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If you use the Safer Parks Dashboard in your work, we ask that you please acknowledge and cite us.
If you use insights, screenshots, text, or images from the dashboard in your work, please cite us as follows:
Maeve Murphy Quinlan, Anna Barker, Melissa Barrientos, Victoria Houlden, Francesca Pontin (2026): The Safer Parks Dashboard. University of Leeds. https://doi.org/10.5518/1951. Accessed:
Please include a caption containing this information in any published screenshots from the dashboard (note that this is not required for internal documents):
Source: Screenshot from the Safer Parks Dashboard (Murphy Quinlan et al., 2026), University of Leeds. DOI: 10.5518/1951. Includes OpenStreetMap-derived content and map data, together with derived datasets incorporating information licensed under the UK Open Government Licence (OGL). Where shown, toilet facility data are © 2025 Toilet Map (CC BY 4.0). Other third-party data may be subject to separate licences and attribution requirements; full layer attribution statements are available on the Safer Parks Dashboard. Captured: [Include date of capture here].
Please update the timestamp to match the date the image was captured or downloaded. Where multiple screenshots or images are included in a document, this can be included once as opposed to duplicated for each.
For other citations, data reuse etc., please see the citation and licensing guide (opens in a new tab).
A schematic background map that provides visual context for the park and its surroundings. It shows roads, buildings, green spaces, and water in a simple, stylised format.
Unlike other layers, it is not analytical; it helps orient users and place other data layers within their spatial context.
Understanding surroundings: Identifying nearby streets, buildings, and green spaces around the park boundary.
Orientation: Locating yourself within the dashboard before exploring other layers.
Cross-referencing: Comparing analytical layers with the area’s overall layout.
This layer uses OpenStreetMap data.
Cycling and walking infrastructure within and around parks. The network is displayed as lines.
Use this layer to understand how parks connect to the wider active travel network and where improvements could support safer, more inclusive use, considering:
Visibility and lighting on active routes: Are key routes through the park visible, open, and well-lit, especially those used at peak times? Poor visibility can deter use, even on busy routes (See Visibility and Openness: The Safer Parks guidance and Lighting: The Safer Parks guidance).
Connections to access points: Do routes connect well to access and exit points? Weak connections can reduce use and natural footfall (See Escape: The Safer Parks guidance and Access and Location).
Signage and wayfinding: Is navigation clear along routes within or alongside the park? (See Wayfinding and Layout: The Safer Parks guidance).
Catchment coverage: Does the network serve the full local population, including those who rely on walking or cycling?
For deeper insight, use alongside the Predicted Route Popularity, Street Lighting, Visibility Analysis, Access and Exit Points, and Demographic Data layers.
This layer uses OpenStreetMap data downloaded via Geofabrik and filtered to extract active travel infrastructure across England and Wales. The data is split into two layers: cycling (cycleways, cycle lanes, shared paths, bridleways, and tracks) and walking (footways, pedestrian areas, shared paths, bridleways, and tracks). Shared paths, bridleways, and tracks appear in both layers as they serve both cyclists and pedestrians.
This data combines Ordnance Survey and OpenStreetMap greenspace datasets into a single park boundary layer for parks smaller than 5000 square metres. Parks and formal gardens were identified using the following classifications: Public Park or Garden (Ordnance Survey), Park (OpenStreetMap), and Garden (OpenStreetMap).
The boundary of each park. The boundary is displayed as a polygon.
Use the Park Boundary layer as the spatial reference for all other data. It helps to:
Define park extent: What area does the park cover? all other layers are interpreted in relation to this boundary.
Natural surveillance: What surrounds the park perimeter? Nearby streets, buildings, and open land influence visibility and natural surveillance (See Escape: The Safer Parks guidance).
Connectivity: How well does the boundary connect to the surrounding street network? Strong connections can improve access and movement (See Escape: The Safer Parks guidance).
This data combines Ordnance Survey and OpenStreetMap greenspace datasets into a single park boundary layer. Parks and formal gardens were identified using the following classifications: Public Park or Garden (Ordnance Survey), Park (OpenStreetMap), and Garden (OpenStreetMap).
The location of external park entrances or access and exit points. These are displayed as points.
Identify entry and exit points across your chosen park to consider:
Visibility at entry points: Are entrances clearly visible from surrounding streets and from within the park? Poor visibility can undermine feelings of safety (See Escape: The Safer Parks guidance for more information).
