Abstract:This paper offers a state-of-the-art overview of the intertwined privacy, confidentiality, and security issues that are commonly encountered in health research involving disaggregate geographic data about individuals. Key definitions are provided, along with some examples of actual and potential security and confidentiality breaches and related incidents that captured mainstream media and public interest in recent months and years. The paper then goes on to present a brief survey of the research literature on … Show more
“…This is especially true in research on public health or social issues (see Boulos, Curtis, and AbdelMalik 2009 for an overview of locational privacy in biomedical and public health research literature). For example, there is considerable interest in the effect of neighbourhood on crime, delinquency, and immigrant assimilation (Simcha-Fagan and Schwartz 1986;Brooksgunn et al 1993;Sampson, Morenoff, and Earls 1999;Sampson, Morenoff, and Gannon-Rowley 2002;Jackson, Pebley, and Goldman 2010;Sastry and Pebley 2010;Mennis et al 2011;Mennis and Mason 2012).…”
Section: Confidentiality Issues Associated With Georeferenced Datamentioning
confidence: 99%
“…Ironically, geocoding has had some unintentional built-in protections due to errors (completeness and positional error) (Zandbergen 2009). However, as the accuracy of geocoding techniques improve, the chance of successfully re-engineering mapped locations has also improved (Boulos, Curtis, and AbdelMalik 2009). As a result, these researchers are calling for standards and guidelines for the display of maps derived from georeferenced data that require privacy protections (Curtis, Mills, and Leitner 2006b;Boulos, Curtis, and AbdelMalik 2009).…”
Section: Confidentiality Issues Associated With Georeferenced Datamentioning
The ability to replicate, or reproduce, research is fundamental to the scientific process. Research combining a variety of georeferenced data is spreading rapidly across scientific domains and international borders. This suggests a growing potential for the use and integration of new and existing data sets to create new multi-disciplinary scientific collaborations. 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 or when combined with sensor data or other related geospatial data sets. The potential opportunities of collaboration may thus be constrained by the need to protect the locational privacy (geoprivacy) and confidentiality of subjects in research using georeferenced data. This paper reviews the obstacles to and potential methods for sharing georeferenced data in order to support a growing and dynamic geospatial research community and build capacity for data-intensive research across the social and environmental sciences. The development and implementation of a geospatial virtual data enclave methodology is proposed as an innovative and viable solution to share and archive georeferenced data among researchers while protecting the geoprivacy of research subjects and the confidentiality of these data. The ability to share confidential geospatial data among researchers is crucial to ensuring replicability of scientific research, and to enable researchers to verify and build upon the research of others.
“…This is especially true in research on public health or social issues (see Boulos, Curtis, and AbdelMalik 2009 for an overview of locational privacy in biomedical and public health research literature). For example, there is considerable interest in the effect of neighbourhood on crime, delinquency, and immigrant assimilation (Simcha-Fagan and Schwartz 1986;Brooksgunn et al 1993;Sampson, Morenoff, and Earls 1999;Sampson, Morenoff, and Gannon-Rowley 2002;Jackson, Pebley, and Goldman 2010;Sastry and Pebley 2010;Mennis et al 2011;Mennis and Mason 2012).…”
Section: Confidentiality Issues Associated With Georeferenced Datamentioning
confidence: 99%
“…Ironically, geocoding has had some unintentional built-in protections due to errors (completeness and positional error) (Zandbergen 2009). However, as the accuracy of geocoding techniques improve, the chance of successfully re-engineering mapped locations has also improved (Boulos, Curtis, and AbdelMalik 2009). As a result, these researchers are calling for standards and guidelines for the display of maps derived from georeferenced data that require privacy protections (Curtis, Mills, and Leitner 2006b;Boulos, Curtis, and AbdelMalik 2009).…”
Section: Confidentiality Issues Associated With Georeferenced Datamentioning
The ability to replicate, or reproduce, research is fundamental to the scientific process. Research combining a variety of georeferenced data is spreading rapidly across scientific domains and international borders. This suggests a growing potential for the use and integration of new and existing data sets to create new multi-disciplinary scientific collaborations. 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 or when combined with sensor data or other related geospatial data sets. The potential opportunities of collaboration may thus be constrained by the need to protect the locational privacy (geoprivacy) and confidentiality of subjects in research using georeferenced data. This paper reviews the obstacles to and potential methods for sharing georeferenced data in order to support a growing and dynamic geospatial research community and build capacity for data-intensive research across the social and environmental sciences. The development and implementation of a geospatial virtual data enclave methodology is proposed as an innovative and viable solution to share and archive georeferenced data among researchers while protecting the geoprivacy of research subjects and the confidentiality of these data. The ability to share confidential geospatial data among researchers is crucial to ensuring replicability of scientific research, and to enable researchers to verify and build upon the research of others.
