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Residential-exposure-to-visible-blue-space--but-not-green-space-_2016_Health.pdf

Health & Place 39 (2016) 70–78

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Health & Place

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Residential exposure to visible blue space (but not green space) associated with lower psychological distress in a capital city

Daniel Nutsford a, Amber L. Pearson b,c,n, Simon Kingham a, Femke Reitsma a

a University of Canterbury, Department of Geography, Christchurch, New Zealand b Michigan State University, Department of Geography, 673 Auditorium Road, East Lansing, MI 48824, USA c University of Otago, Department of Public Health, 23A Mein Street, Wellington 6242, New Zealand

a r t i c l e i n f o

Article history: Received 29 July 2015 Received in revised form 8 February 2016 Accepted 3 March 2016 Available online 11 March 2016

Keywords: Mental health Visibility analysis Blue space Green space Urban planning

x.doi.org/10.1016/j.healthplace.2016.03.002 92/& 2016 Elsevier Ltd. All rights reserved.

esponding author at: Michigan State Universi itorium Road, East Lansing, MI 48824, USA. ail address: [email protected] (A.L. Pearson)

a b s t r a c t

As urbanisation escalates globally, urban neighbourhood features which may improve physical and mental health are of growing importance. Using a cross-sectional survey of adults and the application of novel geospatial techniques, this study investigated whether increased visibility of nature (green and blue space) was associated with lower psychological distress (K10 scores), in the capital city of Well- ington, New Zealand. To validate, we also tested whether visibility of blue space was associated missing teeth in the same sample. Cluster robust, linear regression models were fitted to test the association between visibility of nature and K10 scores, adjusted for age, sex, personal income, neighbourhood population density, housing quality, crime and deprivation. Higher levels of blue space visibility were associated with lower psychological distress (β¼ �0.28, po0.001). Importantly, blue space visibility was not significantly associated with tooth loss. Further research is needed to confirm whether increased visibility of blue space could promote mental well-being and reduce distress in other cities.

& 2016 Elsevier Ltd. All rights reserved.

1. Introduction

The last decade has seen mounting evidence to suggest that the presence of natural environments in urban neighbourhoods is associated with positive physical and mental health. These en- vironments, often called “green space” and “blue space”, are places for recreational opportunities (Barton and Pretty, 2010), social connection and enhanced social ties (Nutsford et al., 2013), often providing respite in urban settings to assist mental and physical recuperation (White et al., 2010; De Ridder et al., 2004). While the definition of green space may vary slightly between users, this term tends to include open areas of vegetation (e.g. parks, sports fields) and conservation areas (e.g. forests), but can also include backyard gardens, farms or any other space predominantly cov- ered in vegetation. Blue space includes water bodies (e.g. lakes, oceans, rivers, etc.) but rarely includes human-made features (e.g. water fountains or sculptures). The mental health benefits of green and blue spaces have become increasingly of interest, with mental health contributing significantly to the global disease burden (Lim et al., 2012). Anxiety and depression are also precursors for chronic physical conditions such as asthma, arthritis, diabetes, stroke and

ty, Department of Geography,

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heart disease (Pretty et al., 2005). As such, exploration of the ways in which urban neighbourhood features, such as exposure to green and blue spaces, may promote mental health, is of great im- portance to reducing this major source of global disease burden.

In our previous work (Nutsford et al., 2015) we outlined three primary causal pathways through which green and blue spaces may directly or indirectly influence mental health. First, there is strong evidence of an indirect pathway whereby increased phy- sical activity has been associated with improved mental health (Barton and Pretty, 2010; Pretty et al., 2005). The benefits of physical activity include reduced blood pressure, increased self- esteem and reduced anxiety (Pretty et al., 2005), all of which may improve mental health. Nearby green spaces, particularly useable green spaces, provide recreational opportunities and are asso- ciated with higher levels of physical activity (Sugiyama et al., 2008). Particularly relevant to the research here, the proximity to both green (Pretty et al., 2005), and blue space (Barton and Pretty, 2010) has shown a significant impact on self-esteem, especially amongst the mentally ill. A weakness of this research is that proximity to green or blue space is often used as a proxy for use or engagement in physical activity. In fact, a recent review associa- tions between proximity, in particular to green spaces, and phy- sical activity concluded that findings are inconsistent across stu- dies (Bell et al., 2014).

