基于多源大数据的轨道交通站域街道品质多维评价分析——以成都市三环内地铁站域街道为例

为了科学测度、准确评价站域街道的空间品质并实现有效优化，选取成都市73个地铁站域，以街道网络、POI（point of interest）、街景图片等多源大数据为支撑，运用机器学习、sDNA分析（spatial design network analysis）等技术，构建了以便捷性、功能性与舒适性为核心的评价体系，进行站域街道空间品质的大规模定量评价，并针对不同等级的站点提出了导控策略。结果表明，在城市整体层面，68.03%的站域街道评分低于中等水平，街道功能性与舒适性普遍较好，便捷性较差；在站域层面，街道空间品质呈现出南高北低、西高东低、内高外低的分布特征。研究使人本尺度的分析精度、站点尺度的分析深度和城市尺度的分析广度得以兼顾，有助于创建高效的城市管理动态反馈机制。

Multi dimensional evaluation and analysis of street quality in rail transit station area based on multi-source big data:

To scientifically measure and accurately evaluate the spatial quality within the station area and realize effective optimization, seventy-three subway stations in Chengdu City were selected to support multi-source big data such as street network, POI(point of interest), street view pictures, etc., then machine learning and spatial design network analysis(sDNA) and other technologies were used to construct an evaluation system with convenience, functionality and comfort as the core. Large-scale quantitative evaluation of street space quality within the station area was carried out, and guidance and control strategies for different levels of stations were proposed. The results show that 68.03% of the station area streets score is lower than the medium level, the street function and comfort are generally good, and the convenience is poor; at the station level, the street space quality shows the distribution characteristics of high in the South and low in the north, high in the West and low in the East, and high in the inside and low in the outside. The proposed method takes into account the analysis accuracy of human-oriented scale, the analysis depth of site scale and the analysis breadth of urban scale, which is helpful to create an efficient dynamic feedback mechanism of urban management.