Sufficiency of access: Are there enough entry and exit points? Limited options can create a feeling of entrapment (See Escape: The Safer Parks guidance ).
Location on popular routes: Are entrances located on or near popular routes? Access points on busier paths often feel safer (See Busyness and Activation: The Safer Parks guidance ).
Secondary access points: Are there well-used secondary access points? Frequently used secondary routes may indicate key access points or desire lines that could benefit from improvements such as enhanced surfacing, visibility, signage or lighting.
Lighting: Are access and exit points adequately lit? (See Lighting: The Safer Parks guidance ).
Public transport links: Are entrances well connected to public transport, such as bus stops or cycle stations (See Access and Location: The Safer Parks guidance).
For deeper insight, use alongside the Predicted Route Popularity, Visibility Analysis, and Street Lighting layers.
Access and exit points distinguish between primary entrances (as mapped by Ordnance Survey and OpenStreetMap data) and secondary or additional entry points to the park (i.e. calculated where a footpath intersects with a Park Boundary).
This layer combines features within parks and nearby features. For the selected park, it shows features whose mapped extents intersect the park boundary or an 800 m buffer around it. The buffer is applied as an efficient expanded bounding box, so features near the corners of that box may be slightly farther than 800 m away.
Use this layer to understand how facilities are distributed across your chosen park.
Parks with a wider range of features tend to attract more visitors and a broader mix of people. Certain facilities, such as cafes and playgrounds, can encourage co-presence and increase activity, contributing to greater ‘eyes on the park’. Consider:
Gaps in provision: Are there areas with few or no facilities? These areas may attract less footfall and feel more isolated (See Busyness and Activation: The Safer Parks guidance).
Gendered provision: Do existing facilities reflect the needs and interests of women and girls? Parks dominated by certain features (e.g. sports pitches or Multi-Use Games Areas) may not support inclusive use (See Belonging and Familiarity: The Safer Parks guidance).
Visibility and accessibility: Are facilities visible and accessible from popular routes? Can you see one destination within the park from another? Well-located features can support use and improve perceived safety (See Wayfinding and Layout: The Safer Parks guidance).
Reflecting local diversity: Does provision reflect the needs of the local population? Ese alongside the Demographics Data layer to assess alignment (See Access and Location: The Safer Parks guidance).
For deeper insight, use alongside the Predicted Route Popularity and Demographics Data layers.
This data comes from combined Ordnance Survey and OpenStreetMap data.
This layer shows how visually connected different parts of the park are. It considers factors such as elevation, tree canopy height, and shrub height. The layer is displayed as a hexagonal grid.
Areas with higher visibility allow people to see and be seen more easily. Use this layer to identify areas of higher and lower visibility in your chosen park. Consider:
Vegetation management: Are there areas where dense vegetation limits visibility? Targeted management, such as reducing hedge height or lower-level planting along paths – can improve sightlines (See Visibility and Openness: The Safer Parks guidance).
Tree canopy height: Are low tree canopies obstructing views? Raising canopies (i.e. above 2 metres) can help improve lines of sight (See Visibility and Openness: The Safer Parks guidance).
Visibility at access and exit points: Are entrances and exits clearly visible from inside and outside the park? Improving visibility at these points can enhance feelings of safety (See Escape: The Safer Parks guidance).
Wayfinding and signage: Are there areas were low visibility may make navigation difficult? Additional signage and clear routes can support wayfinding (See Wayfinding and Layout: The Safer Parks guidance).
For deeper insight, use alongside the Foliage, Street Lighting, and Predicted Route Popularity layers.
Note on vegetation and visibility: Parks play an important ecological and wellbeing role, and vegetation is a key part of this. While some vegetation can reduce visibility and block sightlines, the aim is not widespread clearance. Use the dashboard with local knowledge to identify targeted, proportionate improvements along key routes and around facilities.
This layer uses DEFRA LIDAR data and author-generated analysis. For England, it draws on the 2022 composite dataset (Digital Surface Model and Digital Terrain Model). For Wales, a 32-bit LiDAR dataset is used. Full details are available in the Safer Parks GitHub repository: https://github.com/Safer-Parks/park-vga; and https://github.com/Safer-Parks/park-vga/blob/main/notebooks/finding_lidar_tiles/README_downlaoding.md).
Tree locations and vegetation coverage within parks. It is displayed as a heatmap. Note that this layer is derived fromLidar scans, and so will also capture buildings and other structures that are not vegetation.
