Privacy Threats and Protection Recommendations for the Use of Geosocial Network Data in Research

Kounadi, Ourania; Resch, Bernd; Petutschnig, Andreas

doi:10.3390/socsci7100191

Cited by 18 publications

(12 citation statements)

References 80 publications

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“…Another important aspect of using geotagged data is that the study adheres to adequate privacy by design guidelines [52,53] to protect the users who contributed the data.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the Representativeness of Socio-Demographic Variables over Time for Geo-Social Media Data

Petutschnig

Resch

Lang

et al. 2021

IJGI

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Geo-social media data are widely used as a data source to model populations and processes in a variety of contexts. However, if the data do not adequately represent the population they are drawn from, analysis results will be biased. Unaddressed, these biases may lead to false interpretations and conclusions. In this paper, we propose a generic methodology for investigating the representativeness of geo-social media data for population groups of similar statistical predictive power based on reference data. The groups are designed to be spatially coherent regions with similar prediction errors. Based on these units, we investigate the influence of different socio-demographic covariates on the representativeness. We perform experiments based on over 1.6 billion tweets and 90 socio-demographic covariates. We demonstrate that Twitter data representativeness varies strongly over time and space. Our results show that densely populated areas tend to be underrepresented consistently in non-spatial models. Over time, some covariates like the number of people aged 20 years exhibit highly different effects on the prediction models, whereas others are much more stable. The spatial effects can most frequently be explained using spatial error models, indicating spatially related errors that indicate the necessity of additional covariates. Finally, we provide hints for interpreting the results of our approach for researchers using the concepts presented in this paper.

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“…Another important aspect of using geotagged data is that the study adheres to adequate privacy by design guidelines [52,53] to protect the users who contributed the data.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the Representativeness of Socio-Demographic Variables over Time for Geo-Social Media Data

Petutschnig

Resch

Lang

et al. 2021

IJGI

Self Cite

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“…Only researchers with (restricted) access to the table that provides links between independent IDs from both datasets should be able to run a full geospatial analysis, especially if the data are meant to be shared (e.g., as public use files [ 68 ]). While this procedure is (becoming) the norm for social surveys, it cannot yet be assumed to be fully observed in GIScience, although increasing efforts are being made in the community [ 69 ]. However, as the sample sizes grow, and as the value obtained from having access to more and more unique information about participants increases, this will become a prerequisite in the future.…”

Section: Discussion and Sociological Considerationsmentioning

confidence: 99%

An Interdisciplinary Mixed-Methods Approach to Analyzing Urban Spaces: The Case of Urban Walkability and Bikeability

Resch

Puetz

Bluemke

et al. 2020

IJERPH

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Human-centered approaches are of particular importance when analyzing urban spaces in technology-driven fields, because understanding how people perceive and react to their environments depends on several dynamic and static factors, such as traffic volume, noise, safety, urban configuration, and greenness. Analyzing and interpreting emotions against the background of environmental information can provide insights into the spatial and temporal properties of urban spaces and their influence on citizens, such as urban walkability and bikeability. In this study, we present a comprehensive mixed-methods approach to geospatial analysis that utilizes wearable sensor technology for emotion detection and combines information from sources that correct or complement each other. This includes objective data from wearable physiological sensors combined with an eDiary app, first-person perspective videos from a chest-mounted camera, and georeferenced interviews, and post-hoc surveys. Across two studies, we identified and geolocated pedestrians’ and cyclists’ moments of stress and relaxation in the city centers of Salzburg and Cologne. Despite open methodological questions, we conclude that mapping wearable sensor data, complemented with other sources of information—all of which are indispensable for evidence-based urban planning—offering tremendous potential for gaining useful insights into urban spaces and their impact on citizens.

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“…Because location data contain sensitive private information, their use for DDR may raise concerns about privacy issues (Kounadi et al, 2018), particularly in developing countries (Taylor, 2016), introducing issues for research reproducibility and replicability (Tullis and Kar, 2021). Several anonymization technologies have been developed for location data.…”

Section: Challengesmentioning

confidence: 99%

Human mobility data and analysis for urban resilience: A systematic review

Haraguchi

Nishino

Kodaka

et al. 2022

Environment and Planning B: Urban Analytics and City Science

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The impacts of disasters are increasing due to climate change and unplanned urbanization. Big and open data offer considerable potential for analyzing and predicting human mobility during disaster events, including the COVID-19 pandemic, leading to better disaster risk reduction (DRR) planning. However, the value of human mobility data and analysis (HMDA) in urban resilience research is poorly understood. This review highlights key opportunities for and challenges hindering the use of HMDA in DRR in urban planning and risk science, as well as insights from practitioners. A gap in research on HMDA for data-driven DRR planning was identified. By examining human mobility studies and their respective analytical and planning tools, this paper offers deeper insights into the challenges that must be addressed to improve the development of effective data-driven DRR planning, from data collection to implementation. In future work on HMDA, (i) the human mobility of vulnerable populations should be targeted, (ii) research should focus on disaster mitigation and prevention, (iii) analytical methods for evidence-based disaster planning should be developed, (iv) different types of data should be integrated into analyses to overcome methodological challenges, and (v) a decision-making framework should be developed for evidence-based urban planning through transdisciplinary knowledge co-production.

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Privacy Threats and Protection Recommendations for the Use of Geosocial Network Data in Research

Cited by 18 publications

References 80 publications

Evaluating the Representativeness of Socio-Demographic Variables over Time for Geo-Social Media Data

Evaluating the Representativeness of Socio-Demographic Variables over Time for Geo-Social Media Data

An Interdisciplinary Mixed-Methods Approach to Analyzing Urban Spaces: The Case of Urban Walkability and Bikeability

Human mobility data and analysis for urban resilience: A systematic review

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