Second, green and blue spaces offer opportunities for social interaction, whether planned or not, which is also linked to

D. Nutsford et al. / Health & Place 39 (2016) 70–78 71

improved moods (Sugiyama et al., 2008; Miles et al., 2012). In- creased social interaction is particularly beneficial for the elderly, where increased social interactions improve community cohe- siveness and has been associated with lower suicide rates, lower fear of crime and better physical health (Kweon et al., 1998). However, Bell et al., (2014) identify a number of limitations un- derpinning extant research and conclude that there is limited evidence to support the social interaction pathway. The vast ma- jority of research makes the assumption that green space use is correlated with locational access measures, although actual use os often not directly measured. Much of this work argues that such use of green spaces are also driven by personal characteristics and the amentities and quality of the green spaces.

Last and most salient to the present study, green and blue spaces are recognised as therapeutic or salutogenic places and may lower psychological distress by serving as calming backdrops in residential neighbourhoods, in contrast to built-up features of the urban setting (White et al., 2010; Pretty et al., 2005; Ulrich et al., 1991). This backdrop is theorised to reduce sensory stimuli and thus promote mental relaxation (Ulrich et al., 1991). In fact, it is theorised that humans have an innate imperative to favour natural environments in contrast to urban environments due to their evolutionary importance as key resources (Newell, 1997). As a result of this evolutionary connection, the human brain pro- cesses natural environments more efficiently than built-up en- vironments, thereby further increasing opportunity for relaxation (Ulrich et al., 1991; Heerwagen and Orians, 1986) and combating the onset of stress (Igarashi et al., 2015). Kearney (Kearney, 2006) conducted a qualitative study which found that prisoners in Eng- land with non-natural views had a 24% higher frequency of sick calls than prisoners with a view of farm land. Similarly, it was observed that increased views of nature from residential windows increased neighbourhood satisfaction, which has been linked to improved psychological stress (Kearney, 2006). A literature review conducted by Völker and Kistemann (2013), identified numerous benefits of visible waterscapes including emotional, recreational and direct health benefits. Specifically, they identify work by Kar- manov and Hamel (2008) conducted in the Netherlands that found blue spaces to stress-reduce and enhance mood in urban en- vironments. Other research by Laumann et al. (2003) observed increased levels of attentiveness in study participants when ex- posed to a simulated coastal environment as opposed to an urban setting.

The majority of existing studies evaluating relationships be- tween exposure to green and blue spaces and indicators of mental health have employed basic measures of proximity (e.g. distance from home) (Wheeler et al., 2012; White et al., 2013) or the pro- portion of parks within a given distance from home (Nutsford et al., 2013; Richardson et al., 2013, 2010). These measures of lo- cational access are useful when exploring the potential pathways of physical activity and social interactions in reducing psycholo- gical distress, as easy access to a park from one's home could fa- cilitate such processes. However, proximity measures are unable to accurately characterise one's visual exposure to natural environ- ments and thus to explore the potential salutogenic aspects of green or blue backdrops. Advances in geospatial technologies permit more precise quantification of the visibility of green and blue spaces (Nutsford et al., 2015) and thus offer a new way to test the effect of visibility of green and/or blue space on psychological distress, to complement existing qualitative evidence (Herzog, 1985; Rose, 2012).

Quantitative measures of green and blue space visibility have been employed in various disciplines. However, they have not yet been employed in health research. For example, Hamilton and Morgan (2010) incorporated views of blue space into house va- luation price models and found a clear favouritism and

willingness-to-pay for houses with ocean views. In 2005, Putra and Yang (2005) developed a GIS-based 3D visibility analysis in the hope that it would map environmental perceptions of the built environment. Similarly Miller et al. (2009) employed spatial ana- lysis tools to quantify the visual perception of green spaces for a case study in Edinburgh, UK. Their work illustrates the potential for including visual measures in future research and concludes that three aspects could influence access to green spaces: loca- tional access, cultural access and visual exposure. These studies highlight that the visual structure of residential environments is important and quantifiable, however, to date these advances in visibility quantification have not been applied to understanding the relationship between the visual exposure of green and blue spaces and mental health.

This study serves as the first of its kind, to our knowledge, and investigated whether self-reported psychological distress was as- sociated with visibility of green and blue spaces in residential neighbourhoods for a sample of adult New Zealanders in Well- ington, using the New Zealand Health Survey (NZHS). To validate, we also tested whether visibility of blue space was associated with missing teeth in the same sample (a theoretically unrelated out- come). The study made use of a novel geospatial method for measuring the visibility of natural environments (Nutsford et al., 2015).

