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A Day in the Life
of Metadata

Mapping the Consequences of Smartphone Location Data



A Day in the Life of Metadata (ADITLOM) is a multidisciplinary collaboration between social scientists and computer scientists to address a simple yet challenging
research question: 
what do surveillance data look like?

ADITLOM addresses the question by empirically examining a particular kind of surveillance data: smartphone location data.

 Our goal is to spread awareness of smartphone location data privacy and ethics issues in two ways: by demystifying how they are created, transformed, and distributed by smartphone hardware, operating systems, Apps, and third parties, and by critically mapping how they are used by industry and government.

ADITLOM is driven by numerous sub-projects, including Big Data Exposed, The API Diver, Surveillance Data Storytelling, and The Industrial Universe of Raw Measurements.

ADITLOM's outputs include interactive learning and teaching software, publications, as well as public and academic presentations.

ADITLOM is supported by a large network of collaborators from around the globe.
If you are interested in working with us, please click here to connect with our Principal Investigator, Dr. Tommy Cooke.


ADITLOM began at Queen's University in 2018 and is the result of over $200,000 CDN in combined funding support from the Social Sciences and Humanities Research Council of Canada, the Centre for Advanced Internet Studies, the Centre for Advanced Computing, and Queen's University's Wicked Ideas Competition.

Big Data Exposed


What stories do organizations tell when they use our location data?


Every second of every day our smartphones produce and share data about our physical interactions with the world around us. But those data are not merely GPS coordinates. They consist of thousands of tiny measurements, such as changes in our elevation to the length of time it takes to connect to a satellite. Hundreds of organizations across the world collect these data to analyze our behaviour -
and often without our knowledge, understanding, or permission.

Funded by the Wicked Ideas research competition at Queen's University, Big Data Exposed (BDE) is a data experiment that launched in September 2021 to investigate the privacy effects of these processes. To do so, the ADITLOM team retrofitted smartphones with specialized software to monitor how hard-to-see location data were being collected by foreign organizations in real-time. The goal of the project is to promote awareness not only about how these other, hidden location data exist, but also what they contain, how they are being used and for what purposes.

We are excited to announce that BDE is brought on two new collaborators from the University of Maryland, Baltimore County, including Dr. Dillon Mahmoudi and Alicia Sabatino. In 2023, Dillon and Alicia are launching a pilot project in Baltimore, Maryland using BDE's software.

We are also pleased to share that ADITLOM's PIs, Dr. Tommy Cooke and Dr. David Lyon, will be presenting the first round of major findings of BDE at the International Association of Sociology World Congress in June 2023. 

An introduction to Big Data Exposed

A demonstration of BDE's Discrete Data Visualizer (2DV)


The API Diver


How do Apps retrieve and transmit our location data?

The API Diver is a sub-project of ADITLOM that indexes what are called Application Programming Interfaces (APIs) found inside of Android operating systems on Google smartphones. When an App wants location data, developers program asks the operating system to provide them. The requests the App makes are formulated in very specific code, which Google documents and provides App developers.


For example, if an App wants to access location data about how fast a user is travelling, the developer would program their App to include getSpeed() - a specific method or code instruction provided by Google, which is found within Location class. The Android operating system would then return speed measurements to the App.

The API Diver is an interactive tool that allows users to see the entire range of methods and data types that Apps receive when they ask the operating system for location data. The goal of The API Diver is to simply provide non-expert researchers a visually comprehensible sense of what range of possibilities Apps and their advertisers have when it comes to receiving location data. We hope that the tool allows you to critically expand your own understanding of location data.


To access the tool, please follow this link


Surveillance Data Storytelling


How do we educate young people about location privacy?

In collaboration with the Dr. Valerie Steeves and Dr. Jane Bailey, co-leaders of the eQuality Project at The University of Ottawa, Surveillance Data Storytelling is a new sub-project that aims to educate high school students about location privacy.

By utilizing ADITLOM's Discrete Data Visualizer (2DV), we are working together to design an educational video that guides university instructors and students on how to ask hard questions and harness their curiousity about location data. 

What exactly are location data?

What do they look like?

How are they used, and for what reasons?

What exact is location privacy, and is it important?

Stay tuned throughout 2023 for an announcement from ADITLOM and the eQuality project about the release of content that you can use in your classroom.


The Industrial Universe of Raw Measurements


What exactly are location data, and how are they used by third parties?

The Industrial Universe of Raw Measurements is a sub-project of ADITLOM that is exploring a specific type of location data: raw measurements.


While we as everyday smartphones users tend to think about location data as mailing addresses, GPS coordinates, and postal codes, there is a completely different kind of location data inside of our devices that our phones' operating system needs before it can determine its own location. These other types of location data, or raw measurements for short, are produced by a piece of hardware called the Global Navigation Satellite System (GNSS) chipset. 

The GNSS chipset is responsible for detecting and decoding signals sent to our phones from navigation satellites. The output produced by the chipset are raw measurements, which come in two protocols or data languages: GNSS Raw Measurements and NMEA 0183.

These two data protocols contain a wide range of unusual location-related measurements, including navigation satellite identification numbers, characteristics of the signal itself, and information about the flight path, velocity, and altitude of the navigation satellites themselves.

While these two data protocols seem innocuous, the research team behind The Industrial Universe of Raw Measurements is investigating the organizations, practices, and processes behind the uptake and usage of them. Our research has revealed that these other location data types are being extracted from smartphones to fuel industrial innovations in tracking accuracy. 

The Industrial Universe of Raw Measurements sub-project is driven by ADITLOM's PI, Dr. Tommy Cooke, along with Dr. Kirstie Ball, Dr. Benjamin Muller, and Alicia Sabatino

We have recently published on this sub-project in The Conversation. Stay tuned for forthcoming academic publications in 2023.

Big Data Exposed
The API Diver

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