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Landscape and Urban Planning 116 (2013) 1– 12

Contents lists available at SciVerse ScienceDirect

Landscape and Urban Planning

jou rn al hom ep age: www.elsev ier .com/ locate / landurbplan

esearch paper

reen networks for people: Application of a functional approach to support thelanning and management of greenspace

arren Moseleya,∗, Mariella Marzanoa, Jordan Chetcuti a, Kevin Wattsb

Forest Research, Centre for Ecosystems, Society and Biosecurity, Northern Research Station, Roslin, Midlothian EH25 9SY, UKForest Research, Centre for Ecosystems, Society and Biosecurity, Alice Holt Lodge, Farnham, Surrey GU10 4LH, UK

i g h l i g h t s

We describe a model to plan and manage greenspace as a functional green network.Two profiles: a leisure user and a utilitarian user identify access to greenspace.Conventional methods may substantially overestimate greenspace provision/access.Outputs used by planners to target health inequalities and promote active travel.

a r t i c l e i n f o

rticle history:eceived 10 September 2012eceived in revised form 12 February 2013ccepted 6 April 2013vailable online 4 May 2013

eywords:ccesseast-cost modellingser profilesealth inequalitiesctive travel

a b s t r a c t

Well planned and managed greenspaces enhance urban environments, providing opportunities for peopleto relax and to engage with nature. However, greenspace provision has typically focussed upon meetingset targets related to proximity of residential areas and been given a low priority within economic devel-opment, after transport, housing and business. In applying proximity criteria, most planning authoritieshave used a uniform (Euclidean) distance buffer as this is a relatively simple procedure within a Geo-graphic Information System. Such approaches to greenspace may limit its potential, particularly as part ofa green network resource, which considers the movement of people. We describe a modelling approach toplan and manage greenspace as a functional green network. Our approach incorporates data on the typeand quality of each greenspace and examines the use of greenspace and connecting routes through theperspective of two user profiles: a leisure user and a utilitarian user. These profiles are mapped to repre-sent use based upon the existing green network resource and compared with the conventional Euclidean

buffer approach. Our results suggest that conventional methods may substantially overestimate provi-sion/accessibility to greenspace (by a factor of three for leisure greenspace). This is particularly the casewhen examining who has access; a concern in areas of higher social deprivation where Governmentinitiatives attempt to address health inequalities. The green network approach can help plan and man-age where improvements to greenspace quality and access can be targeted to promote regular exercisethrough incorporating opportunities for active travel and improving daily interaction with greenspace.

. Introduction

The role of green environments in promoting physicalnd mental health and wellbeing is well documented (Maas,

erheij, Groenewegen, De Vries, & Spreeuwenberg, 2006;akano, Nakamura, & Watanabe, 2002), with evidence suggest-ng greenspace can provide significant physical, psychological

∗ Corresponding author. Tel.: +44 01314456947.E-mail addresses: darren.moseley@forestry.gsi.gov.uk (D. Moseley),

ariella.marzano@forestry.gsi.gov.uk (M. Marzano),ordan.chetcuti@forestry.gsi.gov.uk (J. Chetcuti),evin.watts@forestry.gsi.gov.uk (K. Watts).

169-2046/$ – see front matter. Crown Copyright © 2013 Published by Elsevier B.V. All rittp://dx.doi.org/10.1016/j.landurbplan.2013.04.004

Crown Copyright © 2013 Published by Elsevier B.V. All rights reserved.

and physiological benefits (e.g. Bell et al., 2008; Mitchell &Popham, 2008; Tzoulas et al., 2007). Government policy and ini-tiatives recognises these benefits and their role in addressinghealth inequalities and in providing improved living condi-tions, e.g. Healthy Lives, Healthy People (Department of Health,2010), Good Places, Better Health (The Scottish Government,2008).

These considerations feature prominently in planning, e.g. inthe U.K. through Planning Policy Guidance 17 (PPG 17), ScottishPlanning Policy 11 (SPP 11), Physical Activity and Open Space,

and Planning and Open Space (PAN 65); in the USA through ini-tiatives such as the National Physical Activity Plan (http://www.physicalactivityplan.org); and in Australia through the Public SpaceAdvisory Committee (http://publicspace.planning.sa.gov.au/).

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Nevertheless, the planning of greenspace in urban areas has notlways been accorded the same status as physical urban structuresuch as buildings and roads (Sandström, 2002) or maximised itsotential as part of a green network resource, resulting in frag-ented greenspaces which may have less value and functional

se for the intended users. This paper develops a green networkpproach to address the current limited knowledge surroundinghe integrative role and spatial function of greenspace.

.1. Greenspace or green network?

The term green network is often used interchangeably withreen infrastructure, and both focus on the social, biological andhysical environment functions of greenspace and interactionsetween them. Benedict and Mcmahon (2006, p. 1) refer to a net-ork in their definition of green infrastructure as “an interconnected

etwork of natural areas and other open spaces that conserves natu-al ecosystem values and functions, sustains clean air and water, androvides a wide array of benefits to people and wildlife”. The termreen network can evoke a sense of flow and movement throughreenspaces; it has been adopted by the planning authorities acrosscotland which is the location of our case study. In this papere will use the terms greenspace for the individual components

such as parks, gardens, etc.) and green network (GN) to refer tohe configuration and management of greenspace as a functionalystem.

