2.2. Characterising Visitors to Greenspaces
To address our three research questions we drew upon open- and closed-ended questions embedded within a longer structured survey assessing values associated with biodiversity and the natural world [
7,
24]. All study materials underwent ethics review and verbal consent was obtained from participants following a brief description of the study. We wished to engage with as wide a range of people using the riparian zones as possible. Therefore, each site was visited at least four times, covering daytime and early evening during weekends and weekdays, using a rule of thumb of approaching every third person who passed by the study site. Over half (54.3%) of those asked to participate did so, yielding 1108 completed questionnaires (median = 34 per site). Participants were predominantly of European ethnicity (91.7%; broadly in line with Sheffield’s population as a whole, which is 91.2% of European descent), represented both genders (62% male), and covered a broad array of age groups (16 to 70+) and household income (below £10,000 to above £70,000 per year). We acknowledge that our sample is self-selected (
i.e., we only interview those people who have already chosen to visit greenspaces). However, our intention is not to understand what differentiates users from non-users, but to quantify the drivers of usage frequency among existing users.
For research question (I) (
Do frequent users of urban greenspaces report higher psychological well-being gains associated with their visit than less frequent users?), visit frequency was quantified through a closed-ended question which asked how often an individual came to that particular riparian greenspace. Possible responses were daily, weekly, monthly and less than monthly. We used self-reported psychological well-being measures to assess the benefits of greenspace usage, in line with previous research [
7,
8] and theoretical considerations including reflection/contemplation [
14,
25,
27] and sense of place (e.g., [
28,
29]). Seven statements measured reflection/contemplation, while a further 14 assessed sense of place constructs. Participants answered on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree) in response to the stem question “Please indicate how much you agree with each statement about this stretch of river and the neighbouring banks”. We then used factor analysis (principal axis factoring; oblique rotation) to identify meaningful interpretable well-being factors: reflection (ability to think and gain perspective), attachment (degree of emotional ties with the stretch of river), and continuity with past (extent to which sense-of-identity is linked to the stretch of river through continuity across time). Continuous measures were derived by calculating the participant’s average rating of the set of statements forming each factor (see [
7] for full details of the measures) (
Table 1).
To address question (II) (
what determines how often people visit greenspaces?) our questionnaire included closed-ended socio-demographic questions. In addition, we assessed participants’ knowledge about the natural world via a wildlife identification skill test [
7]. This was done by asking people to identify photographs of four species of bird, butterfly and plant commonly encountered within our sites. We used the answers to generate continuous measures of “wildlife knowledge” by summing the number of correct responses to give a score from 0 to 12 for each participant (
Table 1). We also assessed the biophysical properties of our study sites, as outlined in
Section 2.3 below.
Table 1.
For 1108 visitors to 34 riparian greenspaces in Sheffield, England, site-level medians (range) for: biophysical site properties, self-reported psychological well-being of visitors (measured on a 1–5 scale: 1 = strongly disagree to 5 = strongly agree, in response to the stem question “Please indicate how much you agree with each statement about this stretch of river and the neighbouring banks”) and the socio-demographic characteristics of visitors. Wildlife identification skill gives the median number of correctly identified images (up to a maximum of 12 in total). Household income before tax is given in GBP thousands per annum.
Table 1.
For 1108 visitors to 34 riparian greenspaces in Sheffield, England, site-level medians (range) for: biophysical site properties, self-reported psychological well-being of visitors (measured on a 1–5 scale: 1 = strongly disagree to 5 = strongly agree, in response to the stem question “Please indicate how much you agree with each statement about this stretch of river and the neighbouring banks”) and the socio-demographic characteristics of visitors. Wildlife identification skill gives the median number of correctly identified images (up to a maximum of 12 in total). Household income before tax is given in GBP thousands per annum.
Variable | Median (Min–Max) |
---|
Biophysical site properties | |
Travel time (minutes) | 10 (1–340) |
Number of bird species | 12 (4–18) |
Tree cover (proportion) | 0.37 (0.05–0.91) |
Greenspace neglect | 2 (0–6) |
Psychological well-being | |
Reflection | 3.99 (3.26–4.43) |
Attachment | 4.32 (3.42–4.67) |
Continuity with past | 3.26 (2.40–3.86) |
Wilidlife Knowledge | |
Wildlife identification skill (number of photographs out of 12 correctly identified) | 2.09 (0.78–3.17) |
Socio-demographic characteristics | |
Gender | 62% male |
Age | 40 (18–70) |
Household income | £20 (£10–£75) |
Study sample size | |
Number of participants | 34 (10–46) |
For question (III) (
are the motivations for visiting greenspaces different between high and low frequency users?), we elicited peoples’ own descriptions of why they were visiting a particular greenspace. To do this, we included an open-ended question (“As for today, what are the two main reasons that brought you to this stretch of river?”) [
9]. To minimise the potential influence of subsequent closed-ended questions on responses, this question was asked first; seven individuals did not provide an answer. Responses from the remaining 1,101 participants were iteratively content-analysed [
30] by two researchers (KNI/MD) following the rationale, analysis protocol and identified taxonomy from Irvine
et al. [
9] as a guide. Visit-motivation responses were first sorted into codes (e.g., the comments “countryside in a very urban setting” and “see some nature” were both placed in a “Natural Setting” code) informed by participants’ language; Kappa analysis [
31] indicated a substantial agreement between the two researchers who were independently assigning codes (90.4%; Kappa = 0.89). All mismatched codes were resolved by consensus agreement. Following the general approach of content analysis [
30], codes were then grouped into descriptive themes, the development of which was informed by commonly mentioned words/phrases and meanings within the codes, previous research findings and theoretical constructs. Themes were subsequently categorised into nine domains grounded in existing theoretical constructs regarding the relationship between people and nature [
14,
32,
33], holistic models of health [
33] and previous research (e.g., [
9,
34]). Here we concentrate on the domain and theme level data only.
