Zero Waste Scotland contracted Exegesis to support Scottish local authorities in meeting the requirement for land zoning data. This involved:

• Providing practical technical advice to local authority staff on the use of GIS to zone land.
• Creating first cut spatial datasets of relevant land to be zoned.
• Processing data on behalf of local authorities in various ways.
• Supporting local authority staff as they determine and apply zones to their data.
• Quality assuring data and preparing it for the Litter Monitoring System.

Data processing was generally undertaken in the PostgreSQL extension PostGIS, using QGIS to view the data in PostGIS. Some processing was also undertaken in GRASS where this was beneficial, such as cleaning and removing small areas from the spatial data (using v.clean, especially the rmarea tool). Advice to local authority staff was tailored to their corporate GIS, mainly ESRI ArcGIS and Pitney Bowes MapInfo.

One of the requirements was that zoned land was split into 1,000 m² sample locations. Each sample location defined an area of land that could be surveyed as part of litter monitoring. Part of the support provided to local authorities was to run the pre-existing QGIS Polygon Divider plugin on supplied data to create the sample locations. This highlighted the need to enhance the Polygon Divider, which we undertook following discussion with Zero Waste Scotland. This included:

• The addition of an approach to split complex polygons into more manageable chunks to the Polygon Divider interface.
• Improved data handling to use memory more efficiently and reduce crashes and failures.
• Implementation of PostGIS compatibility.
• Improved message logging.
• Various minor bug fixes.

The changes to the Polygon Divider were submitted for inclusion in the released tool.

Further information from Richard Griffiths.

The work involved consultation with staff at JNCC and the British Oceanographic Data Centre (BODC) to ensure a faithful import and production of meaningful layers that are useful to JNCC marine staff when assessing the current extent of UK marine monitoring according to each, broad marine discipline (e.g. biodiversity, physical oceanography and fisheries). In addition, users can create custom layers, selecting only the monitoring parameters that may be relevant to a particular analysis.

Further information from Andy Brewer

Natural England had collated a series of spatial datasets relating to surveys of subtidal and intertidal marine habitats. Though all were in digital Geographical Information System format, they were from a variety of sources with differing formats. These were standardised to MESH Translated Habitat DEF, including an assessment of MESH confidence, data cleansing and validation, quality assurance and production of MEDIN metadata. We used a range of tools within ArcGIS, QGIS, GRASS and MapInfo in order to quickly and efficiently produce accurate and reliable outputs. We also utilised four standard habitat translation tables to manually assure the EUNIS habitat identified. A generic process diagram was also created, that can be used to guide all future MESH translation work.

The data to be imported into Marine Recorder were reviewed so that issues could be identified and the approach to import could be agreed. Biotopes were assigned through expert assessment of the data, based upon faunal groupings from Bray-Curtis analysis, particle size distribution, geographic location and field survey notes for each sample. These were then compared with the Marine Habitat Classification for Britain and Ireland (Connor et al., 2004) for the final biotope assignment. The Marine Recorder spreadsheet import function was used to import the resulting data, with particle size and biotope entered manually. The imported data in the snapshot were then independently checked to ensure they were correctly attributed.

Further information from Claire Lush.

Exegesis were contracted to investigate the distribution of non-native species on protected sites in England, in order to help develop a programme of work to tackle INNS. This involved:

• the development of a master list of 3,687 non-native species (NNS)
• the collation of nearly five million records from the National Biodiversity Network (NBN) Gateway and a range of Natural England and third party datasets
• spatial analysis of NSS distribution against Special Area of Conservation (SAC), Special Protection Area (SPA) and Site of Special Scientific Interest (SSSI) boundaries in SQL Server

We presented the results in Microsoft Excel workbooks showing the NNS that had been recorded from each site. Each spreadsheet could be filtered, allowing only the data within a specific site or region to be displayed. This showed that 98% of SACs, 99% of SPAs and 87% of SSSIs had records of NNS. Potential INNS intersected with 90% of SACs, 96% of SPAs and 75% of SSSIs.

The results for seven sites were reviewed against on the ground knowledge by Natural England site staff to determine any differences. We also made a comparison against data in Natural England’s ENSIS database. These comparisons demonstrated the case for increasing data flow by increasing awareness.

Based on the results, we undertook a review of the recording, systems and data flow processes within Natural England. We recommended and costed the use of the NBN Gateway as a data repository and the use of existing Natural England systems for recording and interrogating INNS data. Additional recommendations were made to improve data flow more generally.

Read the report.

Further information from Claire Lush.

CMSi

The BARS Export module for CMSi was commissioned by Natural England to help site managers to convert their CMSi projects to BARS actions, without the need to re-type them into the BARS website.

The contract included a period of consultation with the BARS technical team to establish the best translation between CMSi and BARS concepts. Next, a plugin interface was designed. The design goals for the interface were and to make it as quick, easy and transparent as possible for CMSi users to export their projects to BARS actions.

The solution was a single form in CMSi consisting of two panes and a set of editing controls. The top pane displays all CMSi projects in a grid with icons indicating whether they are ready for export or have been modified since the last export. These projects can then be copied down into the bottom export pane. Here the projects can be edited to match BARS term lists using auto-completion controls for bulk editing, filtering and sorting.

The module also remembers previous exports and displays a complete history including whether projects have been modified since the last export. This ensures that subsequent exports are even easier for users and the whole process is completely transparent.