CIF21 DIBBs: Scalable Capabilities for Spatial Data Synthesis

该项目将通过基于云计算、CyberGIS和其他现有工具的可扩展数据聚合和集成，开发一套空间数据综合工具。许多科学问题需要聚集和整合来自众多来源的大量不同的空间数据，然而现有的方法和软件无法有效地综合通常可用的大量空间数据。该项目将解决与使用大量空间数据相关的问题，从而促进依赖于此类数据的科学问题解决工作，例如人口动态和城市可持续性研究。从研究活动中获得的学习材料将通过CyberGIS科学门户开放获取。有针对性的大规模开放在线课程开发将提供廉价而有效的方法来教授学生空间数据综合的能力和基本科学原理。项目后半段将开设暑期学校，提供更集中、更深入的培训活动。该研究项目将通过云计算和CyberGIS为空间数据合成创造可扩展的能力。该项目将从开发解决特定科学问题的能力开始，然后继续与更广泛的社区合作，以验证和改进核心能力。研究将包括两个相互关联的主题：(1)基于一系列社会、环境和物理因素和过程来衡量城市的可持续性；(2)通过综合多个人口数据源和社交媒体数据来研究人口动态。该项目将提供的空间数据综合能力包括提取元数据和处理空间参考和单元问题。该项目还将开发表征数据及其传播中的不确定性的基本能力。

This project will develop a set of tools for spatial data synthesis through scalable data aggregation and integration based on cloud computing, CyberGIS, and other existing tools. Many scientific problems require the aggregation and integration of large and varied spatial data from a multitude of sources, yet existing approaches and software cannot effectively synthesize the enormous amounts of spatial data that often are available. This project will resolve problems associated with the use of massive spatial data, thus facilitating work dependent on this type of data for scientific problem solving, such as research on population dynamics and urban sustainability. Learning materials derived from the research activities will be openly accessible through the CyberGIS Science Gateway. Targeted massive open online course development will provide inexpensive and efficient ways to teaching students about the capabilities and underlying scientific principles of spatial data synthesis. A summer school will be offered during the second half of the project to provide a more focused and in-depth training event.This research project will create scalable capabilities for spatial data synthesis enabled by cloud computing and CyberGIS. The project will begin by developing the capabilities for solving specific scientific problems and then move on to engage a broader community for validating and improving the core capabilities. The research will incorporate two interrelated themes: (1) measuring urban sustainability based on a number of social, environmental, and physical factors and processes; and (2) examining population dynamics by synthesizing multiple population data sources with social media data. Spatial data synthesis capabilities that the project will provide include extracting metadata and dealing with problems of spatial references and units. The project also will develop a fundamental capability to characterize uncertainty in data and its propagation.