Vegetation plays an important ecological role while also shaping sightlines and overall character of the park.
Use this layer to identify where trees and dense vegetation are located within your chosen park. The dashboard reflects a ‘worst case’ visibility scenario, assuming full leaf coverage in summer. Consider:
Sightlines along paths: Are there areas where vegetation close to paths reduces visibility? Dense or overgown planting may obstruct views and affect perceived safety. Use alongside the Visibility Analysis layer to understand the relationship (See Visibility and Openness: The Safer Parks guidance).
Targeted maintenance: Are there locations were maintenance could improve visibility? Measures such as raising tree canopies or managing shrub and hedge height can enhance sightlines without widespread removal (See Visibility and Openness: The Safer Parks guidance).
Boundary and access point visibility: Does vegetation near park edges or entrances restrict visibility from surrounding streets? Improving visibility at these locations can support safety and wayfinding (See Escape: The Safer Parks guidance).
Note on vegetation and visibility: Parks provide important ecological and wellbeing benefits, and vegetation is central to this. While some planting can reduce visibility, the aim is not widespread clearance. Use the dashboard alongside local knowledge to identify targeted, proportionate improvements along key routes and around facilities.
For deeper insight, use alongside the Visibility Analysis layer.
This layer uses DEFRA LIDAR data capturing elevation, tree canopy height, and shrub height. For England, it uses the 2022 composite dataset (Digital Surface Model and Digital Terrain Model). For Wales, a 32-bit LiDAR dataset is used. Full details are available in the Safer Parks GitHub repository: https://github.com/Safer-Parks/park-vga; and https://github.com/Safer-Parks/park-vga/blob/main/notebooks/finding_lidar_tiles/README_downlaoding.md).
Tree locations and vegetation coverage within parks. It is displayed as a heatmap. Note that this layer is derived fromLidar scans, and so will also capture buildings and other structures that are not vegetation.
Vegetation plays an important ecological role while also shaping sightlines and overall character of the park.
Use this layer to identify where trees and dense vegetation are located within your chosen park. The dashboard reflects a ‘worst case’ visibility scenario, assuming full leaf coverage in summer. Consider:
Sightlines along paths: Are there areas where vegetation close to paths reduces visibility? Dense or overgown planting may obstruct views and affect perceived safety. Use alongside the Visibility Analysis layer to understand the relationship (See Visibility and Openness: The Safer Parks guidance).
Targeted maintenance: Are there locations were maintenance could improve visibility? Measures such as raising tree canopies or managing shrub and hedge height can enhance sightlines without widespread removal (See Visibility and Openness: The Safer Parks guidance).
Boundary and access point visibility: Does vegetation near park edges or entrances restrict visibility from surrounding streets? Improving visibility at these locations can support safety and wayfinding (See Escape: The Safer Parks guidance).
Note on vegetation and visibility: Parks provide important ecological and wellbeing benefits, and vegetation is central to this. While some planting can reduce visibility, the aim is not widespread clearance. Use the dashboard alongside local knowledge to identify targeted, proportionate improvements along key routes and around facilities.
For deeper insight, use alongside the Visibility Analysis layer.
This layer uses DEFRA LIDAR data capturing elevation, tree canopy height, and shrub height. For England, it uses the 2022 composite dataset (Digital Surface Model and Digital Terrain Model). For Wales, a 32-bit LiDAR dataset is used. Full details are available in the Safer Parks GitHub repository: https://github.com/Safer-Parks/park-vga; and https://github.com/Safer-Parks/park-vga/blob/main/notebooks/finding_lidar_tiles/README_downlaoding.md).
Tree locations and vegetation coverage within parks. It is displayed as a heatmap. Note that this layer is derived fromLidar scans, and so will also capture buildings and other structures that are not vegetation.
Vegetation plays an important ecological role while also shaping sightlines and overall character of the park.
Use this layer to identify where trees and dense vegetation are located within your chosen park. The dashboard reflects a ‘worst case’ visibility scenario, assuming full leaf coverage in summer. Consider:
Sightlines along paths: Are there areas where vegetation close to paths reduces visibility? Dense or overgown planting may obstruct views and affect perceived safety. Use alongside the Visibility Analysis layer to understand the relationship (See Visibility and Openness: The Safer Parks guidance).
Targeted maintenance: Are there locations were maintenance could improve visibility? Measures such as raising tree canopies or managing shrub and hedge height can enhance sightlines without widespread removal (See Visibility and Openness: The Safer Parks guidance).
