The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City

Abstract

Studying the social dynamics of a city on a large scale has tra- ditionally been a challenging endeavor, requiring long hours of observation and interviews, usually resulting in only a par- tial depiction of reality. At the same time, the boundaries of municipal organizational units, such as neighborhoods and districts, are largely statically defined by the city government and do not always reflect the character of life in these ar- eas. To address both difficulties, we introduce a clustering model and research methodology for studying the structure and composition of a city based on the social media its res- idents generate. We use data from approximately 18 million check-ins collected from users of a location-based online so- cial network. The resulting clusters, which we call Livehoods, are representations of the dynamic urban areas that comprise the city. We take an interdisciplinary approach to validating these clusters, interviewing 27 residents of Pittsburgh, PA, to see how their perceptions of the city project onto our findings there. Our results provide strong support for the discovered clusters, showing how Livehoods reveal the distinctly charac- terized areas of the city and the forces that shape them.

Leveraging Mobility Flows from Location Technology Platforms to Test Crime Pattern Theory in Large Cities

“…Data from online location platforms captures human activity at high resolution. Accordingly, this type of data was instrumental in basic research on human dynamics, where it helped in identifying universal patterns of human urban mobility (Noulas et al 2012) and in discovering dynamic clusters of urban activity (Cranshaw, Hong, and Sadeh 2012).…”

“…It is well established in scientific literature (Arribas-Bel and Bakens 2018) that data from online location services reflects only a subset of the general population -for instance, Foursquare users used to be young, professionals, smartphones owners back in 2012/2013 (Cranshaw, Hong, and Sadeh 2012). Another potential caveat could be that businesses may have artificially inflated their check-ins.…”

Targeted Interest-Driven Advertising in Cities Using Twitter

“…Urban computing using geotagged data Among the works that leverage geotagged data of particular interest are the tasks of finding local experts on Twitter (Cheng et al 2014), and characterizing city areas such as neighborhoods in terms of the local activities (Cranshaw et al 2012;Le Falher, Gionis, and Mathioudakis 2015). The latter two works are the most relevant to ours, and exploit data from location-based services.…”

No Walk in the Park: The Viability and Fairness of Social Media Analysis for Parks and Recreational Policy Making

“…In the domain of urban computing (Zheng et al 2014;Silva et al 2019), location-based social media has been shown as a useful means of identifying and understanding semantic areas of cities (Cranshaw et al 2012;Noulas et al 2011;Zhang and Zhou 2018), providing valuable insights into people's opinions to support the well-being status of urban communities (De Choudhury, Sharma, and Kiciman 2016;Venerandi et al 2015;Hecht and Stephens 2014). These results, demonstrating the utility of user generated content, have caught the attention of practitioners and policy-makers who aim to make cities more livable and equitable.…”