This paper describes results from a research project undertaken to explore the technical issues associated with integrating unstructured crowd sourced data with authoritative national mapping data. The ultimate objective is to develop methodologies to ensure the feature enrichment of authoritative data, using crowd sourced data. Users increasingly find that they wish to use data from both kinds of geographic data sources. Whilst map matching techniques can be deployed, the result is often confusing, creating obfuscation more than clarity. In addition, when integrating different forms of data at the feature level, these attempts are often time consuming and require more technical ability and resources than that typically available. To tackle these problems, this project aims at developing a methodology for automated conflict resolution, linking and merging of geographical information from disparate authoritative and crowd-sourced data sources. To integrate road vector data from disparate sources, the method presented in this paper first converts input data sets to ontologies, and then merges these ontologies into a new ontology. This new ontology is then checked and modified to ensure that it is consistent.
- data conflaction