Grid square estimates of agricultural census data for England Scotland and Wales supplied by EDINA. Request specific areas or national coverage.

The data in this file was based on the OpenURL Router data available from http://openurl.ac.uk/doc/data/data.html. It was developed by EDINA as part of a JISC-funded project, more information on the project is available at http://edina.ac.uk/projects/Using_OpenURL_Activity_data_summary.html

The script used by Dimitrios to generate the data file is given in full in the discussion page for this item as it doesn't display on the wiki page properly.

These data are the urban woodland habitat networks of eleven different cities: Nottingham, Plymouth, Stoke-on-Trent, Milton Keynes, Coventry, Wolverhampton, Northampton, Birkenhead, Derby, Luton and Kingston-Upon-Hull.Three types of data are used to create the shape files:The OS MasterMap Topography (EDINA Digimap Ordnance Survey Service, 2024) ‘Natural Environment’ layer.This was overlain upon the latest version of the LandCover Map (EDINA Environment Digimap Service, 2022) for each urban area using QGIS (https://qgis.org/). Urban area boundaries were determined and clipped using the experimental urban extent polygons for the UK (ONS, 2019).ReferencesEDINA Digimap Ordnance Survey Service (2024) OS MasterMap® Topography Layer [GeoPackage geospatial data], Scale 1:1250, Tiles: GB, Updated: 1 February 2024, Ordnance Survey (GB). Available at: https://digimap.edina.ac.uk (Accessed: 10 July 2024).EDINA Environment Digimap Service (2022) Land Cover Map 2021 [FileGeoDatabase geospatial data], Scale 1:250000, Tiles: GB, Updated: 10 August 2022, CEH. Available at: https://digimap.edina.ac.uk (Accessed: 10 July 2024).ONS (2019) Experimental urban extent for UK - Office for National Statistics. Available at: https://www.ons.gov.uk/aboutus/transparencyandgovernance/experimentalurbanextentforuk (Accessed: 26 August 2024).

[from EDINA's description of Land-form PANORAMA data: "http://edina.ac.uk/digimap/description/products/panorama.shtml"]

Land-Form PANORAMA is a digital representation of the contours from Ordnance Survey's 1:50 000 scale Landranger maps. Contours are at 10 metre vertical intervals together with breaklines, lakes, coastline and a selection of spot heights to the nearest metre. Digital contour accuracy values are typically better than 3 metres root mean square error.

The Ordnance Survey has used the dataset to derive mathematically a digital terrain-model (DTM) dataset. The dataset consists of a grid of height values at 50 metre intervals interpolated from the contour data. Height values are rounded to the nearest metre. Accuracy varies according to the complexity of the terrain, from 2 metres in a hilly rural area to 3 metres in an urban lowland area. This data is only available for downloading to your machine.

DTM data can be used for terrain analysis of lines of sight and in applications such as visual impact studies, drainage analysis, site planning.

This is the data associated with the paper submitted entitled:

A Novel Approach for Mapping Exposure to Land Cover at the Small Statistical Geography Level

Joanne K. Garrett¹, Lewis R. Elliott¹, Rebecca Lovell¹, Benedict W. Wheeler¹, Tom Marshall², Fränze Kibowski², Benjamin B. Philips³, Kevin J. Gaston³

This dataset includes the Living England percentage land cover at the LSOA level calculated by two methods. These methods are referred to as the "proposed" method and the "typical" method. Our proposed method uses data at both LSOA and postcode (sub-LSOA) levels for England, first calculating the percentage coverage of land cover types within 300 m postcode buffers, then averaging these at the LSOA level weighted by the number of domestic postal delivery addresses (as a proxy for population per postcode). It mitigates edge effects by allowing habitat exposure to extend beyond the LSOA boundary through the use of a 300-metre postcode buffer and maintains consistency across varying LSOA sizes. We argue that the new proposed approach reduces the potential for exposure misclassification associated with variable unit size at the small statistical geography level.

The variables are described in the included file data_variable_descriptions.xlsx

The code is available on Github at github.com/j-k-garrett/RENEW_mapping.

