Publication Type
Journal Article
Version
acceptedVersion
Publication Date
5-2016
Abstract
In this paper, given a set of check-in data, we aim at discovering representative daily movement behavior of users in a city. For example, daily movement behavior on a weekday may show users moving from one to another spatial region associated with time information. Since check-in data contain both spatial and temporal information, we propose a mobility evolution pattern to capture the daily movement behavior of users in a city. Furthermore, given a set of daily mobility evolution patterns, we formulate their similarity distances and then discover representative mobility evolution patterns via the clustering process. Representative mobility evolution patterns are able to infer major movement behavior in a city, which could bring some valuable knowledge for urban planning. Specifically, mobility evolution patterns consist of segments with the spatial region distribution and the corresponding time interval. To measure good segmentation from a set of check-in data, we formulate the problem of mining evolution patterns as a compression problem. In particular, we compute the representation length of the patterns based on the Minimum Description Length principle. Since the number of daily mobility evolution patterns is huge, we further cluster the daily mobility evolution patterns into groups and discover representative patterns. Note that we use the concept of locality-sensitive hashing to accelerate the cluster performance. To evaluate our proposed algorithms, we conducted experiments on the Gowalla and Brightkite datasets, and the experimental results show the effectiveness and efficiency of our proposed algorithms.
Keywords
Data mining, Mobility pattern, Pattern clustering, Urban planning
Discipline
Numerical Analysis and Scientific Computing | Social Media | Urban Studies and Planning
Research Areas
Data Science and Engineering
Publication
Knowledge and Information Systems
Volume
47
Issue
2
First Page
381
Last Page
403
ISSN
0219-3116
Identifier
10.1007/s10115-015-0853-4
Publisher
Springer
Citation
CHEN, Chien-Cheng; CHIANG, Meng-Fen; and PENG, Wen-Chih.
Mining and clustering mobility evolution patterns from social media for urban informatics. (2016). Knowledge and Information Systems. 47, (2), 381-403.
Available at: https://ink.library.smu.edu.sg/sis_research/3631
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/s10115-015-0853-4
Included in
Numerical Analysis and Scientific Computing Commons, Social Media Commons, Urban Studies and Planning Commons