Sensing Urban Social Geography
Using Online Social Networking Data
Santi Phithakkitnukoon and Patrick Olivier
Culture Lab
School of Computing Science
Newcastle University, United Kingdom
santi@mit.edu, p.i.olivier@newcastle.ac.uk
Abstract
Growing pool of public-generated bits like online social networking data provides possibility to sense social dynamics in the urban space. In this position paper, we use a location-based online social networking data to sense geo-social activity and analyze the underlying social activity distribution of three different cities: London, Paris, and New York. We find a non-linear distribution of social activity, which follows the Power Law decay function. We perform inter-urban analysis based on social activity distribution and clustering. We believe that our study sheds new light on context-aware urban computing and social sensing
Conclusion
Urban spaces are being blanketed by streams of digital data generated by inhabitants. This large pool of bits creates a unique opportunity for harvesting and analyzing behavioral data to better understand about the city and people. In this position paper, we analyze the aggregated data over about 16 months from a location-based online social network called Foursquare. The data has been collected from London, Paris, and New York. With the unique characteristic of the Foursquare network that allows the users to interact (check in) with the physical landscape (venue), we are able perform a geo-social and inter-urban analyses. With different venue’s categories in our data, we find that social activity is distributed in a non-linear fashion and is following a Power Law distribution with Food and Nightlife social activity being the strongest social hubs across the three cities. Statistically we show that New York has a low variation in social distributions among different activity types compared with other two cities. Moreover, we observe a similar social clustering between Food and Arts in London and New York, but Paris on the other hand appears to have Arts and Nightlife social activity clustered comparably. There are however some limitations in this study. The demographics and penetration of the Foursquare network in the
Figure 5: Social clusters of each activity category in London 38
Figure 6: Social clusters of each activity category in Paris
Figure 7: Social clusters of each activity category in New York
cities of study can also impact to the results. The arbitrary selection of number of centroids in our k-means clustering may not be a representative for social centers. Nonetheless we believe that to some extent this study helps us realize the usefulness of online social network data that can be utilized to better understand physical space and sociality. As our future direction, we will continue to investigate on the sole use of this data to understand the city as well as integrating it with data from other sources e.g. transportation, telecommunication to enrich context inference.
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