text: 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.
  @cite_10: 
  @cite_23: I study the spatial organization of retail commercial activities. These are organized in a network comprising antilinks,'' i.e., links of negative weight. From pure location data, network analysis leads to a community structure that closely follows the commercial classification of the U.S. Department of Labor. The interaction network allows one to build a quality'' index of optimal location niches for stores, which has been empirically tested.
  @cite_7: The paper examines the geography of three street centrality indices and their correlations with various types of economic activities in Barcelona, Spain. The focus is on what type of street centrality (closeness, betweenness and straightness) is more closely associated with which type of economic activity (primary and secondary). Centralities are calculated purely on the street network by using a multiple centrality assessment model, and a kernel density estimation method is applied to both street centralities and economic activities to permit correlation analysis between them. Results indicate that street centralities are correlated with the location of economic activities and that the correlations are higher with secondary than primary activities. The research suggests that, in urban planning, central urban arterials should be conceived as the cores, not the borders, of neighbourhoods.
  @cite_20: Measuring the spatial distribution of locations of many entities (trees, atoms, economic activities, ...), and, more precisely, the deviations from purely random configurations, is a powerful method to unravel their underlying interactions. I study here the spatial organization of retail commercial activities. From pure location data, network analysis leads to a community structure that closely follows the commercial classification of the US Department of Labor. The interaction network allows to build a 'quality' index of optimal location niches for stores, which has been empirically tested.
summary: One of our main hypothesis is that geographic factors can play a major role in the popularity of places during highly active seasons such as the Olympic Games. An inspiration is drawn from Jensen's work on quantifying the optimal spatial positioning of retail stores @cite_20 @cite_23 . Jensen as well as Porta et. al. @cite_10 @cite_7 demonstrate that pure spatial organization can be indicative of the quality of retail and economic activities in the cities of Lyon (France), Bologna (Italy) and Barcelona (Spain).