BCC-SBE: Addressing Challenges for Geospatial Data-Intensive Research Communities: Research on Unique Confidentiality Risks & Geospatial Data Sharing within a Virtual Data Encl

结合各种密集的地理参考数据流的研究正在扩展到许多科学领域，从环境科学到交通运输再到流行病学，并且创建新的多学科和数据密集型科学合作的机会正在扩大。然而，地理参考数据的独特特征给此类合作带来了特殊的挑战。这些数据在地图和其他可视化中呈现时具有高度可识别性。合作的潜在机会和好处受到使用地理参考数据的研究中保护受试者的位置隐私和机密性的需要的限制。这项研究通过关注地理空间数据及其可视化的独特机密特征、披露风险以及在虚拟数据飞地内共享地理空间数据的潜力，解决了地理空间数据密集型研究界面临的挑战。美国地理学家协会和密歇根大学政治与社会研究大学间联盟将让地理空间研究界参与 (1) 对大型地理参考数据集的独特机密特征以及操纵这些数据的可行方法进行研究数据及其地理可视化以保护机密性和隐私； (2) 研究评估和减少源自位置可识别数据的地图和其他研究项目的披露风险的方法和程序； (3) 研究使用虚拟数据飞地共享和归档机密地理参考研究数据的可行性，以便在保护研究对象隐私的条件下对这些数据进行复杂的分析，以及 (4) 在地理空间虚拟数据飞地内集中测试保密方法，以降低披露风险并制定披露审查标准。地理技术（例如地理信息系统、计算机测绘和 GPS）的进步为研究人员提供了新的、日益强大的工具，以可视化和分析从环境到环境等各种现象。运输以预防疾病。与此类现象相关的地理数据的地理分析和可视化为广泛学科的创新和高级研究创造了机会。然而，对与特定位置相关的个人级数据（即地理参考数据）的分析和映射可以揭示人们与其社会和物理环境之间的交互模式。当个人位置（例如，家庭或工作）和行程（例如，上班）可用时，个人变得更容易识别。这项研究将探讨保护研究人员收集和共享的地理数据的机密性和位置隐私的方法。具体来说，该项目将针对使用和共享机密地理参考研究数据及其可视化的独特挑战进行研究，包括：（1）开发和测试技术和程序，以保护地理数据的机密性，同时允许研究人员访问和共享重要数据，以及（2）制定保护个人隐私的信息披露标准。

Research combining a variety of intensive geographically-referenced data streams is spreading across many scientific domains, ranging from environmental science to transportation to epidemiology, and opportunities to create new multi-disciplinary and data-intensive scientific collaborations are expanding. Yet, the unique characteristics of georeferenced data present special challenges to such collaborations. These data are highly identifiable when presented in maps and other visualizations. The potential opportunities and benefits of collaboration are constrained by the need to protect the locational privacy and confidentiality of subjects in research using georeferenced data. This research addresses the challenges facing geospatial data-intensive research communities by focusing on the unique confidentiality characteristics of geospatial data and their visualizations, on disclosure risks, and on the potential for sharing geospatial data within a Virtual Data Enclave.The Association of American Geographers and the Interuniversity Consortium for Political and Social Research at the University of Michigan will engage the geospatial research community to (1) conduct research on the unique confidential characteristics of large georeferenced data sets and on viable ways to manipulate these data and their geovisualizations to protect confidentiality and privacy; (2) conduct research on methods and procedures to assess and reduce disclosure risks in maps and other research projects derived from locationally identifiable data; (3) conduct research regarding the viability of sharing and archiving confidential georeferenced research data using a Virtual Data Enclave to enable sophisticated analyses of these data under conditions that protect the privacy of research subjects, and (4) intensively test confidentiality methods within the geospatial Virtual Data Enclave to reduce disclosure risk and develop standards for disclosure review.Advances in geographic technologies (such as geographic information systems, computer mapping, and GPS) have provided new and increasingly powerful tools for researchers to visualize and analyze a wide range of phenomena ranging from the environment to transportation to disease prevention. Geographic analyses and visualizations of geographic data associated with such phenomena have created opportunities for innovation and advanced research across a broad range of disciplines. Yet, analysis and mapping of individual-level data associated with specific locations (i.e., georeferenced data) may reveal patterns of interaction between people and their social and physical environments. Individuals become more identifiable when their individual locations (e.g., home or work) and itineraries (e.g., travel to work) are available. This research will examine ways to protect the confidentiality and locational privacy of geographic data collected and shared among researchers. Specifically, this project will conduct research on the unique challenges to using and sharing confidential georeferenced research data and their visualizations, including: (1) the development and testing of technologies and procedures to protect confidentiality of geographic data while also allowing access to and sharing of important data among researchers, and (2) the development of standards for disclosure of information that protect individuals' privacy.