用机器学习评估城市街区活力，辅助规划决策
District Vitality Index Using Machine Learning Methods for Urban Planners
- 构建短期与长期活力指数，融合多维度指标
- 15年趋势预测准确，交互地图助力实时监测
- 适合城市规划、公共政策与智能城市建设者
城市领导者在预算分配和投资优先级上面临关键决策。如何识别需振兴的城区？本文提出当前活力指数与长期活力指数，基于精心筛选的指标体系。缺失数据采用K-近邻插补，随机森林用于筛选关键特征，k-means聚类生成有意义的数据分组以增强长期活力监控。当前活力通过交互地图可视化，长期活力则基于15年数据，使用多层感知机或线性回归进行预测。结果经城市规划师验证，已具初步成效，随着更多数据积累可进一步优化。本研究展示机器学习在优化城市规划、提升市民生活质量中的潜力。
原文摘要 · Abstract (English)
City leaders face critical decisions regarding budget allocation and investment priorities. How can they identify which city districts require revitalization? To address this challenge, a Current Vitality Index and a Long-Term Vitality Index are proposed. These indexes are based on a carefully curated set of indicators. Missing data is handled using K-Nearest Neighbors imputation, while Random Forest is employed to identify the most reliable and significant features. Additionally, k-means clustering is utilized to generate meaningful data groupings for enhanced monitoring of Long-Term Vitality. Current vitality is visualized through an interactive map, while Long-Term Vitality is tracked over 15 years with predictions made using Multilayer Perceptron or Linear Regression. The results, approved by urban planners, are already promising and helpful, with the potential for further improvement as more data becomes available. This paper proposes leveraging machine learning methods to optimize urban planning and enhance citizens' quality of life.
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