1. Vvisual comparision of data Measuring the quality of Volunteered Geographic Information (VGI) datasets such as OpenStreetMap is often attempted without the ability to measure the VGI data against some established ground-truth dataset. Our first results from measing the quality of OpenStreetMap was a fully manual approach of comparing a number of selected Irish cities from OpenStreetMap with the corresponding representation in Google Maps and Bing Maps. Road network data was extracted from the OpenStreetMap database in KML format and overlayed in a web-map application on Google Maps and Bing Maps. An example of Dublin is shown to the right of this text box. Comparison was made visually (by using zoom and pan features of the web map application) to analyse: disagreement amongst road and street naming in the three systems, missing roads and roundabouts, and incorrect road designation (ie one-way street marked as two-way traffic). No data was available from Google or Bing to perform this type of analysis using computational techniques. This meant that this method was very time consuming and was consequently restricted to 5KM grid squares in cities and towns. OpenStreetMap performed very well in this comparison with particular strength in the area of road and street names and the currency of the data. Bing Maps performed poorly for some motorway sections caused by the time lag between the Bing Map tiles and the ground-truth reality. Automated Measurement of Quality Metrics Visual comparison is not feasible. VGI data changes very frequently and it is preferably that some automated method of measuring the quality of VGI be developed. Haklay (2010) and Zielstra and Zipf (2010) have developed quality assessment methodologies for OpenStreetMap based on comparison to ground-truth vector datasets. In this poster we show two brief examples: First is the comparison of OpenStreetMap Ireland with a ground-truth dataset and then the development of un-supervised methods where the characteristics of the OSM data is used to generate OSM-specific quality evaluations. Measuring Quality of Volunteered Collected Datasets Błażej Ciepłuch and Peter Mooney Measuring the quality of VGI Example: Represented secondary class roads in Ireland This map on the left presents a comparison of the representation of secondary roads in an Ordnance Survey Ireland (OSI) roads dataset (made available through the STRAT-AG project) and OpenStreetMap. The OSI data is at a scale of 1:5000. The map is coloured using the simple convention: if a 5KM grid cell has more kilometers of OSI data than OSM then it is coloured red otherwise it is coloured green. Blue corresponds to water bodies or no data. The OSI dataset only contains for the Republic of Ireland. There are a number of interesting observations. OSI has better coverage of roads in remote and difficult to survey areas (for example North West): that is their polyline representations contains more points and are more accurate. The OSM dataset has more coverage in the midlands and east. There are several reasons for this (1) the OSM data is more current than the OSI dataset from 2007 since which time several road redesignations have taken place and (2) there are more contributors to OSM in these areas than other parts of the country. In a current paper which is in the review process we have outlined a more detailed comparison of the test OSI dataset and the OSM database. Several lessons have been learned from this comparison. For example both OSI and OSM use different models to classify roads. Both have different concepts for representation of single and dual carriageways, service roads, track roads and boreens (Irish laneways). Number of Points as Quality indicator in VGI systems For OpenStreetMap we have generated two heat maps with the first representing Ireland and the second Lithuania. Both contain 5KM grid squares which are coloured from Dark Blue (no OSM data) to Red colours which corresponds the highest number of points in either country. Analysis of points in OSM are the simplest and most easily understood indicator to inform us of how much work was done in particular area. Every feature in OSM is built from point geometries. There are some interesting charactersitics of the maps above. The most intense data collection for OSM is focused around principal citites and towns and centers of population (note east coast of Ireland). The road and highway network for both country is used as a base layer in both maps. Much of the OSM activity in both countries is concentrated along motorway aerteries. Overall the coverage of OSM in both countries in very inhomogenous. Many locations away from centers of high population density have only very moderate (light green) concentration of OSM data. In the near future we will produce a series of similiar maps for all EU 27 countries in order to analysis OSM coverage in Europe. This analysis will extend to analysis of the currency of OSM data in each country, the number of contributors of data, and the types of data contributed. Research presented in this poster was funded by a Strategic Research Cluster Grant (07/SRC/I1168) by Science Foundation Ireland under the National Development Plan. The authors gratefully acknowledge this support. Where is OSM data collected? Comparing OSI and OSM roads