Abstract
With the arrival of the big data era, mobile phone data have attracted increasing attention due to their rich information and high sampling rate. Currently, researchers have conducted various studies using mobile phone data. However, most existing studies have focused on macroscopic analysis, such as urban hot spot detection and crowd behavior analysis over a short period. With the development of the smart city, personal service and management have become very important, so microscopic portraiture research and mobility pattern of an individual based on big data is necessary. Therefore, this paper first proposes a method to depict the individual mobility pattern, and based on the long-term mobile phone data (from 2007 to 2012) of volunteers from Beijing as part of project Geolife conducted by Microsoft Research Asia, more detailed individual portrait depiction analysis is performed. The conclusions are as follows: (1) Based on high-density cluster identification, the behavior trajectories of volunteers are generalized into three types, and among them, the two-point-one-line trajectory and evenly distributed behavior trajectory were more prevalent in Beijing. (2) By integrating with Google Maps data, five volunteers’ behavior trajectories and the activity patterns of individuals were analyzed in detail, and a portrait depiction method for individual characteristics comprehensively considering their attributes, such as occupation and hobbies, is proposed. (3) Based on analysis of the individual characteristics of some volunteers, it is discovered that two-point-one-line individuals are generally white-collar workers working in enterprises or institutions, and the situation of a single cluster mainly exists among college students and home freelancer. The findings of this study are important for individual classification and prediction in the big data era and can also provide useful guidance for targeted services and individualized management of smart cities.
Highlights
With the development of various positioning tools, individual’s mobility behavior can be continuously captured from mobile phones and GPS appliances [1,2]
(3) Based on analysis of the individual characteristics of some volunteers, it is discovered that two-point-one-line individuals are generally white-collar workers working in enterprises or institutions, and the situation of a single cluster mainly exists among college students and home freelancer
Most research based on mobile GPS data has focused on macroscopic analysis, such as the identification of working and living space, division of functional area, and population type identification [9,10]
Summary
With the development of various positioning tools, individual’s mobility behavior can be continuously captured from mobile phones and GPS appliances [1,2]. Based on mobile phone GPS data across Korea over a week, Lee et al (2018) analyzed and compared the urban activities and mobility patterns across 10 cities and examined the spatial dispersion of residential areas [11]. By analyzing mobile phone GPS data in Spain over five weeks, Louail et al (2015) proposed an origin–destination (O-D) matrix identification method for the commute of residents in cities and clarified the spatial distribution patterns of the working and living spaces in Spain [12]. Selecting the central city region in Shanghai as an example, Niu et al (2015) proposed a method for urban spatial structure examination based on mobile phone data. This paper performs long-term behavior analysis and portraiture research of individuals relying on mobile phone data to provide a foundation for the personalized management of smart cities.
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