This statistic shows the percentage of green space in major cities in the United Kingdom in 2016. The major Scottish cities of Edinburgh and Glasgow have the highest percentage of green space, with 49.2 and 32 percent respectively.

This statistic shows the green space per inhabitant in the city of London in the United Kingdom as of 2018, broken down by category. According to data published by

A pilot-scale research project between the UK and Taiwan funded jointly under ESRC and NSTC (ES/W000172/1), exploring how green spaces can support neighbourhoods in adapting to extreme hot weather under a changing climate. Pilot workshops with residents were held in two cities: Glasgow (Scotland, UK) and Taipei (Taiwan). The project demonstrated a methodology for engaging residents on the links between green spaces and reducing heat risk, and illustrated the breadth of benefits that green spaces can provide to residents' wellbeing and resilience.

The project develops a network of UK and Taiwan-based researchers capable of understanding the lived experience of climate change at the neighbourhood level, and of how citizens may experience the climate risk reduction benefits provided by green spaces in their neighbourhood. Globally, there is increasing interest within environmental politics and human geography scholarship in the role that institutions working at the sub-national level - such as city governments - can play in responding to the climate challenge. This is supported by an upswell of interest in 'nature-based solutions' (responses to social and environmental challenges through the management of natural spaces) across the social and natural sciences. Yet urban planners and third sector organisations are becoming more interested in the neighbourhood as the scale at which people experience climate change - and our responses to it - in their daily lives. Nevertheless, there remains a need for more concrete evidence on how climate impacts and responses play out at the sub-national level; and in the urban studies literature in particular it is increasingly recognised that subtropical Asian cities are under-represented in the climate risk reduction and governance literature.

The proposed research responds to these challenges by evaluating how neighbourhoods in two cities taking climate and resilience leadership outside of formal UN channels - Glasgow, Scotland; and Taipei, Taiwan - experience risks from climate change and feel the benefits of city-led nature-based resilience strategies. Given its policy relevance in each city, excess urban heat is taken as a focal point to assess in more depth one climate risk which may be mitigated via urban greening. At the core of the project is pilot-scale research centered on a small number of neighbourhoods in each city, which understands residents' and decision-makers' narrative experiences of climate change and urban greenspace, evaluates planning and urban development histories, and uses publicly-available data to quantitatively assess inequality in access to heat risk reduction benefits from green spaces across the city. The aim of this pilot research is to develop and exemplify a methodology for understanding the interface between lived experience, climate risk and greenspace across different urban contexts, to build credibility among the UK-Taiwan team ahead of further larger-scale research collaboration.

Indeed, a key aim of the project is to lay the foundations for subsequent transdisciplinary research encompassing not only social scientists and natural scientists, but also stakeholders from local government, planning consultancies, NGOs and community organisations in both Glasgow and Taipei. To this end, academic workshops and international transdisciplinary dialogues will be held in both Glasgow and Taipei (or virtually depending on the COVID situation) to (a) create a broader network of social- and natural science academics to engage in follow-on research; and (b) pro-actively engage stakeholders in both Glasgow and Taipei in city-to-city learning and in the co-creation of research questions and knowledge needs for subsequent larger-scale projects.

The research is jointly led by Dr Leslie Mabon (environmental sociology) and Dr Wan-Yu Shih (urban planning), who will facilitate wider buy-in and impact through their links in each city. In Glasgow, Mabon can draw on academic contacts through his position in the Young Academy of Scotland, and stakeholder links via his continued collaboration with Glasgow City Council and Climate Ready Clyde. In Taipei, Shih can draw in academics via her role in Future Earth Taipei, and stakeholders via her close association with the Classic Landscape and Planning Company, who will support the stakeholder engagement elements of the project.

Dataset displaying open spaces in Runnymede. This includes parks & gardens, outdoor sports facilities, amenity green spaces, play areas and more.

This dataset has been released as open data. You are encouraged to use and re-use the Information that is available under this licence freely and flexibly, with only a few conditions as outlined in the Open Government Licence.

