Geospatial data, otherwise known as location data, plays a significant role in a diverse range of sectors across the UK economy including the public sector. If used effectively, location data drives economic, social and environmental value.
Geospatial data does not always have an established market price: its full value may only be determined after it has been used or applied. This can make it challenging to understand and appraise the value of new or improved geospatial data and can lead to underinvestment in the geospatial data ecosystem. The United Kingdom Geospatial Commission commissioned Frontier Economics to develop a practical framework for appraising benefits in this context.
The guidance developed provides a methodical framework for measuring the economic, social and environmental benefits associated with geospatial data investments in the public sector. It is a practical resource to value improvements in location data and can be particularly useful for public sector bodies when building a business case for investment.
The framework for appraising geospatial investment contains seven steps:
The first five steps of this seven-step framework involve development of a conceptual theory of change. This theory of change articulates the rationale behind the intervention, classifies the investment, links it to specific data characteristics (if relevant) and identifies use cases and their benefits. In doing so it enables the user to map out the expected pathways to impact and value.
For further details on non-quantified impacts see the Case Studies Annex to the study – a brief summary of two non-quantified case studies is outlined below:
His Majesty’s Land Registry
HMLR was considering valuing its existing datasets which will require ongoing maintenance and curation. A valuation model was developed that initially applied to four HMLR datasets and focused on the economic benefits facilitated by that data. The composition of the HMLR model, that provides three outputs, is summarised as:
Output – Revenue Generation (RG)
Definition – Estimated revenue generated by UK private sector organisations as a result of dataset(s) usage (private sector only)
Formula – RG = SIC revenue * Revenue related to data * Business relevance factor * Business dependency rating * Adoption rate
Output – Approximate Gross Value Added (aGVA) / GVA
Definition – Estimated gross value added by UK private sector organisations as a result of dataset(s) usage
Formula – GVA = Industry aGVA * Revenue potentially related data * Business relevance factor * Business dependency rating * adoption rate
Output – Cost savings
Definition – Estimated potential staff cost savings by UK private and public sector organisations as a result of dataset(s) usage
Formula – Cost savings = Staff costs * Staff related to data * Business relevance factor * Business dependency rating * Efficiency rating
Public Sector Geospatial Agreement (PGSA)
The Public Sector Geospatial Agreement is a collective purchase licensing arrangement that sets out how the OS provides world-leading location expertise to the public sector, developers and OS Partners across Britain to support the delivery of critical infrastructure and services. One of the key improvements introduced by the PSGA is the move away from fixed specification products to the licensing of the individual componentized building bricks of data, with access to the data for PSGA members enabled through the OS Data Hub, Ordnance Survey’s new customer engagement platform.
Economic value: Accessing the data building bricks, rather than the fixed specification products, offers the greatest level of flexibility and ease of use for both the public and private sector. It also offers greater opportunities for innovation and increased competition within the market at the end user level, by reducing the costs of access to the private sector by allowing them to access only the data content they require for their particular application rather than having to purchase, store, and manage the elements within the existing products they do not require. The reduction in the data management burden is also replicated within the public sector, thereby delivering efficiencies in terms of reduced storage and processing costs, as well as time savings. Other economic benefits are associated with additional revenue for OS from flexibility to respond to new opportunities.
Social value: the new Data Hub will attract analysts who previously avoided using OS products and the increased quality of analysis will have social value via evidence-based policy making.
Environmental value: the new data content provided in the contest of PSGA has some indirect environmental benefits such as better reporting on carbon targets and more accurate environmental.
For further details on quantifiable impacts see the Case Studies Annex to the study – a brief summary of two quantified case studies is outlined below:
National Underground Asset Register (NUAR)
NUAR is a secure data sharing platform which facilitates shared access to underground asset data. In particular it provides digital records of underground assets (i.e. pipes and cables) in a combined, interactive, and standardised way in order to improve the efficiency and safety of underground works. A use case approach was taken to quantitatively value this intervention (including both direct and indirect impacts). This was accompanied by consideration of wider benefits in a qualitative way. A conservative estimate of the benefits of a national approach to digitalising underground asset data calculates £30 worth of benefits for every £1 invested and the estimated total monetised benefit of the NUAR programme is £3.4bn, which is £347m per year over ten years.
TFL Open Data Initiative
TfL decided to release information on timetables, service status, live arrivals, disruptions, air quality and accessibility in an open format, available for use by registered users. Data was made available under a version of the Open Government License so it could be used for commercial and non-commercial purposes. In order to estimate the benefits of TfL’s data, Deloitte considered the cost savings and incremental value to Passengers, London and TfL itself. Their findings include:
Find this report and supporting documents at:
Case Studies Annex at: