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.
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).
@cite_10 @cite_23 @cite_7 @cite_20
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This paper examines the relationship between street centrality and densities of commercial and service activities in the city of Bologna, northern Italy. Street centrality is calibrated in a multiple centrality assessment model composed of multiple measures such as closeness, betweenness, and straightness. Kernel density estimation is used to transform datasets of centrality and activities to one scale unit for analysis of correlation between them. Results indicate that retail and service activities in Bologna tend to concentrate in areas with better centralities. The distribution of these activities correlates highly with the global betweenness of the street network, and also, to a slightly lesser extent, with the global closeness. This confirms the hypothesis that street centrality plays a crucial role in shaping the formation of urban structure and land uses. 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. 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. Historically, papers have been physically bound to the journal in which they were published; but in the digital age papers are available individually, no longer tied to their respective journals. Hence, papers now can be read and cited based on their own merits, independently of the journal's physical availability, reputation, or impact factor (IF). We compare the strength of the relationship between journals' IFs and the actual citations received by their respective papers from 1902 to 2009. Throughout most of the 20th century, papers' citation rates were increasingly linked to their respective journals' IFs. However, since 1990, the advent of the digital age, the relation between IFs and paper citations has been weakening. This began first in physics, a field that was quick to make the transition into the electronic domain. Furthermore, since 1990 the overall proportion of highly cited papers coming from highly cited journals has been decreasing and, of these highly cited papers, the proportion not coming from highly cited journals has been increasing. Should this pattern continue, it might bring an end to the use of the IF as a way to evaluate the quality of journals, papers, and researchers.  2012 Wiley Periodicals, Inc.