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Mining spatiotemporal mobility patterns is crucial for optimizing urban planning, enhancing transportation systems, and improving public safety by providing useful insights into human movement and behavior over space and time. As an unsupervised learning technique, time series clustering has gained considerable attention due to its efficiency. However, the existing literature has often overlooked the inherent characteristics of mobility data, including high-dimensionality, noise, outliers, and time distortions. This oversight can lead to potentially large computational costs and inaccurate patterns. To address these challenges, this paper proposes a novel neural network-based method integrating temporal autoencoder and dynamic time warping-based K-means clustering algorithm to mutually promote each other for mining spatiotemporal mobility patterns. Comparative results showed that our proposed method outperformed several time series clustering techniques in accurately identifying mobility patterns on both synthetic and real-world data, which provides a reliable foundation for data-driven decision-making. Furthermore, we applied the method to monthly county-level mobility data during the COVID-19 pandemic in the U.S., revealing significant differences in mobility changes between rural and urban areas, as well as the impact of public response and health considerations on mobility patterns.
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This content will become publicly available on September 2, 2026
Equivalence and Clustering in Worker Flows: Stochastic Blockmodels for the Analysis of Mobility Tables
Mobility scholars are increasingly turning to computational methods to analyze mobility tables. Most of these approaches start with the detection of mobility clusters, namely, sets of occupations within which the flow of workers is dense and across which the flow is sparse. Yet clustering is not the only way worker flows can be structured. This article shows how a degree-corrected stochastic blockmodel can detect patterns of mobility that are more general than clustering and consistent with the homogeneity criterion laid out by Goodman as well as the internal homogeneity thesis proposed by Breiger. Because of the intractable marginal likelihood of the model, parameters are estimated using a variational expectation maximization algorithm. Simulation results suggest the estimation algorithm successfully recovers (conditionally) stochastically equivalent mobility classes. Analysis of two real-world examples shows the model is able to detect meaningful mobility patterns, even in situations in which commonly used community detection algorithms fail.
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- PAR ID:
- 10655858
- Publisher / Repository:
- Sage Journals
- Date Published:
- Journal Name:
- Sociological Methodology
- ISSN:
- 0081-1750
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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