Today, problems of spatial data integration have been further complicated by the rapid development in communication technologies and the increasing amount of available data sources on the World Wide Web. Thus, web-based geospatial data sources can be managed by different communities and the data themselves can vary in respect to quality, coverage, and purpose. Integrating such multiple geospatial datasets remains a challenge for geospatial data consumers. This paper concentrates on the integration of geometric and classification schemes for official data, such as Ordnance Survey (OS) national mapping data, with volunteered geographic information (VGI) data, such as the data derived from the OpenStreetMap (OSM) project. Useful descriptions of geometric accuracy assessment (positional accuracy and shape fidelity) have been obtained. Semantic similarity testing covered feature classification, in effect comparing possible categories (legend classes) and actual attributes attached to features. The model involves ‘tokenization’to search for common roots of words, and the feature classifications have been modelled as an XML schema labelled rooted tree for hierarchical analysis. The semantic similarity was measured using the WordNet:: Similarity package. Among several proposed semantic similarity methods in WordNet:: Similarity, the Lin approach has been adopted to give normalised comparison scores. The results reveal poor correspondence in the geometric and semantics integration of OS and OSM.
The estimation process is one of the pillars of the statistical inference process as well as the hypothesis test, and the assessment is based on the collection of information and conclusions about the teacher or the community's teachers on the basis of the result