基于轨迹和兴趣点数据的城市功能区动态识别与时变规律可视分析

Dynamic Identification of Urban Functional Areas and Visual Analysis of Time-varying Patterns Based on Trajectory Data and POIs

目前,多数城市功能区识别方法仅依据路网和土地利用类型进行功能区的划分与识别,无法反映功能区范围及功能性随人类活动的动态变化.为此,提出基于轨迹数据挖掘与兴趣点语义分析的城市功能区识别与时空特征分析方法.通过考虑车辆行驶状况与区域功能的相关性,对特征轨迹点进行自适应密度聚类,并基于聚类中心利用Voronoi图合理划分功能区范围.为了有效地评价区域的复合功能性,利用潜在狄利克雷分布(latent Dirichlet allocation,LDA)模型对区域内兴趣点的类别信息挖掘主题词并计算相应的概率,在此基础上提出功能性强弱量化计算方法.基于轨迹数据的时变特性,构建交互式可视分析系统UFAVIS(urban functional areas visualization),进一步发掘人类活动对功能区时空模式的影响.利用结合时空特征分析的功能区识别方法对北京市真实数据进行了实验验证和具体案例分析,结果表明,UFAVIS能够准确识别区域的复合功能性,并发现功能区随人类活动的时空变化规律,为城市规划和政策制定提供依据.

At present, most of the methods for urban functional areas identification are based on the road net- work and the types of land utilization, and cannot reflect the dynamic changes of the coverage areas and the functionalities of functional areas, accompanying with the changes of human activities. In this paper, we pro- pose a method to identify the urban regions and analyze their spatial-temporal features based on trajectory data mining and POIs （point of interest） semantic analysis. Through taking the correlation between vehicle running conditions and functions of regions into account, the characteristic points in trajectory data are clustered adap- tively based on their densities. The functional areas are divided reasonably through building Voronoi diagrams based on the cluster centers. In order to effectively evaluate the compositional functions of the regions, the topic words are mined and the corresponding probabilities are calculated based on the POIs＇ categories in each region, using LDA （latent Dirichlet allocation） topic model. Furthermore, we propose a quantifiable method of computing the function strength based on the results of LDA. Moreover, based on the time-variant characteris- tics of trajectory data, an interactive visual analysis system called UFAVIS （urban functional areas visualization） is constructed to explore the impact of human activities on the spatial-temporal patterns of the functional areas. Using the method of functional area recognition with spatial-temporal feature analysis, experimental verifica- tions and multiple case studies of the real data in Beijing are performed. The results demonstrate that UFAVIS can effectively identify the compositional functions of urban areas and find their spatial-temporal patterns changing with the variations of human activities, which provides guidance for urban planning and policy deci- sion.