Creating Woodland Access with Gis - a Literature Review

Modelling access to greenspaces: A review of GIS methods

1 Introduction

Studies have shown that frequent visits to green spaces in urban areas can reduce stress (Ulrich 1984; 2001), increase physical & mental wellbeing (Jackson, 2003), and enhance the overall health of a population (Bedimo-Rung, 2005).

In a report by the UN it was found that 50% of the world population is now located in urban areas and this figure is expected to reach 6.3billion by 2050 (UNWUP, 2011). The lack of access to green spaces for an urban population this size would have significant impacts on the mental and physical wellbeing of residents (Bedimo-Rung et al, 2005; Kessel et al, 2009; Van Den Berg et al, 2010; Lachowycz & Jones, 2011;Mitchell, 2012) leading to socio-economic pressures on already strained health services.

Grahn (2003) investigated the link between stress and stress related illnesses in Sweden and found a relationship between the level of stress and the frequency of visits to urban green spaces. The report showed that least stressed individuals had visited UGS for >311hours per year compared to the most stressed adults with a <185 hours.

Pretty et al (2006) showed how green surroundings can be positive for mental health whilst exercising. A sequence of 30 scenes was projected onto a wall whilst participants used a tread mill. The physiological affects had remained the same throughout all scenes (lower blood pressure) but both self-esteem and mood were enhanced when participants were shown either urban or rural greenery.

However, further research found that the proximity to green spaces was a major influence on whether a person will visit or not.

Schipperijn et al (2010) found that that the frequency of visits to green spaces was negatively correlated with the distance it took to reach them; the number of daily visits dropped by almost 40% when living more than 300m from a green space.

Coles & Bussey (2000) suggest that the low frequency of visits to green spaces is not only attributed to distance but rather the length of time it takes to walk there. By conducting a survey of residents living adjacent to urban woodlands they found that 5-10 minutes seems to be the threshold time beyond which people would not travel on foot to visit the woodland (see figure 1). This suggests that it is not only the 'availability' of green spaces which will encourage the public to frequent parks and urban green spaces but 'access' is equally important.

Figure 1. The effect of woodland location on usage patterns (Coles & Bussey, 2000; 182)

In 1995 the UK statutory body for nature conservation, England Nature (now Natural England), provided a hierarchal set of standards to ensure provision of, and access to, urban green spaces for as much of the urban population as possible (Pauleit et al, 1995). The Accessible Natural Greenspace Standards model (ANGSt) was based on ideas presented in a report by Harrison et al (1995) which had been produced for Natural England. The standards set by the ANGSt model require that no individual should live beyond a 300m distance from the nearest green space and no smaller than 2ha; that there should be at least 2ha of nature reserves locally per 1000 population & every individual should be able to access a 20,100 & 500 ha site within 2,5 & 10km from home.

2 Modelling access

2.1 Early methods

Early methods for measuring access were mostly based on access to services such as GPs/health centres or post offices and usually consisted of laborious methods of counting the source to population ratio (Wing, 1988; Kindig & Rickets, 2010) or measuring the distance between a population and the source (Joseph & Bantock, 1982). Advances in Geographic Information Science (GIS) however, has allowed a more precise form of modelling which is both more efficient and realistic.

2.2 Current methods of access modelling

White (1997) used GIS to assess changes in the distance travelled to post offices before and after planned closures over a 20 year period. This was achieved by applying buffers, using regression analyses and conducting network modelling. White(1997) had acknowledged though, that buffers were at best a descriptive analysis which only represent a straight line distance between points, whereas the actual time and travel routes to reach post offices should be considered. White found that the distance travelled to reach post offices has increased from 2.5km in 1979 to 2.9km in 1994. Demographics such as mode of travel had been included in the analysis which highlighted the difficulty of certain proportions of the population in reaching post offices. Although informative, at the time the analysis was not able to estimate the travel times to post offices which may have varied with the mode of transport being used.

In order to overcome the inaccuracies involved in a buffer analysis, distances along road networks which people would normally travel would need to measured and calculated. This can be achieved by conducting a network analysis along routes from source to destination (roads, pathways etc.) and takes into consideration the road speed limit.

Comber et al (2008) conducted a study on the provision of green space access to different ethnic and religious groups within Leicester and comparing results to the ANGSt model. Network analysis was chosen as the technique for distance measurement in line with Handley et al (2003); in this study Handley et al argued that GIS was the optimum tool for measuring access as it provided a more accurate and a more realistic view of catchments. By utilising the network analysis tool it was possible in the study by Comber et al (2008) to assess which percentages of the population within Leicester met the standards set out in the ANGSt model (see table 1).

ANGSt standards % of population meeting ANGSt standards

Rule 1 2ha within 300m 10.3

Rule 2 2ha per 1000 population 100+

Rule 3 20ha within 2km 60.1

Rule 4 100ha within 5km 100 + *

Rule 5 500ha within 10km No 500ha sites in county

Table