1. Spatial and transpatial networks Paola Monachesi

2. Public spaces

3. Fusion of physical and online spaces

4. Physical vs. Online space  Online space as a version of the “real” world  Urban space as a version of online space  People: they are the link

5. Cities as big data producers

6. Goal  Understand collective and interaction behaviour of city/buildings’ inhabitants both online and offline. Focus is on people’s social ties  Kostakos and Venkatanathan (2010) Making friends in life and online: Equivalence, micro-correlation and value in spatial and transpatial social networks. Proceedings of IEEE SocialCom, Minneapolis, USA, pp. 587-594

7. People’s ties  Face to face interactions are rich communicative experience but bound to space => spatial social networks  Online tools lack the richness of physical interactions but go beyond space and time => transpatial social networks  Combination is a fused network => overview of people’s social engagement

8. Questions  How do online and face-to-face networks relate to each other?  Do individuals assume similar roles in each network?  Do transpatial networks offer greater value than spatial networks wrt. navigation through social ties?

9. Data  2602 participants  Co-presence data [A was co-located with B]  Subset of actual physical encounters, March 2007  Facebook friendship network [A is friends with B]  Recorded after Bloetooth data collection lasted 10 days

10. System  Cityware application:  People’s Bluetooth-enabled devices  Cityware nodes  Cityware servers  FB servers  FB application  For each registered user, the system knows Bluetooth ID and FB profile ID

11. Spatial and transpatial networks

12. Types of networks  Encounter Network (Spatial network)  Users linked if they were co-located during the study  Facebook Network (Transpatial network)  Users linked if they were friends on FB  Fused Network  Encounter and FB networks fused  3 types of ties: Encounter, FB and “fused”

14. Networks  Each node represents a cohort member  Links represent respective ties  Blue: low betweenness  Red: high betweenness

15. Structural characteristics  Measure of structural properties  Size and number of edges  Density  Size of the largest connected component  Average number of links (degree)  Longest shortest path of each network (diameter)  Average shortest distance between pairs of nodes  Each network’s transitivity

16. Values structural properties

17. Structural characteristics

19. Fused Network  Blue: links resulting from physical encounters  Red: links resulting from FB friendship  White: links resulting from both

20. Links  Significant effect of link type on link betweenness (p<0.0001)  In the fused network  Types of links in order of importance: Encounter, FB, Fused

21. Average betweenness in Fused NW

22. Triads

23. Structural Characteristics

24. Resilience

25. Analysis results - Equivalence  Encounter network and FB network with similar characteristics  Fused: increased density and core but diameter of the core does not shorten and average path length increases  Conclusion: when users adopt FB, they increase their local connectivity but globally futher away from everyone since network is larger

26. Analysis results - roles  Similarities between encounter’s network and FB network with respect to effort to maintain the network (high correlation of degree 0.696)  Online only relationships are more likely to be weak, but unclear whether Granovetter’s work also applies to online communities

27. Analysis results – Value of links  Links of encounter networks are more important than links of FB networks  Links that exist in both networks are of least importance  Spatial networks might be more important because they are better at mediating the establishment of new social ties  Physical co-presence enforces trust  Do you agree?

28. A generative model  Assume a fixed number of locations and people  At each location people encounter each other randomly  If two people encounter each other, there is a probability that they become friends on FB  People may become friends on FB even if they have not met face to face  Some FB friends might visit each other  People might travel to locations even if they know no one there

29. Model results  Model is a simplified version of dynamics that generate fused networks  Similarity between model and observed data  Support for the methodological validity of relying on Bluetooth and Facebook proxies for spatial and transpatian network proxies.

30. Summary: results  Bluetooth and Facebook networks exhibit very similar structural characteristics  As proxies to user’s SN they reflect similar aspects  Fused ties least important  They are more likely with close relatives or colleagues (cf. Granovetter 1973)  Spatial ties more important than transpatial ties  Bluetooth has the potential to record “familiar strangers”

31. Fusion physical and online worlds  It becomes possible to:  Keep track of how people move in physical space  Investigate the effect of movement through the digital trace they leave behind  Analyze the data which is often in natural language through language technology techniques  Formalize the information extracted  Analyze: information diffusion knowledge exchange

32. Another use of mobility data  Can we use mobility data through smart cards (i.e. Oyster cards) in order to get insights into the cities communities?  The Hidden Image of the City: Sensing Community Well-Being from Urban Mobility N. Lathia, D. Quercia, J. Crowcroft, The Computer laboratory, University of Cambridge, In Pervasive 2012. Newcastle, UK. June 18-22, 2012.Pervasive 2012

33. Answer  Analyze the relation between London urban flow of public transport and census based indices of the various communities (i.e. community well being)  Analyze the trips made by people it can be inferred which communities they belong to  Goal: monitor urban spaces

34. Visibility

35. Image of a city: London

36. Approach

37. Data  Well being data: IMD  Index of multiple deprivation  Socially deprived communities have higher IMD  Richer communities have lower IMD  Oyster card data  All journeys made during March 2010  Data cleaning: No bus trips, inconsistencies  ~76 million journeys, by 5.1 million users  Mapping between stations and IMD scores

38. Geographical distribution IMD values Each circle is a station, darker values have higher IMD

39. Methodology

40. Infer familiar location  Identify communities that each traveller is familiar with  Entries and exits of each traveller  Top 2 most visited stations (~ work-home)  At least 2 trips in period of observation  Inferred station must not be a major rail station

41. Create user visit matrix  It counts the visits of each traveller to a given station  Binary matrix  Visit = entry-exit

42. Community flow matrix  It represents which location community members visit  Each entry counts the people who live in j and who have visited i  Frequency not taken into account

43. Correlate IMD and flow  Correlation is computed using the Pearson correlation coefficient  Given a vector X and a vector Y the correlation is defined as the covariance of the two variables divided by the product of the standard deviations

44. Compute social equaliser index  It measures the extent to which an area attracts people from areas of varying deprivation  If index is high the area attracts visitors from areas of varying deprivations  If index is low that people within a given area tend to flow within areas with people of similar social deprivation

45. Compute heterogeneity index  It measures the extent to which an area attracts people from areas with similar deprivation  If index is high, it attracts areas different from itself

46. Main results  The more deprived the area the more it tends to be visited  Londoners do not tend to visit communities that have deprivation scores similar to theirs  Rich areas tend to attract people that come from areas of various deprivations  Rich people do not tend to visit communities that are deprived  Segregation effects only in deprived areas

47. Limitations  Do not know exact home location of travelers  Do not know penetration of Oyster card in various communities  Do not have data about urban density  Only analyze portions of the city covered by public transport

48. Who can use this data?  Urban planners  Policy makers to help make decisions Transport infrastructure