Boundaries for land designated by a local planning authority as being green belt, grouped using the greenbelt core category. This data is compiled by the Ministry of Housing, Communities and Local Government for the purposes of gathering green belt statistics.

Boundaries of all designated Green Belts in and around the City of Edinburgh. These boundaries come from the Edinburgh Local Development Plan.

Green Belt boundary from the 2019 Adopted Local Development Plan

Spatial Data layers referenced in City Development Plan Policy and Proposals & Supplementary Guidance Maps. Third party data displayed in the above mentioned maps are not included herein.

This map was created by the GLA in 2017 as a preliminary analysis of the potential for woodland creation in London’s Green Belt, which covers 35,000 hectares. The map shows land in London’s Green Belt which could have potential for woodland creation, described as, ‘plantable areas’, based on an assessment of land use data.

Green belt as defined in the 2012 adpoted Surrey Heath Local Plan. This data was first published 2023-07 and last updated 2023-07. Downloads are projected in British National Grid (EPSG:27700), with the exception of the GeoJSON which is projected in EPSG:4326. View on map. Open Government License (OGL)

Dataset showing Green Belt (Policy CS9) as identified in Local Plan Core Strategy submission proposals map adopted June 2013.

1. Cultural Ecosystem Services (CES) encompass a range of social, cultural and health benefits to local communities, for example recreation, spirituality, a sense of place and local identity. However, these complex and place-specific CES are often overlooked in rapid land management decisions and assessed using broad, top–down approaches. 2. We use the Toolkit for Ecosystem Service Site-based Assessment (TESSA) to examine a novel approach to rapid assessment of local CES provision using inductive, participatory methods. We combined free-listing and participatory geographic information systems (GIS) techniques to quantify and map perceptions of current CES provision of an urban green space. The results were then statistically compared with those of a proposed alternative scenario with the aim to inform future decision-making. 3. By identifying changes in the spatial hotspots of CES in our study area, we revealed a spatially-specific shift toward positive sentiment regarding several CES under the alternative state with variance across demographic and stakeholder groups. Response aggregations in areas of proposed development reveal previously unknown stakeholder preferences to local decision-makers and highlight potential trade-offs for conservation management. Free-listed responses revealed deeper insight into personal opinion and context. 4. This work serves as a useful case study on how the perceptions and opinions of local people regarding local CES could be accounted for in the future planning of an urban greenspace and how thorough analysis of CES provision is important to fully-inform local-scale conservation and planning for the mutual benefit of local communities and nature.

Cheshire West and Chester Council Green Belt boundary digitised as polygons. Available to view online at http://maps.cheshirewestandchester.gov.uk/cwac/localplan/

Local planning authorities designate sites in their area as being brownfield land, and publish a register of brownfield sites annually following GOV.UK guidance. Each site references the following categories: ownership-status planning-permission-status planning-permission-type site-category

SUMMARYTo be viewed on combination with the dataset ‘Area of accessible green and blue space per 1000 population (England)’ and its associated metadata.This dataset identifies administrative areas for which Public Right of Way (PRoW) data was not available. While some gaps in the PRoW data will have been partially filled in by the OS MasterMap Highways Network Paths dataset, due to overlap between the two, some gaps will still remain. The area of accessible green/blue space in the areas highlighted by this dataset could be slightly under represented in the ‘Area of accessible green and blue space per 1000 population (England)’ dataset.COPYRIGHT NOTICEProduced by Ribble Rivers Trust. Contains Ordnance Survey data © Crown copyright and database right 2020. Contains public sector information licensed under the Open Government Licence v3.0.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

Green belt as defined in the 2012 adpoted Surrey Heath Local Plan.
This data was first published 2023-07 and last updated 2023-07.
Downloads are projected in British National Grid (EPSG:27700), with the exception of the GeoJSON which is projected in EPSG:4326.
View on map.
https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/>Open Government License (OGL)