After reviewing current approaches to plan, assess and deter-ine use of greenspace (Section 1), we describe the development

f a GN approach within a case study area, highlighting the datased, development of user profiles and modelling (Section 2). Wehen compare the GN approach with a more traditional approach tossess and plan greenspace provision and accessibility (Section 3).he practicalities of applying the GN approach to address spatialnequality and help deliver health-promoting initiatives are dis-ussed in Section 4, with suggestions for modifications where datare currently lacking.

.2. Current methods in greenspace planning

Greenspace planning is often based upon simple quantitativetandards which require the provision of a set area of greenspaceer population such as 2.4 ha of greenspace per 1000 populationNational Playing Fields Association, 1992); or a set amount ofreenspace within specific proximity of the surrounding popula-ion such as 2 ha of greenspace within 500 m of each residenceBox & Harrison, 1993). There is some evidence that planners doot always explicitly think of urban greenspace as a multifunctionalystem (e.g. Sandström, Angelstam, & Khakee, 2006). Although con-ideration may be given to how people move around using pathsr cycle routes, this is not often linked explicitly to greenspacelanning and management, thereby neglecting the elements ofonnectivity that contribute to health improvements such as sus-ainable green travel initiatives.

Standards and guidance have been developed over the lastwenty years, often reflecting categories or sizes of greenspace inelation to travel distance. Many UK planning authorities assessnd plan greenspace using standards based on Box and Harrison1993) (Table 1). Although such standards can be useful for deter-

ining the spatial provision of greenspace, the figures are oftenrbitrary, e.g. a 10 min walk (Barker & Graf, 1989), and do not takento account regional (or even urban – peri-urban) variations inreenspace configuration.

Many of the standards that specify a maximum distance toreenspace can be traced back to Hart’s (1979) study of younghildren’s perception of the landscape undertaken in a small Newngland town, and to Matthews’ (1987) paper investigating the

ban Planning 116 (2013) 1– 12

influence of gender-related differences in cognitive mapping abil-ity. These studies specified criteria for how far you would expectsomeone to walk from their home to get to their nearest areaof usable greenspace (often a play park for children). However,a field and desk study of the distances and times taken to walkto local parks indicated that critical distances relevant for defin-ing accessible local open spaces are smaller than those assumedby earlier studies (London Planning Advisory Committee, 1992).Subsequent studies have surveyed greenspace users to define walk-ing time for accessible woodland such as 6–8 min or up to 400 m(Coles & Bussey, 2000) or up to 5 min for greenspace (Coles &Caserio, 2001). In the absence of locally collected data on thedistance users of local greenspace are prepared to travel, manylocal authorities use either the Accessible Natural Greenspace Stan-dard developed by Natural England (Table 1), or define their owndistance.

1.3. Human dimensions of greenspace use

Little is known about how people use and interact with GN in awider landscape context and there is a lack of baseline data on howpeople perceive and use greenspaces (Bell et al., 2008) and GNs.There is some information about why people do not use greenspace(e.g. Morris et al., 2011; Tzoulas & James, 2010; Wridt, 2010), butless is known about how such barriers could be overcome. There isevidence to suggest that in many urban residential environmentsthere is unequal distribution of, and access to, greenspaces withfewer trees, private gardens and open spaces in these areas (Maaset al., 2009). Certainly green environments can help tackle healthinequalities by promoting good health. As Mitchell and Popham(2008, p. 1655) state, “We know, for example, that people withlow socio-economic status are less likely to exercise. . . than thosewith high socio-economic status, partly because the environmentsin which they live are less conducive to it”. The Scottish Index ofMultiple Deprivation (SIMD) takes into account indicators such ashousehold incomes, employment, health, education, geographicalaccess to services, social environment and housing. People livingin areas of high multiple deprivation are likely to have less accessto good quality greenspaces and networks. Some studies suggestthat the attractiveness of greenspace can influence the distancepeople are prepared to travel to reach it, with the most attrac-tive greenspaces able to draw people across greater distances,particularly when there is a lack of such appealing greenspacelocally (Giles-Corti et al., 2005; Maat & Vries, 2006). A variety ofinformation related to greenspace use is collected both nationally(e.g. Census, greenspace surveys, park satisfaction surveys) andwithin planning authorities (usually for individual sites), but hasnot traditionally been applied to the planning and managementof GN.

Planning authorities may undertake a survey of current (anddesired) use of greenspace, however, common methods often donot capture whether visitors are using specific greenspaces orpassing through to another location, or consider their motiva-tions for use (e.g. exercise, aesthetics or social interaction). Arange of approaches have been developed to gather informationon social values relating to greenspace use, although it is rare tofind evidence relating to the motivations and barriers to using GNs.Approaches undertaken in the UK and overseas (e.g. Morris et al.,2011; Tyrvainen, Makinen, & Schipperijn, 2007) have attemptedto profile local populations and their use of woodland and othergreenspace as a way of understanding people’s attitudes, habitsand motivations. Datasets are primarily collected at the national

and/or regional level and involve some level of segmentation, e.g.Barbosa et al. (2007) used Experian’s Mosaic UK geodemographicdatabase to classify social diversity and access to greenspace acrossSheffield, UK.

D. Moseley et al. / Landscape and Urban Planning 116 (2013) 1– 12 3

Table 1Summary of standards and guidance for travel distance to greenspace.

