The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XXXIX-B4
27 Jul 2012
 | 27 Jul 2012


M. Erdoğan, A. Torun, and D. Boyacı

Keywords: Mapping, Database, Feature, GIS, Quality, Vector

Abstract. Immense use of topographical data in spatial data visualization, business GIS (Geographic Information Systems) solutions and applications, mobile and location-based services forced the topo-data providers to create standard, up-to-date and complete data sets in a sustainable frame. Data quality has been studied and researched for more than two decades. There have been un-countable numbers of references on its semantics, its conceptual logical and representations and many applications on spatial databases and GIS. However, there is a gap between research and practice in the sense of spatial data quality which increases the costs and decreases the efficiency of data production. Spatial data quality is well-known by academia and industry but usually in different context. The research on spatial data quality stated several issues having practical use such as descriptive information, metadata, fulfillment of spatial relationships among data, integrity measures, geometric constraints etc. The industry and data producers realize them in three stages; pre-, co- and post data capturing. The pre-data capturing stage covers semantic modelling, data definition, cataloguing, modelling, data dictionary and schema creation processes. The co-data capturing stage covers general rules of spatial relationships, data and model specific rules such as topologic and model building relationships, geometric threshold, data extraction guidelines, object-object, object-belonging class, object-non-belonging class, class-class relationships to be taken into account during data capturing. And post-data capturing stage covers specified QC (quality check) benchmarks and checking compliance to general and specific rules. The vector data quality criteria are different from the views of producers and users. But these criteria are generally driven by the needs, expectations and feedbacks of the users. This paper presents a practical method which closes the gap between theory and practice. Development of spatial data quality concepts into developments and application requires existence of conceptual, logical and most importantly physical existence of data model, rules and knowledge of realization in a form of geo-spatial data. The applicable metrics and thresholds are determined on this concrete base. This study discusses application of geo-spatial data quality issues and QA (quality assurance) and QC procedures in the topographic data production. Firstly we introduce MGCP (Multinational Geospatial Co-production Program) data profile of NATO (North Atlantic Treaty Organization) DFDD (DGIWG Feature Data Dictionary), the requirements of data owner, the view of data producers for both data capturing and QC and finally QA to fulfil user needs. Then, our practical and new approach which divides the quality into three phases is introduced. Finally, implementation of our approach to accomplish metrics, measures and thresholds of quality definitions is discussed. In this paper, especially geometry and semantics quality and quality control procedures that can be performed by the producers are discussed. Some applicable best-practices that we experienced on techniques of quality control, defining regulations that define the objectives and data production procedures are given in the final remarks. These quality control procedures should include the visual checks over the source data, captured vector data and printouts, some automatic checks that can be performed by software and some semi-automatic checks by the interaction with quality control personnel. Finally, these quality control procedures should ensure the geometric, semantic, attribution and metadata quality of vector data.