Where Businesses Thrive: Predicting the Impact of the Olympic Games on Local Retailers through Location-based Services Data
Eighth International AAAI Conference on Weblogs and Social Media (2014)
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Georgiev, P., Noulas, A., & Mascolo, C. (2014). Where Businesses Thrive: Predicting the Impact of the Olympic Games on Local Retailers through Location-based Services Data. Eighth International AAAI Conference on Weblogs and Social Media (2014) http://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/view/8071
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.
We acknowledge the support of Microsoft Research and EPSRC through grant GALE (EP/K019392).
This record's URL: https://www.repository.cam.ac.uk/handle/1810/245856