The Rural Payments Agency (RPA) makes payments of over £2 billion per year to farmers, traders and land owners, under the EU’s Common Agricultural Policy, to support farmers’ incomes and rural development. Many farmers rely on the payments, therefore prompt, accurate payments are essential.
Data for the Rural Payment service is collected and managed through two components: A Customer Portal and the Land Management System (LMS). The LMS holds the geospatial master data, including 2.5 million land parcels and 3.4 million land cover records required to manage farm payments. It is supported by a team of land digitisers who convert farmers’ paper submissions into geospatial data and manage any subsequent changes, updates or corrections. The LMS enables the verification of changes using remotely sensed geospatial data (such as aerial photography) and farm inspections.
The Rural Payments Agency – and its sponsor, the Department for Environment, Food & Rural Affairs (Defra) – have an aim of becoming more digital and data-driven; using data to support faster, more accurate decisions. To achieve this, the LMS data must be kept accurate and current.
The LMS is a bespoke business system that has been built by integrating different, best of breed, open source and commercial, off the shelf (COTS) software and custom code. This includes 1Spatial’s 1Integrate software and custom code. The system publishes a number of web mapping services, using open source technology, which are consumed by other components of the LMS and systems used by RPA.
Accelerating the update pipeline
When 1Spatial started to provide support to the live Rural Payment service, the validation of geospatial data updates was a major constraint on the system. Updates submitted by inspectors or from aerial photography needed validation by the land digitisers. At the start of the contract, there was a back-log of hundreds of updates that required checking.
Using its experience of similar situations and deep knowledge of its own 1Integrate software, the 1Spatial team reviewed the pre-existing automated data validation rules. Several improvements made automatic validation much more efficient. As a result, a greater proportion of data submissions were checked and validated automatically, reducing the number requiring expert, manual intervention to an average of 10-12 at any given time. RPA’s team of land digitisers could then focus their expertise on the remaining, more difficult exceptions.