Details
| Original language | English |
|---|---|
| Pages (from-to) | 56-68 |
| Number of pages | 13 |
| Journal | Geo-Spatial Information Science |
| Volume | 19 |
| Issue number | 1 |
| Publication status | Published - 25 Mar 2016 |
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.
Keywords
- OpenStreetMap (OSM), Spatial data quality, trajectory data mining
ASJC Scopus subject areas
- Social Sciences(all)
- Geography, Planning and Development
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
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In: Geo-Spatial Information Science, Vol. 19, No. 1, 25.03.2016, p. 56-68.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Quality assessment of OpenStreetMap data using trajectory mining
AU - Basiri, Anahid
AU - Jackson, Mike
AU - Amirian, Pouria
AU - Pourabdollah, Amir
AU - Sester, Monika
AU - Winstanley, Adam
AU - Moore, Terry
AU - Zhang, Lijuan
N1 - Funding information: EU FP7 Marie Curie Initial Training Network MULTI-POS (Multi-technology Positioning Professionals), Science Foundation Ireland
PY - 2016/3/25
Y1 - 2016/3/25
N2 - 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.
AB - 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.
KW - OpenStreetMap (OSM)
KW - Spatial data quality
KW - trajectory data mining
UR - http://www.scopus.com/inward/record.url?scp=84961755358&partnerID=8YFLogxK
U2 - 10.1080/10095020.2016.1151213
DO - 10.1080/10095020.2016.1151213
M3 - Article
AN - SCOPUS:84961755358
VL - 19
SP - 56
EP - 68
JO - Geo-Spatial Information Science
JF - Geo-Spatial Information Science
SN - 1009-5020
IS - 1
ER -