Distributed spatial infrastructures leveraging cloud computing technologies can tackle issues of disparate data sources and address the need for data-driven knowledge discovery and more sophisticated spatial analysis central to the COVID-19 pandemic. We implement a new, open source spatial middleware component (libgeoda) and system design to scale development quickly to effectively meet the need for surveilling county-level metrics in a rapidly changing pandemic landscape. We incorporate, wrangle, and analyze multiple data streams from volunteered and crowdsourced environments to leverage multiple data perspectives. We integrate explorative spatial data analysis (ESDA) and statistical hotspot standards to detect infectious disease clusters in real time, building on decades of research in GIScience and spatial statistics. We scale the computational infrastructure to provide equitable access to data and insights across the entire USA, demanding a basic but high-quality standard of ESDA techniques. Finally, we engage a research coalition and incorporate principles of user-centered design to ground the direction and design of Atlas application development.

中文翻译：

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美国新冠地图集：用于交互式探索疫情的动态网络基础设施监控系统

利用云计算技术的分布式空间基础设施可以解决不同数据源的问题，并满足对数据驱动的知识发现和对 COVID-19 大流行至关重要的更复杂的空间分析的需求。我们实施了一个新的开源空间中间件组件（libgeoda）和系统设计，以快速扩展开发规模，从而有效满足在快速变化的流行病形势下监测县级指标的需求。我们整合、整理和分析来自志愿和众包环境的多个数据流，以利用多个数据视角。我们以数十年的地理信息科学和空间统计研究为基础，整合探索性空间数据分析 (ESDA) 和统计热点标准来实时检测传染病集群。我们扩展计算基础设施，以便在整个美国提供对数据和见解的公平访问，这要求 ESDA 技术具有基本但高质量的标准。最后，我们建立了一个研究联盟，并结合以用户为中心的设计原则，为 Atlas 应用程序开发的方向和设计奠定基础。