Modeling data quality for risk assessment of GIS

This paper presents a methodology to determine three data quality (DQ) risk characteristics: accuracy, comprehensiveness and nonmembership. The methodology provides a set of quantitative models to confirm the information quality risks for the database of the geographical information system (GIS). Four quantitative measures are introduced to examine how the quality risks of source information affect the quality of information outputs produced using the relational algebra operations Selection, Projection, and Cubic Product. It can be used to determine how quality risks associated with diverse data sources affect the derived data. The GIS is the prime source of information on the location of cables, and detection time strongly depends on whether maps indicate the presence of cables in the construction business. Poor data quality in the GIS can contribute to increased risk or higher risk avoidance costs. A case study provides a numerical example of the calculation of the trade-offs between risk and detection costs and provides an example of the calculation of the costs of data quality. We conclude that the model contributes valuable new insight.