Overview The Green Infrastructure Areas for the Black Country (GIBC) data identifies green infrastructure, green infrastructure function (or ecosystem service) and pinch point mapping for the Black Country area which includes Wolverhampton, Walsall, Sandwell and Dudley. The analysis was carried out in two stages and as a result of this, four datasets were produced: ‘GIBC 01 - Typology & Functionality OS’ ‘GIBC 02 – Pinch Points OS’ ‘GIBC 03 – Pinch Points Consortium Housing’ ‘GIBC 04 – Pinch Points Housing Land Availability’ First Stage Processing Ordnance Survey data was used as the base layer for all the processing. The first process produced the ‘GIBC 01 - Typology & Functionality OS’ dataset. This dataset is then subsequently used as a basis for the production of the second, third and fourth datasets during the second stage. In the first stage each OS polygon was assigned a green infrastructure category based on the SPADES project from the list below.
Green Infrastructure Categories are: Garden Green corridor Pocket park Park or garden Outdoor sports facility Children’s play space Youth area Broadleaved woodland Mixed woodland Coniferous woodland Natural and semi-natural open spaces Pasture or meadow Wetland Watercourse Fresh water body Allotments Orchard Cemetery or churchyard Open space around premises Agricultural land Road island/verge Railway corridor Abandoned, ruderal and derelict area The OS polygon was then assigned “A function beneficial to people” dependent on the Green Infrastructure Category already assigned. The 26 categories that perform a “function beneficial to people” are: Accessible water storage Carbon storage Community cohesion Connection with local environment Corridor for wildlife Culture Encouraging green travel Evaporative cooling Flow reduction through surface roughness Habitat for wildlife Heritage Inaccessible water storage Learning Local food production Pollination Pollutant removal from soil/water Providing jobs Recreation - private Recreation - public Recreation - public with restrictions Shading from the sun Trapping air pollutants Visual contribution to landscape character Water conveyance Water infiltration Water interception In its simplest form the process flow looks like this: OS base layer → assigned green infrastructure → assigned function (Eg - OS polygon → coniferous woodland → shading from the sun) The first dataset, ‘GIBC 01 - Typology & Functionality OS’, therefore shows the following: The green infrastructure category The function beneficial to people Shows areas of greatest need for this function. Shows areas whereby the function has been met. Shows areas whereby the function has not been met. Second Stage Processing The second stage expands on the first by establishing which areas suffer from stress associated with investment in growth and redevelopment of land. These areas are known as ‘pinch points’. Wherever there is a high level of need for a particular function (identified in the first process), a potential ‘pinch point’ exists. The pinch point categories that have been identified are: Air Quality Pinch Culture Pinch Flooding Pinch Heat Stress Pinch Heritage Pinch Local Community Pinch Mental Health Pinch Nature Pinch Physical Activity Pinch Recreation Pinch Sustainable Travel Pinch The second dataset, ‘GIBC 02 – Pinch Points OS’, takes the analysis from the first stage process and using the Ordnance Survey data as a base layer, displays the ‘pinch points’ analysis results. The third dataset, ‘GIBC 03 – Pinch Points Consortium Housing’, takes the analysis from the first stage process and rather than using the OS as a base uses the Consortium Housing Site data instead to display ‘pinch points’. The fourth dataset, ‘GIBC 04 – Pinch Points Housing Land Availability’, takes the analysis from the first stage process and rather than using the OS as a base used the Strategic Housing Land Availability data instead to display ‘pinch points’. Conclusion The ‘pinch point’ mapping can help identify particular areas of stress, which when used in conjunction with the green infrastructure and function mapping from the first stage process can be used to help alleviate those stresses.

The proposals map for Salford reflects the spatial policies of the adopted development plan. It contains policies from the UDP, the Minerals DPD and the Waste DPD. The datasets included are listed below.

UDP Datasets:

Archaeology / Ancient Monument, UDP Policy: CH5 (layer name: UDP_ANCIENT_MONUMENT)

Barton Aerodrome, UDP Policy: A14 (layer name: UDP_BARTON_AERODROME)

Chapel Street Frontage, UDP Policy: MX2 (layer name: UDP_CHAPEL_STREET_FRONTAGE)

Conservation Areas (Updated 2010), UDP Policy CH3 (layer name: UDP_CONSERVATION_AREAS_NEW)

Conservation Areas (at time of adoption, June 2006), UDP Policy: CH3 (layer name: UDP_CONSERVATION_AREAS_OLD)

Education, Health or Community Development Site, UDP Policy: EHC9 and EHC10 (layer name: UDP_EDUC_HEALTH_AND_COMM_DEV)