2.3. Characterising the Biophysical Properties of the Study Greenspaces
Urban river corridors show a high degree of environmental variation and can support diverse biological communities (e.g., [
35]). In Sheffield, previous work has shown that there is substantial variation in the biophysical properties of these riparian greenspaces [
23,
36], which could influence the decisions people make regarding how often to visit. For the purposes of this study we characterized each study site using four different metrics (
Table 1): (I) site accessibility; (II) number of bird species; (III) proportion of tree cover; and (IV) neglect/maintenance. We deliberately excluded the presence/absence of built facilities, such as a café or playground, as a possible explanatory variable for visit frequency as these were present on only three sites.
Site accessibility was quantified by using the proxy of travel time, which was measured by asking each participant to state how long (in minutes) it took them to reach the greenspace. We included the number of bird species as birds play a central role in human-wildlife interactions in England [
37]: (I) bird feeding is a common and widespread activity [
38,
39]; (II) bird watching is a popular and fast growing leisure interest [
40]; (III) bird-focussed citizen science initiatives successfully engage large numbers of people [
41,
42]; and, (IV) birds are more likely to be recognised by the general public than other common and widespread plant and animal groups [
7]. The number of bird species was surveyed on each of the 34 sites. Following standard protocols [
43], two visits were made in spring and early summer to coincide with the breeding season, with the second at least six weeks after the first. To ensure that the maximum number of species was encountered, visits began between one and three hours after sunrise (the time of highest avian activity) and were only carried out in suitable weather conditions (low wind, no rain or mist). A single observer (MD) recorded the identity of each bird that was seen or heard from the survey point (the same location at which questionnaires were administered) over a five minute period (see [
23] for details of bird and ecological survey methods and complete results). A list of all species encountered during both visits was collated (
Table 1), among them only three species (feral geese, feral pigeon
Columbia livia and the rose-ringed parakeet
Psittacula krameri) were non-native, with the latter species only observed on a single occasion [
7,
23].
Tree cover is a further highly visible aspect of the natural world, readily appreciated and noted by visitors. Tree cover was mapped in a Geographical Information System (GIS) by manually tracing around each tree or group of trees shown in aerial photographs [
44]. The proportion of cover in a 50 m radius around each location was then determined. Finally, we included a metric of greenspace neglect. This was derived from field surveys where we counted the number of large (e.g., furniture) and small (e.g., food packaging) items of litter that were on both banks and in the river channel itself in an area 40 m up and downstream from the location where participants were invited to complete the questionnaire. For both large and small items, each site was given a score of 0 where no litter was present, 1 where the amount of litter was less than the average across all sites, or 2 where the amount of litter was greater than average. Sites were also characterised by the presence of graffiti and abandoned buildings (both scored 1/0 for presence/absence). All four scores were combined into a single greenspace neglect index which had a theoretical maximum of 6 and minimum of 0 (
Table 1).
2.4. Statistical Analyses
We undertook the following statistical analyses to answer our research questions:
(I) Do frequent users of urban greenspaces report higher psychological well-being gains associated with their visit than less frequent users? We used Kruskal-Wallis tests to determine whether there were significant differences in self-reported well-being between visit-frequency categories.
(II)
What determines how often people use greenspaces? Using an ordered logistic regression approach, we modelled visit frequency as the response variable against a suite of explanatory variables including site biophysical properties (travel time, number of bird species, tree cover, greenspace neglect), the socio-demographic make-up of participants (age, gender, income) and their ability to identify wildlife. Ordered logistic regression is an extension of logistic regression (used when the response is binary) that can be employed when there are more than two possible responses and where the order of responses is informative. Ordered logistic regression assumes that the parameter estimates that describe the relationship between the highest category of the response variable and all other categories are the same as those that describe the relationship between the next highest category and all others, hence there is a single parameter estimate for each variable. All analyses were carried out using the “polr” command in the MASS package [
45] of the R statistical software [
46]. The model coefficients can be difficult to interpret due to the log scale, so we converted them into proportional odds ratios by taking the exponential of the coefficients. The outputs can then be understood in the same way as odds ratios from a standard logistic regression model.
(III) Are the motivations for visiting greenspaces different between high and low frequency users?
We used chi-squared tests to determine whether the frequency of the types of motivation given at the domain and theme levels varied according to visit frequency.