A Stepwise Spatio-Temporal Flow Clustering Method for Discovering Mobility Trends
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8432425/ |
| _version_ | 1818558640281354240 |
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| author | Xin Yao Di Zhu Yong Gao Lun Wu Pengcheng Zhang Yu Liu |
| author_facet | Xin Yao Di Zhu Yong Gao Lun Wu Pengcheng Zhang Yu Liu |
| author_sort | Xin Yao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T00:14:55Z |
| format | Article |
| id | doaj.art-f4f6777fd3f94ed9aebd631aed34ad1d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T00:14:55Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f4f6777fd3f94ed9aebd631aed34ad1d2022-12-21T23:25:36ZengIEEEIEEE Access2169-35362018-01-016446664467510.1109/ACCESS.2018.28646628432425A Stepwise Spatio-Temporal Flow Clustering Method for Discovering Mobility TrendsXin Yao0https://orcid.org/0000-0002-0109-2643Di Zhu1https://orcid.org/0000-0002-3237-6032Yong Gao2https://orcid.org/0000-0003-1562-6228Lun Wu3Pengcheng Zhang4Yu Liu5Institute of Remote Sensing and Geographical Information Systems, Peking University, Beijing, ChinaInstitute of Remote Sensing and Geographical Information Systems, Peking University, Beijing, ChinaInstitute of Remote Sensing and Geographical Information Systems, Peking University, Beijing, ChinaInstitute of Remote Sensing and Geographical Information Systems, Peking University, Beijing, ChinaGuangzhou Urban Planning & Design Survey Research Institute, Guangzhou, ChinaInstitute of Remote Sensing and Geographical Information Systems, Peking University, Beijing, ChinaMassive 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.https://ieeexplore.ieee.org/document/8432425/Flow datasimilarity measurementspatial clusteringtemporal clusteringmobility trend |
| spellingShingle | Xin Yao Di Zhu Yong Gao Lun Wu Pengcheng Zhang Yu Liu A Stepwise Spatio-Temporal Flow Clustering Method for Discovering Mobility Trends IEEE Access Flow data similarity measurement spatial clustering temporal clustering mobility trend |
| title | A Stepwise Spatio-Temporal Flow Clustering Method for Discovering Mobility Trends |
| title_full | A Stepwise Spatio-Temporal Flow Clustering Method for Discovering Mobility Trends |
| title_fullStr | A Stepwise Spatio-Temporal Flow Clustering Method for Discovering Mobility Trends |
| title_full_unstemmed | A Stepwise Spatio-Temporal Flow Clustering Method for Discovering Mobility Trends |
| title_short | A Stepwise Spatio-Temporal Flow Clustering Method for Discovering Mobility Trends |
| title_sort | stepwise spatio temporal flow clustering method for discovering mobility trends |
| topic | Flow data similarity measurement spatial clustering temporal clustering mobility trend |
| url | https://ieeexplore.ieee.org/document/8432425/ |
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