Employment Development Site, UDP Policy: E4 (layer name: UDP_EMPLOYMENT_DEVELOPMENT)

Existing Strategic Recreation Route, UDP Policy: R5 (layer name: UDP_EXISTING_STRAT_REC_ROUTES)

Green Belt, UDP Policy: EN1 (layer name: UDP_GREEN_BELT)

Housing Development Site, UDP Policy: H9 (layer name: UDP_HOUSING_DEVELOP_SITE)

Innovation Park, UDP Policy: E2 (layer name: UDP_INNOVATION_PARK)

Irwell Valley, UDP Policy: EN5 (layer name: UDP_IRWELL_VALLEY)

Key Recreation Area, UDP Policy: R4 (layer name: UDP_KEY_RECREATION_AREA)

Knowledge Capital, UDP Policy: E3 (layer name: UDP_KNOWLEDGE_CAPITAL)

Major Highway Proposal, UDP Policy: A9 (layer name: UDP_MAJOR_HIGHWAY_PROPOSALS)

Manchester Airport Safeguarding Zone – Buildings, structures, erections and works exceeding 90m in height, UDP Policy: DEV7(i) (layer name: UDP_MAN_AIRPORT_90M_CONSULT)

Manchester Airport Safeguarding Zone – Developments likely to attract birds, and/or applications connected with aviation use, UDP Policy: DEV7(ii) (layer name: UDP_MAN_AIRPORT_ATTRACT_BIRDS)

City Boundary and Manchester Airport Safeguarding Zone - Wind turbine development, UDP Policy: DEV7(iii) (layer name: UDP_MAN_AIRPORT_SAFEGUARD_ZONE)

Manchester, Bolton and Bury Canal, UDP Policy: CH7 (layer name: UDP_MBBC)

Metrolink Extension (Lowry Spur), UDP Policy: A3 (layer name: UDP_METROLINK_EXT_LOWRY_SPUR)

Mixed-Use Area, UDP Policy: MX1 (layer name: UDP_MIXED_USE_AREAS)

Mixed-Use Development Site, UDP Policy: MX3 (layer name: UDP_MIXED_USE_DEVELOP_SITE)

Mosslands, UDP Policy: EN11 (layer name: UDP_MOSSLANDS)

Mossland Heartland, UDP Policy: EN11 (layer name: UDP_MOSSLANDS_HEARTLAND)

New and Improved Recreation Land and Facilities, UDP Policy: R6 (layer name: UDP_NEW_AND_IMPROVED_REC_LAND)

New Sports Stadium, UDP Policy: E1C (layer name: UDP_NEW_SPORTS_STADIUM)

Parks and Gardens of Historic Interest, UDP Policy: CH6 (layer name: UDP_PARKS_AND_GARDENS)

Proposed Strategic Recreation Route, UDP Policy R5 (layer name: UDP_PROPOSED_STRAT_REC_ROUTES)

Retail Development Site, UDP Policy: S5 (layer name: UDP_RETAIL_DEVELOPMENT_SITE)

River Irwell Flood Control, UDP Policy: EN20 (layer name: UDP_RIVER_IRWELL_FLOOD_CONTROL)

Safeguarded Potential Transport Route, UDP Policy: A15 (layer name: UDP_SAFEGUARDED_POT_TRANSPORT)

Sites of Biological Importance (Updated Aug 2012 (2010 Survey)), UDP Policy: EN8 (layer name: UDP_SBI_NEW)

Sites of Biological Importance (at time of adoption, June 2006), UDP Policy: EN8 (layer name: UDP_SBI_OLD)

Strategic Regional Site, Barton, UDP Policy: E1 (layer name: UDP_STRATEGIC_REGIONAL_SITE)

Town / Neighbourhood Centre, UDP Policy: S1 (layer name: UDP_TOWN_AND_NEIGH_CENTRES)

Wildlife Corridor Key Area of Search, UDP Policy: EN9 (layer name: UDP_WILDLIFE_CORRIDOR)

Worsley Greenway, UDP Policy: EN2 (layer name: UDP_WORSLEY_GREENWAY)

Minerals DPD Datasets

Area of Search for Gravel (Minerals DPD Policy: 3 (layer name: MDPD_GRAVEL_AREA_OF_SEARCH)

