When worlds collide: combining Ordnance Survey and Open Street Map data

Anand, Suchith; Morley, Jeremy; Jiang, Wenchao; Du, Heshan; Hart, Glen; Jackson, Mike

Abstract

The context of this paper is the progress of national and international spatial data infrastructures such as the UK Location Programme and INSPIRE, contrasted against crowd-sourced geospatial databases such as Open Street Map. While initiatives such as INSPIRE tend towards a top-down process of harmonised data models and services using ISO & OGC standards, the OSM approach is one of tagged data with attribute tags agreed through consensus, but a tag set that can change with time (with inherent related issues of data quality). There is a danger that should the more formal approaches simply ignore the crowd sourced initiatives then they will miss an opportunity to evolve to better meet growing demands for geographic information. In any case both formal and informal data will increasingly coexist begging the question of how an end user gains maximum benefit from both.
Ordnance Survey as the national mapping agency of Great Britain provides authoritative datasets with published data specifications driven by a combination of user need and the history of national mapping with a remit to ensure real-world feature changes are reflected in the OS large-scale data within 6 months. OSM in contrast relies on the availability of local mapping enthusiasts to capture changes but through its more informal structure can capture a broader range of features of interest to different sub-communities such as cyclists or horse riders.
This research has been carried out to understand the issues of data integration between crowd sourced information and authoritative data. The aim of the research was to look into the mid-term and long-term effects of crowd sourcing technologies for understanding their effects on the change intelligence operations of national mapping agencies (NMAs) in the future. Mobile phones, with more computing power than the desktop machine of 5 years ago and incorporating built-in GPS receivers and cameras have become widespread and give people a multi-sensor capability. This combined with CCTV, sensor webs, RFID etc. offers the potential to make data capture pervasive and ubiquitous. All key sectors of modern economies will be affected by the developments in crowd sourcing of information. The synergies created by new technologies will create the conditions for exciting new developments in geospatial data integration. This has an impact in the spatial data collection domain especially in collecting vernacular and crowd-sourced information. Individual users will be able to use these technologies to collect location data and make it available for multiple applications without needing prior geospatial skills.
The basic question behind our research is how do we combine data from authoritative OS data sets with feature-rich, informal OSM data, recognising the variable coverage of OSM while capturing the best of both worlds? There have been previous studies (Al-Bakri and Fairbairn, 2010) focussing on geometric accuracy assessment of crowd-sourced data(OSM) with OS data.
Another important context is the rapid developments in Open Source GIS. The availability of free and open source GIS has made possible for large number of government organizations and SMEs to make use of GIS tools in their work. The Open Source Geospatial Foundation (OSGeo) is an excellent example of community initiative to support and promote the collaborative development of open geospatial technologies. OSGeo’s key mission is to promote the use of open source software in the geospatial industry and to encourage the implementation of open standards and standards based interoperability in its projects.

Citation

Anand, S., Morley, J., Jiang, W., Du, H., Hart, G., & Jackson, M. (2010). When worlds collide: combining Ordnance Survey and Open Street Map data.

• Conference Name: AGI Geocommunity '10
• Publication Date: Jan 1, 2010
• Deposit Date: Jun 24, 2014
• Publicly Available Date: Jun 24, 2014
• Peer Reviewed: Peer Reviewed
• Keywords: Open Street Map data; OpenStreetMap data; Ordnance Survey; geospatial information
• Public URL: https://nottingham-repository.worktribe.com/output/1012283