  An important challenge for ubiquitous computing is the development of
techniques that can characterize a location vis-a-vis the richness and
diversity of urban settings. In this paper we report our work on correlating
urban pedestrian flows with Google search queries. Using longitudinal data we
show pedestrian flows at particular locations can be correlated with the
frequency of Google search terms that are semantically relevant to those
locations. Our approach can identify relevant content, media, and
advertisements for particular locations.
