Detecting Home and Work Locations from Mobile Phone Cellular Signaling Data

Yingkun Yang · Xiong Chen · Junfan Zhuo · Ming Cai

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Human Mobility and Location-Based Analysis Indoor and Outdoor Localization Technologies Data Management and Algorithms

Abstract

Obtaining the distribution of home and work locations is essential for city planning, as it defines the structure and mobility pattern of a city. With the development of telecommunication networks, mobile network data, having the advantages of large coverage and strong followability, have produced large amounts of information about human activities. Thus, it has become a popular research subject for human position detection. In this study, we proposed a new method to detect home and work locations based on the extraction of focal points in traces, identifying an individual’s working and resting hours, and analyzing the characteristics of city grids using mobile phone cellular signaling data (CSD). At the individual level, we validated the algorithm on ground-truth volunteer data and achieved a small deviation of under 500 and 565 m for home and work location detection 85% of the time. At the aggregate level, we tested it on a city-wide anonymized CSD set and found a high Pearson correlation between our result and the census data of 0.93. Compared to existing studies, this study improved the granularity and location accuracy of home and work location detection, as well as validated the method using both individually labeled ground-truth data and aggregate data for the first time. Applying the algorithm in a city, we captured the population distribution, commuting patterns, and job-housing balance of the city and demonstrated the potential in using mobile network data for urban planning and policy formulation.

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References

2014A Survey of Digital Map Processing Techniques 1971ERROR ON CHOROPLETHIC MAPS: DEFINITION, MEASUREMENT, REDUCTION 2015Origin–destination trips by purpose and time of day inferred from mobile phone data 2013Unravelling daily human mobility motifs 2014Development of origin–destination matrices using mobile phone call data 2016Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore 2011Identifying Important Places in People’s Lives from Cellular Network Data 2010Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones

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