{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:23:31Z","timestamp":1760239411093,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T00:00:00Z","timestamp":1604620800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFB2100700"],"award-info":[{"award-number":["2018YFB2100700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871375 and 41907389"],"award-info":[{"award-number":["41871375 and 41907389"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>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\u2019 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.<\/jats:p>","DOI":"10.3390\/ijgi9110666","type":"journal-article","created":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T09:03:04Z","timestamp":1604653384000},"page":"666","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Understanding Individual Mobility Pattern and Portrait Depiction Based on Mobile Phone Data"],"prefix":"10.3390","volume":"9","author":[{"given":"Chengming","family":"Li","sequence":"first","affiliation":[{"name":"Chinese Academy of Surveying and mapping, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaxi","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoxin","family":"Dai","sequence":"additional","affiliation":[{"name":"Chinese Academy of Surveying and mapping, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zixian","family":"Fan","sequence":"additional","affiliation":[{"name":"Chinese Academy of Surveying and mapping, Beijing 100044, China"},{"name":"Department of Civil Engineering, Shenyang Jianzhu University, Shenyang 110168, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Wu","sequence":"additional","affiliation":[{"name":"Chinese Academy of Surveying and mapping, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3095","DOI":"10.1109\/ACCESS.2017.2676168","article-title":"Performance Analysis of Smartphone-Sensor Behavior for Human Activity Recognition","volume":"5","author":"Chen","year":"2017","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.1109\/TITS.2017.2727281","article-title":"Urban Mobility Analysis with Mobile Network Data: A Visual Analytics Approach","volume":"19","author":"Senaratne","year":"2017","journal-title":"IEEE Trans. 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