Big Data Connects Users to their Worlds in Incredible New Ways
The importance of online social networks has moved well beyond the margins of connecting and communicating with friends. New startups and services allow users to find and provide the answers to a wide range of topics. Whether one wants to discover the quietest place in Tokyo to read a novel, or what political topics are trending at a given moment, social networks allow this information to be retrieved and recorded instantaneously. Beneath the surface of their glossy interfaces, however, is an ever-growing collection of network data sets[1] – often referred to as “Big Data” in the tech industry – that holds the true power of online networks. The applications of this data are endless and, more often than not, help generate new ideas that evolve social networks online and offline through a better understanding of its populace.
Foursquare, a popular social network for logging and sharing locations, is no stranger to data analysis. Using proprietary algorithms, Foursquare goes beyond simple “check-ins” by providing its community of over 40 million users with personalized recommendations and deals based on one’s current location[2].
In September, Foursquare applied their collection of Big Data in a new and mesmerizing way: visualizing the flow of daily life in major cities around the world. Like a bird’s-eye view, the animated visualizations show not only “where people are but, more significantly, how, when, and why they’re going there.” [3] To say the least, these visualizations are absolutely stunning and convey graphically the connectivity of paths (physical locations and routes) and components (users) in an innovative way so as to show giant components of users’ behaviors [4] including working, eating, shopping, traveling, or even enjoying the night life.
While currently released in this highly visual form for demonstrative purposes, there are certainly a number of important applications for Foursquare’s data. One such application might be providing location recommendations that factor in the current traffic and current social activity of a given area. It is not hard to imagine an updated algorithm that strives for equilibrium [5] with the payoffs for each user including a reduction overpopulating locations, shorter wait times at restaurants and stores, and reduced vehicular traffic. Based purely on the knowledge of how many users are going to which places for which reasons and at what times, Foursquare and other location recommendation services could soon influence and transform the way in which users navigate through their worlds [6] – all thanks to the analysis and harvesting of user generated Big Data.
[1] p. 35. Networks, Crowds, and Markets, Easly and Kleinberg.
[2] https://foursquare.com/about
[3] http://www.fastcodesign.com/3018574/infographic-of-the-day/foursquare-data-viz-shows-the-pulse-of-london-chicago-tokyo)
[4] p. 4. Networks, Crowds, and Markets, Easly and Kleinberg.
[5] p 150. Ibid
[6] p 13. Ibid