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Quality assessment of OpenStreetMap data using trajectory mining

Basiri, Anahid; Jackson, Mike; Amirian, Pouria; Pourabdollah, Amir; Sester, Monika; Winstanley, Adam; Moore, Terry; Zhang, Lijuan

Authors

Anahid Basiri

Mike Jackson

Pouria Amirian

Amir Pourabdollah

Monika Sester

Adam Winstanley

Terry Moore

Lijuan Zhang



Abstract

OpenStreetMap (OSM) data are widely used but their reliability is still variable. Many contributors to OSM have not been trained in geography or surveying and consequently their contributions, including geometry and attribute data inserts, deletions, and updates, can be inaccurate, incomplete, inconsistent, or vague. There are some mechanisms and applications dedicated to discovering bugs and errors in OSM data. Such systems can remove errors through user-checks and applying predefined rules but they need an extra control process to check the real-world validity of suspected errors and bugs. This paper focuses on finding bugs and errors based on patterns and rules extracted from the tracking data of users. The underlying idea is that certain characteristics of user trajectories are directly linked to the type of feature. Using such rules, some sets of potential bugs and errors can be identified and stored for further investigations

Citation

Basiri, A., Jackson, M., Amirian, P., Pourabdollah, A., Sester, M., Winstanley, A., …Zhang, L. (2016). Quality assessment of OpenStreetMap data using trajectory mining. Geo-Spatial Information Scienc, 19(1), https://doi.org/10.1080/10095020.2016.1151213

Journal Article Type Article
Acceptance Date Dec 19, 2015
Online Publication Date Mar 25, 2016
Publication Date Jan 1, 2016
Deposit Date May 5, 2016
Publicly Available Date Mar 29, 2024
Journal Geo-spatial Information Science
Electronic ISSN 1009-5020
Publisher Taylor & Francis Open
Peer Reviewed Peer Reviewed
Volume 19
Issue 1
DOI https://doi.org/10.1080/10095020.2016.1151213
Keywords Spatial data quality; OpenStreetMap (OSM); trajectory data mining
Public URL https://nottingham-repository.worktribe.com/output/979742
Publisher URL http://dx.doi.org/10.1080/10095020.2016.1151213

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