Comparison of crowd source data with authorized data and assessment of data quality

With the increasing popularity of data produced by Volunteer Geographic Information and crowdsourcing paradigms, in many fields, national mapping agencies have started to evaluate the potential of data produced by these paradigms to update their data. Due to the nature of these two paradigms, data quality needs to be assessed. This thesis is dedicated to assessing the quality and applicability of spatial data derived from Volunteer Geographic Information and crowdsourcing approaches by juxtaposing it with data from authoritative sources. Through a case study conducted in a specifically chosen area within the Trabzon province, this research seeks to elevate the quality of spatial data. The principal goal is to refine the existing spatial data's accuracy and accessibility, employing the frameworks of the Spatial Data Quality Ontology and the newly created Spatial Data Enhancement Ontology. A significant portion of this process entails the integration and meticulous analysis of datasets from the Turkish General Directorate of Mapping and OpenStreetMap. Leveraging an ontology-based approach, an algorithm that surpasses existing methods in spatial accuracy has been devised using MATLAB software. The efficacy and success of the proposed method have been validated through its application to real-world datasets, demonstrating its potential to significantly improve spatial data quality.