Study/standard Maximum distance to travel togreenspace

Greenspace size and type

London Boroughs(since 1976)

1/4 mile (0.4 km) Small and local parks3/4 mile (1.2 km) District parks

Open Space Planning in London. LondonPlanning Advisory Committee (1992)

Direct line radius of approximately280 m

6 Acre Standard National Playing FieldsAssociation (NPFA) (1992)

100 m (1 min walk) Local areas for play400 m (5 min walk) Local equipped areas for play1000 m (15 min walk) Neighbourhood equipped areas for play

Natural Spaces in Urban Places(Box & Harrison, 1993)

0.5 km Natural greenspace of at least 2 ha2 km Natural greenspace of at least 20 ha5 km Natural greenspace of at least 100 ha10 km Natural greenspace of at least 500 ha

Minimum 1 ha Local Nature Reserve (LNR) in every urbanarea per 1000 population

ANGSt (Natural England’s Accessible NaturalGreenspace Standard, Harrison et al., 1995)

Every home should be within 300 m of An accessible natural greenspace of at least 2 ha, plus2 km of At least one accessible 20 ha site5 km of At least one accessible 100 ha site10 km of At least one accessible 500 ha site

Provision of at least 1 ha LNR per 1000 population

Natural environments – healthyenvironments? (De Vries, Verheij,Groenewegen, & Spreeuwenberg, 2003)

3 km radius around the centre of theneighbourhood

London Plan. London’s Public Open SpaceHierarchy (Greater London Authority, 2004)

8 km Regional (over 400 ha)3.2 km Metropolitan (60–400 ha)1.2 km District (20–60 ha)400 m (280 m*) Local parks (2–20 ha)400 m (280 m*) Small local parks (0.4–2 ha)400 m (280 m*) Pocket parks (less than 0.4 ha)Where feasible Linear open spaces (variable)

Space for People(Woodland Trust, 2002)

500 m Accessible woodland of at least 2 ha4 km Accessible woodland of at least 20 ha

Is green space in the living environmentassociated with people’s feelings of socialsafety? (Maas et al., 2009)

1 km or 3 km radius around homes Metropolitan

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It is clear that greenspace could be planned a lot better; currentlyssessment within planning authorities is too simplistic and needso integrate social factors to maximise its full potential.

.4. Methods of assessment

In applying proximity criteria, most planning authorities havesed a straight line or ‘as the crow flies’ distance as this is a rela-ively simple procedure within a Geographic Information SystemGIS). This produces a uniform (Euclidean) buffer or ‘catchment’reas around greenspaces with the assumption that any residencesithin the buffer can access one or more greenspace. However, itoes not take into account the ability of the population to accesshese areas through entrance points nor the factors that may pro-

ote or impede travel from homes to greenspace. For example,he distance that people are prepared to travel can be reducedy ‘severance’ factors such as busy roads, private land or undesir-ble areas, and constraining factors such as slope and rough terrainHarrison, Burgess, Millward, & Dawe, 1995). Some studies take intoccount severance factors by reducing straight line distances, e.g.rom 400 m to 280 m (London Planning Advisory Committee, 1992).

An alternative approach is to analyse travel distance alongoutes, i.e. paths, pavements and tracks as this more realistically

eflects actual travel. Network and least-cost analyses use mappedoutes (paths and roads) to model the potential movement ofeople (e.g. Comber, Brunsdon, & Green, 2008). Least-cost analysis,hich provides a measure of distance modified by a ‘cost’ for

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movement through the landscape (Adriaensen et al., 2003), hasan advantage over network analysis in that movement throughgreenspaces is also considered. Least-cost analysis has been appliedto determine habitat networks for a range of species (e.g. Opdam,Steingrover, & Van Rooij, 2006; Watts et al., 2010) and to determineroutes for people (e.g. Chiou, Tsai, & Leung, 2010). Approaches tomodel GNs have been explored previously in relation to populationprovision (e.g. Jim & Chen, 2003; Kong, Yin, Nakagoshi, & Zong,2010) and at different spatial scales (Tan, 2006), whilst Zhang, Lu,and Holt (2011) developed a population-weighted distance modelto measure spatial accessibility of neighbourhood parks.

1.5. A new approach

Our aim is to develop a more integrative approach to greenspaceplanning and management, by taking account of: the functionalaspects of greenspace (types and qualities of greenspace); howpeople move to and through greenspace, and how people usegreenspace, as part of a wider functionally connected GN. An inte-grated approach necessarily includes:

a) utilising existing spatial GN resource data (typologies and qual-ity) to weight areas in terms of drivers of use;

b) utilising existing GN user information to infer GN use anddevelop user profiles;

(c) mapping user profiles to represent current use based upon theexisting GN resource.

4 D. Moseley et al. / Landscape and Urban Planning 116 (2013) 1– 12

Fig. 1. The case study area located within the east of Kirkintilloch, East Dunbartonshire, a planning authority to the north of Glasgow, Scotland, UK. Underlying OS data ©C cence

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. Methods: the development of a GN approach

This section describes the modelling method used, its applica-ion within a case study area, and the development of a conceptualramework for GN planning and management.

.1. Case study area

An integrative GN approach was developed through discussionsith local planning authorities, with the aim of producing a method

hat utilises currently available data. A case study was undertakenithin Kirkintilloch (55◦56′ N, 4◦9′ W), a sub-region of East Dun-

artonshire planning authority (EDPA), located to the north oflasgow, Scotland, UK (Fig. 1). The site is representative of manylanning authorities in its peri-urban context, range of greenspaceequirements, mix of social groups and pockets of deprivedreas.

