Evaluation of a volunteered geographical information trust measure in the case of OpenStreetMap

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.The presence of Volunteered Geographical Information is attracting research because its high availability and diversity make it an interesting source of information. For many organisations it is important that quality of geographical information is of a certain level. Recent developments in studies related to VGI direct towards the estimation of its quality through the notion of trust as a proxy. For this thesis is investigated which factors have an important influence on trust and a simple approach was used to come up with an indication of trust levels for geographical features. The indicators were selected based on a literature review and on a dataset extracted from the open mapping project OpenStreetMap. Numbers of users, versions and confirmations were counted or calculated and involved as positive indicators, while numbers of various corrections were treated as indicators having a negative influence on the development of trust in information. Analysis of the dataset and thinking about how to incorporate what in the trust measure showed for example how ideas about time decay could be different. Importance of tags was determined based on a method adopted from documentation studies and applied on the dataset. It allowed for generating lists of tags to be described when publishing information about particular features. This was of importance for assessing information completeness in measuring the quality of the data. The results of the trust measure have been compared to those if the quality measure and an evaluation of this comparison shows significant signs of support for the hypothesis that VGI data quality can be estimated based on a trust model that incorporates data provenance. On the other hand there is also a significant number of features of which both measures show opposite indications of quality. Various single assumptions, simplifications and the relatively small size of the dataset restricted the possibilities for obtaining more accurate results. Confirmation or denial of the ideas that resulted from this research can be made by enlarging the dataset and experimenting with different methods. Automating all the data processing would be necessary