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The questionnaire method is one of the most commonly used methods of data collection in urban planning. The questionnaires to investigate the satisfaction of residents can provide an in-depth understanding of the masses to meet the needs of residents better. Traditional research on resident satisfaction is mostly based on statistical analysis, while the emergence of machine learning can dig deeper into its intrinsic information. To accurately predict resident satisfaction and analyze its influencing factors, three combinations of Tree-MLP, GCN-MLP, and Tree + GCN-MLP are proposed. The experimental results show that the three proposed models outperform other individual models in multiple evaluation metrics.

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Multi-label Prediction of Resident Satisfaction Based on Combinatorial Model

  • 会议论文

  • 发表日期：

    2025年

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  • Ang Sha,
  • Wei Zhao,
  • Xiaolin Zang,
  • Zhiguo Che,
  • Fuen Xue,
  • Yong Zhang

摘要

The questionnaire method is one of the most commonly used methods of data collection in urban planning. The questionnaires to investigate the satisfaction of residents can provide an in-depth understanding of the masses to meet the needs of residents better. Traditional research on resident satisfaction is mostly based on statistical analysis, while the emergence of machine learning can dig deeper into its intrinsic information. To accurately predict resident satisfaction and analyze its influencing factors, three combinations of Tree-MLP, GCN-MLP, and Tree + GCN-MLP are proposed. The experimental results show that the three proposed models outperform other individual models in multiple evaluation metrics.