Prior to reading this article I felt quite comfortable in the privacy literature in preparation of my talk on VGI, however Toch et al.’s (2010) gave many new insights in the field of locational privacy.
The first of these (and the most marking to me) was the concept of ‘locational entropy’, being the measure of diversity of visitors in a given location. I found this metric quite scary as it becomes quite clear to infer attributes of a location (i.e. private residence, business building, university campus) simply based on the location and time stamp of citizens visiting them. I found this parallel’s the ‘How Fast is a Cow’ reading prescribed for Rudy’s movement GIS talk, as it infers behaviour/attributes from limited data though in the traditional GIS method of layering location, time, and geographic context.
Furthermore, locational entropy is then used to infer social networks, by linking social media (Facebook) to the accounts, and seeing areas in common users may visit. I have heard that apps like Tinder already use algorithms like this to pair users who ‘go out’ more with like people, as well as users who may visit certain similar areas frequently and meet a similar social profile (inferred by the software of course).
In this paper I would have liked to see the possibility of differences in volunteered location between different age groups as well as cultures, as I can imagine that although Toch et al.’s findings on high entropy areas being considered less private may work in the chosen study area, it could be further complicated and user dependent in different circumstances. Furthermore, I feel a newer paper could be made, as although this paper only came out 7 years ago, so much has changed in subtleties such as the option to disclose locational information during certain hours of the day largely disappearing from most apps, and privacy sections moving from overt web page options to the subtleties of user terms of service and cell phone plans in more and more coercive forms of VGI.
-MercatorGator