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

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

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

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?

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

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

Spatial and transpatial networks

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”

Networks

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

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

Values structural properties

Structural characteristics

Fused Network

Blue: links resulting from physical encounters

Red: links resulting from FB friendship White: links resulting from both

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

Average betweenness in Fused NW

Triads

Structural Characteristics

Resilience

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

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

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?

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

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.

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”

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

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.

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

Visibility

Image of a city: London

Approach

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

Geographical distribution IMD values

Each circle is a station, darker values have higher IMD

Methodology

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

Create user visit matrix

It counts the visits of each traveller to a given station

Binary matrix Visit = entry-exit

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

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

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

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

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

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

Who can use this data?

Urban planners Policy makers

to help make decisionsTransport infrastructure