UNDERSTANDING RESIDENT MOBILITY IN MILAN THROUGH INDEPENDENT COMPONENT ANALYSIS OF TELECOM ITALIA MOBILE USAGE DATA

• 成果类型: Article
• 署名作者: Zanini, Paolo; Shen, Haipeng; Truong, Young
• 署名单位: Polytechnic University of Milan; University of Hong Kong; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/16-AOAS913
• 发表日期: 2016
• 页码: 812-833
• 关键词: SPECTRAL DENSITY

摘要

We consider an urban planning application where Telecom Italia collected mobile-phone traffic data in the metropolitan area of Milan, Italy, aiming to retrieve meaningful information regarding working, residential, and mobility activities around the city. The independent component analysis (ICA) framework is used to model underlying spatial activities as spatial processes on a lattice independent of each other. To incorporate spatial dependence within the spatial sources, we develop a spatial colored ICA (scICA) method. The method models spatial dependence within each source in the frequency domain, exploiting the power of Whittle likelihood and local linear log-spectral density estimation. An iterative algorithm is derived to estimate the model parameters through maximum Whittle likelihood. We then apply scICA to the Italian mobile traffic application.