“…To answer this call, work on data collection techniques and spatial cluster analysis methods that incorporate a notion of an individual's activity space and mobility (Orellana and Wachowicz, 2011;Zenk et al, 2011), or residential history (Meliker and Sloan, 2011), should be encouraged throughout the disciplines involved with spatial cluster analysis. Building such an awareness throughout this research community will also bring more macroscopic issues to the front, such as privacy issues in health research caused by the distribution of sensitive, location-specific health data (Boulos et al, 2009;Meliker et al, 2009).…”
Section: Future Research Recommendationsmentioning
Abstract. Spatial cluster analysis is a uniquely interdisciplinary endeavour, and so it is important to communicate and disseminate ideas, innovations, best practices and challenges across practitioners, applied epidemiology researchers and spatial statisticians. In this research we conducted a scoping review to systematically search peer-reviewed journal databases for research that has employed spatial cluster analysis methods on individual-level, address location, or x and y coordinate derived data. To illustrate the thematic issues raised by our results, methods were tested using a dataset where known clusters existed. Point pattern methods, spatial clustering and cluster detection tests, and a locally weighted spatial regression model were most commonly used for individual-level, address location data (n = 29). The spatial scan statistic was the most popular method for address location data (n = 19). Six themes were identified relating to the application of spatial cluster analysis methods and subsequent analyses, which we recommend researchers to consider; exploratory analysis, visualization, spatial resolution, aetiology, scale and spatial weights. It is our intention that researchers seeking direction for using spatial cluster analysis methods, consider the caveats and strengths of each approach, but also explore the numerous other methods available for this type of analysis. Applied spatial epidemiology researchers and practitioners should give special consideration to applying multiple tests to a dataset. Future research should focus on developing frameworks for selecting appropriate methods and the corresponding spatial weighting schemes.
“…Confidentiality constraints often preclude the release of disaggregate data about individual cancer patients [102,103]. Access to individually geocoded (disaggregate) data often involves lengthy and cumbersome procedures through review boards and committees for approval (and sometimes is not possible).…”
Section: Gastro-intestinal Tract Cancermentioning
confidence: 99%
“…Exposure assessment data are generally collected for different areas than health and demographic data [102]. Kamel Boulos et al [103] provide a comprehensive state-of-the-art review of data privacy issues (including privacy-preserving solutions and recommendations) in health GIS studies.…”
Geographic information systems (GIS) offer a very rich toolbox of methods and technologies, and powerful research tools that extend far beyond the mere production of maps, making it possible to cross-link and study the complex interaction of disease data and factors originating from a wide range of disparate sources. Despite their potential indispensable role in cancer prevention and control programmes, GIS are underrepresented in specialised oncology literature. The latter has provided an impetus for the current review. The review provides an eight-year snapshot of geospatial cancer research in peer-reviewed literature (2002-2009), presenting the clinico-epidemiological and methodological findings and trends in the covered corpus (93 papers). The authors concluded that understanding the relationship between location and cancer/cancer care services can play a crucial role in disease control and prevention, and in better service planning, and appropriate resource utilisation. Nevertheless, there are still barriers that hinder the wide-scale adoption of GIS and related technologies in everyday oncology practice.
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