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Automatic analysis of positional plausibility for points of interest in OpenStreetMap using coexistence patterns
University of Melbourne, Australia. (Department of Infrastructure Engineering)ORCID-id: 0000-0002-2395-3184
University of Melbourne, Australia. (Department of Infrastructure Engineering)ORCID-id: 0000-0002-4256-3173
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.ORCID-id: 0000-0001-5629-0981
University of Melbourne, Australia. (Department of Infrastructure Engineering)
2019 (engelsk)Inngår i: International Journal of Geographical Information Science, ISSN 1365-8816, E-ISSN 1365-8824, Vol. 33, nr 7, s. 1420-1443Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

In the past decade, Volunteered Geographic Information (VGI) has emerged as a new source of geographic information, making it a cheap and universal competitor to existing authoritative data sources. The growing popularity of VGI platforms, such as OpenStreetMap (OSM), would trigger malicious activities such as vandalism or spam. Similarly, wrong entries by unexperienced contributors adds to the complexities and directly impact the reliability of such databases. While there are some existing methods and tools for monitoring OSM data quality, there is still a lack of advanced mechanisms for automatic validation. This paper presents a new recommender tool which evaluates the positional plausibility of incoming POI registrations in OSM by generating near real-time validation scores. Similar to machine learning techniques, the tool discovers, stores and reapplies binary distance-based coexistence patterns between one specific POI and its surrounding objects. To clarify the idea, basic concepts about analysing coexistence patterns including design methodology and algorithms are covered in this context. Furthermore, the results of two case studies are presented to demonstrate the analytical power and reliability of the proposed technique. The encouraging results of this new recommendation tool elevates the need for developing reliable quality assurance systems in OSM and other VGI projects.

sted, utgiver, år, opplag, sider
Taylor & Francis Group, 2019. Vol. 33, nr 7, s. 1420-1443
Emneord [en]
OSM, coexistence patterns, spatial data quality, spatial association rules, spatial data mining, points of interest
HSV kategori
Forskningsprogram
datalogi
Identifikatorer
URN: urn:nbn:se:umu:diva-157025DOI: 10.1080/13658816.2019.1584803ISI: 000468585300008Scopus ID: 2-s2.0-85062505209OAI: oai:DiVA.org:umu-157025DiVA, id: diva2:1294256
Tilgjengelig fra: 2019-03-06 Laget: 2019-03-06 Sist oppdatert: 2019-06-11bibliografisk kontrollert

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