EDPA operates within the Glasgow & Clyde Valley (GCV)trategic Planning Region (GCVSDPA, 2012) and is part of theentral Scotland Green Network (CSGN) Region (CSGN, 2011),ne of only 14 National developments designated in the Nationallanning Framework 2 for Scotland (The Scottish Government,009). This designation places the CSGN on a statutory footingnd encourages planning authorities to undertake activities tomprove GN provision. Our aim was to work with local plan-ers and delivery organisations to align the GN methodology toxisting planning considerations within GCV and the CSGN and

rovide a sound basis for policy delivery, e.g. the CSGN ambi-ion that ‘every home in Central Scotland is within 300 m ofn attractive, safe, and well-maintained greenspace or accessibleountryside’.

number [100021242].

2.2. Modelling approach

In this paper we use a least-cost approach to reflect the ease bywhich people can move through greenspace and the surroundingarea. For example, a person will likely find it preferable to walkalong a well-constructed and well-maintained route with pleas-ant surroundings and many greenspaces have specific and limitedpoints of access. In applying our approach we consider the intimatemixture of urban greenspaces and residences. In contrast, studiesanalysing proximity to greenspace in the USA often consider accessvia motorised transport as many trips to parks are undertaken inthis way (e.g. Witten, Hiscock, Pearce, & Blakely, 2008). In our studywe focus upon access within catchment areas around greenspacesstarting at the household doorstep as this allows planning to con-sider how residents can engage with green networks through activetravel and as part of a healthy lifestyle.

To map the components of GN use, we identified greenspacesand travel routes which people use to move from their homes toother areas or facilities within the community. The components canbe represented within a GIS landcover dataset and parameterisedto model how they come together to form a GN for people. Thisconceptual framework then allows comparison with an approachthat uses a Euclidean buffer for determining access to greenspace.Fig. 2 outlines these steps, which are described thereafter in moredetail.

2.3. Parameterising the model

2.3.1. Utilise spatial GN dataExisting spatial GN resource data supplied by EDPA, compris-

ing of a greenspace audit and quality assessment, greenspace entry

D. Moseley et al. / Landscape and Ur

2.3.2 Deve lop the us er profiles

National Social dataLocal survey data

User profiles

2.3.1. Utilise spatial GN data

Greenspace auditsGreenspace quality

Greenspace entry pointsCore paths

Greenspace map

2.3.3. Integration of data and GN modelling

2.4. Comparison of outputs with

conventional approa ch

Fig. 2. Green Network conceptual framework steps, beginning with utilisation ofexisting spatial GN data (Section 2.3.1), development of user profiles (Section 2.3.2),io

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ntegration of the data and profiles within a model (Section 2.3.3) and comparisonf the outputs with a conventional approach (Section 2.4).

oints, and core paths were gathered to build a map of greenspacend potential connectivity elements (Fig. 3).

Greenspace data comprised a standard open space datasetTM

onstructed within a GIS using MasterMap topological data

rom Ordnance Survey (OS) with descriptive attributes relatingo typology and size (ha). Discussions with planning authoritiesithin the GCV identified a subset of the PAN 65 typologies

Fig. 3. Accessible greenspace, greenspace quality, entry points a

ban Planning 116 (2013) 1– 12 5

(Table 2), reflecting those that are most likely to be accessedby the public for formal and informal recreation and as travelroutes.

Greenspace quality data provided by EDPA focussed upon func-tion, condition, quality and a combined score. To allow replicabilityacross other planning authority areas, the combined quality scorefor the greenspaces were divided into 5 classes, from low to highquality, using Jenks Natural Breaks (Jenks, 1967). This process aimsto reduce the variance within each class, but maximise variancebetween classes, i.e. resulting in distinctive classes.

The quality scores were used to weight the catchment areaaround greenspaces, i.e. higher quality greenspace was given a posi-tive weighting (more than 1) to produce a larger catchment; lowerquality greenspace was given a negative weighting (less than 1)(Table 3). Analyses were undertaken for each greenspace qualitycategory and the outputs were then combined into a single net-work.

To reflect how people access greenspaces, each greenspace poly-gon was classified in terms of broad physical entry restrictions, i.e.if an entrance is the only entry point or if the greenspace can beentered at any point. The classifications were: restricted access(to one or more entry points); partially restricted access (e.g. anentrance on one or more sides, but open access on other sides); andopen access (without physical barriers). Entry points were suppliedby the planning authority and checked for accuracy using GoogleStreet View.

Footpaths and other routes which people may use to accessGN are part of OS MasterMap data and have PAN 65 typologies(Table 2). Some paths have been designated as ‘core paths’ through

of the Land Reform (Scotland) Act 2003 (Scottish Executive, 2003:section 17) that every local planning authority in Scotland should‘draw up a plan for a system of paths (‘core paths’) sufficient for the

nd core paths within the case study area of Kirkintilloch.

6 D. Moseley et al. / Landscape and Urban Planning 116 (2013) 1– 12

Table 2Landcover typologies used in the analysis with unique primary land use (PLU) codes and descriptions. Core paths consist of footpaths and tracks but do not have an individualPLU. PAN 65 typologies designated as accessible open/greenspace for public recreation are annotated with an ‘X’. Resistance values were applied to model movement throughthe landcover (see Step 3), with low values for highly permeable areas and exceptionally high values for typologies where movement is prohibited due to physical or socialfactors.

