Green Belt boundary from the 2019 Adopted Local Development Plan

City Development Plan Policy and Proposals

Green Belt - The functions of the Green Belt are to: check the unrestricted sprawl of large built-up areas; prevent neighbouring towns from merging into one another; assist in safeguarding the countryside from encroachment; preserve the setting and special character of historic towns; and assist in urban regeneration, by encouraging the recycling of derelict and other urban land.

For more information please see our online map Unitary Development Plan 2006

This data has been derived from Ordnance Survey base mapping. (C) Crown copyright [and database rights] (2019) OS (licence 100023069).

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.

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

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.

Analysis of Ordnance Survey (OS) data on access to private gardens, public parks and playing fields in Great Britain, available by country, region, Local Authority and Middle Layer Super Output Area. This page also includes Natural England survey data on garden access in England, broken down by personal characteristics such as age and ethnicity.

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.

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.

The 'Climate Just' Map Tool shows the geography of England’s vulnerability to climate change at a neighbourhood scale.

The Climate Just Map Tool shows which places may be most disadvantaged through climate impacts. It aims to raise awareness about how social vulnerability combined with exposure to hazards, like flooding and heat, may lead to uneven impacts in different neighbourhoods, causing climate disadvantage.

Climate Just Map Tool includes maps on:

• Flooding (river/coastal and surface water)
• Heat
• Fuel poverty.

The flood and heat analysis for England is based on an assessment of social vulnerability in 2011 carried out by the University of Manchester. This has been combined with national datasets on exposure to flooding, using Environment Agency data, and exposure to heat, using UKCP09 data.

Data is available at Middle Super Output Area (MSOA) level across England. Summaries of numbers of MSOAs are shown in the file named Climate Just-LA_summaries_vulnerability_disadvantage_Dec2014.xls

Indicators include:

Climate Just-Flood disadvantage_2011_Dec2014.xlsx

Fluvial flood disadvantage index
Pluvial flood disadvantage index (1 in 30 years)
Pluvial flood disadvantage index (1 in 100 years)
Pluvial flood disadvantage index (1 in 1000 years)

Climate Just-Flood_hazard_exposure_2011_Dec2014.xlsx

Percentage of area at moderate and significant risk of fluvial flooding
Percentage of area at risk of surface water flooding (1 in 30 years)
Percentage of area at risk of surface water flooding (1 in 100 years)
Percentage of area at risk of surface water flooding (1 in 1000 years)

Climate Just-SSVI_indices_2011_Dec2014.xlsx

Sensitivity - flood and heat
Ability to prepare - flood
Ability to respond - flood
Ability to recover - flood
Enhanced exposure - flood
Ability to prepare - heat
Ability to respond - heat
Ability to recover - heat
Enhanced exposure - heat
Socio-spatial vulnerability index - flood
Socio-spatial vulnerability index - heat

Climate Just-SSVI_indicators_2011_Dec2014.xlsx

% children < 5 years old
% people > 75 years old
% people with long term ill-health/disability (activities limited a little or a lot)
% households with at least one person with long term ill-health/disability (activities limited a little or a lot)
% unemployed
% in low income occupations (routine & semi-routine)
% long term unemployed / never worked
% households with no adults in employment and dependent children
Average weekly household net income estimate (equivalised after housing costs) (Pounds)
% all pensioner households
% households rented from social landlords
% households rented from private landlords
% born outside UK and Ireland
Flood experience (% area associated with past events)
Insurance availability (% area with 1 in 75 chance of flooding)
% people with % unemployed
% in low income occupations (routine & semi-routine)
% long term unemployed / never worked
% households with no adults in employment and dependent children
Average weekly household net income estimate (equivalised after housing costs) (Pounds)
% all pensioner households
% born outside UK and Ireland
Flood experience (% area associated with past events)
Insurance availability (% area with 1 in 75 chance of flooding)
% single pensioner households
% lone parent household with dependent children
% people who do not provide unpaid care
% disabled (activities limited a lot)
% households with no car
Crime score (IMD)
% area not road
Density of retail units (count /km2)
% change in number of local VAT-based units
% people with % not home workers
% unemployed
% in low income occupations (routine & semi-routine)
% long term unemployed / never worked
% households with no adults in employment and dependent children
Average weekly household net income estimate (Pounds)
% all pensioner households
% born outside UK and Ireland
Insurance availability (% area with 1 in 75 chance of flooding)
% single pensioner households
% lone parent household with dependent children
% people who do not provide unpaid care
% disabled (activities limited a lot)
% households with no car
Travel time to nearest GP by walk/public transport (mins - representative time)
% of at risk population (no car) outside of 15 minutes by walk/public transport to nearest GP
Number of GPs within 15 minutes by walk/public transport
Number of GPs within 15 minutes by car
Travel time to nearest hospital by walk/public transport (mins - representative time)
Travel time to nearest hospital by car (mins - representative time)
% of at risk population outside of 30 minutes by walk/PT to nearest hospital
Number of hospitals within 30 minutes by walk/public transport
Number of hospitals within 30 minutes by car
% people with % not home workers
Change in median house price 2004-09 (Pounds)
% area not green space
Area of domestic buildings per area of domestic gardens (m2 per m2)
% area not blue space
Distance to coast (m)
Elevation (m)
% households with the lowest floor level: Basement or semi-basement
% households with the lowest floor level: ground floor
% households with the lowest floor level: fifth floor or higher