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跨越许多科学领域，从环境科学到交通运输到流行病学，创造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性给这种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限制，需要保护地理参考数据的研究对象的位置隐私和机密性。这项研究通过关注地理空间数据及其可视化的独特保密性特征，披露风险，美国地理学家协会和密歇根大学政治和社会研究大学间联合会将使地理空间研究界参与，（1）研究大型地理参考数据集的独特保密特性，以及操纵这些数据及其地理可视化以保护机密性和隐私的可行方法;（2）研究评估和减少从可识别地点的数据生成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存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密集测试保密方法，以降低披露风险并制定披露审查标准。（如地理信息系统、计算机制图、和GPS）为研究人员提供了新的和越来越强大的工具，以可视化和分析从环境到交通到疾病预防的各种现象。对与这些现象相关的地理数据进行地理分析和可视化，为跨广泛学科的创新和高级研究创造了机会。然而，与特定位置相关的个人级别数据的分析和映射（即，地理参考数据）可以揭示人与其社会和物理环境之间的互动模式。当个人的个人位置（例如，家庭或工作）和行程（例如，上班）。这项研究将探讨如何保护研究人员之间收集和共享的地理数据的机密性和位置隐私。具体而言，该项目将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，及（2）制订保障个人私隐的资料披露标准。

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.