PLU code PLU description Accessible greenspace Resistance values

1.1–1.5 Roads and tracks, roadside, parking and loading 12.2 Tidal water 10,0002.3 Foreshore/rocks 53 Railway 10,0004 Footpath 15.1–5.5 Residential/commercial/institutional buildings,

glasshouses, airports and other structures10,000

6.1 Public parks and gardens X 16.21 Private gardens 16.22 School grounds 56.23 Institutional grounds 16.31 Amenity residential open space X 16.32 Amenity business open space X 16.33 Amenity transport open space 56.4 Playspace for children and teenagers X 16.51 Playing fields X 16.52 Golf coursea X 56.53 Tennis courts 10,0006.54 Bowling green 10,0006.55 Other sports 56.61 Green access route X 16.62 Riparian route X 16.71 Woodland X 16.72 Open semi-natural greenspace X 16.73 Open water X 10,0006.81 Allotment 10,0006.82 Churchyard X 56.83 Cemetery X 16.84 Other functional greenspace, e.g. caravan park 206.9 Civic space X 17.1 Farmland 57.2 Moorland 17.3 Other, e.g. landfill, quarries 10099 Areas undergoing change 100

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a Only those areas with woodland.

urpose of giving the public reasonable access throughout theirrea’. Planning authorities consider core paths to be part of a GN.

.3.2. Develop the user profilesThe development of the user profiles was informed by findings

rom a survey undertaken in the Hillhead and Milngavie areas ofDPA of core path users (East Dunbartonshire Council, 2008). Theim of the core path survey was to understand usage and to identifyarriers to usage. The study involved a survey of 500 adults aged6+ on and off the paths, in addition to two focus groups. Overall,ore paths were found to have two main uses: leisure activitiese.g. exercise for health reasons) and practical/utilitarian purposese.g. using the paths to get to a particular destination such as shops,arks, local facilities).

Building on these findings two user profiles were developed. Therst profile is of a leisure user who will utilise greenspace and coreaths for recreational purposes like walking and for other activitiesuch as taking their children to play or walking the dog. The secondser profile is a utilitarian user who will use the greenspace andore paths to travel to another destination such as the workplacer shops, visiting friends and family.

.3.3. Integration of data and GN modellingLeast-cost modelling within a GIS requires parameterisation

ith a ‘cost’ to reflect the ease of movement through differentandscape features. Where empirical data are unavailable expertpinion can be utilised (Eycott, Marzano, & Watts, 2011). Valuesere derived following discussion with experts within EDPA and

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at project steering meetings. These values (Table 2) represent howpeople move through the different types of landcover with lowvalues reflecting more permeable areas and high values reflect-ing areas which people might find more difficult or unappealing tomove through. A ‘maximum’ distance for movement is used (300 mwas used to align with the CSGN access ambition), reflecting howfar people are expected to move through the most appealing areas.This maximum distance is effectively reduced when people startto encounter the less desirable areas (with high resistance and lowpermeability) or increased through more favourable areas (withlow resistance and high permeability). For example, if we assumepeople will walk up to a maximum of 300 m, then through a highlypermeable area (with a low resistance of 1) the distance walked willbe 300/1 = 300 m; however, through a low permeability area (witha resistance of 20) it would be reduced by a factor of 20 (300/20)to 15 m. Applying these values across a landscape with a range oflandcover types produces a ‘cost-surface’. As a user moves froma source (greenspace) across the landscape (cost-surface) a ‘cost’is accrued until the maximum distance is reached. The resultingbuffer around the source greenspace is compressed or extended bythe underlying landscape permeability.

The least-cost analysis used greenspaces associated with theuser-profile as the origin for movement (in a similar way thatpreferred habitat is used for modelling habitat networks, e.g.

Watts et al., 2010). Analysis for the leisure users considered thosegreenspaces associated with exercise/leisure (6.1 public parks andgardens, 6.4 playspace, 6.5x Sports areas (Table 2)), with the excep-tion of school grounds as use is often restricted. An exceptionally

D. Moseley et al. / Landscape and Ur

Table 3Weightings applied for the five greenspace quality classes, defined using Jenks Nat-ural Breaks.

Greenspace quality class Weighting

Lowest 0.8Lower 0.9Average 1.0

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igh value (10,000) was applied to prevent movement throughestricted areas, e.g. buildings, water, inaccessible greenspace, andhe parts of greenspaces with restricted entry. Where access wasimited to entry points, these were used as the starting point for

ovement. For open-sided polygons movement was modelled tossume movement from all parts of the polygon. Movement alongore paths had a weighting of 0.5 applied (in effect the user couldotentially move twice the distance whilst on the core path) reflect-

ng their selection by the public and planning authority as the mostopular and important routes.

For the utilitarian user a two step approach was used. First, allhe accessible greenspace typologies identified in Table 2 (6.1, 6.31,.32, 6.4, 6.51, 6.52, 6.61, 6.62, 6.71, 6.72) were used to produce aeneral greenspace network. Next, a destination network was pro-uced around shops, post offices, libraries and schools (identifiedrom OS AddressPoint). Those parts of the destination network thatntersected the greenspace network were then merged with thereenspace network to produce the utilitarian network.

The analysis was undertaken in ArcGIS, using the spatial analystxtension and applying a spatial resolution of 1 m. This very highesolution ensures accurate representation of narrow linear fea-ures, such as paths, and avoids problems associated with least-costaster cracks (Rothley, 2005).