Boundary and access point visibility: Does vegetation near park edges or entrances restrict visibility from surrounding streets? Improving visibility at these locations can support safety and wayfinding (See Escape: The Safer Parks guidance).
Note on vegetation and visibility: Parks provide important ecological and wellbeing benefits, and vegetation is central to this. While some planting can reduce visibility, the aim is not widespread clearance. Use the dashboard alongside local knowledge to identify targeted, proportionate improvements along key routes and around facilities.
For deeper insight, use alongside the Visibility Analysis layer.
This layer uses DEFRA LIDAR data capturing elevation, tree canopy height, and shrub height. For England, it uses the 2022 composite dataset (Digital Surface Model and Digital Terrain Model). For Wales, a 32-bit LiDAR dataset is used. Full details are available in the Safer Parks GitHub repository: https://github.com/Safer-Parks/park-vga; and https://github.com/Safer-Parks/park-vga/blob/main/notebooks/finding_lidar_tiles/README_downlaoding.md).
Illustrates how people are likely to move through streets and paths, showing movement patterns at a neighbourhood scale, around a 10-minute walk. Higher values indicate greater potential for pedestrian movement, footfall, and co-presence. It is displayed as a line network.
Some routes that are more likely to be used for movement through and around the park, based on the structure of the surrounding street network.
Use this layer to identify busier routes and assess how movement patterns relate to safety and design. Consider:
Busyness and perceived safety: Which routes are likley to have higher footfall? These routes may feel safer due to increased presence of people (See Busyness and Activation: The Safer Parks guidance).
Lighting and visibility on popular routes: Are popular routes open, visible, and well lit? Poor lighting or sightlines can undermine safety, event where routs are busy (See Visibility and Openness: The Safer Parks guidance; Lighting: The Safer Parks guidance).
Connection to entrances: Do higher-use routes connect clearly to access and exit points? Stronger connections can support movement, accessibility and natural footfall (See Wayfinding and Layout: The Safer Parks guidance).
Supporting safety on key routes: Are primary routes, particularly those that may be used after dark, supported by lighting, signage, or passive surveillance? Additional measures may be needed to support feelings of safety such as increased visibility from busier areas, patrols, CCTV, or locating activity-generating features (e.g. cafés, ranger bases) nearby.
Improving the path network: Are there gaps, dead ends, or misaligned routes? Enhancing connections and aligning with desire lines can improve movement and perceived safety (See Wayfinding and Layout: The Safer Parks guidance).
Placement of features and amenities: Are facilities located in areas with sufficient footfall? Positioning amenities on or nearby busier routes can increase use and passive surveillance (See Busyness and Activation: The Safer Parks guidance).
For deeper insight, use alongside the Visibility Analysis, Street Lighting, Access and Exit points, and Park Features layers.
This layer is based on Space Syntax (University College London, UCL) analysis, using Normalised Angular Choice (NACH) at 800m (approximately a 10-minute walk), derived from OpenStreetMap street network data.
The location of toilets within or nearby parks. The layer is displayed as points.
Good quality public toilets are important. They not only help women
and girls remain in the space for longer but also act as a signal that
they are welcome in the park. Well-used seating areas can also provide
greater ‘eyes on the park’, helping spaces feel safer.
Use this layer to understand where toilets are—and are not—located
within your chosen park. Consider:
Availability of toilets: Are there toilets available in or near the park? Lack of toilets may discourage use, particularly by women, older people, parents, and disabled users (See Belonging and Familiarity: The Safer Parks guidance).
Signposting toilets: If there aren’t toilets available in the park can the nearest public toilets be signposted (See Access and Location: The Safer Parks guidance).
Location and entrances: Are toilets located in visible, well-lit areas? Toilet entrances in concealed or isolated spots may feel unsafe or attract unwanted behaviour (See Visibility and Openness: The Safer Parks guidance)
For deeper insight, use alongside the Predicted Route Popularity and Visibility Analysis layers.
This layer uses data from The Toilet Map, the
UK’s largest database of publicly accessible toilets, with over 14,000
facilities. The Toilet Map is run by Public Convenience, a joint venture
between researchers Dr Jo-Anne Bichard and Gail Ramster, who created the
map at the RCA Helen Hamlyn Centre for Design, and the software company
Neontribe, who designed and built it.