2. Methods

2.1. Ethical approval

Approval to access the NZHS data was granted by the New Zealand Ministry of Health. Spatial data were linked to survey responses, then participant identifying information was removed, both conducted by the Ministry of Health. As such, all data used in analyses were anonymized prior to our use and did not require University ethical approval for use. All de-identified data were password protected and kept in a secure computer facility.

2.2. Spatial data and the creation of the visibility measures

Spatial data on green spaces and oceanic blue spaces were compiled from three national datasets (the Land Class DataBase II, Department of Conservation land register, and the Land Informa- tion New Zealand parcel database), as previously used by Ri- chardson et al. (2010). Freshwater blue space features such as lakes and rivers were made available by Land Information New Zealand (LINZ) and obtained from an online spatial data archive (www. koordinates.com, downloaded April 2013). See Fig. 1 for the spatial distribution of green and blue spaces across the study area.

Visibility measures were generated for population-weighted centroids of meshblocks, called viewpoints hereafter, in which study participants resided (New Zealand's finest administration boundary, study area n¼46, national n¼46,263, mean area ¼0.1 km2 in 2006) across each cell in a 5 m resolution gridded digital elevation surface and extended 15 km from each viewpoint. A novel method of visibility was applied, termed the Vertical Visibility Index (VVI), which accounts for the slope, aspect, dis- tance and elevation of visible areas relative to the observer loca- tion (see Fig. 2). This method is described in detail elsewhere (Nutsford et al., 2015).

In brief, the VVI is a visual summation of green and blue spaces expressed as degrees of visibility. For observers, green and blue spaces are visible across a broad spectrum of degrees, both verti- cally and rotationally. Observers surrounded by buildings, for ex- ample, are likely to have limited visibility of green and blue spaces and this visibility may be limited to specific angles. These

Fig. 1. Distribution of natural environments throughout Wellington City and the greater region.

Fig. 2. Cross-sectional diagram of the VVI measure, highlighting how visibility can be represented as degrees of visibility. The observer on the left (indicated by the dot) has higher visibility of the grassy hill (indicated by a higher VVI value).

D. Nutsford et al. / Health & Place 39 (2016) 70–7872

differences are captured with the VVI while accounting for the impacts of slope, aspect, distance and elevation, unlike standard viewshed analysis. The VVI is generated through an iterative pro- cess where the elevation, aspect and slope of each visible cell in the Digital Elevation Model is computed relative to the viewpoint. This measure provides information about whether a cell is visible (standard viewshed analysis), but also how significant the visual

impact of that cell is. For example, a hill sloping down towards a viewer is of much greater visual significance than a surface sloping away perpendicular to a viewer or indeed, to a level surface with no slope. The summation of these visibility angles across each visible cell for a single viewpoint provides a more accurate mea- sure of visibility from a human perspective. The influence of buildings in visibility analysis is incorporated in the initial stages

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of analysis where building heights are added in the standard viewshed analysis. Using line of sight algorithms, buildings effec- tively obstruct cells which are candidates, thus preventing them from being included in the further visibility analysis. These methods were applied to generate quantities of visible green space and blue space for each meshblock in the study area, expressed as degrees of visibility.

Each measure was also divided into four distance bands, visible areas: (i) r300 m; (ii) 300 m to 3 km; (iii) 3–6 km; and (iv) 6– 15 km. These bands were selected to categorise visible areas as foreground objects, background objects, or objects somewhere in between. Through this notion questions such as “does distant green space have a stronger influence on health compared to nearby green space?” can be answered. After preliminary analyses, blue space distance bands were not included in further analyses due to the lack of blue space in nearby distance bands (i.e., almost all visible blue space was at distances43 km). Furthermore, there was a lack of variation between the distant bands simply because locations with plenty of visible blue space between 3 and 6 km were also likely to have visible blue space beyond 6 km due to the expansive nature of oceanic blue space. Each visibility score (de- grees of visibility) was finally transformed to an ordinal scale be- tween 1 and 10, where 1 represents the lowest 10% of visibility scores and 10 represents the highest 10%.