.4. Comparison of outputs with conventional approach

The conventional assessment of greenspace provision (basedpon a Euclidean buffer distance analysis of 300 m undertakeny planning authorities) was compared with the spatial functionpproach developed here to identify where there may be potentialaps in GN provision (see Table 4). Three Euclidean analyses werendertaken. The first considered all accessible greenspace to com-are with overall greenspace provision, the second focused uponAN 65 typologies 6.1, 6.4 and 6.51 (Table 2) to compare againstrovision of parks and playspace provision. The third analysis used

eisure greenspaces (6.1, 6.4, 6.5x Sports areas) to allow direct com-

arison with the leisure user network. The comparison considered:

a. The extent of the GNs/buffer areas

able 4omparison of approaches examining area of greenspace considered, network or buffer aref residences within the network or buffer areas.

Approach Area (ha) of greenspaceconsidered

Network/buffer area(ha)

1. Leisure user network 126 278

2. Utilitarian user network b 123

3. Euclidean buffer of all accessiblegreenspace

399.3 1491

4. Euclidean buffer of parks andplay spaces

87.5 868

5. Euclidean buffer of leisure usergreenspace

126 982

a Selection within 20 m as the network analyses do not intersect with buildings.b Area of greenspace not applicable as the analysis considers only part of the greenspac

ban Planning 116 (2013) 1– 12 7

b. The number of residences within these areas (using OS AddressPoint)

c. The spatial distribution of the provision/perceived access as aproportion of the study area and as the proportion of residences

d. Social equality – the spatial distribution of the provi-sion/perceived access in relation to areas with higher levels ofmultiple deprivation (using the SIMD).

3. Results and comparison

3.1. Green networks in the case study area

GIS modelling provided an indication of GNs for leisure users(Fig. 4) and utilitarian users (Fig. 5) within the case study area. TheEuclidean buffer analysis, representing conventional greenspaceplanning and management for a range of greenspace criteria (allaccessible greenspace, leisure greenspaces, parks and playspacesonly) indicates the perceived extent of greenspace provision (Fig. 6).

3.2. Comparison of perceived provision of greenspace

Figs. 4–6 provide a visual assessment of perceived provision ofgreenspace within the study area and can help to quickly iden-tify where there are potentially deficiencies. Quantification of theextent of each of the approaches allows a more detailed compari-son to be undertaken (Table 4). As least-cost analyses use routes toand through the potential GN rather than a broad Euclidean buffer,provision across the study area appears relatively low with under20% coverage for the leisure user network and less than 10% for theutilitarian network (approaches 1 and 2, Table 4).

The Euclidean buffer generated using all accessible greenspaceencompasses the whole study area, whilst the extent of perceivedgreenspace provision is just over half the study area when focus-ing upon parks and play spaces (Fig. 6). When all greenspace areasconsidered for leisure are buffered, the extent is slightly larger thanthat for parks and play spaces with around two thirds of the studyarea covered. The area of the leisure user network, which uses thesame greenspaces as the leisure Euclidean buffer is less than a thirdof the Euclidean buffer area (Table 4).

3.3. Comparison of perceived greenspace accessibility

The second comparison considered accessibility in relationto planning authority requirements; in this case that residencesshould be within 300 m of greenspace. The approach with aEuclidean buffer around all accessible greenspace encompasses

the whole study area, which may lead planners to the con-clusion that each residence has access to at least one area ofaccessible greenspace (approach 3, Table 4). However, examin-ing greenspace for leisure using a least-cost analysis suggests that

as expressed in hectares (ha) and as a percentage of the study area, and the number

Network/buffer as % ofstudy area (1491 ha)

Number/% ofresidences withinnetwork/buffer

Proportion of area ofhigher deprivationwithin buffer/network

18.6% 9781 (72.6%)a 52.0%8.2% 5638 (41.9%)a 29.5%100% 13,466 (100%) 100%

58.2% 11,308 (84.0%) 99.5%

65.9% 11,658 (86.6%) 99.9%

e network that intersects a destination network.

8 D. Moseley et al. / Landscape and Urban Planning 116 (2013) 1– 12

Fig. 4. Leisure user networks (light shading) around Kirkintilloch, with the areas of greenspace considered for leisure (dark shading). Areas classified as within the 15% mostdeprived parts of Scotland in the Scottish Index of Multiple Deprivation are highlighted by simple hatching.

Fig. 5. Utilitarian networks (light shading) around Kirkintilloch with core paths. Areas classified as within the 15% most deprived parts of Scotland in the Scottish Index ofMultiple Deprivation are highlighted by simple hatching.

D. Moseley et al. / Landscape and Urban Planning 116 (2013) 1– 12 9

Fig. 6. Perceived extent of greenspace access using a 300 m Euclidean buffer for a range of greenspace criteria (parks and playspaces only (cross hatching), leisure greenspaces( ost deb

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solid grey), all accessible greenspace (stippled)). Areas classified as within the 15% my simple hatching.

ust over 72% of residences can potentially access the leisure useretwork. Comparison of these two approaches, indicates that aarkedly smaller proportion (14% less) of the local population have

ccess to these greenspace than is indicated by a Euclidean bufferTable 4).

Examination of accessibility to parks and playspaces using auclidean buffer indicates only 84% of the population can reachhese areas from their homes. This would suggest that the NPFAriteria for access to areas equipped for play (at 400 m and 1000 m)ay not be met for all residents. However, it is worth noting

hat this analysis considers a much smaller amount of greenspacehan the Euclidean leisure analysis (87.5 ha of park/playspacegainst 126 ha for leisure greenspace) which may have impli-ations for what types of greenspaces planning authoritiesrovide.

