Abstract
The assessment of data quality from different sources can be considered as a key challenge in supporting effective geospatial data integration and promoting collaboration in mapping projects. This paper presents a methodology for assessing positional and shape quality for authoritative large-scale data, such as Ordnance Survey (OS) UK data and General Directorate for Survey (GDS) Iraq data, and Volunteered Geographic Information (VGI), such as OpenStreetMap (OSM) data, with the intention of assessing possible integration. It is based on the measurement of discrepancies among the datasets, addressing positional accuracy and shape fidelity, using standard procedures and also directional statistics. Line feature comparison has been undertaken using buffering techniques and statistics, whilst shape metrics, including moments invariant, have been applied to assess polygon matching. The analyses are presented with a user-friendly interface which eases data input, computation and output of results, and assists in interpretation of the comparison. The results show that a comparison of positional and shape characteristics of OS data or GDS data, with those of OSM data, indicates that their integration for large scale mapping applications is not viable.
Highlights
The rapid development of geospatial data collection techniques, and the growth of the World WideWeb for a wide range of ―geo‖-applications, has resulted in recent increases in the availability of geospatial data
The assessment of matching of position was undertaken by adapting established methods for spatial data accuracy measurement used by mapping agencies to establish absolute accuracy standards for map products
Such statistical methods have long been used by formal mapping agencies for characterising maps and include the National Map Accuracy Standard (NMAS) [15], the Engineering
Summary
The rapid development of geospatial data collection techniques, and the growth of the World Wide. The INSPIRE (Infrastructure for Spatial Information in Europe) initiative, which initially aimed to create a Europe-wide spatial data infrastructure (SDI) exclusively from governmental and official geospatial datasets [1], could potentially benefit from considering cheaper and more timely contributions from citizen-based sources [2] Such integration of informal and formal datasets must be considered, and is increasingly implemented, in other contemporary projects such as development of databases for in-car navigation systems, and rapid mapping solutions in emergency response situations. Further discussion considers the utility of such a system, and its contribution to collaborative mapping projects
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