Urban Mobility and Knowledge Extraction from Chaotic Time Series Data: A Comparative Analysis for Uncovering COVID-19 Effects

Abstract

The COVID-19 pandemic exerted devastating effects on the global economy, public health, and urban life. The geographical consequences lie in spatial time series dimensions, but the chaotic characteristics hinder intuitive understanding. Aiming to discover the variance of urban mobility in the pandemic context, this article presents a hybrid clustering technique called whole time sequence mixture (WTSM). The combination of whole time and subsequence techniques ensures robustness to data volume, dimensionality, sampling, distortion tasks, and a prior constraint. For yellow taxi trips in New York City, the case study spanned the pre-, mid-, and postpandemic periods and compared the performance of the established methods (symbolic aggregate approximation and dynamic time warping) and WTSM techniques. Findings revealed COVID-19 trends and social restriction-induced mobility variations and determined that vaccine supply did not lead to immediate mobility restoration. Meanwhile, concepts of validation indexes and computational complexity corroborate the superiority of WTSM. The transient cluster arising from temporal dissimilarity is a unique finding of WTSM, which led to high cluster cohesion and separation. WTSM can obtain new knowledge and rationale for urgent government intervention and epidemic management, not sacrificing computational efficiency and clustering quality. With the improved storage capability and desire for multidimensional pattern extraction, the increased accessibility to the chaotic time series data sets could promote further studies and help administrative schemes.