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Big Data Exposed: GNSS & the Quest for Accuracy in the Digital City
Topics: Urban Geography
, Digital Geographies
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Keywords: gnss, smartphone, accuracy, digital city Session Type: Virtual Paper Abstract Day: Tuesday Session Start / End Time: 3/1/2022 09:40 AM (Eastern Time (US & Canada)) - 3/1/2022 11:00 AM (Eastern Time (US & Canada)) Room: Virtual 62
Authors:
Dan Cohen, Queen's University
Tommy Cooke, Queen's University
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Abstract
This paper presents “Big Data Exposed”: a multidisciplinary pilot project that brings social scientists and computer scientists together to demystify and critically investigate how corporations and governments extract smartphone location metadata for profiling purposes. Launched in the Fall of 2021 at Queen’s University, the project retrofitted two smartphones with data tracking software. As the devices were carried during three separate routes across Kingston, Ontario, Canada, the software monitored how a specific App connected to a Mobile Location Analytics (MLA) corporation in The Netherlands profiled these devices’ movement in real-time. Our paper discusses the project’s first finding – the MLA’s discrete extraction of a single variable produced by the Global Navigation Satellite System (GNSS) receiver, which reflects growing industrial interest in cloud-driven predictive analytics solutions that aim to increase location accuracy in smartphones. Our goal is to critically contextualize these developments within a discretely burgeoning geoscientific data economy that aims to construct new meanings of urban mobility and urban space, at the cost of location privacy.
Big Data Exposed: GNSS & the Quest for Accuracy in the Digital City