@inproceedings{4ea137b491b940e4bc50aaea9e1cb003,
title = "A Social Attribute Inferred Model Based on Spatio-Temporal Data",
abstract = "Understanding the social attributes of urban residents, such as occupations, settlement characteristics etc., has important significance in social research, public policy formulation and business. Most of the current methods for obtaining people{\textquoteright}s social attributes by analyzing of social networks cannot reflect the relationship between the occupational characteristics and their daily movements. However, the current methods of using spatio-temporal data analysis are limited by the characteristics of the samples, and focus more on travel patterns and arrival time predictions. Based on coarse-grained CDR (Call Detail Record) data, this paper proposes an approach to infer occupation attribute by analyzing the travel patterns of personnel and incorporating more enhanced information. Finally we uses the CDR data of 6 million people to analyze and extract two types of people: college students in Beijing and urban hummingbirds and the F1 score of our proposed model is more than 0.95.",
keywords = "Semi-supervised model, Social attribute, Sptio-temporal data, Time-series data classification, Travel patterns",
author = "Tongyu Zhu and Peng Ling and Zhiyuan Chen and Dongdong Wu and Ruyan Zhang",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 14th International Conference on Knowledge Science, Engineering and Management, KSEM 2021 ; Conference date: 14-08-2021 Through 16-08-2021",
year = "2021",
doi = "10.1007/978-3-030-82147-0\_30",
language = "英语",
isbn = "9783030821463",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "364--375",
editor = "Han Qiu and Cheng Zhang and Zongming Fei and Meikang Qiu and Sun-Yuan Kung",
booktitle = "Knowledge Science, Engineering and Management - 14th International Conference, KSEM 2021, Proceedings",
address = "德国",
}