Using Geometric Properties to Evaluate Possible Integration of Authoritative and Volunteered Geographic Information

Abstract

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.

Harnessing the power of volunteers, the internet and Google Earth to collect and validate global spatial information using Geo-Wiki

“…Some papers suggest that VGI quality is not currently sufficient for this purpose, e.g. (Fairbairn and Al-Bakri, 2013), while others recognize the need for experts to be involved in the training and quality control (Jackson et al, 2013). For Geo-Wiki this is also an area of concern.…”

Recent Developments and Future Trends in Volunteered Geographic Information Research: The Case of OpenStreetMap

“…In their method, they compared the road centerlines with referenced satellite imagery in the U.S. Based on several test areas, their proposed approach was able to improve the positional point accuracy and to recover the positional street displacement of OSM data. In a different study [89], a variety of methods were applied to evaluate positional and linear geometric accuracy and area shape similarity among datasets for integration purposes in different study areas for the UK and Iraq. The researchers concluded that the integration of OSM into the official dataset caused several issues from the geometrical matching perspective.…”

Experiences with Citizen-Sourced VGI in Challenging Circumstances

“…This involved a RMSE analysis of coordinated corner points of land parcels, assessing the discrepancy between the positions captured using the three methods utilized and the coordinates of those points as shown in the official map records. A customized dashboard tool, developed in MATLAB for calculating and visualizing the RMSE and other measures [28], was used to quantify the discrepancies between VGI and official data ( Figure 5). The calculated RMSE values were consistent for each data collection technology within a locality, but the summary statistics presented in Table 2 do indicate some variation.…”

“…Fairbairn and Al Bakri [28] reviewed the spatial correspondence between VGI and official government data, finding that the RMSE for OpenStreetMap data against official topographic mapping data was consistently higher than established tolerances, with errors attributed to the low-precision devices, for example, personal GPS units and commercial imagery services, commonly used in VGI data collection. Such measures can vary within a dataset which covers different areas: Zielstra and Zipf [29] found that the quality of VGI became worse the further away it was collected from the urban core.…”