Name
Abstract | ||
|---|---|---|
Massive flows that represent the individual level of movements and communications can be easily obtained in the age of big data. Generalizing spatial and temporal flow patterns from such data is essential to demonstrate spatial connections and mobility trends. Clustering approaches provide effective methods to handle data sets that contain massive individual-level flows. However, existing flow clustering studies obscure the geometric properties of flow data, such as direction and length, which significantly indicate movement trends. In addition, temporal information is often ignored because previous approaches have mainly focused on the perspective of spatial clusters of flow data, resulting in a loss of temporal patterns. In this paper, we introduce new spatial and temporal similarity measurements between flows and propose a new clustering approach of flow data based on a stepwise strategy. This method can identify clusters from distinct flow distributions and discover significant spatio-temporal trends from large flow data. Simulated experiments with synthetic flows and a case study using Beijing taxi trip data are conducted to validate the usefulness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ACCESS.2018.2864662 | IEEE ACCESS |
Keywords | Field | DocType |
Flow data,similarity measurement,spatial clustering,temporal clustering,mobility trend | Cluster (physics),Data mining,Data set,Generalization,Computer science,Length measurement,Flow (psychology),Cluster analysis,Big data,Distributed computing,Temporal similarity | Journal |
Volume | ISSN | Citations |
6 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |