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On Data Quality Assurance and Conflation Entanglement in Crowdsourcing for Environmental Studies
Open AccessArticle

Assessing Crowdsourced POI Quality: Combining Methods Based on Reference Data, History, and Spatial Relations

1
Univ. Paris-Est, LASTIG COGIT, IGN, ENSG, F-94160 Saint-Mande, France
2
Hellenic Military Geographical Service, Evelpidon 4, Athens 11362, Greece
*
Authors to whom correspondence should be addressed.
Academic Editors: Alexander Zipf, David Jonietz, Linda See and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2017, 6(3), 80; https://doi.org/10.3390/ijgi6030080
Received: 5 December 2016 / Revised: 27 February 2017 / Accepted: 8 March 2017 / Published: 14 March 2017
(This article belongs to the Special Issue Volunteered Geographic Information)
With the development of location-aware devices and the success and high use of Web 2.0 techniques, citizens are able to act as sensors by contributing geographic information. In this context, data quality is an important aspect that should be taken into account when using this source of data for different purposes. The goal of the paper is to analyze the quality of crowdsourced data and to study its evolution over time. We propose two types of approaches: (1) use the intrinsic characteristics of the crowdsourced datasets; or (2) evaluate crowdsourced Points of Interest (POIs) using external datasets (i.e., authoritative reference or other crowdsourced datasets), and two different methods for each approach. The potential of the combination of these approaches is then demonstrated, to overcome the limitations associated with each individual method. In this paper, we focus on POIs and places coming from the very successful crowdsourcing project: OpenStreetMap. The results show that the proposed approaches are complementary in assessing data quality. The positive results obtained for data matching show that the analysis of data quality through automatic data matching is possible but considerable effort and attention are needed for schema matching given the heterogeneity of OSM and the representation of authoritative datasets. For the features studied, it can be noted that change over time is sometimes due to disagreements between contributors, but in most cases the change improves the quality of the data. View Full-Text
Keywords: crowdsourced data quality; point of interest; crowdsourced data evolution; data matching; OpenStreetMap; intrinsic data quality assessment; extrinsic data quality assessment; spatial relations crowdsourced data quality; point of interest; crowdsourced data evolution; data matching; OpenStreetMap; intrinsic data quality assessment; extrinsic data quality assessment; spatial relations
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Touya, G.; Antoniou, V.; Olteanu-Raimond, A.-M.; Van Damme, M.-D. Assessing Crowdsourced POI Quality: Combining Methods Based on Reference Data, History, and Spatial Relations. ISPRS Int. J. Geo-Inf. 2017, 6, 80.

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