This dataset has ben superseded by the following dataset, which contains all years and accuracy of site location has been improved: https://data.gov.uk/dataset/big-tree-plant-sites

This data shows the locations of the Big Tree Plant scheme locations for 2012/2013 .

The Grant Scheme is now closed to new applicants.

The Big Tree Plant:

• was a national campaign that brought together a partnership of national organisations who already plant trees, as well as civic and community groups working with Defra and the Forestry Commission
• was launched in December 2010 to increase the number of trees (individual, small groups, street trees and fruit trees) planted in towns, cities and neighbourhoods throughout England
• encouraged and supported people to establish, protect and enhance local green spaces
• was supported by a Forestry Commission funding scheme, giving up to £4 million in grants (2011 to 2015) to help pay for the planting of at least one million new trees in urban areas
• aimed to make neighborhoods more attractive, healthy places to live; particularly in areas of greatest deprivation and/or where there is little greenery

Attributes:

FinYear = Financial year in in which the project is assigned ProjectNo = Big Tree Plant project reference number OrgName = Name of the organisation who applied for the Big Tree Plant project Attribution statement: Contains OS data © Crown copyright [and database right] [year].

A range of indicators for a selection of cities from the New York City Global City database.

Dataset includes the following:

Geography

City Area (km2)

Metro Area (km2)

People

City Population (millions)

Metro Population (millions)

Foreign Born

Annual Population Growth

Economy

GDP Per Capita (thousands $, PPP rates, per resident)

Primary Industry

Secondary Industry

Share of Global 500 Companies (%)

Unemployment Rate

Poverty Rate

Transportation

Public Transportation

Mass Transit Commuters

Major Airports

Major Ports

Education

Students Enrolled in Higher Education

Percent of Population with Higher Education (%)

Higher Education Institutions

Tourism

Total Tourists Annually (millions)

Foreign Tourists Annually (millions)

Domestic Tourists Annually (millions)

Annual Tourism Revenue ($US billions)

Hotel Rooms (thousands)

Health

Infant Mortality (Deaths per 1,000 Births)

Life Expectancy in Years (Male)

Life Expectancy in Years (Female)

Physicians per 100,000 People

Number of Hospitals

Anti-Smoking Legislation

Culture

Number of Museums

Number of Cultural and Arts Organizations

Environment

Green Spaces (km2)

Air Quality

Laws or Regulations to Improve Energy Efficiency

Retrofitted City Vehicle Fleet

Bike Share Program

This is the first natural capital account for London, and was supported by the Mayor of London, the National Trust and the Heritage Lottery Fund. The natural capital account assesses the economic value of different benefits that London and Londoners gain from the city’s public parks and other green spaces. For more information on the methodology and results of this analysis, please see the London.gov.uk website.

This dataset is published as Open Data and replaces all previously published versions.A council development plan may designate a green belt around a city or town to support the spatial strategy by:directing development to the most appropriate locations and supporting regeneration;protecting and enhancing the character, landscape setting and identity of the settlement; andprotecting and providing access to open space.

The People and Nature Survey for England is one of the main sources of data and statistics on how people experience and think about the environment. It began collecting data in April 2020 and has been collecting data since.

The survey builds on the Monitor of Engagement with the Natural Environment (MENE) survey which ran from 2009 to 2019. Data from the People and Nature Survey for England enables users to:

• understand how people use, enjoy, and are motivated to protect the natural environment
• monitor changes in use of the natural environment over time, at a range of different spatial scales and for key groups within the population
• understand how being in the natural environment can influence wellbeing
• understand environmental attitudes and the actions people take at home, in the garden and in the wider community to protect the environment

This data contributes to Natural England’s delivery of statutory duties, informs Defra policy and natural capital accounting, and contributes to the outcome indicator framework for the 25 Year Environment Plan.

Different versions of the People and Nature Survey for England are available from the UK Data Archive under Open Access (SN 9092) conditions, End User Licence (SN 9093), and Secure Access (SN 9094).

