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Spatiotemporal systems are ubiquitous in a large number of scientific areas, representing underlying knowledge and patterns in the data. Here, a fundamental question usually arises as how to understand and characterize these spatiotemporal systems with a certain data-driven machine learning framework. In this work, we introduce an unsupervised pattern discovery framework, namely, dynamic autoregressive tensor factorization. Our framework is essentially built on the fact that the spatiotemporal systems can be well described by the time-varying autoregression on multivariate or even multidimensional data. In the modeling process, tensor factorization is seamlessly integrated into the time-varying autoregression for discovering spatial and temporal modes/patterns from the spatiotemporal systems in which the spatial factor matrix is assumed to be orthogonal. To evaluate the framework, we apply it to several real-world spatiotemporal datasets, including fluid flow dynamics, international import/export merchandise trade, and urban human mobility. On the international trade dataset with dimensions {country/region, product type, year}, our framework can produce interpretable import/export patterns of countries/regions, while the low-dimensional product patterns are also important for classifying import/export merchandise and understanding systematical differences between import and export. On the ridesharing mobility dataset with dimensions {origin, destination, time}, our framework is helpful for identifying the shift of spatial patterns of urban human mobility that changed between 2019 and 2022. Empirical experiments demonstrate that our framework can discover interpretable and meaningful patterns from the spatiotemporal systems that are both time-varying and multidimensional.
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This content will become publicly available on August 1, 2026
A Video Machine Learning Framework for Spatiotemporal Analysis of Complex Urban Dynamics
Urban dynamics is complex and interconnected across various social and environmental systems. To better understand such dynamics, this study proposes a scalable and flexible video machine learning framework for spatiotemporal analysis of urban dynamics. The framework is based on a space–time cube representation and decomposes the cube structure along the temporal dimension into a sequence of time‐series spatial aggregation, similar to a video. State‐of‐the‐art video machine learning models including ConvLSTM, predRNN, predRNN‐V2, and E3D‐LSTM are utilized for spatiotemporal modeling and prediction. The scalability of this cyberGIS‐enabled framework is shown by its applicability to diverse geographic regions, its ability to address various urban problems, and its capacity to integrate heterogeneous geospatial data. Moreover, the framework's flexibility is further enhanced by adjustable spatial and temporal granularity. The framework's effectiveness is validated through two case studies: (1) a real‐world urban heat analysis in Cook County, Illinois, USA in 2018, which achieved an RMSE of 0.60535°C, representing a 46% improvement over established benchmarks; and (2) a simulated dataset analysis demonstrating the framework's adaptability for spatial heterogeneity and temporal changes. A series of evaluations demonstrate the effectiveness of the proposed framework in spatiotemporal analysis of complex urban dynamics.
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- Award ID(s):
- 2118329
- PAR ID:
- 10637193
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Transactions in GIS
- Volume:
- 29
- Issue:
- 5
- ISSN:
- 1361-1682
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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