a pca-based approach for exploring space-time structure of urban mobility dynamics
Understanding of urban mobility dynamics benefits both aggregate human mobility in wireless communications, and the planning and provision of urban facilities and services. Due to the high penetration of cell phones, the cellular networks provide information for urban dynamics with large spatial extent and continuous temporal coverage. In this paper, a novel approach is proposed to explore the space-time structure of urban dynamics, based on the original data collected by cellular networks in a southern city of China, recording population distribution by dividing the city into thousands of pixels. By applying principal component analysis, the intrinsic dimensionality is revealed. The structure of all the pixel population variations could be well captured by a small set of eigen pixel population variations. According to the classification of eigen pixel population variations, each pixel population variation can be decomposed into three constitutions: deterministic trends, short-lived spikes, and noise. Moreover, the most significant eigen pixel population variations are utilized in the applications of forecasting and anomaly detection.
human mobility principal component analysis urban computing