A NEW HIERARCHICAL CLUSTERING APPROACH FOR SPARSE MOBILE PHONE TRAJECTORIES
- 1Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen 518040, China
- 2Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services and Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
- 3Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation, Shenzhen University, Shenzhen 518060, China
- 4College of Civil Engineering, Shenzhen University, Shenzhen 518060, China
- 5School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan, China
Keywords: Trajectory, Mobile phone data, Human activities, Hierarchical clustering, Human behavior
Abstract. Understanding the pattern of human activities benefits both the living service providing for the public and the policy-making for urban planners. The development of location-aware technology enables us to acquire large volume individual trajectories with different spatial and temporal resolution, such as GPS trajectories, mobile phone positioning data, social media check-in data, Wifi, and Bluetooth. However, the highest population penetrated mobile phone positioning trajectories are hard to infer human activity pattern directly, because of the sparsity in both space and time. This article presents a hierarchical clustering approach by using the move and stay sequences inferred from spare mobile phone trajectories to uncover the hidden human activity pattern. Personal stays at some places and following moves are first extracted from mobile phone trajectories, considering the spatial uncertainty of position. The similarity of trajectories is measured with a new indicator defined by the area of a spatial-temporal polygon bound with normalized trajectories. Finally, a hierarchical clustering method is developed to group trajectories with similar stay-move chains from the bottom to the top. The obtained clusters are analyzed to identify human activity patterns. An experiment with mobile phone users’ one-day trajectories in Shenzhen, China was conducted to test the performance of the proposed clustering approach. The results indicate all used trajectories are classified into 10 clusters representing typical daily activity patterns from the simple home-staying mode to complex home-working-social activity daily cycles. This study not only unravels the hidden activity patterns behind massive sparse trajectories but also deepens our understanding of the interaction of human activity and urban space.