Profiling Residents’ Mobility with Grid-Aggregated Mobile Phone Trace Data Using Chengdu as the Case

Xuesong Gao,Hui Wang,Lun Liu

doi:10.3390/su132413713

Xuesong Gao, Hui Wang + Show 1 more

Open Access

https://doi.org/10.3390/su132413713

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Abstract

People’s movement trace harvested from mobile phone signals has become an important new data source for studying human behavior and related socioeconomic topics in social science. With growing concern about privacy leakage of big data, mobile phone data holders now tend to provide aggregate-level mobility data instead of individual-level data. However, most algorithms for measuring mobility are based on individual-level data—how the existing mobility algorithms can be properly transformed to apply on aggregate-level data remains undiscussed. This paper explores the transformation of individual data-based mobility metrics to fit with grid-aggregate data. Fifteen candidate metrics measuring five indicators of mobility are proposed and the most suitable one for each indicator is selected. Future research about aggregate-level mobility data may refer to our analysis to assist in the selection of suitable mobility metrics.

Highlights

Sustainability 2021, 13, 13713. https://With the proliferation of mobile phone use in recent years, it has become possible to track the movements of people through mobile phone signals so that human behavior and a range of social issues can be better understood
We develop our aggregate-level mobility metrics based on the gridaggregate data provided by China Unicom, one of the largest mobile phone service providers in China
This paper explores the transformation of individual-level mobility metrics to work with grid-aggregate mobility data in response to growing concerns about privacy leakage through mobility data

Summary

Introduction

With the proliferation of mobile phone use in recent years, it has become possible to track the movements of people through mobile phone signals so that human behavior and a range of social issues can be better understood. This data presents a major new source for urban studies in the era of big data [1,2]. The data are wide coverage, being retrieved from all mobile phone users in a given area, and can be harvested over a long period for more reliable results This new data source has been used by many studies about understanding human mobility patterns [10,11]

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