Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/80868
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Land Surveying and Geo-Informatics | - |
dc.creator | Shi, Z | - |
dc.creator | Pun Cheng, LSC | - |
dc.date.accessioned | 2019-06-27T06:36:12Z | - |
dc.date.available | 2019-06-27T06:36:12Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/80868 | - |
dc.language.iso | en | en_US |
dc.publisher | Molecular Diversity Preservation International (MDPI) | en_US |
dc.rights | © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Shi Z, Pun-Cheng LS. Spatiotemporal Data Clustering: A Survey of Methods. ISPRS International Journal of Geo-Information. 2019; 8(3):112 is available at https://doi.org/10.3390/ijgi8030112 | en_US |
dc.subject | Clustering | en_US |
dc.subject | Spatiotemporal data | en_US |
dc.subject | Survey | en_US |
dc.title | Spatiotemporal data clustering : a survey of methods | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 8 | - |
dc.identifier.issue | 3 | - |
dc.identifier.doi | 10.3390/ijgi8030112 | - |
dcterms.abstract | Large quantities of spatiotemporal (ST) data can be easily collected from various domains such as transportation, social media analysis, crime analysis, and human mobility analysis. The development of ST data analysis methods can uncover potentially interesting and useful information. Due to the complexity of ST data and the diversity of objectives, a number of ST analysis methods exist, including but not limited to clustering, prediction, and change detection. As one of the most important methods, clustering has been widely used in many applications. It is a process of grouping data with similar spatial attributes, temporal attributes, or both, from which many significant events and regular phenomena can be discovered. In this paper, some representative ST clustering methods are reviewed, most of which are extended from spatial clustering. These methods are broadly divided into hypothesis testing-based methods and partitional clustering methods that have been applied differently in previous research. Research trends and the challenges of ST clustering are also discussed. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | ISPRS international journal of geo-information, 2019, v. 8, no. 3, 112 | - |
dcterms.isPartOf | ISPRS international journal of geo-information | - |
dcterms.issued | 2019 | - |
dc.identifier.scopus | 2-s2.0-85063686092 | - |
dc.identifier.eissn | 2220-9964 | - |
dc.identifier.artn | 112 | - |
dc.description.validate | 201906 bcma | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
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Shi_Spatiotemporal_data_clustering.pdf | 971.58 kB | Adobe PDF | View/Open |
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