标題: A regionalization method for clustering and partitioning based on trajectories from NLP perspective

作者: Yizhuo Li, Teng Fei*, Fan Zhang

來源出版物: INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENC 卷: 33 期: 12 DOI: https://doi.org/10.1080/13658816.2019.1643025 出版年: DEC 2 2019

摘要：Regionalization attempts to group units into a few subsets to partition the entire area. The results represent the underlying spatial structure and facilitate decision-making. Massive amounts of trajectories produced in the urban space provide a new opportunity for regionalization from human mobility. This paper proposes and applies a novel regionalization method to cluster similar areal units and visualize the spatial structure by considering all trajectories in an area into a word embedding model. In this model, nodes in a trajectory are regarded as words in a sentence, and nodes can be clustered in the feature space. The result depicts the underlying socio-economic structure at multiple spatial scales. To our knowledge, this is the first regionalization method from trajectories with natural language processing technology. A case study of mobile phone trajectory data in Beijing is used to validate our method, and then we evaluate its performance by predicting the next location of an individual’s trajectory. The case study indicates that the method is fast, flexible and scalable to large trajectory datasets, and moreover, represents the structure of trajectory more effectively.

文獻類型：Article

語種：English

關鍵詞：regionalization, spatial data mining, Word2Vec, trajectory

通訊作者地址：Teng Fei, School of Resource and Environmental Sciences, Wuhan University, Wuhan, China

通訊作者電子郵件地址：feiteng@whu.edu.cn

作者地址：

[Yizhuo Li, Teng Fei] School of Resource and Environmental Sciences, Wuhan University, Wuhan, China

[Fan Zhang] State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

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