Detecting Home and Work Locations from Mobile Phone Cellular Signaling Data

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.