Open AccessA VERSION-SIMILARITY BASED TRUST DEGREE COMPUTATION MODEL FOR CROWDSOURCING GEOGRAPHIC DATA
Author(s) -
Xiaog Zhou,
Yingjie Zhao
Publication year - 2016
Publication title -
the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.264
H-Index - 71
eISSN - 1682-1777
pISSN - 1682-1750
DOI - 10.5194/isprsarchives-xli-b2-327-2016
Subject(s) - crowdsourcing , volunteered geographic information , reputation , computer science , similarity (geometry) , data mining , degree (music) , quality (philosophy) , information retrieval , proxy (statistics) , trustworthiness , computation , data science , machine learning , artificial intelligence , world wide web , computer security , image (mathematics) , algorithm , social science , philosophy , physics , epistemology , sociology , acoustics
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.