Paper short abstract:

This paper examines how the personalized local search engine Foursquare mediates its users' navigations and practices of place-finding in urban space, by profiling them as "subjects of locational tastes".

Paper long abstract:

By directing their users to potential destinations (restaurants, cafés and other places of interest around them), local search engines play an important role in contemporary practices of urban navigation and place-finding. On such spatial media, the definition of what is a relevant and valuable destination to display can be inscribed in very different ways in the app's algorithms. Are the most relevant places, the closest ones? The best-rated? Or the ones recommended by "experts"? Just like any map, these local search engines encapsulate specific meanings and values which they further display to their users.

This paper examines how relevance is inscribed in the algorithms of Foursquare, a local search engine, whose displayed destinations are personalized in accordance with each user's profile. In Foursquare's ranking of places, a certain understanding of the user as a "subject of locational tastes" is inscribed. The paper examines how this subject is framed as a bundle of past location data, momentarily connected to other data-producers (friends or calculated publics of similar spatial practices). Conceptually drawing on previous attempts to frame these data-subjects as "monads", "made up of complex, unique, dynamic and always varying metrics" (Ruppert 2012) and "distributed across varied populations of different kinds that intersect through them" (Mackenzie 2016), the paper asks: how does this ever-evolving, flexible and context-dependent mode of knowing subjects impact on users' practices of place-finding and experiences of urban space? From an empirical viewpoint, the paper builds on a study of Foursquare's uses in New York City.