Quality assessment of OpenStreetMap data using trajectory mining
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, incon...
| Main Authors: | , , , , , , , |
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| Format: | Article |
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Taylor & Francis
2016
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| Online Access: | https://eprints.nottingham.ac.uk/33132/ |
| _version_ | 1848794564885741568 |
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| author | Basiri, Anahid Jackson, Mike Amirian, Pouria Pourabdollah, Amir Sester, Monika Winstanley, Adam Moore, Terry Zhang, Lijuan |
| author_facet | Basiri, Anahid Jackson, Mike Amirian, Pouria Pourabdollah, Amir Sester, Monika Winstanley, Adam Moore, Terry Zhang, Lijuan |
| author_sort | Basiri, Anahid |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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 |
| first_indexed | 2025-11-14T19:18:12Z |
| format | Article |
| id | nottingham-33132 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:18:12Z |
| publishDate | 2016 |
| publisher | Taylor & Francis |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-331322020-05-04T20:04:58Z https://eprints.nottingham.ac.uk/33132/ Quality assessment of OpenStreetMap data using trajectory mining Basiri, Anahid Jackson, Mike Amirian, Pouria Pourabdollah, Amir Sester, Monika Winstanley, Adam Moore, Terry Zhang, Lijuan 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 Taylor & Francis 2016 Article PeerReviewed Basiri, Anahid, Jackson, Mike, Amirian, Pouria, Pourabdollah, Amir, Sester, Monika, Winstanley, Adam, Moore, Terry and Zhang, Lijuan (2016) Quality assessment of OpenStreetMap data using trajectory mining. Geo-spatial Information Science, 19 (1). pp. 56-68. ISSN 1009-5020 Spatial data quality; OpenStreetMap (OSM); trajectory data mining http://dx.doi.org/10.1080/10095020.2016.1151213 doi:10.1080/10095020.2016.1151213 doi:10.1080/10095020.2016.1151213 |
| spellingShingle | Spatial data quality; OpenStreetMap (OSM); trajectory data mining Basiri, Anahid Jackson, Mike Amirian, Pouria Pourabdollah, Amir Sester, Monika Winstanley, Adam Moore, Terry Zhang, Lijuan Quality assessment of OpenStreetMap data using trajectory mining |
| title | Quality assessment of OpenStreetMap data using trajectory mining |
| title_full | Quality assessment of OpenStreetMap data using trajectory mining |
| title_fullStr | Quality assessment of OpenStreetMap data using trajectory mining |
| title_full_unstemmed | Quality assessment of OpenStreetMap data using trajectory mining |
| title_short | Quality assessment of OpenStreetMap data using trajectory mining |
| title_sort | quality assessment of openstreetmap data using trajectory mining |
| topic | Spatial data quality; OpenStreetMap (OSM); trajectory data mining |
| url | https://eprints.nottingham.ac.uk/33132/ https://eprints.nottingham.ac.uk/33132/ https://eprints.nottingham.ac.uk/33132/ |