  In the context of rapid urbanization, understanding the patterns of urban
residents' activities and mobility is crucial for optimizing transportation
systems and enhancing urban management efficiency. This study addresses the
limitations of traditional travel analysis methods in handling high-dimensional
and large-scale spatiotemporal data by incorporating Topological Data Analysis
(TDA) techniques, specifically using persistent homology. This method allows
for the extraction of information from the topological structure of data,
enabling the effective identification and analysis of complex spatiotemporal
behavior patterns without reducing the data's dimensionality. We utilized
mobile signaling data from a community in Shenzhen, which includes detailed
geographic and temporal information, providing an ideal sample for analyzing
urban residents' behavior patterns. Using our pattern mining framework, we
successfully identified five main patterns of residents' activities and travel,
revealing daily behavioral habits and reflecting the activity heterogeneity
among residents with different socio-economic attributes. These findings not
only assist urban planners in better session design but also provide new
characteristics for predictive mobility models.