LSOAs were obtained for the year 2011 [1]. Postcode locations were obtained from the UK’s Ordnance Survey dataset of postcode locations (Codepoint; [2]), which is accessible through the Edina Digimap service for UK educational and research institutions. The Living England Habitat Map is a probability-based map showing the extent and distribution of broad habitats across England [3]. Estuaries and rias were obtained from the Coastal Physiographic features product from JNCC [4.5]. Boundary data for Scotland and Wales were obtained from the Ordnance Survey dataset Boundary-Line [6].

The study was funded by the Natural Environment Research Council ‘Renewing biodiversity through a people-in-nature approach (RENEW)’ project (NE/W004941/1)

References

[1] Office for National Statistics: Lower Layer Super Output Areas (December 2011) Boundaries Full Clipped (BFC) EW V3. https://geoportal.statistics.gov.uk/datasets/1f23484eafea45f98485ef816e4fee2d_0/explore; 2021.

[2] Ordnance Survey: Code-Point. August 2021. EDINA Digimap Ordnance Survey Service; 2021.

[3] Kilcoyne A, Clement M, Moore C, Picton Phillipps G, Keane R, Woodget A, Potter S, Stefaniak A, Trippier B: Living England: Technical User Guide. NERR108. http://nepubprod.appspot.com/publication/4918342350798848; 2022: 38.

[4] Joint Nature Conservation Committee: Coastal Physiographic Features - Estuaries. https://www.data.gov.uk/dataset/225fb0e1-5cfd-43fa-a6bf-c108091f3825/coastal-physiographic-features-estuaries; 2018.

[5] Joint Nature Conservation Committee: Coastal Physiographic Features - Ria https://www.data.gov.uk/dataset/71bb8571-6214-45ba-8f14-a9b8d014b90c/coastal-physiographic-features-ria; 2018.

[6] Ordnance survey: Boundary-Line, Scotland and Wales region. 23rd April 2022 edn. https://digimap.edina.ac.uk/os: EDINA Digimap Ordnance Survey Service; 2022.

This dataset maps the location of anti-social graffiti around the University of Edinburgh's central campus. The data was collected over a 2 week period between the 19th May and the 2nd June 2014. The data was collected using a smartphone through an app called Fieldtrip GB (http://fieldtripgb.blogs.edina.ac.uk/). Multiple asset collectors were deployed to use a pre-defined data collection form which allowed users to log the following attributes: Date / Name of asset collector / Type of graffiti (image/tag/words/advert/.....) / What the graffiti was on (building/wall/lamppost/....) / What medium was used (paint/paper/chalk/....) / Density of graffiti / Photograph / Location. The data is by no means complete and realistically captured only around 50% of the graffiti in the study area. It is hoped that this dataset will be updated every 3 months to chart the distribution of graffiti over time. data was collected using the app Fieldtrip GB Once collected, data from multiple asset collectors was merged in FtGB's authoring tool and exported as a CSV file. This was then imported into QGIS and saved as a vector dataset in ESRI Shapefile format. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2014-06-06 and migrated to Edinburgh DataShare on 2017-02-22.

This dataset shows the location of Higher Education (HE) and Further Education (FE) institutes in the Great Britain. This should cover Universities and Colleges. Many institutes have more than one campus and where possible this is refelcted in the data so a University may have more than one entry. Postcodes have also been included for instities where possible. This data was collected from various sources connected with HEFE in the UK including JISC and EDINA. This represents the fullest list that the author could compile from various sources. If you spot a missing institution, please contact the author and they will add it to the dataset. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2011-02-01 and migrated to Edinburgh DataShare on 2017-02-21.

Database of nearest resilience infrastructure from each Scottish postcode, commissioned and funded by the National Centre for Resilience.

Data are in geopackage format, which is curated by the open geospatial consortium. The data format can be read by GDAL, and hence all major analytical and spatial software (e.g. R, Python, QGIS). The database has two main tables:

• datazones - a spatial table of boundary polygons. Available from data.gov.uk under the open government licence.
• postcode_to_POI - a table of postcode to POI type distances with lookups for a range of administrative boundaries.

An example query (in R) is shown in 10.5281/zenodo.3386179 to access data in the geopackage.

The database was built using the following datasets:

• Ordnance Survey Open Roads
• Ordnance Survey Code-Point Open
• Ordnance Survey Points of Interest

The following copyright licences apply to this dataset:

© Crown Copyright and Database Right 2019. Ordnance Survey (Digimap Licence).
This material includes data licensed from PointX Database Right/Copyright 2019.
Contains NRS data © Crown copyright and database right 2019.

