Yingjie Zhang, Beibei Li, and Jason I Hong


ACM Transactions on Intelligent Systems and Technology (TIST)


September 2017


The pervasiveness of mobile technologies today have facilitated the creation of massive online crowdsourced and geotagged data from individual users at different locations in the city. Such ubiquitous user-generated data allow us to study the social and behavioral trajectories of individuals across both digital and physical environments. Œis information, combined with traditional economic and behavioral indicators in the city (e.g., store purchases, restaurant visits, parking), can help us beŠer understand human behavior and interactions with cities. In this study, we take an economic perspective and focus on understanding human economic behavior in the city by examining the performance of local businesses based on the value learned from crowdsourced and geotagged data. Specifi€cally, we extract multiple trac and human mobility features from publicly available data sources geo-mapping and geo-social-tagging techniques, and examine the effects of both static and dynamic features on booking volume of local restaurants. Our study is instantiated on a unique dataset of restaurant bookings from OpenTable for 3,187 restaurants in New York City from November 2013 to March 2014. Our results suggest that foot traffic can increase local popularity and business performance, while mobility and traffic from automobiles may hurt local businesses, especially the well-established chains and high-end restaurants. We also €find that on average one more street closure (caused by events or construction projects) nearby leads to a 4.7% decrease in the probability of a restaurant being fully booked during the dinner peak. Our study demonstrates the potential of how to best make use of the large volumes and diverse sources of crowdsourced and geotagged user-generated data to create matrices to predict local economic demand in a manner that is fast, cheap, accurate, and meaningful.

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