Spatial mapping of the extreme weather event datasets
Data were exported from R as ascii text files with grid cell centroid locations provided as absolute integer coordinates in British National Grid projection to facilitate import into ArcGIS 10.5 for visualisation and further analyses. Null values (NA) representing offshore locations were recoded to (-9999), ensuring compliance with numeric format prior to import. The point locations were plotted and then spatially joined to a pre-calculated vector 5 km grid, whereupon joined null values and their corresponding grid squares were identified and removed. The resulting datasets were then used to create thematic maps.
Geoprocessing (clipping) was used to extract underlying published land cover data (Rowland et al, 2017). The resulting land cover data required planimetric areas to be re-calculated, and these were subsequently summarized by ecosystem type and aggregate area. The UK land cover categories (Rowland et al. 2017) were grouped into four broad classes each providing specific ecosystem services and levels of biodiversity: (1) Agriculture, incorporating arable/horticultural and improved grasslands (provisioning), (2) Woodlands, incorporating broadleaf and coniferous woodlands (provisioning, regulating, and biodiversity), (3) Conservation, incorporating National Parks and Sites of Special Scientific Interest (SSSIs) (supporting, regulating and biodiversity), (4) Carbon stores, incorporating heathland, heath grasslands and bogs (regulating).
Where the analyses had revealed significant change, a field attribute selection was used to identify the corresponding grid squares, extracted, and then exported as separate geospatial datasets. To facilitate further quantification of land cover types affected, the boundaries between resulting significant grid squares were dissolved, so that only the perimeters of aggregated squares remained. These two datasets were combined to produce a map for each of the four land cover categories overlain with areas of significant increase in frequency of each extreme event metric.