1 / 48

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.

lucien
Télécharger la présentation

Spatial and transpatial networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related