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

ABSTRACT

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.

KEYWORDS:

• Regionalization
• spatial data mining
• Word2Vec
• trajectory

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed here

Additional information

Funding

This work was supported by the [the National Key Research and Development Plan of China] under Grant [2017YFB0503601]; and [the National Natural Science Foundation of China] under Grant [41571437].

Notes on contributors

Yizhuo Li

Yizhuo Li earned his bachelor's degree in geographical information science from the school of Resource and Environmental Sciences, Wuhan University, China, and continued his study at Wuhan University as a MSc student. His current research interest is urban data mining and spatial analysis.

Teng Fei

Teng Fei obtained his bachelor's degree in remote sensing from Wuhan University, China, and received his PhD degree in Twente University, the Netherlands. He currently works as an Associate Professor in the School of Resource and Environmental Sciences, Wuhan University, China. His research focuses on remote sensing, urban data analysis, health geography and ecological modelling.

Fan Zhang

Fan Zhang works as an Associate Professor in State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, China. His research focuses on LIDAR remote sensing and applications.