  The Olympic Games are an important sporting event with notable consequences
for the general economic landscape of the host city. Traditional economic
assessments focus on the aggregated impact of the event on the national income,
but fail to provide micro-scale insights on why local businesses will benefit
from the increased activity during the Games. In this paper we provide a novel
approach to modeling the impact of the Olympic Games on local retailers by
analyzing a dataset mined from a large location-based social service,
Foursquare. We hypothesize that the spatial positioning of businesses as well
as the mobility trends of visitors are primary indicators of whether retailers
will rise their popularity during the event. To confirm this we formulate a
retail winners prediction task in the context of which we evaluate a set of
geographic and mobility metrics. We find that the proximity to stadiums, the
diversity of activity in the neighborhood, the nearby area sociability, as well
as the probability of customer flows from and to event places such as stadiums
and parks are all vital factors. Through supervised learning techniques we
demonstrate that the success of businesses hinges on a combination of both
geographic and mobility factors. Our results suggest that location-based social
networks, where crowdsourced information about the dynamic interaction of users
with urban spaces becomes publicly available, present an alternative medium to
assess the economic impact of large scale events in a city.
