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{'author': None, 'author_email': None, 'creator_user_id': '64e47517-dce0-4a13-9ab7-ef1cfcd1d892', 'doi': '10.15493/BEA.DATA.250320-20', 'featured': 'false', 'id': '695aa705-9c99-41b3-983b-6bbc25d36d45', 'isopen': False, 'license_id': None, 'license_title': None, 'lineage_statement': "A cost surface using a digital elevation model (SRTM v3) [~30m] was used as the basis, with main water features (rivers and dams) and a slope threshold of 15 degrees removed as obstacles from the surface. Start points (derived from a 1km x 1km raster grid, and end points (the NGI road layer) rasterised at a 100m x 100m resolution, both using the StatsSA BSU grid projection ['+proj=aea +lat_1=-22 +lat_2=-38 +lat_0=-30 +lon_0=25 +x_0=1400000 +y_0=1300000 +datum=WGS84 +units=m +no_defs]) were used as location inputs. Non-NA raster values from these location inputs were converted to points (the centroid of each Non-NA pixel), then reprojected to the same resolution as the cost surface (e.g. EPSG:4326 - WGS 84 – Geographic). A variable resolution window was used for modelling [based on 250 start points at a time, each linked to 100 closest road points [end points] to create an extent and perform the modelling operation in reasonably sized chunks].", 'maintainer': None, 'maintainer_email': None, 'metadata_created': '2025-09-11T14:42:20.726226', 'metadata_date': '2023-01-01T00:00', 'metadata_modified': '2025-09-11T14:43:53.300875', 'metadata_thumbnail': '', 'name': 'virtual_roads_for_the_bioenergy_atlas', 'notes': "Virtual roads representing theoretical paths [roads] starting every 1km apart (across South Africa) from areas where no roads exists to the closest road segment in South Africa's National Geo-spatial Information (NGI) 2019 road layer (http://www.ngi.gov.za) -- for use in the SAEON BioEnergy Atlas feasibility model. A cost surface using a digital elevation model (SRTM v3) [~30m] was used as the basis, with main water features (rivers and dams) and a slope threshold of 15 degrees removed as obstacles from the surface. Start points (derived from a 1km x 1km raster grid, and end points (the NGI road layer) rasterised at a 100m x 100m resolution, both using the StatsSA BSU grid projection ['+proj=aea +lat_1=-22 +lat_2=-38 +lat_0=-30 +lon_0=25 +x_0=1400000 +y_0=1300000 +datum=WGS84 +units=m +no_defs]) were used as location inputs. Non-NA raster values from these location inputs were converted to points (the centroid of each Non-NA pixel), then reprojected to the same resolution as the cost surface (e.g. EPSG:4326 - WGS 84 – Geographic). A variable resolution window was used for modelling [based on 250 start points at a time, each linked to 100 closest road points [end points] to create an extent and perform the modelling operation in reasonably sized chunks]. The scikit-image python package was used to find the shortest path through each cost surface array cropped using the variable window extent, using a fully connected (i.e diagonal pixels) minimum cost path (MCU) algorithm. All outputs were merged to create a seamless Virtual roads layer with 1 051 696 individual paths covering South Africa.", 'num_resources': 1, 'num_tags': 1, 'organization': {'id': 'c4a18544-cded-4488-9f43-c02a1094ad39', 'name': 'saeon', 'title': 'NRF-SAEON', 'type': 'organization', 'description': 'The South African Environmental Observation Network (SAEON) is a long-term environmental observation and research facility of the National Research Foundation (NRF). SAEON’s three focus areas are environmental observation, data management and education outreach. The Department of Science, Technology and Innovation (DSTI) provides core funding for these activities. 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