A Robust and Reliable Resource for Accessing, Sharing, and Analyzing Confidential Geospatial Research Data

该项目将开展一系列相互关联的研究活动，以加强一个强大和可靠的地理空间虚拟数据飞地，这将促进地理空间研究数据的共享，同时也为由于地理空间数据的独特性而身份可能面临风险的个人保密。 美国地理学家协会（AAG），伊利诺伊大学厄巴纳-香槟分校和密歇根大学政治和社会研究校际联盟（ICPSR）之间的这一合作项目将加强基础设施资源，通过在社会，环境，以及使用地理空间数据的相关科学。 GVDE资源将提供广泛的地理空间，空间统计和地理信息系统软件，使研究人员能够共享，访问，建立和进行涉及飞地内机密数据的新研究。 这将是一个安全的研究环境，可用于共享机密地理空间研究数据，从而能够复制以前涉及机密地理空间数据的研究。 GVDE将为科学家共享数据创建新的研究基础设施，以便研究成果可复制，并将国家在数据创建方面的投资保留下来供后续分析。 该项目将培训研究人员掌握保护地理空间数据机密性的技术。 研究生将参与加强这一基础设施。复制和再生产研究的能力是科学方法的基石。 地理空间和位置数据的生成和分析处于许多科学领域的前沿，但由于需要保护研究对象的位置隐私和机密性，这些数据的独特性对数据共享做法提出了特殊挑战。 科学研究界的许多成员尚未充分了解机密地理空间数据的隐私挑战。 因此，研究工作往往因地理空间数据保密性的不确定性或因缺乏安全共享和获取机密地理空间研究数据以供分析或复制的有效方法而受到阻碍。 为了解决这些问题，研究人员将加强GVDE及其共享、访问、分析和复制地理空间数据的核心能力。 他们将评估和实施保密保护技术，使研究人员能够匿名和导出GVDE中进行的分析得出的地图，分析和可视化。 他们将开发和实施研究人员资格认证系统，为训练有素且值得信赖的研究人员提供持久的数字标识符，以安全访问和使用GVDE。 他们还将确保全球地理信息数据交换系统的长期可持续性，并通过外联、传播和编写关于系统使用、数据保密道德、认证要求、研究复制、该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识产权进行评估来支持。优点和更广泛的影响审查标准。

This project will undertake a set of interrelated research activities to enhance a robust and reliable geospatial virtual data enclave (GVDE) that will facilitate the sharing of geospatial research data while also maintaining the confidentiality of individuals whose identities may be at risk because of the distinctive characteristics of geospatial data. This collaborative project between the American Association of Geographers (AAG), the University of Illinois at Urbana-Champaign, and the University of Michigan's Inter-university Consortium for Political and Social Research (ICPSR) will enhance an infrastructural resource that will advance scientific research by building capacity for data sharing in geospatial data-intensive research communities in the social, environmental, and related sciences that use geospatial data. The GVDE resource will make a wide range of geospatial, spatial statistical, and geographic information system software available to allow researchers to share, access, build on, and conduct new research involving confidential data within the enclave. It will be a secure research environment for sharing confidential geospatial research data, thereby enabling the replication of prior research involving confidential geospatial data. The GVDE will create new research infrastructure for scientists to share data so that research results are replicable and national investments in data creation are preserved for subsequent analysis. The project will train researchers in techniques to protect confidentiality in geospatial data. Graduate students will be involved in the enhancement of this infrastructure.The ability to replicate and reproduce research is a corner-stone of scientific methodology. The generation and analysis of geospatial and locational data is at the frontier of many scientific domains, yet the unique characteristics of these data present special challenges to data-sharing practices due to the need to protect the locational privacy and confidentiality of research subjects. The privacy challenges of confidential geospatial data are not yet fully understood by many members of the scientific research community. As a result, research is often impeded by uncertainty regarding geospatial data confidentiality or by a lack of effective methods for safely sharing and accessing confidential geospatial research data for analysis or replication. To address these problems, the investigators will enhance the GVDE and its core capabilities to share, access, analyze, and replicate geospatial data. They will evaluate and implement confidentiality protection techniques to enable researchers to anonymize and export maps, analyses, and visualizations derived from analyses conducted in the GVDE. They will develop and implement a researcher credentialing system to provide trained and trusted researchers with a durable digital identifier to safely access and use the GVDE. They will also ensure the long-term sustainability of the GVDE system and expand its availability to the broader geospatial research community through outreach, dissemination, and the development of training materials on system use, data confidentiality ethics, credentialing requirements, research replication, and best practices.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.