Visualizing the Data City

Abstract

There is growing interest across a wide range of subfields of urban studies in understanding the role played by location-based social media and the impact of the increasing availability of urban digital data from different sources. The way people experience the city is affected by a complex, dense, and reactive information landscape: the data city presents itself with an unprecedented quantity of information in the form of geo-located comments from Twitter, reviews from Pickles, and check-ins from Foursquare. This fragmented proliferation of information generated by urban inhabitants offers potential benefits both for the research community and urban decision makers, who can use the data to generate broad and analytical visions of the uses of urban space. This book explores and presents methods and tools to collect, analyze, and represent time-based geo-located social media data at the urban scale. The aim is to investigate possible perspectives for the use of these data as a source of knowledge for urban planning, design, and management. We ask whether geo-located social media data can be useful in the creation of indicators of urban life as it is perceived and communicated by city users. In fact, although traditional data collection methods such as surveys, interviews, questionnaires, and, more recently, data harvesting and analysis techniques (e.g., using geographical location data from mobile devices) have provided interesting insights into the social life of urban spaces, nowadays, they can be complemented using geo-located social media data. On one side, the book reviews the existing literature, projects, and approaches related to data visualization and the geo-located social mining techniques used to investigate topics of urban interests. On the other side, the book presents the design experiments we conducted in collaboration with urban stakeholders at various levels and in various US and European cities. These case studies document our research activity with geo-located social mining techniques and offer some insights distilled from our experience. As a conclusion, we propose recommendations for the exploitation of geolocated social media data in order to answer hitherto unsolved urban questions and—as such—to generate knowledge for urban planning and management.