflecting entrenched spatial disparities. Interpretable machine learning models reveal key threshold effects: when housing prices fall below 80,590 yuan/m² and migrant ratios exceed 32%, inequalities rise sharply. Furthermore, more than 18.98% of communities with high migrant population proportions exhibited cumulative inequality, where multiple disadvantage factors overlap and reinforce each other. These findings highlight how spatial, economic, and demographic vulnerabilities intersect, underscoring the urgent need for data-informed, equity-oriented urban planning to foster more inclusive, resilient, and sustainable cities.