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Spatial and transpatial networks

Spatial and transpatial networks. Paola Monachesi. Public spaces. Fusion of physical and online spaces. 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. Cities as big data producers. Goal.

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Spatial and transpatial networks

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  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”

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

  14. 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

  15. Values structural properties

  16. Structural characteristics

  17. Fused Network • Blue: links resulting from physical encounters • Red: links resulting from FB friendship • White: links resulting from both

  18. 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

  19. Average betweenness in Fused NW

  20. Triads

  21. Structural Characteristics

  22. Resilience

  23. 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

  24. 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

  25. 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?

  26. 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

  27. 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.

  28. 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”

  29. 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

  30. 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 MobilityN. Lathia, D. Quercia, J. Crowcroft, The Computer laboratory, University of Cambridge, In Pervasive 2012. Newcastle, UK. June 18-22, 2012.

  31. 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

  32. Visibility

  33. Image of a city: London

  34. Approach

  35. 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

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

  37. Methodology

  38. 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

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

  40. 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

  41. 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

  42. 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

  43. 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

  44. 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

  45. 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

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

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