The Secure Access version includes the same data as the End User Licence version, but includes more detailed variables including:

• age as a continuous variable
• sex
  
• whether gender is the same as at birth
• sexual orientation
• more detailed ethnicity
• where journey to recent visit to green and natural space started from
• visit date
• detailed home geography, including local authority districts, local nature recovery strategies areas, national character areas; urban/rural area, and Index of Multiple Deprivation
• a number of variables that have not been top-coded, including number of children and number of children in household, food and drink expenditure, and income

The Open Access version includes the same data as the End User Licence version, but does not include the following variables:

• age band
• gender identity
  
• marital status
• number of children living in household
• number of children
• work status
• student working status
• income
• qualification
• ethnicity and consent to answer ethnicity question
• number of vehicles
• presence of dog in household
• physical activity
• various health data

Researchers are advised to review the Open Access and/or the End User Licence versions to determine if these are adequate prior to ordering the Secure Access version.

Natural England's statistical practice is regulated by the Office for Statistics Regulation (OSR). OSR sets the standards of trustworthiness, quality and value in the Code of Practice for Statistics that all producers of official statistics should adhere to.

These accredited official statistics were independently reviewed by the Office for Statistics Regulation in January 2023. They comply with the standards of trustworthiness, quality and value in the Code of Practice for Statistics and should be labelled ‘accredited official statistics’.

Users are welcome to contact Natural England directly at people_and_nature@naturalengland.org.uk with any comments about how they meet these standards. Alternatively, users can contact OSR by emailing regulation@statistics.gov.uk or via the OSR website.

Since the latest review by the Office for Statistics Regulation, Natural England have continued to comply with the Code of Practice for Statistics, and have made the following improvements:

1. Published a development plan with timetables for future work, which will be updated annually
2. Ensured that users have opportunities to contribute to development planning through their biannual Research User Group
3. Enabled wider access to the data by publishing raw data sets through the UK Data Service
4. Provided users with guidance on how statistics from their products can be compared with those produced in the devolved nations
5. Published guidance on the differences between PaNS and MENE
6. Improved estimates of the percentage of people visiting nature in the previous 14 days by reducing the amount of respondents answering ‘don’t know’.

These data are available in Excel, SPSS, as well as Open Document Spreadsheet (ODS) formats.

Latest edition information

For the seventh edition (March 2025), data for April to June 2024 (Quarter 17) have been added.

A series of flow based classifications of commuting for England and Wales based on MSOA origin-destination data from the 2011 Census. It consists of 9 super-groups and 40 sub-groups. The evidence can be used to target funding for an 'into-work-scheme' to help the most disconnected community. The toolkit allows the policymaker to explore levels of commuting and compare the level of connectivity of each neighbourhood to major employment centres. The underlying rationale for the research is that the toolkit will help deliver efficiencies in public and private sector investment. This is crucial at a time when the government is promoting the need for smarter economic growth but doing so in a challenging context in which public sector resources are scarce and the private sector is risk averse.

