从城市体检到动态监测*——以上海城市体征监测为例
From City Medical to Dynamic Diagnosis: A Case of Shanghai City Sign Diagnosis
林文棋
清华大学建筑学院，副教授 北京清华同衡规划设计研究院有限公司 总规划师
段冰若
深圳市城市交通规划设计研究中心有限公司 资深数据分析师，硕士
马 靓
伦敦帝国理工学院土木与环境工程学院 硕士研究生
吴梦荷
北京清华同衡规划设计研究院有限公司技术创新中心 室主任工，硕士
孙小明
北京清华同衡规划设计研究院有限公司技术创新中心 城市未来研究部 副部长，硕士
李 栋
北京清华同衡规划设计研究院有限公司技术创新中心 常务副主任，博士
蔡玉蘅
北京清华同衡规划设计研究院有限公司技术创新中心 项目经理，硕士
摘要: 2015年中央城市工作会议要求建立常态化的城市体检评估机制。通过城市体检识别问题，有针对性地制定解决方案，成为促进城市精细化管理及高质量建设发展的重要途径。以上海城市体征监测为例，提出从城市体检到动态监测的分析模式，利用多源数据，以“人地兼顾、动静结合”为原则，构建包含属性指数、动力指数、压力指数以及活力指数在内的4个一级指数、10个二级指数和27个三级指数的多维指标体系，并以职住空间为例开展专题评估，最后基于机器学习开展政策措施单一场景及综合场景下的城市体征变化模拟和量化对比。研究结果表明，动态的城市体征监测能够更实时、更精准地多角度定量评价城市实时运行状态及各主体时空分布，可以有效支撑专题评估，并通过决策情景模拟等技术，为政府日常管理、运行监测和治理决策优化提供有力的工具支撑。
Abstract: City medical has become annual routine work for implementation assessment of urban planning. Based on the massive multi-dimensional data provided by the government and big data providers, we established multidimensional indicators for urban assessment and diagnosis in Shanghai. Through spatial analysis, the basic situation or problems of each community unit and the overall structure of the city can be judged more timely and accurately. Planning scenario assessment by machine learning was also carried out. By comparing different scenario's influence to indicators, the model can help government's decision making effectively.
关键词：城市体检 | 体征监测 | 大数据 | 机器学习
Keyword: City medical | Urban diagnosis | Big data | Machine learning
中图分类号：TU981
文献标识码: A
资金资助
国家重点研发计划 “村镇发展模拟系统和智能决策管控平台关键技术” 2018YFD1100805
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