2.3. Health and demographic data

Kessler Psychological Distress Scale (K10) scores were obtained for Wellington adults (n¼442, 15 years and older) who partici- pated in the 2011/2012 New Zealand Health Survey (NZHS) which covers population health, long-term conditions, health service utilisation and patient experience, health risk and protective fac- tors, health status and socio-demographics (Ministry of Health, 2013). The national survey uses a multi-stage, stratified, prob- ability-proportional-to-size (PPS) sampling design. K10 scores are a simple measure of psychological distress, designed for large sample population studies where scores range from 0 to 40 (higher values indicate increased distress). This measure involves 10 questions about personal feelings over the previous month and has proven to be an accurate predictor of anxiety and mood dis- orders (Oakley Browne et al., 2010) and diagnosed mental dis- orders (Andrews and Slade, 2001). In 2001, the Victorian Popula- tion Survey determined thresholds to represent the likelihood of a mental health disorders based on K10 responses, which are now widely accepted (Oakley Browne et al., 2010; Kessler et al., 2003). Scores of 0–5 were considered'none or low’; 6–11 were ‘moder- ate’; 12–19 were ‘high’ and 20–40 were ‘very high’ in regards to the likelihood of having a mental health disorder. K10 scores were treated as an ordinal dependent variable in regression models. Other demographic variables from the NZHS included sex (cate- gorical), age groups (ordinal), ethnicity (categorical), and personal income groups (ordinal).

To account for other features of the urban environment which might be important in understanding the relationship between visibility of natural environment and mental health, we also compiled data for neighbourhood socioeconomic status, popula- tion density, and crime rates. We obtained deciles of neighbour- hood deprivation (ordinal) and population density (continuous) for 2006 from Statistics New Zealand at the meshblock level. We calculated average annual neighbourhood crime rates 2007–2010 (continuous) based on New Zealand Police data at the census area unit level (meshblocks are nested within census area units or CAUs). To further adjust for socioeconomic position, we included a variable to represent the housing quality of the neighbourhood. Many poor neighbourhoods are characterised by poor quality housing (Braubach and Fairburn, 2010). We compiled dwelling

quality ratings from Quotable Value New Zealand (QV) data. QV is a crown-owned independent entity which holds and maintains data on all New Zealand properties and rates properties as “su- perior” (1), “average” (0) to “poor” (�1). Most ratings are assigned on the basis of exterior inspection from the street, as closer in- spections only occur following work requiring a building consent. While there is no formal validation of these ratings, QV ratings for overall dwelling condition have been found to be broadly similar to standardised assessment-based ratings assigned by the Building Research Association of New Zealand (BRANZ, 2005). A Housing Quality Index (HQI) was then created at CAU level, by averaging the quality value over the CAU, then ranking these averages and dividing them into deciles, where higher values indicate better quality. Each participant was assigned the HQI for the CAU in which they resided.

Last, to validate our findings, we also tested whether visibility of blue space was associated missing teeth in the same sample. We selected tooth loss due to its high correlation with socioeconomic status while being theoretically unrelated to blue space exposure. In studies from a variety of countries, tooth loss has been asso- ciated with measures of individual socioeconomic status, including income and education (Ito et al., 2012; Buchwald et al., 2013; Wennström et al., 2013; Wu et al., 2014). Many such studies were conducted in older adult and elderly populations (Wu et al., 2014; Gaio et al., 2012). A recent longitudinal study among adult (38–50 years old), but not elderly, women in Sweden also found that fewer remaining teeth was significantly higher for women of lower social group, or living alone, in all analyses over a 36 year- period (Wennström et al., 2013). In this Swedish study, various logistic regression models were fitted to compare those with 1 or more teeth to those with none, those with 11 or more teeth to those with 0–10 teeth, those with 21 or more teeth to those with 0–20 teeth, etc. In their large sample, they were able to find sig- nificant results for each of these comparisons (Wennström et al., 2013). In a Japanese study, those with the lowest household in- come had increased risk of tooth loss (OR¼1.25) compared to those with the highest income (Ito et al., 2012).

In our sample (n¼442), most respondents reported having all their teeth (n¼239) and very few reported having no teeth (n¼14). In fact, only 5% of the sample reported having less than half of their teeth (n¼23). For these reasons, we compared those with missing teeth to those without missing teeth, as done in si- milar research (Ito et al., 2012).

2.4. Statistical analyses

Approximately 20% of the Wellington NZHS respondents had missing personal income data (n¼95). Thus, multiple imputations by chained equations were used to impute these missing values using Stata v12 software (College Station, TX, USA). Follow- ing White et al. (2011) recommendations: (1) 20 replicates of the dataset were generated to roughly reflect the percentage of missing data; (2) data were assumed missing at random; and (3) all independent and dependent variables included in the final analytical regression models were used as variables for the mul- tinomial logistic chained equation model.

