Journal
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
Volume 61, Issue -, Pages 39-48Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.compenvurbsys.2016.08.007
Keywords
Land Use detection; Topic models; Mobile phone data; Human mobility; Latent Dirichlet allocation; Big data
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Funding
- Business Intelligence Research Center (CEINE) from University of Chile
- Complex Engineering Systems Institute (ISCI) [ICM-FIC: P05-004-F, CONICYT:FB0816]
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Today we have the opportunity without precedents to analyze human land use or mobility behavior in a city, country or even the globe. Some studies have analyzed existing data generated daily by mobile networks, mostly using geo-localization in Twitter, Foursquare or cell phone records. Most of these studies use a small portion of data (a few days or a couple million records). This time we will show a novel way to apply latent semantic topic models to detect Land Use Patterns in a real big dataset of 880,000,000 calls made in Santiago City (Chile) over 77days by about 3 million customers of a major telecommunications company. We proposed to use a latent variables clustering technique which allow us to detect four interesting clusters. We found out that the application of LDA allow us to discover two well known clusters (residential and office area clusters) but also we discover two new clusters: Leisure-Commerce and Rush Hour patterns. (C) 2016 Elsevier Ltd. All rights reserved.
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