International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B4, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
REVISITING THE PROCEDURES FOR THE VECTOR DATA QUALITY ASSURANCE 
IN PRACTICE 
M. Erdogan *, A. Torun, D. Boyaci 
General Command of Mapping, Photogrammetry Department, 06100 Dikimevi Ankara, Turkey, - (mustafa.erdogan, 
abdulvahit.torun, dijle.baysal)@hgk.msb.gov.tr 
Commission IV, WG IV/1 
KEY WORDS: 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. 
1. INTRODUCTION virtual landscapes. Therefore, techniques that allow 
visualization of terrain and geospatial vector data are required. 
  
Data quality has been studied and researched for years and it 
also depends on use. Since the use of geodata becomes wider, 
QC is increasingly getting more importance. 
In geoscience, analytical information symbolizes one of the 
main categories maintained by geoinformation systems. The 
vector data is generally showed by lists of coordinates defining 
points, lines, polygons, etc. These primitives are traditionally 
used for describing geographic entities, for example buildings, 
rivers, vegetation or soil types. In GIS, vector data has 
important applications in the evaluation and management of 
  
* Corresponding author. 
Such techniques have to evolve the vector data of the terrain 
surface and should ensure a precise and efficient mapping. 
(Dai et al, 2008) 
Many disciplines like environmental planning, documentation 
and analysis uses geodata and the quality of the geodata and 
description of that quality are very important subjects to get 
good results. Additionally a quality description in terms of 
geometric and thematic reliability and completeness is a must. 
For verifying the quality of geodata, it is required to make sure 
that a production process of geodata offers the desired quality. 
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