Mining and clustering mobility evolution patterns from social media for urban informatics

• Main Authors: CHEN, Chien-Cheng, CHIANG, Meng-Fen, PENG, Wen-Chih
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: Data mining; Mobility pattern; Pattern clustering; Urban planning; Numerical Analysis and Scientific Computing; Social Media; Urban Studies and Planning
• Online Access: https://ink.library.smu.edu.sg/sis_research/3631; https://ink.library.smu.edu.sg/context/sis_research/article/4633/viewcontent/Mining_and_clustering_mobility.pdf
• Institution: Singapore Management University

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.