The utilitarian network analysis indicates that less than halfaround 42%) of residents can access this type of GN (approach 2,able 4), indicating substantial scope for the planning authoritieso identify improvements in GN routes to promote active travel innd around greenspaces.

.4. Increasing access to GNs

Examination of each of the networks illustrates that the spa-ial extent of GNs is an important consideration and it is not justhe proportion of the population that has access to the GN, buthere the population is disconnected. Spatial analysis of perceived

ccess for areas with higher social deprivation indicates that meth-ds of greenspace provision using Euclidean buffers would suggestlmost complete provision (approaches 3–5, Table 4). However,here are gaps evident when analysing with a GN approach, which

prived parts of Scotland in the Scottish Index of Multiple Deprivation are highlighted

suggests that barely a half of the higher deprivation area (52%) isconnected to the leisure network and less than a third of the area(29.5%) connected to the utilitarian network. These disconnectedareas are very apparent when viewing an inverse GN provisionimage (Fig. 7), which importantly, indicates where access to theleisure or utilitarian user networks is lacking.

4. Discussion

Whilst conventional approaches have been useful in help-ing planners to make a quantitative provision of greenspace orplayspace, many of the principles have not moved on from theminimum distances to greenspace set out by Hart (1979) andMatthews (1987). The comparison between the conventional andGN approaches suggests than using simple Euclidean buffersaround greenspace is inadequate for assessing provision and acces-sibility of greenspace within a planning authority. The greennetwork analysis for leisure users suggests the area of provision isless than one third of that implied by the Euclidean buffer approach.It is evident from these results that the conventional approach togreenspace provision, whether that be on an area basis or for thenumber of residences, may substantially overestimate who is ableto access the ‘accessible’ greenspace and where (e.g. 86% vs. 72% ofresidences for leisure greenspace).

The ‘who’ is particularly important when considering equalityof access, as it is often people living within deprived areas whoare the target of Government initiatives to encourage healthier

lifestyles. Here we have identified that only half of the deprived areahas functional connection to the leisure user network, which maycompound the challenges faced in engaging the local population.There is an increasing interest across Governments internationally

10 D. Moseley et al. / Landscape and Urban Planning 116 (2013) 1– 12

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ig. 7. Inverse image of green network provision. The shaded areas represent whereas classified as within the 15% most deprived parts of Scotland in the Scottish In

n ways to encourage healthy and sustainable behaviours that aren contrast to formal regulation (Vallgårda, 2012) such as ‘nudge’focusing on how to nudge citizens into changing their behaviourThaler & Sunstein, 2008)) or ‘think’ (to provide a forum for citi-ens to discuss and think collectively about instituting sustainableractices (John, Smith, & Stoker, 2009)). Conventional approachesend to treat everyone similarly and not differentiate between theange of social situations, types of greenspace use and motiva-ions for engaging with greenspace. The GN method described here,ogether with locally obtained data, can enable planning authori-ies to identify where they can target efforts to improve access toN.

More widely, given its focus on movement and inter-connectionhe GN approach can help to consider how to promote regular exer-ise through incorporating active travel (utilitarian user network)nd improving daily interaction with greenspace (leisure user net-ork). The utilitarian user network developed here is particularly

nnovative, as traditionally greenspace provision has focused uponeisure use but there is an increasing interest in promoting activeravel within planning authorities (Barton, 2009; Chillón, Evenson,aughn, & Ward, 2011). Our study indicates lower accessibility

o the utilitarian user network than to the leisure user network42% vs. 72%), which may be a function of the spatial configura-ion of greenspace in relation to residences and to destinations.t may also reflect a planning authority focus on greenspace pro-ision for leisure rather than considering how greenspace andNs can help sustainable travel initiatives. Destination is consid-red to be a key determinant in promoting active travel (Pikora,iles-Corti, Bull, Jamrozik, & Donavan, 2003) and needs to be

onsidered within green networks. However, the type of activityndertaken is also important as some travel may be purely forhe purpose of exercise (Handy, Boarnet, Ewing, & Killingsworth,002).

ere is no access to either the leisure user network or the utilitarian user network.f Multiple Deprivation are highlighted by simple hatching.

4.1. Practicalities of approach, use by planning authorities andothers

Current approaches employed by planning authorities to planand manage greenspace often focus on individual greenspacesrather than as part of a GN. The reasons for this may bedue to organisational convention (it has always been done likethis) or to meet internal or external reporting mechanisms andstandards for greenspace provision. The leisure user networksdescribed here can aid the delivery of strategies to improve thehealth and well-being of the local population, whilst the utili-tarian networks enable active or green travel strategies withinGNs.

On a practical level, comparison of current conventionalapproaches to greenspace provision with the GN approach iden-tified the missing potential of greenspace in terms of highlightingwhere people do not currently have access to the GN. Incorporatinga weighting for greenspace quality within the GN approach enablesplanning and management to consider how improving greenspacequality and functionality can increase GN extent. For example, byrunning the model iteratively a planning authority could determinethe increase in green network extent by improving the quality of anumber of greenspaces or increasing the provision of access points.It is recognised that some planning authorities may lack all thedata used here and that, initially, a simpler approach may needto be taken using just a greenspace and path dataset. As resourcesbecome available, additional data can be generated and the outputsrefined. Table 5 outlines expected data availability and sugges-tions for data collection. Although the GN approach involves more

complicated GIS procedures than a Euclidean buffer approach, theprocesses can be largely automated, reducing the requirement foradditional training and resources. This model also facilitates the useof the approach internationally, as the only model parameters that

D. Moseley et al. / Landscape and Urban Planning 116 (2013) 1– 12 11

Table 5Data sources, expected availability, requirements and their use in green network modelling.

