GIS and Academic Portfolio
The webinar by Mei-Po Kwan serves as a useful introduction to the issues of data privacy by analyzing mobility data through the lens of COVID-19. In their COVID-19 mitigation efforts, many countries have begun to gather location data of individuals from smartphone and credit card activity and facial recognition cameras. While these methods are invaluable from a public health perspective, they create concerns for data privacy.
We have spent much time in this class discussing the importance of reproducibility in data sciences, and a key aspect of that is open data. It’s very difficult to fact-check or peer-review statistics without access to the original data. And in the case of COVID-19 mobility data, this fact checking is highly important in slowing the spread of the virus and ensuring equity in the response to the pandemic. However, as Mei-Po Kwan demonstrates, even in datasets that do not store key identifying information, it is often quite easy to surmise who an individual is just from their movement patterns. When someone begins and ends every day at the same location, for example, it’s easy to deduce one’s household. By including more data points or checkins to that individual’s profile (e.g. from credit card purchases), more private data becomes exposed. These concerns about data privacy are particularly important for people of marginalized groups, who often have greater incentive to keep their data private from the government or corporations. Once these datasets are created, issues of access must be placed at the forefront of their use.
Some questions that I would like to see discussed in class tomorrow would be: