Understanding communities using mobile network big data CPRsouth 2015
Date
2015-07
Journal Title
Journal ISSN
Volume Title
Publisher
LIRNEasia, Colombo, LK
Abstract
Understanding the strength and boundaries of human connections can help identify communities amongst a population, and is valuable knowledge for modeling disease spread, information flow, and mobility patterns. Administrative boundaries, formed by history and geography, do not necessarily reflect the actual communities or social interaction patterns within a region. In this study we employ community detection algorithms to a mobile Call Detail Records (CDR) network in Sri Lanka in order to compare natural communities existing in the interaction network against administrative regions of Sri Lanka. Additionally we explore how these communities segment into a further level of sub-communities.
Description
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Conference Paper
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Keywords
SPATIAL ANALYSIS, MOBILE PHONES, URBAN PLANNING, GEOGRAPHICAL REFERENCED DATA, MODE OF DATA COLLECTION, BIG DATA, DATA MINING, HUMAN GEOGRAPHY, CULTURAL GEOGRAPHY, SOCIAL STRUCTURE, URBAN PLANNING, ETHNIC FACTORS, ADMINISTRATIVE DEVELOPMENT
Citation
DOI
Collections
IDRC Research Results / Résultats de recherches du CRDI
2010-2019 / Années 2010-2019
Data / Données
Research Results (I&N) / Résultats de recherches (I&R)
Research Results (INASSA) / Résultats de recherche (IRAAS)
Research Results (Networked Economies) / Résultats de recherche (Économies en réseaux)
2010-2019 / Années 2010-2019
Data / Données
Research Results (I&N) / Résultats de recherches (I&R)
Research Results (INASSA) / Résultats de recherche (IRAAS)
Research Results (Networked Economies) / Résultats de recherche (Économies en réseaux)