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.