This archive contains code and data to go with the paper An Open-Source Automatic Survey of Green Roofs in London using Segmentation of Aerial Imagery.

This archive contains geospatial data, as well as the code used to generate the geospatial data.

The geospatial data consists of georeferenced polygons identifying areas which are covered by green roofs in London (GBR) generated from 2019 aerial imagery.

The data is described in detail in the manuscript An Open-Source Automatic Survey of Green Roofs in London using Segmentation of Aerial Imagery. See abstract below.

GeoJSON format:

GeoJSON is a format for encoding geospatial data, see https://geojson.org/.

GeoJSON can be read using GIS programs including ArcGIS, QGIS, OGR.

Contents:

geospatial_data/buffered_polygons_2021.zip a zip archive containing a geojson file. It is the estimated locations of green roofs in London in 2021 and is the main result, which can be opened in any GIS program after being unzipped.

geospatial_data/buffered_polygons_2019.zip a zip archive containing a geojson file. It is the estimated locations of green roofs in London in 2019 and is a secondary result, which can be opened in any GIS program after being unzipped. The predictions were made with the same model as the 2021 results.

geospatial_data/labelled_area.zip a zip archive containing a geojson file. Identifies the area which was hand-labelled.

geospatial_data/manual_2021.zip a zip archive containing a geojson file. Manually labelled green roof from 2021 imagery.

geospatial_data/manual_2019.zip a zip archive containing a geojson file. Manually labelled green roof from 2019 imagery.

segmentation_code contains the code used to produce the segmentation from the aerial imagery.

analysis_code contains the code used to produce the plots and tables for the paper.

Imagery availability:

Unfortunately the aerial imagery and building footprint data cannot be shared directly, as you will require the proper license. Both can be found at Digimap provided your institution has the license.

Abstract:

Green roofs can mitigate heat, increase biodiversity, and attenuate storm water, giving some of the benefits of natural vegetation in an urban context where ground space is scarce. To guide the design of more sustainable and climate resilient buildings and neighbourhoods, there is a need to assess the existing status of green roof coverage and explore the potential for future implementation. Therefore, accurate information on the prevalence and characteristics of existing green roofs is needed, but this information is currently lacking. Segmentation algorithms have been used widely to identify buildings and land cover in aerial imagery. Using a machine-learning algorithm based on U-Net to segment aerial imagery, we surveyed the area and coverage of green roofs in London, producing a geospatial dataset \cite[]{simpson_charles_2022_6861929}. We estimate that there was 0.23 km^2 of green roof in the Central Activities Zone (CAZ) of London, (1.07 km^2) in Inner London, and (1.89 km^2) in Greater London in the year 2021. This corresponds to 2.0% of the total building footprint area in the CAZ, and 1.3% in Inner London. There is a relatively higher concentration of green roofs in the City of London, covering 3.9% of the total building footprint area. Test set accuracy was 0.99, with an f-score of 0.58. When tested against imagery and labels from a different year (2019), the model performed just as well as a model trained on the imagery and labels from that year, showing that the model generalised well between different imagery. We improve on previous studies by including more negative examples in the training data, and by requiring coincidence between vector building footprints and green roof patches. We experimented with different data augmentation methods, and found a small improvement in performance when applying random elastic deformations, colour shifts, gamma adjustments, and rotations to the imagery. The survey covers 1558 km^2 of Greater London, making this the largest open automatic survey of green roofs in any city. The geospatial dataset is at the single-building level, providing a higher level of detail over the larger area compared to what was already available. This dataset will enable future work exploring the potential of green roofs in London and on urban climate modelling.

EDINA agcensus data provides agricultural census data for England, Scotland and Wales at 2km, 5km or 10km grid square resolution. The Agricultural Census is conducted in June each year by the government departments dealing with Agriculture and Rural Affairs for Scotland, England, and Wales. Farmers are surveyed in each year via a postal questionnaire, with the farmer declaring the agricultural activity on their land. In Scotland the census covers all major agricultural holdings, but in England and Wales a stratified sample of holdings are surveyed. Data for non-surveyed farms is extrapolated from previous years and trends on comparable farms. The respective government departments publish information relating to farm holdings for recognised geographies for the 150 items of data. Algorithms developed by Edinburgh University Data Library convert small area data provided by the government department into grid squares of 2, 5 or 10 km. Dates covered: Great Britain: 1969-1994; England and Wales: 1969-1997; England: 2000-present; Wales: 2000-present; Scotland: 1969-present. The frequency of updating is dependent upon the respective government department collating and publishing the census data for each year prior to supplying EDINA with the data for their processing. EDINA data is a paid for service. There is a nominal fee per year for unlimited access for UK tertiary education institutions. Non-academic subscriptions are based on the number of potential users or on a per project basis.

GB mid year population estimates by Local Authority from 1981 - 2009. Mid year estimates reflect the population at 30 June of the reference year. Population data was downloaded from Nomis (www.nomisweb.co.uk/) and joined to Local Authority (district, unitary authority and borough) boundaries downloaded from Ordnance Survey OpenData Boundary Line dataset and joined using ArcGIS. Contains public sector information licensed under the Open Government Licence v1.0. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2010-11-11 and migrated to Edinburgh DataShare on 2017-02-21.

About

UKBORDERS provides digitised boundary datasets of the UK, available in many Geographic Information System (GIS) formats (MapInfo MIF/MID, ArcView Shape, Arc/Info Export and several others), for teachers and researchers in the UK Higher and Further Education community to download and use in their work.

Re-use

Available for re-use in UK HE/FE.

Wind Farms - follows on from the 'Dave' Data Download case study. View and symbolise OS raster and height data and Wind Farm location data. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2014-04-10 and migrated to Edinburgh DataShare on 2017-02-22.

Points of interest of English Heritage features, derived from a POI GPX file. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2010-09-03 and migrated to Edinburgh DataShare on 2017-02-22.

Several datasets and workbook for use in the Visualising Arts and Humanities Data Workshop at the FOSS4G UK 2016 conference in Southampton. Tiff data generated from OpenStreetMap in QGIS as a screen Grab. (CC BY_SA). London Local Authorities derived from Open Government Data (OGL). Geoparsed text data derived from a book using the Edinburgh Geoparser, this data has been randomised and annonymised so is open data(ODbl). Hexagons created in QGIS using the MMQGIS plugin and is open data (ODbl). Other. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2016-06-10 and migrated to Edinburgh DataShare on 2017-02-22.

Country boundary polygon for Wales created from merging the European regions from OS Open Boundary Line data (May 2010). GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2010-07-29 and migrated to Edinburgh DataShare on 2017-02-21.

UK boundary dataset corresponding to the geography of the ONS UACNTY09 coding frame. The dataset consists of 143 features corresponding to a standard GB county/unitary authority geography with the following exceptions. Orkney, Shetland and the Western Isles are included as a single feature; The Isles of Scilly are not included; Greater London is split into 2 Inner and Outer features corresponding to the super geographies within which the Inner and Outer London boroughs fall; Northern Ireland is provided as a single feature and is shown alongside the rest of GB in British National Grid. The GB portions of this dataset was derived from Ordnance Survey Boundary-Line data available as part of the Ordnance Survey OpenData initiative. The full terms and conditions of use of Ordnance Survey OpenData which apply to this dataset may be viewed at the following location: http://www.ordnancesurvey.co.uk/opendata/docs/os-opendata-licence.pdf. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2012-10-15 and migrated to Edinburgh DataShare on 2017-02-21.

Environment Roam exercise. Zip file contains exercise plus annotations file and Quick Guide. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2014-04-10 and migrated to Edinburgh DataShare on 2017-02-22.

This data shows the local authority districts, council areas (Scotland) and unitary authorities for Great Britain. The attached Layer File can be used to symbolise the different types of boundaries. A JPEG image of the map is also contained in the download. This dataset was made from the OS OpenData Boundary Line product http://www.ordnancesurvey.co.uk/oswebsite/products/boundary-line/index.html. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2012-05-10 and migrated to Edinburgh DataShare on 2017-02-21.

GB Roman Roads extracted from Ordnance Survey Strategi data which is available from OS Open Data. Roman Roads were extracted from the land_use_polyline dataset and merged to create a GB wide dataset. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2010-10-07 and migrated to Edinburgh DataShare on 2017-02-21.