Land Use detection with cell phone data using topic models: Case Santiago, Chile

Abstract

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.