Enhancing DSS Exploitation Based on VGI Quality Assessment: Conceptual Framework and Experimental Evaluation

The latest advances in spatial information technology have led to the emergence of Volunteered Geographic Information (VGI) as enrichment to existing spatial data sources. Additionally, Decision Support Systems (DSS) are among the fields that have seen major advances. Volunteered Geographic Information (VGI) has great potential as a valuable data source to decision support systems. Several studies have been proposed to integrate VGI data into DSS. However, as VGI data may have different levels of quality, integrating VGI data with poor quality may affect the decision-making process. In fact, VGI data with poor quality. that are obsolete or incomplete, could, if integrated into a spatial DSS, lead to inappropriate analysis results. This paper presents an approach that aims to enhance spatial DSS analysis and exploitation by integrating high quality VGI data that are appropriate to the user requirements, and that have a good indicator completeness and time relevance. The approach introduces a conceptual framework that evaluates VGI data quality and integrates only high quality VGI data into spatial DSS. The proposed approach is experimented on a road maintenance project in Grand-Tunis. We develop the Map-Report prototype, and we evaluate the efficiency of our approach in enhancing data analysis and exploitation in spatial DSS by reducing the error rate and providing accurate and precise analysis results.

Automated summary

The latest advances in spatial information technology have led to the emergence of Volunteered Geographic Information (VGI) as enrichment to existing spatial data sources. Additionally, Decision Support Systems (DSS) are among the fields that have seen major advances. Volunteered Geographic Information (VGI) has great potential as a valuable data source to decision support systems. Several studies have been proposed to integrate VGI data into DSS. However, as VGI data may have different levels of quality, integrating VGI data with poor quality may affect the decision-making process.