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许多科学领域蔓延，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特征给这种合作带来了特殊的挑战。当以地图和其他可视化方式呈现时，这些数据是高度可识别的。在使用地理参考数据的研究中，需要保护受试者的位置隐私和机密性，这限制了合作的潜在机会和利益。本研究通过关注地理空间数据及其可视化的独特机密性特征、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社区面临的挑战。美国地理学家协会和密歇根大学校际政治与社会研究联盟将与地理空间研究界合作：(1)对大型地理参考数据集的独特机密特征进行研究，并研究操纵这些数据及其地理可视化的可行方法，以保护机密性和隐私；(2)研究基于位置可识别数据的地图和其他研究项目中评估和降低信息披露风险的方法和程序；(3)就使用虚拟数据Enclave共享和存档机密地理参考研究数据的可行性进行研究，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；(4)在地理空间虚拟数据Enclave中深入测试保密方法，以降低披露风险并制定披露审查标准。地理技术的进步（如地理信息系统、计算机制图和全球定位系统）为研究人员提供了新的和日益强大的工具来可视化和分析从环境到交通到疾病预防的各种现象。与这些现象相关的地理数据的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，对与特定地点有关的个人一级数据（即地理参考数据）的分析和绘图可能揭示人与其社会和自然环境之间相互作用的模式。当个人的位置（例如，家或工作）和行程（例如，上班旅行）可用时，个人就更容易被识别。本研究将探讨保护研究人员之间收集和共享的地理数据的保密性和位置隐私的方法。具体而言，该项目将研究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，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；(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.