In popular culture, there is only one city that never sleeps – New York. Even Google seems to agree, as the search term ‘city that never sleep’s brings up the Wikipedia article on NYC as the first hit. Is it true though, is New York really more dynamic at night that any other city on the globe? A JISC funded project found a clever way of answering this question by mining geographical information from Twitter.
Last week I attended the All Hands Meeting in Cardiff, and one of the most interesting presentations came from the NeISS project. NeISS, National e-Infrastructure for Social Simulation, is a funded by the JISC as part of the Information Environment Programme. The NeISS project is working on providing a platform to meet the demand for powerful simulation tools by social scientists, public and private sector policymakers. The tools developed through the project will enable researchers to create workflows to run their own simulations, visualise and analyse results, and publish them for future discovery, sharing and re-use. While the field of social simulation is of much interest to researchers, its findings are also of high relevance for the wider public due to its forecasting applications for scenarios in transport, housing, education, healthcare etc.
How will British cities look like in thirty years from now? What effects will planning decisions have on the population structure? How can urban planning help to allocate resources or prepare better for an ageing population? It is these kinds of questions that social simulation tools can help find answers to. Compared to that, the question of whether New Yorkers do really party harder than anyone else in the world may appear to be trivial. What makes it interesting beyond its entertainment value is the approach the NeISS project has taken to address it, an approach that can be used to answer other questions as well. Instead of starting a survey, the researchers went to a source that provides data on the activities of millions of people over the globe: Twitter. The microblogging service is increasingly accessed through mobile devices that share the location of their users. Tweet-o-Meter makes use of the data by logging all geolocated tweets across several large cities across the globe.
Visualising the number of tweets in a city over the course of a 24h period, which is what the Tweet-o-Meter does, will, for instance, tell you how active Twitter users are. In most of the cities Tweet-o-Meter monitors, they appear to go to bed around midnight. New York, which also happens to be home of the most active Twitter users, is different, as New Yorkers tend to stay up about two hours longer than everyone else. For most of us this may be just an amusing fact, but an analysis of geolocated data from services such as Twitter can also be used to answer a much wider range of questions. It becomes possible to track the movement patterns of an increasingly larger part of urban populations throughout they day, helping to further our understanding of the social and temporal dynamics of cities. Analysing geolocated data generated through social networks can help with urban planning and could, for instance, also be used to think about much more flexible public transportation systems. This makes the approache used by Tweet-o-Meter much more than just an amusing comment on sleeping patterns of the digital generation.