Most of the information is crowdsourced. Anyone can add, edit or remove
toilets on the website. It is supplemented with open data and with
information requested from councils, OpenStreetMap, private companies
and other organisations. Because of this, coverage and accuracy vary
between areas. Some toilets may be missing, out of date, closed, or open
only at certain times. Check details such as opening hours,
accessibility and baby-changing provision against local authority
records and local knowledge.
The data was downloaded on 10/06/2026. For this dashboard it has been
filtered to show toilets within or near (within 800m of) park
boundaries.
Licence: The Toilet Map dataset is open data released under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Contains data from the Toilet Map © 2026, CC BY 4.0. The data has been modified (filtered by location) for use in this dashboard.
The location of seating within parks. The layer is displayed as points.
Seating encourages people to spend time in the park, supporting activity, comfort and a sense of belonging. Well-used seating areas can also provide greater ‘eyes on the park’, helping spaces feel safer.
Use this layer to understand where seating is—and is not—located within your chosen park. Consider:
Gaps in seating: Are there areas with no or limited seating? Lack of seating may discourage use, particularly by women, older people, parents, and disabled users (See Belonging and Familiarity: The Safer Parks guidance).
Location and safety: Is seating located in visible, well-lit areas? Seating in concealed or isolated spots may feel unsafe or attract unwanted behaviour (See Visibility and Openness: The Safer Parks guidance)
Accessibility and diversity of seating: Does the type and distribution of seating meet a range of needs? Features such as circular seating, back-rests accessible picnic tables, and shelter from sun or rain can support inclusive use. Consider this alongside the Demographics Data layer (See Access and Location: The Safer Parks guidance).
For deeper insight, use alongside the Visibility Analysis, Foliage, Predicted Route Popularity, and Street Lighting layers.
This layer uses Ordnance Survey and OpenStreetMap.
Cycling and walking infrastructure within and around parks. The network is displayed as lines.
Use this layer to understand how parks connect to the wider active travel network and where improvements could support safer, more inclusive use, considering:
Visibility and lighting on active routes: Are key routes through the park visible, open, and well-lit, especially those used at peak times? Poor visibility can deter use, even on busy routes (See Visibility and Openness: The Safer Parks guidance and Lighting: The Safer Parks guidance).
Connections to access points: Do routes connect well to access and exit points? Weak connections can reduce use and natural footfall (See Escape: The Safer Parks guidance and Access and Location).
Signage and wayfinding: Is navigation clear along routes within or alongside the park? (See Wayfinding and Layout: The Safer Parks guidance).
Catchment coverage: Does the network serve the full local population, including those who rely on walking or cycling?
For deeper insight, use alongside the Predicted Route Popularity, Street Lighting, Visibility Analysis, Access and Exit Points, and Demographic Data layers.
This layer uses OpenStreetMap data downloaded via Geofabrik and filtered to extract active travel infrastructure across England and Wales. The data is split into two layers: cycling (cycleways, cycle lanes, shared paths, bridleways, and tracks) and walking (footways, pedestrian areas, shared paths, bridleways, and tracks). Shared paths, bridleways, and tracks appear in both layers as they serve both cyclists and pedestrians.
Cycling and walking infrastructure within and around parks. The network is displayed as lines.
Use this layer to understand how parks connect to the wider active travel network and where improvements could support safer, more inclusive use, considering:
Visibility and lighting on active routes: Are key routes through the park visible, open, and well-lit, especially those used at peak times? Poor visibility can deter use, even on busy routes (See Visibility and Openness: The Safer Parks guidance and Lighting: The Safer Parks guidance).
Connections to access points: Do routes connect well to access and exit points? Weak connections can reduce use and natural footfall (See Escape: The Safer Parks guidance and Access and Location).
Signage and wayfinding: Is navigation clear along routes within or alongside the park? (See Wayfinding and Layout: The Safer Parks guidance).
Catchment coverage: Does the network serve the full local population, including those who rely on walking or cycling?
For deeper insight, use alongside the Predicted Route Popularity, Street Lighting, Visibility Analysis, Access and Exit Points, and Demographic Data layers.
This layer uses OpenStreetMap data downloaded via Geofabrik and filtered to extract active travel infrastructure across England and Wales. The data is split into two layers: cycling (cycleways, cycle lanes, shared paths, bridleways, and tracks) and walking (footways, pedestrian areas, shared paths, bridleways, and tracks). Shared paths, bridleways, and tracks appear in both layers as they serve both cyclists and pedestrians.
