Authors: Zhanya Xu*,
Topics: Geographic Information Science and Systems
Keywords: big data ， time-space clustering
Session Type: Paper
Start / End Time: 4:40 PM / 6:20 PM
Room: Balcony M, Marriott, River Tower Elevators, 4th Floor
Presentation File: No File Uploaded
Based on the large-scale spatio-temporal trajectories data of urban residents with the technology of big data analysis to explore the potential characters of people and provide the services and decisions making has become an efficient method of modern smart city service.In this paper, with the Sina Weibo check-in data for study, the time-space clustering analysis is carried out by using the time-space rearrangement scanning statistic method to explore the temporal and spatial interaction rules of daily behavior of urban residents, then to measure the aggregation range of time and space hot spots at different time scales and activity time. In order to meet the requirements of fast clustering and optimization, a kernel-density and spatial auto correlation spatio-temporal clustering method based on quad-tree optimization and time-dependent similarity is used to improve the accuracy of clustering and g efficiency.