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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. 

The reproducibility and replicability (R&R) crisis poses a significant challenge across disciplines, particularly in spatiotemporal studies. This paper focuses on the unique challenges within spatiotemporal research in the context of R&R, including data availability, methodological conception transparency, interdisciplinary collaboration complexities, the balance between R&R and innovation, and R&R education. Recognizing the potential of Scientific Workflow Management Systems (SWMS) to enhance R&R, we introduce a pioneering SWMS-based integrated spatiotemporal research approach (SISRA) utilizing KNIME, an open-source SWMS, to tackle these R&R challenges. First, we developed a set of KNIME extensions, including Geospatial and Dataverse extensions, to enhance spatiotemporal software availability in SWMS. Then we created spatial data virtual laboratory architecture to support multidisciplinary collaboration. Finally, we suggested a geographical research lifecycle that integrates SWMS-based methods to improve practices, efficiency, and innovation in R&R research and education. Our approach exemplifies how executable workflows can not only alleviate the R&R burden on researchers but also strengthen R&R education in geographical research, illustrating the benefits of our approach in training, teaching, and multidisciplinary collaboration. 

Understanding the space-time dynamics of human activities is essential in studying human security issues such as climate change impacts, pandemic spreading, or urban sustainability. Geotagged social media posts provide an open and space-time continuous data source with user locations which is convenient for studying human movement. However, the reliability of Chinese geotagged social media data for representing human mobility remains unclear. This study compares human movement data derived from the posts of Sina Weibo, one of the largest social media software in China, and that of Baidu Qianxi, a high-resolution human movement dataset from ‘Baidu Map’, a popular location-based service in China with 1.3 billion users. Correlation analysis was conducted from multiple dimensions of time periods (weekly and monthly), geographic scales (cities and provinces), and flow directions (inflow and outflow), and a case study on COVID-19 transmission was further explored with such data. The result shows that Sina Weibo data can reveal similar patterns as that of Baidu Qianxi, and that the correlation is higher at the provincial level than at the city level and higher at the monthly scale than at the weekly scale. The study also revealed spatial variations in the degree of similarity between the two sources. Findings from this study reveal the values and properties and spatiotemporal heterogeneity of human mobility data extracted from Weibo tweets, providing a reference for the proper use of social media posts as the data sources for human mobility studies.