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Place-based attributes predict community membership in a mobile phone communication network.

Place-based attributes predict community membership in a mobile phone communication network.
Place-based attributes predict community membership in a mobile phone communication network.
Social networks can be organized into communities of closely connected nodes, a property known as modularity. Because diseases, information, and behaviors spread faster within communities than between communities, understanding modularity has broad implications for public policy, epidemiology and the social sciences. Explanations for community formation in social networks often incorporate the attributes of individual people, such as gender, ethnicity or shared activities. High modularity is also a property of large-scale social networks, where each node represents a population of individuals at a location, such as call flow between mobile phone towers. However, whether or not place-based attributes, including land cover and economic activity, can predict community membership for network nodes in large-scale networks remains unknown. We describe the pattern of modularity in a mobile phone communication network in the Dominican Republic, and use a linear discriminant analysis (LDA) to determine whether geographic context can explain community membership. Our results demonstrate that place-based attributes, including sugar cane production, urbanization, distance to the nearest airport, and wealth, correctly predicted community membership for over 70% of mobile phone towers. We observed a strongly positive correlation (r?=?0.97) between the modularity score and the predictive ability of the LDA, suggesting that place-based attributes can accurately represent the processes driving modularity. In the absence of social network data, the methods we present can be used to predict community membership over large scales using solely place-based attributes.
1932-6203
e56057
Caughlin, T. Trevor
4ca29eb2-8836-4431-a9d2-3f667d329276
Ruktanonchai, Nick
4fad0f8b-ad7a-4962-a336-c5555b99335a
Acevedo, Miguel A.
489ec1a3-1e09-4b2b-b9be-9ec34fb4ced8
Lopiano, Kenneth K.
dce1f35c-7550-47ae-a8a8-8bbb86794415
Prosper, Olivia
a86d2cc0-656c-493d-a6be-c8ae8e4458d2
Eagle, Nathan
7936c351-0cae-47be-b0c1-e3f0f331d885
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Caughlin, T. Trevor
4ca29eb2-8836-4431-a9d2-3f667d329276
Ruktanonchai, Nick
4fad0f8b-ad7a-4962-a336-c5555b99335a
Acevedo, Miguel A.
489ec1a3-1e09-4b2b-b9be-9ec34fb4ced8
Lopiano, Kenneth K.
dce1f35c-7550-47ae-a8a8-8bbb86794415
Prosper, Olivia
a86d2cc0-656c-493d-a6be-c8ae8e4458d2
Eagle, Nathan
7936c351-0cae-47be-b0c1-e3f0f331d885
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e

Caughlin, T. Trevor, Ruktanonchai, Nick, Acevedo, Miguel A., Lopiano, Kenneth K., Prosper, Olivia, Eagle, Nathan and Tatem, Andrew J. (2013) Place-based attributes predict community membership in a mobile phone communication network. PLoS ONE, 8 (2), e56057. (doi:10.1371/journal.pone.0056057). (PMID:23451034)

Record type: Article

Abstract

Social networks can be organized into communities of closely connected nodes, a property known as modularity. Because diseases, information, and behaviors spread faster within communities than between communities, understanding modularity has broad implications for public policy, epidemiology and the social sciences. Explanations for community formation in social networks often incorporate the attributes of individual people, such as gender, ethnicity or shared activities. High modularity is also a property of large-scale social networks, where each node represents a population of individuals at a location, such as call flow between mobile phone towers. However, whether or not place-based attributes, including land cover and economic activity, can predict community membership for network nodes in large-scale networks remains unknown. We describe the pattern of modularity in a mobile phone communication network in the Dominican Republic, and use a linear discriminant analysis (LDA) to determine whether geographic context can explain community membership. Our results demonstrate that place-based attributes, including sugar cane production, urbanization, distance to the nearest airport, and wealth, correctly predicted community membership for over 70% of mobile phone towers. We observed a strongly positive correlation (r?=?0.97) between the modularity score and the predictive ability of the LDA, suggesting that place-based attributes can accurately represent the processes driving modularity. In the absence of social network data, the methods we present can be used to predict community membership over large scales using solely place-based attributes.

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Published date: 2013
Organisations: Global Env Change & Earth Observation, WorldPop, Geography & Environment, PHEW – S (Spatial analysis and modelling), Population, Health & Wellbeing (PHeW)

Identifiers

Local EPrints ID: 350234
URI: https://eprints.soton.ac.uk/id/eprint/350234
ISSN: 1932-6203
PURE UUID: c8f149db-e534-4f0e-b1c9-b15e7b6749e1
ORCID for Andrew J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X

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Date deposited: 20 Mar 2013 16:30
Last modified: 20 Jul 2019 00:41

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Contributors

Author: T. Trevor Caughlin
Author: Nick Ruktanonchai
Author: Miguel A. Acevedo
Author: Kenneth K. Lopiano
Author: Olivia Prosper
Author: Nathan Eagle
Author: Andrew J. Tatem ORCID iD

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