Putting People on the Map Protecting Confidentiality with Linked Social-Spatial Data (2007) / Chapter Skim
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1 Linked Social-Spatial Data: Promises and Challenges
1 Linked Social-Spatial Data: Promises and Challenges
Pages 7-25
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From page 7... ...
Yet even as researchers are learning from new opportunities offered by precise spatial information, these data raise new challenges because they allow research participants to be identified and therefore threaten the promise of confidentiality made when collecting the social data to which spatial data are linked. Although the difficulties of ensuring access to data while preserving confidentiality have been addressed by previous National Research Council reports (1993, 2000, 2003, 2005a)
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From page 8... ...
THE NEW WORLD OF LOCATIONAL DATA The development of new data, approaches, spatial analysis tools, and data collection methods over the past several decades has revolutionized how researchers approach many questions. The availability of highresolution satellite images of Earth, collected repeatedly over time, and of software for converting those images into digital information about specific locations, has made new methods of analysis possible.
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From page 9... ...
Such data linkage can reveal more information about research participants than can be known from either source alone. Such revelations can increase the fund of human knowledge, but they can also be seen by the individuals whose data are linked as an invasion of privacy or a violation of a pledge of confidentiality.
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From page 10... ...
The Academies were further asked to discuss and evaluate tradeoffs involving data accessibility, confidentiality, and data quality; consider the legal issues raised by releasing remotely sensed data in forms linked to self-identifying data; assess the costs and benefits of different methods for addressing confidentiality in the dissemi nation of such data; and suggest appropriate models for addressing the issues raised by the combined needs for confidentiality and data access. In carrying out our study, it became clear that limiting the study to remotely sensed data unnecessarily restricted the problem domain.
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From page 11... ...
Microdata are especially important because spatial data can compromise confidentiality both by identifying respondents directly and by providing sensitive information that creates risk of harm if linked to identifying data. In addition, spatially precise information may sometimes be associated with small aggregates of individuals or businesses; and care is always needed when sharing data that have exact locations, for example, a cluster of persons or families living near each other.
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From page 12... ...
presents a concise set of recommendations that encourage increased access to publicly produced data. At the same time, these reports and studies have also insisted on the protection of research participants, mostly in the broader context of protecting all human research subjects.
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From page 13... ...
Confidentiality in the research context involves an agreement in which a research participant makes personal information available to a researcher in an exchange for a promise to use that information only for specified purposes and not to reveal the participant's identity or any identifiable information to unauthorized third parties. Identification of an individual in a database occurs when a third party learns the identity of the person whose attributes are described there.
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From page 14... ...
The danger inherent in a breach of confidentiality is not only that private information about an individual might be revealed, but also that the successful conduct of research requires that there be no breaches of confidentiality: any such breach may significantly endanger future research by making potential research participants wary of sharing personal information. Including spatial data in a dataset with social data greatly increases the possibility of identification while at the same time being necessary for certain kinds of analysis.
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From page 15... ...
. This means that research participants must be protected from identification especially, but not only, when identification can harm them.
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From page 16... ...
This project covers 51 villages in Nang Rong district, Northeast Thailand, an agricultural setting in the country's poorest region. The researchers who work on this project have collected data from all households in each village, including precise locations of dwelling units and agricultural parcels.
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From page 17... ...
The project's social data have been merged with the locations of homes, fields, and migration destinations, and then linked to a variety of other types of geographic information including satellite data, aerial photographs, elevation data, road networks, and hydrological features. These linked data have been used for many types of analysis (see Uni versity of North Carolina, 2006)
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From page 18... ...
. Much has already been learned about the effects of context on social outcomes by analyzing social data at relatively imprecise geographic levels, such as census blocks and tracts or other primary sampling units (e.g., Gephart,1997; Smith and Waitzman 1997; Le Clere et al.
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From page 19... ...
Another example of the future of research concerns understanding travel behavior by linking personal data with fine-scale spatial information on actual travel patterns. Researchers could evaluate simultaneously the individual attributes of the research participants, the environmental attributes of the places they live, work, or otherwise frequent, and the detailed travel patterns that lead from one to another.
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From page 20... ...
In both hypothetical examples, the linking of the social data gathered from the participants and the spatial data will permit identification of some or all of the participants. Yet the researchers have made promises of confidentiality, which state that the data will only be analyzed by qualified researchers and that the participants will never be identified in any publication or presentation.
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From page 21... ...
But locational information may also make it possible for a secondary researcher to identify research participants by linking to data from other sources, without requesting permission for that information. Some recent research suggests that it is possible to gauge social, demographic, and economic characteristics from remote sensing data alone (Cowen and Jensen 1998; Cowen et al 1993; Weeks, Larson, and Fugate, 2005)
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From page 22... ...
However, having such data increases the chances that research participants can be identified, thus breaking researchers' promises of confidentiality. In general, as data with detailed locational
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From page 23... ...
One is increased demands from research funders, particularly federal agencies, for improving data access so as to increase the scientific benefit derived from a relatively fixed investment in data collection. The other is the continuing improvement in computer technologies generally, and especially techniques for mining datasets -- techniques that can be used not only to provide more detailed understanding of social phenomena, but also to identify research participants despite researchers' promises of confidentiality.
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From page 24... ...
. It is possible for the linkage of social and spatial data to create signifi 5Because social networks locate individuals within a social space, releasing social network data involve analogous risks to the risks related to spatial network data discussed in this report.
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From page 25... ...
We note, however, that risks of identification and harm by governments or other organizations with strong capabilities for tracking people and mining datasets exist even if social data are not being collected under promises of confidentiality. The key issue for this study concerns the incremental risks of linking confidential social data to precise spatial information about research participants.
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