城市用地功能精细化识别方法：时序动态图嵌入深度学习模型

多源大数据融合背景下的城市功能区识别是复杂非线性系统的模式识别问题，如何有效地从大规模的轨迹数据中提取出多粒度连续性时变和多尺度空间相互作用的信息是进行城市区域功能识别的关键。本研究设计实现了一种基于时序动态图嵌入的深度学习模型，在融合滴滴出行及兴趣点数据（Point of Interest, POI）基础上，提取城市区域存在的时间和空间上的隐式特征，结合聚类分析实现城市用地功能的语义识别。结果表明，成都市中心的用地功能趋向复合多样化的发展，且用地属性随时间发生作用范围和用地类型的变化，呈现出功能随着城市群体活动而变化的时空规律。与相关文献的对比实验表明，本文提出方法在更细粒度的时间段下进行功能区识别，得到的同一类功能区域内集聚度更高，能够更好的捕获复合型区域在不同时间模式下呈现出的用地功能变化。本研究为城市用地功能识别研究提供了新的技术方法，为城市规划研究人员全面理解城区结构属性提供了有效手段，对推动城市空间得到更合理高效的利用具有一定的价值。

Time Series Dynamic Graph Embedding: A Method for Precision Identification of Urban Functions

Urban functional area recognition based on multi-source big data is a complex nonlinear pattern recognition problem. The traditional machine learning methods are limited to effectively extract the information of multi granularity, time-varying, and multi-scale spatial interaction from large-scale trajectory data. Therefore, this paper designs and implements a Deep Learning model based on time series dynamic graph embedding, integrates Didi travel and Point of Interest (POI), extracts urban areas' spatiotemporal implicit features, and realizes the semantic recognition combined with cluster analysis. The results show that the land use functions in the center of Chengdu tend to be complex and diversified, and the land use attributes change with time. Furthermore, the scope and land use functions show a temporal and spatial law that they change with the activities of urban groups. The comparative experiments with the relevant literature show that the proposed method can identify the functional areas with a finer granularity. Moreover, the agglomeration degree within the same type of functional areas is higher, which can better capture the land use function changes of the composite area in different time modes. This study provides a new technical method for urban land function identification, helping researchers fully understand the structural attributes of urban areas, and has a particular value in improving the use of urban space.