Locations of street lighting within and around parks. The layer is displayed as point data.
Lighting plays a key role in how safe parks feel, particularly after dark, including during early mornings and afternoons in Autumn and Winter. Parks often form part of active-travel routes, but many women and girls feel unsafe using them in low-light conditions.
Use this layer to identify areas that are unlit or poorly lit within and around your chosen park. Consider:
Unlit paths: Which paths lack lighting, particularly those that are well-used or likely to attract higher footfall? (See Lighting: The Safer Parks guidance). These routes may be priority areas for improvement.
Lighting at access and exit points: Are entrances and exit points adequately lit? Poor lighting at these points can deter use and reduce perceptions of safety (See Lighting: The Safer Parks guidance).
Targeting improvements: Where should lighting interventions be prioritised? Combining this layer with Visibility Analysis can help identify locations where lighting and sightlines together present higher risk.
Supporting safe use after dark: Are areas likely to be used in low-light conditions supported by appropriate management measures? Enhancing lighting may require complementary approaches such as patrols, CCTV, or updated park management plans, including decisions about opening and closing times.
Note: Lighting should be applied selectively and strategically. Decisions should balance likely use, safety needs, active travel, and ecological impacts. Lower-level, evenly distributed lighting can improve comfort and visibility while avoiding excessive brightness glare, and deep shadows.
For deeper insight, use alongside the Visibility Analysis, Access and Exit points, and Predicted Route Popularity layers.
This layer uses street lighting data supplied by local authorities in response to Freedom of Information requests. Coverage may be incomplete, and the data was not available under an open licence in every area. Because records may come from several sources, check the licensing information for each lighting asset to see where it came from and the terms that apply. Some lights may switch off or dim at certain times, and the data does not show whether a light is currently working. Use this layer alongside local knowledge, and make sure lights are checked and maintained regularly.
This layer shows the demographic profile of people living within a 15-minute walking distance of park access or exit points. The area is displayed as a polygon. Data currently includes health, economic activity, ethnicity, country of origin, religion, and household deprivation.
Use this layer to understand the communities surrounding your chosen park. It helps identify who the park’s potential users are and whether the provision reflects their needs. Consider:
Catchment population: Which communities fall within walking distance of the park? This helps identify who the primarily serves.
Identifying underserved groups: Does the catchment include groups who may face additional barriers to park use? This may include women, girls, older people, disabled people, and minority ethnic communities (See Belonging and Familiarity: The Safer Parks guidance).
Facilities and design: Do park features and facilities reflect the diverse needs of the local population? Align features and facilities with community characteristics (See Access and Location, The Safer Parks guidance).
Community engagement: How can this data inform co-production and engagement? Understanding local demographics can help shape inclusive design and decision-making (See Co-production and Engagement: The Safer Parks guidance).
Access and connectivity: Are there areas where new or improved access and exit points could better serve nearby communities? This can improve accessibility, visibility, and perceptions of safety.
Catchment coverage: Do access points and routes serve the full catchment area, including those living at its edges? Gaps may limit equitable access (See Access and Location, in the Safer Parks guidance).
Funding applications: Can this data support evidence-based funding bids by demonstrating need and potential impact?
For deeper insight, use alongside the Park Features, Access and Exit points, Visibility Analysis, Catchment Buffer, and Predicted Route Popularity layers.
The catchment buffers are based on an isochrone map, which represents the area reachable from a single point within a specified travel time using a particular mode of transport, such as walking, cycling or public transport.
For each park of 10 hectares or larger (aligned with the Green Infrastructure Framework), we calculated 15-minute walking isochrones from every identified park access and exit point, using the street and path network (assuming 4 km/h walking speed). This approach is effectively the reverse of the DEFRA Access to Greenspace methodology, which also uses 15-minute street-network walking distances, but calculates accessibility from neighbourhoods to the nearest greenspace.
For each park catchment, we examine the people living within it. Demographic data is drawn from Census Output Areas (OAs) which generally contain 40–250 households and 100–625 residents, although rural OAs are much larger in physical size.. We included output areas where at least 20% of the output area is within the catchment area.
To compare the population within the Park Catchment with that of the wider local authority, we calculate the location quotient. (‘Location quotient is a widely used geographic index that measures the relative concentration or distribution of a subarea compared to the entire area’: https://www.sciencedirect.com/topics/computer-science/location-quotient)
Location Quotient (LQ):