Data source Availability Requirement and use

Greenspace audit Every planning authority should have a GIS greenspace datasetor can generate one by populating a landcover dataset withgreenspace types from a survey or digitising paper maps.

Essential for spatial planning greenspace within a GIS.

Greenspace quality May be unavailable. Can be generated using recommendedmethodologies that survey each greenspace.

Optional. Allows greenspace attraction to be modelled and aidsidentification of areas where improving quality may enhanceprovision.

Path network Usually available within a regional GIS landcover dataset. Required to indicate actual travel.

Entry points May be unavailable. Can be captured with a GPS duringsurveys or remotely using aerial photography.

Strongly recommended if actual access is to be represented.

Greenspace use survey May be unavailable. Surveys of users on site or localidet data

Provides information on actual use (and aspirational use for

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households. National or regional surveys may provreasonable information if it is not feasible to colleclocally.

eed to be changed are landcover codes and maximum distancepplied.

This work has focussed upon managing and enhancing the exist-ng GN resource, but it is worth considering how it may influencehe planning of greenspace within new developments to ensure it isncorporated effectively at the planning stage rather than squeezedn at the end. This process may be guided by the analysis indi-ating those areas currently lacking access to networks, therebyighlighting the requirement to plan connections to the GN.

The outputs from this work have been taken forward within anpportunity mapping process that incorporates habitat networks,evelopment growth areas and the SIMD to produce scores across

region, enabling planners to target those areas most in need ofttention. These outputs are now being used and are incorporatednto strategic and local planning across the GCV region and areelping to deliver the CSGN ambition that ‘every home in Centralcotland is within 300 m of an attractive, safe, and well-maintainedreenspace or accessible countryside’.

.2. Suggestions for refinement

A distance of 300 m was used to reflect the distances used byany planning authorities and that specified by the CSGN. This

istance was applied to the Euclidean buffer analyses without mod-fication as this reflects how many planning authorities determinereenspace provision, but it is recognised that once severance fac-ors are taken into account, the actual distance walked to reachhe same point would be closer to 400 m (Harrison et al., 1995). Toecognise this variability and the efforts made by planning author-ties to promote use of the most popular routes, a weighting of.5 was applied uniformly for core paths. This approach could beefined by a weighting that takes into account variability in theuality of the path, signage/promotion, or popularity of use.

The use of a weighting for greenspace quality fits well with thessumption that we should be improving the quality of greenspacesnd evidence that people are more likely to travel further to areasf higher quality greenspace than to lower quality areas (Giles-orti et al., 2005; Maat & Vries, 2006). However, we have not testedhether the relationship between greenspace quality and distance

ravelled is linear and it would be useful to incorporate data onow far local residents are prepared to travel to greenspaces of dif-

erent quality and if this distance is influenced by the number ofreenspaces.

Concern over safety and maintenance of greenspaces can be a

arrier to public use. Thus a focus for planning authorities might beirected to poor condition of paths, lighting and play features, antiocial behaviour and crime, as well as poor environmental qualityaused by dog mess, litter, graffiti and vandalism (e.g. Pikora et al.,

targeting new provision) to develop profile. It may be assumedthat a leisure user would be appropriate for most areas.

2003). As these elements can produce local differences in GN use,accounting for them in the model would highlight where to directaction to improve access and use.

This work has recognised that conventional approaches do notdifferentiate between users or address groups with particular needsby producing two user profiles based upon locally obtained data.This provides planning authorities with more scope for enhancingGNs in relation to use, but further work on profiling local pop-ulations will be useful, for example based upon age, gender, orethnicity. A range of GNs could be devised to identify accessibility ofdifferent user types to preferred greenspace areas or other destina-tions. This could be achieved by incorporating a few extra questionsinto greenspace surveys to obtain the spatially referenced dataneeded to identify different user types. The development of theGN approach has highlighted the lack of spatially referenced socialdata on how people use GNs. Priorities for research to address theseissues are:

1. How people move through the landscape and how they use GNs2. Motivations for using (or not using) GNs3. How to balance competing needs, interests and priorities for

different user groups4. Monitoring and evaluation of effectiveness of GN improvements.

5. Conclusion

Greenspace planning and management using Euclidean bufferscan be considered a blunt tool that fails to consider the differentusers of greenspace or spatial inequalities, i.e. deprived parts of thecommunity. Consequently there is a risk that this approach maylead to under provision of functional greenspace and that the ben-efits of greenspace will not be fully realised. The green networkapproach provides new insights into the process of greenspaceprovision and greater separation of users in analysis. It enablesplanning authorities to determine how changes to the spatialarrangement of different greenspace typologies, their quality, andpoints of access can contribute to initiatives to promote activetravel and increase public use of greenspace for health, particularlyfor areas with higher social deprivation.

Acknowledgements

This work has been developed through funding from Scottish

Natural Heritage, Glasgow & Clyde Valley Green Network Partner-ship and Forestry Commission Scotland. The advice from membersof East Dunbartonshire Council has been invaluable in developingconcepts and the approach. Welcome and constructive comments

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