title: A framework for spatial interaction analysis based on large-scale mobile phone data.
abstract: The overall understanding of spatial interaction and the exact knowledge of its dynamic evolution are required in the urban planning and transportation planning. This study aimed to analyze the spatial interaction based on the large-scale mobile phone data. The newly arisen mass dataset required a new methodology which was compatible with its peculiar characteristics. A three-stage framework was proposed in this paper, including data preprocessing, critical activity identification, and spatial interaction measurement. The proposed framework introduced the frequent pattern mining and measured the spatial interaction by the obtained association. A case study of three communities in Shanghai was carried out as verification of proposed method and demonstration of its practical application. The spatial interaction patterns and the representative features proved the rationality of the proposed framework.
authors.affiliation: Key Laboratory of Road and Traffic Engineering of the Ministry of Education, 4800 Cao'an Road, Tongji University, Jiading District, Shanghai 201804, China.
authors.forename: Weifeng
authors.initials: W
authors.lastname: Li
authors.affiliation: Key Laboratory of Road and Traffic Engineering of the Ministry of Education, 4800 Cao'an Road, Tongji University, Jiading District, Shanghai 201804, China.
authors.forename: Xiaoyun
authors.initials: X
authors.lastname: Cheng
authors.affiliation: Key Laboratory of Road and Traffic Engineering of the Ministry of Education, 4800 Cao'an Road, Tongji University, Jiading District, Shanghai 201804, China.
authors.forename: Zhengyu
authors.identifier: 0000-0002-8807-9045
authors.initials: Z
authors.lastname: Duan
authors.affiliation: Key Laboratory of Road and Traffic Engineering of the Ministry of Education, 4800 Cao'an Road, Tongji University, Jiading District, Shanghai 201804, China.
authors.forename: Dongyuan
authors.identifier: 0000-0002-6961-1120
authors.initials: D
authors.lastname: Yang
authors.affiliation: Key Laboratory of Road and Traffic Engineering of the Ministry of Education, 4800 Cao'an Road, Tongji University, Jiading District, Shanghai 201804, China.
authors.forename: Gaohua
authors.initials: G
authors.lastname: Guo
mesh_terms: D000465:Algorithms; D040421:Cell Phone / Q000706:statistics & numerical data*; D003142:Communication; D003627:Data Interpretation, Statistical; D066264:Datasets as Topic; D040362:Geographic Information Systems; D006801:Humans; D016571:Neural Networks, Computer