Exploring the Mobility of Mobile Phone Users

TL;DR

This paper analyzes a large mobile phone dataset to uncover patterns in human mobility, social behavior, and location preferences, demonstrating effective dimension reduction, location identification, and modeling commuting distances.

Contribution

It integrates multiple behavioral features from mobile data, applying clustering and PCA to identify key locations and validate models of human mobility.

Findings

Most individuals spend time at few locations

Clustering accurately identifies home and work locations

Gravity model explains commuting distances well

Abstract

Mobile phone datasets allow for the analysis of human behavior on an unprecedented scale. The social network, temporal dynamics and mobile behavior of mobile phone users have often been analyzed independently from each other using mobile phone datasets. In this article, we explore the connections between various features of human behavior extracted from a large mobile phone dataset. Our observations are based on the analysis of communication data of 100000 anonymized and randomly chosen individuals in a dataset of communications in Portugal. We show that clustering and principal component analysis allow for a significant dimension reduction with limited loss of information. The most important features are related to geographical location. In particular, we observe that most people spend most of their time at only a few locations. With the help of clustering methods, we then robustly identify home and office locations and compare the results with official census data. Finally, we analyze the geographic spread of users' frequent locations and show that commuting distances can be reasonably well explained by a gravity model.