Contributors have a significant impact on data quality of OpenStreetMap (OSM) because most of them are the non-professional, so clustering analysis of contributors based on different experiences has practical significance. Firstly, this paper obtained 31 behavioural characteristics of contributors from OSM historical data. Then, a weighted principal component analysis (WPCA) method was used to reduce the dimensions of the contributors’ behaviour in the selected region. By using an unsupervised prototype-based Gaussian mixture model (GMM) clustering algorithm, contributors with similar contribution attributes in the London area were clustered into four groups. Finally, the characteristics of four different types of contributors are analysed, and two types of experienced and professional contributors are found, who contribute a large amount of high-quality data.

Due to their status as non-professionals, the reputations of Volunteered Geographic Information (VGI) contributors have a very important impact on data quality. In the process of contributor reputation evaluation in OpenStreetMap (OSM), it is very difficult to calculate the semantic similarity between object versions contributed by volunteers. Aimed at this issue, this paper proposes a model of contributor’s reputation based on semantic similarity of ontology concepts. Firstly, contributors are classified into three categories based on an improved WPCA and classification method. Then, an initial reputation is set for every OSM user in each class according to these categories and related research. Secondly, the related concept ontology is constructed for OSM entities; then, the semantic similarity of the object version is calculated according to the similarity of concept attributes and the semantic distance of concept. The contributor’s evaluation reputation is computed by synthesizing the semantic similarity, geometric similarity, and topological similarity of object versions. Thirdly, the contributor’s evaluation reputation and the initial reputation is aggregated to obtain the contributor’s reputation; finally, the OSM data of Rutland, England, is used as an example to verify the validity of our model. The experimental results show that the proposed model can obtain a more comprehensive contributor’s evaluation by fusing with the semantic similarity of ontology concept. The evaluation bias caused only by the semantic change between versions can be eliminated. Moreover, the obtained user’s reputation is positively correlated with the data quality. The contributor’s reputation evaluation method proposed in this paper is an effective method for evaluating the contributor’s reputation in OSM-like systems.

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