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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. 

ImportancePatients often travel for cancer care, yet the extent to which patients cross state lines for cancer care is not well understood. This knowledge can have implications for policies that regulate telehealth access to out-of-state clinicians. ObjectiveTo quantify the extent of cross-state delivery of cancer services to patients with cancer. Design, Setting, and ParticipantsThis cross-sectional study analyzed fee-for-service Medicare claims data for beneficiaries (aged ≥66 years) with a diagnosis of breast, colon, lung, or pancreatic cancer between January 1, 2017, and December 31, 2020. Analyses were performed between January 1 and July 30, 2024. ExposurePatient rurality. Main Outcomes and MeasuresThe primary outcome of interest was receipt of cancer care across state lines. Frequencies of cancer services (surgery, radiation, and chemotherapy) were summarized by cancer type in relation to in-state vs out-of-state receipt of care based on state of residence for Medicare beneficiaries. Cross-state delivery of cancer services was also quantified by adjacent vs nonadjacent states and overall between-state flows for service utilization. ResultsThe study included 1 040 874 Medicare beneficiaries with cancer. The mean (SD) age of the study population was 76.5 (7.4) years. Most patients were female (68.2%) and urban residing (78.5%); one-quarter (25.9%) were aged between 70 and 74 years. In terms of race and ethnicity, 7.0% of patients identified as Black, 3.4% as Hispanic, and 85.5% as White. Overall, approximately 6.9% of cancer care was delivered across state lines, with the highest proportion (8.3%) occurring for surgical care, followed by radiation (6.7%) and chemotherapy (5.6%) services. Out of all cross-state care, 68.4% occurred in adjacent states. Frequency of cross-state cancer care increased with patient rurality. Compared with urban-residing patients, isolated rural-residing patients were 2.5 times more likely to cross state lines for surgical procedures (18.5% vs 7.5%), 3 times more likely to cross state lines for radiation therapy services (16.9% vs 5.7%), and almost 4 times more likely to cross state lines for chemotherapy services (16.3% vs 4.2%). Conclusions and RelevanceIn this cross-sectional study of Medicare claims data, a notable proportion of cancer services occurred across state lines, particularly for rural-residing patients. These results highlight the need for cross-state telehealth policies that recognize the prevalence of care delivery from geographically distant specialized oncology services. 

Abstract High-quality cancer data are fundamental for public health research and policy, but cancer data for small geographic units and population subgroups in the United States are rarely available due to small-sample suppression rules, spatial coarsening, and data incompleteness. These limitations hinder high-resolution spatial analyses and precision public health interventions. This study provides a high-resolution cancer incidence dataset for the U.S., generated through a multi-constraint Monte Carlo simulation framework that reconstructs suppressed county-level cancer data and systematically disaggregates them to ZIP Code Tabulation Areas (ZCTAs), guided by demographic constraints. This method integrates population subgroup structures and macro-level incidence rates as constraints, ensuring consistency and reliability across spatial scales. The resulting dataset spans multiple geographic units, from state and county levels to ZCTAs, enabling comprehensive analyses of cancer burden, in-depth spatial analyses, and precision public health interventions across multiple scales. 

BackgroundStay-at-home orders were one of the controversial interventions to curb the spread of COVID-19 in the United States. The stay-at-home orders, implemented in 51 states and territories between March 7 and June 30, 2020, impacted the lives of individuals and communities and accelerated the heavy usage of web-based social networking sites. Twitter sentiment analysis can provide valuable insight into public health emergency response measures and allow for better formulation and timing of future public health measures to be released in response to future public health emergencies. ObjectiveThis study evaluated how stay-at-home orders affect Twitter sentiment in the United States. Furthermore, this study aimed to understand the feedback on stay-at-home orders from groups with different circumstances and backgrounds. In addition, we particularly focused on vulnerable groups, including older people groups with underlying medical conditions, small and medium enterprises, and low-income groups. MethodsWe constructed a multiperiod difference-in-differences regression model based on the Twitter sentiment geographical index quantified from 7.4 billion geo-tagged tweets data to analyze the dynamics of sentiment feedback on stay-at-home orders across the United States. In addition, we used moderated effects analysis to assess differential feedback from vulnerable groups. ResultsWe combed through the implementation of stay-at-home orders, Twitter sentiment geographical index, and the number of confirmed cases and deaths in 51 US states and territories. We identified trend changes in public sentiment before and after the stay-at-home orders. Regression results showed that stay-at-home orders generated a positive response, contributing to a recovery in Twitter sentiment. However, vulnerable groups faced greater shocks and hardships during the COVID-19 pandemic. In addition, economic and demographic characteristics had a significant moderating effect. ConclusionsThis study showed a clear positive shift in public opinion about COVID-19, with this positive impact occurring primarily after stay-at-home orders. However, this positive sentiment is time-limited, with 14 days later allowing people to be more influenced by the status quo and trends, so feedback on the stay-at-home orders is no longer positively significant. In particular, negative sentiment is more likely to be generated in states with a large proportion of vulnerable groups, and the policy plays a limited role. The pandemic hit older people, those with underlying diseases, and small and medium enterprises directly but hurt states with cross-cutting economic situations and more complex demographics over time. Based on large-scale Twitter data, this sociological perspective allows us to monitor the evolution of public opinion more directly, assess the impact of social events on public opinion, and understand the heterogeneity in the face of pandemic shocks. 

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.