基于时间序列相似度的城市功能区识别研究

随着城市化的快速发展,城市空间结构愈发复杂,城市功能区的快速有效识别对资源的有效配置和城市规划具有重要意义.传统的功能区识别缺乏对居民这一城市空间活动主体的动态表征,而长时间序列的出租车数据能动态表征居民出行行为,进而反映城市空间结构.动态时间扭曲(DTW)距离比传统的欧氏距离更能有效挖掘高维数据,泛化后的LB_Keo...

Urban Functional Area Identification Based on Similarity of Time Series

With the rapid development of urbanization,the urban spatial structure is becoming much more complex,the rapid and effective identification of urban functional areas is of great significance to the effective allocation of resources and urban planning.Traditional functional area identification doesn’t take residents’ activities into consideration.Long-term serial taxi data can dynamically characterize residents’ travel behavior,and then reflect urban spatial structure.In the case of high-dimensional data mining,the dynamic time warp(DTW)distance has a great advantage over the traditional Euclidean distance.However,it has the disadvantages of high time complexity and asymmetry.These shortcomings are respectively overcome by LB_Keogh distance and LB_Hust distance.To explore the feasibility of clustering algorithm based on time series similarity measure in urban functional area identification,we used the road network data of OpenStreetMap(OSM) to divide the study area,mapping the origins and destination of taxi order data into the units and counting the number by hour separately to constructs the travel time series of residents.Then,we combined four distances with PAM clustering algorithm.Finally,we used Points of Interest(POI) data to identify urban functional areas,and verified the accuracy of the functional zoning results.The result shows that PAM algorithm based LB_Hust distance can be applied to urban functional area identification,and the recognition accuracy of functional area is up to 86%,which can provide a new method for functional area identification based on other time series data.