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Abstract The Spatial Data Lab (SDL) project is a collaborative initiative by the Center for Geographic Analysis at Harvard University, KNIME, Future Data Lab, China Data Institute, and George Mason University. Co-sponsored by the NSF IUCRC Spatiotemporal Innovation Center, SDL aims to advance applied research in spatiotemporal studies across various domains such as business, environment, health, mobility, and more. The project focuses on developing an open-source infrastructure for data linkage, analysis, and collaboration. Key objectives include building spatiotemporal data services, a reproducible, replicable, and expandable (RRE) platform, and workflow-driven data analysis tools to support research case studies. Additionally, SDL promotes spatiotemporal data science training, cross-party collaboration, and the creation of geospatial tools that foster inclusivity, transparency, and ethical practices. Guided by an academic advisory committee of world-renowned scholars, the project is laying the foundation for a more open, effective, and robust scientific enterprise. 

Abstract The Symposium on Human Dynamics Research, first organized at the 2015 AAG Meeting in Chicago, celebrated its 10th anniversary at the 2024 AAG Meeting in Honolulu, marking a decade of transformative advancements in the field. Over the past decade, the focus of human dynamics research has shifted from traditional spatial-temporal analyses to sophisticated modeling of human behavior in a hybrid physical-virtual world. This evolving field now examines the intricate interdependencies between physical and digital environments, addressing critical issues such as urban resilience, public health, social equity, and community sustainability. The symposium emphasized the growing importance of interdisciplinary collaboration, advanced data-driven analytical platforms, and innovative theoretical frameworks to better understand human interactions across these spaces. As human dynamics continue to shape global urban systems, these advancements are pivotal for future research and real-world problem-solving, offering novel insights into the interconnectedness of mobility, technology, and societal well-being in a rapidly changing world. 

Abstract Rapid urbanization, climate change, and aging infrastructure pose significant challenges to achieving sustainability and resilience goals in urban building energy use. Although retrofitting offers a viable solution to mitigate building energy use, there has been limited analysis of its effects under various weather conditions associated with climate change in urban building energy use simulations. Moreover, certain parameters in energy simulations necessitate extensive auditing or survey work, which is often impractical. This research proposes a framework that integrates various datasets, including building footprints, Lidar data, property appraisals, and street view images, to conduct neighborhood-scale building energy use analysis using the Urban Modeling Interface (UMI), an Urban Building Energy Model (UBEM), in a coastal neighborhood in Galveston, Texas. Seven retrofit plans and three weather conditions are considered in the scenarios of building energy use. The results show that decreasing the U-value of building envelopes helps reduce energy use, while increasing the U-value leads to higher energy consumption in the Galveston neighborhood. This finding provides direction for coastal Texas cities, like Galveston, to update building standards and implement retrofit measures.