Numerous research studies use commuting data, collected through the Census of Population, to understand social, economic and environmental challenges in the UK. This commuting data has been used to understand patterns; answer questions regarding the relationship between housing and labour markets; and to see if travel behaviour is becoming more or less sustainable over time. However, there is lots of untapped potential for such data to be used to evaluate transport policy and investment decisions so resources are more effectively and efficiently targeted to places of need. In applied public policy a major shortcoming has been a lack of use of this data to support investment in transport which has major implications for economic growth. If transport investments are inefficiently targeted, this restricts the capacity of places to grow economies to their full potential. This wastes their resources by over investing in transport capacity in areas where it is not needed. Equally, it has long been argued that efficient investment in transport is crucial if labour market exclusion, particularly the case of deprived communities, is to be tackled. The aim of the research is to inform community transportation policy and investment and the socio-spatial dimensions of travel to work flows over time (2001-2011). Our research develops a toolkit to help decision-makers better target investment in transport capacity and infrastructure. The toolkit includes a series of new classifications of commuting flows from the 2001 and 2011 Censuses. It will include a classification of newly developed official Workplace Zones for England to complement official residential population-based classifications alongside various population, deprivation, investment and infrastructure data. The toolkit will bring these classifications and datasets together online through various mapping and analysis tools to understand the dynamics of commuting between different types of residential and workplace locations over time and combine these datasets and analyses with locally-specific transport investment data. The methodology developed will be applied to England as a whole but we will use the Manchester as a test-case for our analysis and for development of the toolkit. The use of open source approaches to build the toolkit means that other locations will have the framework to develop their own toolkit. The flow and area-based (Workplace Zones) classifications for England will complement official ONS residential-based output area classification and existing indices of deprivation. This will be mapped in relation to key transport investments made in Manchester, using local administrative data and overlay these with the results of commuting analysis to support decision-making regarding future targeted public transport infrastructure investment. The toolkit will be interactive so users can pose policy questions to explore commuting relationships between different places. The strength of this approach is that it will enable policy and decision-makers to test various scenarios for future transport investment depending on problems they have posed. In a hypothetical situation, a policymaker in might ask the question of whether a specific deprived community in their city is more or less connected into a major employment centre than another equally deprived community.

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.

Archive contents:
`geospatial_data/green_roofs_220719.geojson` is the main result, which can be opened in any GIS program.
`segmentation_code` contains the Python code used to produce the segmentation from the aerial imagery.
`analysis_code` contains the Python code used to produce the plots and tables for the paper, as well as the OS intersection postprocessing step.

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.

Input data 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](https://digimap.edina.ac.uk) provided your institution has the license.

Abstract:
Green roofs are roofs incorporating a deliberate layer of growing substrate and vegetation. They can reduce both indoor and outdoor temperatures, so are often presented as a strategy to reduce urban overheating, which is expected to increase due to climate change. In addition, they could help decrease the cooling energy demand of buildings thereby contributing to energy and emissions reductions and provide benefits to biodiversity and human well-being. 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 required to estimate any effect of green roofs on temperatures (or other phenomena), but this information is currently lacking. 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. We estimate that there was 0.19 km^2 of green roof in the Central Activities Zone (CAZ) of London, (0.81 km^2) in Inner London, and (1.25 km^2) in Greater London in the year 2019. This corresponds to 1.6% of the total building footprint area in the CAZ, and 1.0% in Inner London. There is a relatively higher concentration of green roofs in the City of London (the historic financial district), covering 3.1% of the total building footprint area. The survey covers 1463 km^2 of Greater London, making this the largest open automatic survey of green roofs in any city. We improve on previous studies by including more negative examples in the training data, by experimenting with different data augmentation methods, and by requiring coincidence between vector building footprints and green roof patches. This dataset will enable future work examining the distribution and potential of green roofs in London and on urban climate modelling.

Woodlands In and Around Towns (WIAT) The Woods In and Around Towns (WIAT) Programme provides the focus for Scottish Forestry's work on improving quality of life in towns and cities. The purpose of WIAT is to: - Bring neglected woodland into active management. - Work with people to help them use their local woodland. There are four key characteristics of woodland that determine whether it improves quality of life: 1. Where it is The woodland must be close to where people live and/or work. We will undertake WIAT related activities within 1km of settlements of over 2000 people (Fig 1). Within the WIAT area, deprived areas are a priority. 2. How it is managed Management for people will be the top priority in most WIAT woodlands. Woods should be safe and welcoming to all. WIAT woodland is also important for other aspects of forestry such as biodiversity. Woodland involved in WIAT should be managed in accordance with the UK Forestry Standard. 3. How it is connected to other woodland and greenspace WIAT will promote the creation and management of woodland that is close to other woodland and greenspace so that it contributes to green networks. Paths should link the networks. 4. How it is connected to people Most of the activity in this programme is directed at the physical elements of WIAT: where it is, how it is managed, and how it is connected into green networks. However, reaching out to people should be part of every WIAT project to help people use woodland.