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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. 

Understanding urban heat exposure dynamics is critical for public health, urban management, and climate change resilience. Near real-time analysis of urban heat enables quick decision-making and timely resource allocation, thereby enhancing the well-being of urban residents, especially during heatwaves or electricity shortages. To serve this purpose, we develop a cyberGIS framework to analyze and visualize human sentiments of heat exposure dynamically based on near real-time location-based social media (LBSM) data. Large volumes and low-cost LBSM data, together with a content analysis algorithm based on natural language processing are used effectively to generate near real-time heat exposure maps from human sentiments on social media at both city and national scales with km spatial resolution and census tract spatial unit. We conducted a case study to visualize and analyze human sentiments of heat exposure in Chicago and the United States in September 2021. Enabled with high-performance computing, dynamic visualization of heat exposure is achieved with fine spatiotemporal scales while heat exposure detected from social media data can be used to understand heat exposure from a human perspective and allow timely responses to extreme heat. 

Abstract Due to climate change and rapid urbanization, Urban Heat Island (UHI), featuring significantly higher temperature in metropolitan areas than surrounding areas, has caused negative impacts on urban communities. Temporal granularity is often limited in UHI studies based on satellite remote sensing data that typically has multi-day frequency coverage of a particular urban area. This low temporal frequency has restricted the development of models for predicting UHI. To resolve this limitation, this study has developed a cyber-based geographic information science and systems (cyberGIS) framework encompassing multiple machine learning models for predicting UHI with high-frequency urban sensor network data combined with remote sensing data focused on Chicago, Illinois, from 2018 to 2020. Enabled by rapid advances in urban sensor network technologies and high-performance computing, this framework is designed to predict UHI in Chicago with fine spatiotemporal granularity based on environmental data collected with the Array of Things (AoT) urban sensor network and Landsat-8 remote sensing imagery. Our computational experiments revealed that a random forest regression (RFR) model outperforms other models with the prediction accuracy of 0.45 degree Celsius in 2020 and 0.8 degree Celsius in 2018 and 2019 with mean absolute error as the evaluation metric. Humidity, distance to geographic center, and PM_2.5concentration are identified as important factors contributing to the model performance. Furthermore, we estimate UHI in Chicago with 10-min temporal frequency and 1-km spatial resolution on the hottest day in 2018. It is demonstrated that the RFR model can accurately predict UHI at fine spatiotemporal scales with high-frequency urban sensor network data integrated with satellite remote sensing data.