Minerals Safeguarding Areas, Minerals DPD Policy: 8 (layer name: MDPD_MINERALS_SAFEGUARDING)

Minerals Site including those under restoration, Minerals DPD Policy: 11 (layer name: MDPD_MINERAL_SITES)

Rail-Linked Mineral Depots, Minerals DPD Policy: 11 (layer name: MDPD_SALFORD_RAIL_DEPOTS)

Waste DPD Datasets

Area Allocation for Waste Management Development, Waste DPD Policy 5 (layer name: WDPD_WASTE_MANAGEMENT_DEVELOP)

Allocation for Non-Hazardous Residual Waste Disposal, Waste DPD Policy: 7 (layer name: WDPD_NON_HAZARDOUS_WASTE)

Upon accessing this licensed data you will be deemed to have accepted the terms of the Public Sector End User Licence - INSPIRE.

Research project relating measuring urban intensity regarding urban forms and performance in China. The study selected eleven towns in the northern part and coastal areas of Zhejiang Province in China, including Guali, Tangqi, Fenshui, Shipu, Zhouxiang, Simen, Longgang, Xinshi, Chongfu, Wangjiangjing,and Zeguo. The data published in this study were collected from multiple sources in 2018, including Google Maps, Open Street Map (OSM), Google Earth satellite images, the regulatory detailed planning (RDP) documents, remote sensing images, and official documents from the local government. The geographical information system incorporated various features such as buildings, transit infrastructure, and natural elements like rivers, lakes, seas, coasts, mountain valleys, grassland, and hills. The data were obtained at a resolution of 15 meters. The Open Street Map (OSM) served as a valuable source of shared vector data for roads and other built environment features in the selected towns, which were subsequently analyzed using ArcMap. The normalized difference vegetation index (NDVI) were calculated through the remote sensing images in 2018. The GDP information was collected from the Zhejiang Province yearbook in 2018.

Overview An updated spatial data set for existing green roofs has been produced for London's Central Activities Zone (CAZ) using 2015 aerial imagery. The CAZ makes up around two per cent of London's total area. Method ArcMap GIS software was used to map green roofs installations. The aerial imagery was loaded into the software in true colour (RGB) and infrared (IR) composites, along with the CAZ boundary and London 500x500m Ordnance Survey grid to aid locating. RGB images are useful for both roof types with IR being very good for identifying vegetation. IR allows for easy identification of vegetation due to it reflecting strongly in the near infrared (NIR) and appearing deep red in colour (healthy vegetation). Challenges and solutions The problem of judging elevation from aerial imagery, particularly of buildings with only a few stories can be partially overcome by comparing the location with Google Maps Earth view. This includes a digital elevation model that makes it easier to judge whether a green area is raised or at street level. A number of small roof terraces in the CAZ have very dense vegetation along the edge of the roof. Where this kind of greening is spatially significant, the vegetation itself has been mapped rather than the full extent of the roof, in order to produce a more appropriate and accurate green area value. Building shadows can mask the texture and colour of the roof. The use of NIR helps with green roof identification, as the contrast of the red of the vegetation with the surrounding area is far higher than in RGB, making the shadow less impactful. Some roofs that appear very uniformly green in RGB may be Astroturf as opposed to a living green roof. This can usually be identified by inspecting the roof in IR, as it will not give the distinctive deep/bright red colours of living vegetation, displaying instead as blue-purple. Even analysing a small area of London, such as the CAZ, takes a long time. This is the main reason why this study methodology has not been extended beyond the CAZ.

A nationally consistent terrestrial Tranquillity & Place resource that identifies visually tranquil areas for use as an evidence base to inform policy intent, practice and provision for well-being benefits.

Storymap link https://storymaps.arcgis.com/stories/865c1876d9f64280a3dfc6e2769a46a5

Tranquillity is associated with the degree to which places and ecosystems deliver a state of quiet, calm, peace and well-being. This can be described as a relative abundance, perception or experience of nature, natural landscapes and features and/or a relative freedom from unwanted visual disturbance, signs of human influence and artificial noise.

Il-konfini tal-Green Belt tal-Kunsill ta’ Cheshire tal-Punent u Chester ġew diġitizzati bħala poligoni. Disponibbli biex tarah online fuq http://maps.cheshirewestandchester.gov.uk/cwac/localplan/