Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 327-333, 2016
https://doi.org/10.5194/isprs-archives-XLI-B2-327-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
07 Jun 2016
A VERSION-SIMILARITY BASED TRUST DEGREE COMPUTATION MODEL FOR CROWDSOURCING GEOGRAPHIC DATA
Xiaoguang Zhou and Yijiang Zhao School of Geosciences and Info-physics, Central South University, Changsha 410083, China
Keywords: Crowdsourcing Geographic Data, Trust Degree, Reputation, Version Similarity, OpenStreetMap Abstract. Quality evaluation and control has become the main concern of VGI. In this paper, trust is used as a proxy of VGI quality, a version-similarity based trust degree computation model for crowdsourcing geographic data is presented. This model is based on the assumption that the quality of VGI objects mainly determined by the professional skill and integrity (called reputation in this paper), and the reputation of the contributor is movable. The contributor’s reputation is calculated using the similarity degree among the multi-versions for the same entity state. The trust degree of VGI object is determined by the trust degree of its previous version, the reputation of the last contributor and the modification proportion. In order to verify this presented model, a prototype system for computing the trust degree of VGI objects is developed by programming with Visual C# 2010. The historical data of Berlin of OpenStreetMap (OSM) are employed for experiments. The experimental results demonstrate that the quality of crowdsourcing geographic data is highly positive correlation with its trustworthiness. As the evaluation is based on version-similarity, not based on the direct subjective evaluation among users, the evaluation result is objective. Furthermore, as the movability property of the contributors’ reputation is used in this presented method, our method has a higher assessment coverage than the existing methods.
Conference paper (PDF, 1240 KB)


Citation: Zhou, X. and Zhao, Y.: A VERSION-SIMILARITY BASED TRUST DEGREE COMPUTATION MODEL FOR CROWDSOURCING GEOGRAPHIC DATA, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 327-333, https://doi.org/10.5194/isprs-archives-XLI-B2-327-2016, 2016.

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