Geosocial Footprints and Geoprivacy Concerns

Weidemann, Christopher D.; Swift, Jennifer; Kemp, Karen

doi:10.2307/j.ctt21h4z6m.11

Cited by 3 publications

(2 citation statements)

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“…Other ubiquitous sources of location data are the geosocial footprints extracted from social media activity and smartphones (Weidemann et al 2018). A New York Times investigation described the extraordinary breadth of location information extracted from a million smartphones in New York City and stored in one database (Harris et al 2018).…”

Section: Tracking Technologiesmentioning

confidence: 99%

Geoprivacy, Convenience, and the Pursuit of Anonymity in Digital Cities

Dobson

Herbert

2021

Urban Informatics

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Cities demand spatial efficiencies that can be achieved only through sharing of information. Current technologies support collection, processing, and dissemination of unprecedented quantities of personal, public, and corporate information. Inherent in this milieu is an inevitable contest among societal efficiency, corporate profits, consumer convenience, personal privacy, and even freedom. The authors examine current trends in technology, data collection, legislation, and public acceptance. They find that without broad specific regulations limiting location data collection and use—including a universal protected right for individuals to pursue anonymity—governments, commercial enterprises, employers, and individuals increasingly will exploit tracking technologies at the expense of geoprivacy.

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Section: Tracking Technologiesmentioning

confidence: 99%

Geoprivacy, Convenience, and the Pursuit of Anonymity in Digital Cities

Dobson

Herbert

2021

Urban Informatics

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“…Big data is changing the way transportation planners work, leading to questions and challenges of justice in how data is used, with recent scholarship, suggesting that substantial changes are necessary to mitigate potential problems (Schweitzer and Afzalan 2017). Smartphone mapping applications such as Google Maps, Waze, Uber, and related technologies create tremendous privacy challenges with personal data behind proprietary algorithms, and also contribute great prospects for understanding travel at high resolution over broad geographies (Weidemann et al 2018). Planners use fitness app data to understand cycling routes, but studies have shown that big data sources provide only a segment of the population, varying significantly from survey data using traditional sampling methods (Bergman and Oksanen 2016a).…”

Section: Introductionmentioning

confidence: 99%

Mitigating Bias in Big Data for Transportation

Griffin

Mulhall

Simek

et al. 2020

J. Big Data Anal. Transp.

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Emerging big data resources and practices provide opportunities to improve transportation safety planning and outcomes. However, researchers and practitioners recognise that big data from mobile phones, social media, and on-board vehicle systems include biases in representation and accuracy, related to transportation safety statistics. This study examines both the sources of bias and approaches to mitigate them through a review of published studies and interviews with experts. Coding of qualitative data enabled topical comparisons and reliability metrics. Results identify four categories of bias and mitigation approaches that concern transportation researchers and practitioners: sampling, measurement, demographics, and aggregation. This structure for understanding and working with bias in big data supports research with practical approaches for rapidly evolving transportation data sources.

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