After imputation, analytical regression models were fitted to examine associations between visibility of green and blue spaces and K10 scores while controlling for individual-level and area-le- vel covariates. In total, separate linear regression models for six visibility measures were fitted, namely: (a) green space; (b) blue space; (c) green space (o300 m); (d) green space (300 m to 3 km); (e) green space (3–6 km); and (f) green space (6–15 km). Initially, a variable representing combined blue and green space visibility was examined, but results were not included here, as green space tended to dominate nearby views and blue space the

Table 1 Descriptive statistics of the survey participants and their neighbourhoods in Wellington City, by sex.

Characteristic Females Males Total

N 260 182 442

Demographic characteristics of participants, %

Sex 58 41 100 Age

15–44 yr 56 54 55 45–64 yr 32 35 33 65þ yr 12 12 12

Ethnicity, Māori (indigenous population)

10 9 10

Personal income $0–$40,000 38 30 34 $41,000–$70,000 26 15 21 $71,000þ 16 36 24

Missing 20 19 20

D. Nutsford et al. / Health & Place 39 (2016) 70–7874

distant views, resulting in a lack of variation between the observer locations. Each of these models included K10 scores as the de- pendent variable and was adjusted for sex, age, income, neigh- bourhood deprivation, neighbourhood housing quality, population density and crime rate. Last, we fitted a logistic model to validate our findings which included tooth loss as the dependent variable of interest and visibility of blue space as the independent variable of interest, adjusted for the same confounders as all other models. In all analyses, adjustments were made for the complex multi- level sampling design of the survey. The clustered sampling design was specified and Taylor series variance estimations were used. All primary analytical models were cluster robust, linear regression with full adjustment for the above covariates and were fitted using Stata v13.

In comparing final analytical model results, it was found that beta coefficients changed o15% between models using non-im- puted versus imputed data. Thus, while descriptive statistics were reported using the non-imputed dataset, all final analytical re- gression results were derived using the imputed data.

VVI measures for participants, mean (sd) Visible green space** 30 (28) 33 (29) 31 (27) Visible green space r300 m** 9 (18) 9 (20) 8 (19) Visible green space 300 m to 3 km** 29 (28) 29 (27) 29 (28) Visible green space 3–6 km** 8 (15) 9 (14) 9 (14) Visible green space between 6 km and 15 km**

5 (17) 9 (24) 7 (20)

Visible blue space** 4 (17) 7 (24) 5 (20)

Neighbourhood characteristics of participants, mean (sd) Neighbourhood deprivation*** 5 (2) 4 (2) 5 (2) Population density (km2) 5535 (3827) 5020

(3571) 5323 (3729)

Crime rate per 100,000 10 (78) 10 (8) 10 (8)

** Possible value ranges 0–100, with higher values indicating higher levels of visibility.

*** Deciles from 1 to 10, 1 is least deprived, 10 is most deprived.

3. Results

3.1. Descriptive statistics

Throughout the study area of Wellington City (including a 15 km buffer to encapsulate visible green and blue spaces beyond the city limit) there was a total of 2076 km2 of green and blue spaces (green space¼795 km2 and blue space ¼1280 km2). Of the visible blue space, the vast majority is oceanic with fresh water contributing o1% of the total. The age composition of participants was similar between males and females, with most participants aged less than 45 years (Table 1). Neighbourhood characteristics and visibility exposure variables did not vary substantially by sex. The visibility measures were also similar across sex, and showed high variation across the study area, as indicated by the standard deviations. The indigenous population (Māori) comprised about 10% of the sample, which is slightly less than the national popu- lation proportion (15%) (Statistics New Zealand, 2013). Table 2 shows that, on average, psychological distress was slightly higher amongst females compared to males (K10¼6.1 and 5.5, respec- tively). The 15–44 year old age group exhibited the highest aver- age psychological distress (K10¼6.4), followed by respondents 65 years and older (K10¼6.0) and those aged 45–64 years (K10¼4.8). Psychological distress was higher on average amongst Māori compared to non-Māori (K10¼8.9 and 5.5, respectively). Psycho- logical distress exhibited a gradient by personal income level (K10¼6.8 in the lowest income group and 4.2 in highest income group), with a high average K10 score in among respondents with missing personal income data (K10¼6.7).

3.2. Regression results

In Table 3, no significant association was detected between green space visibility (independent variable of interest) and K10 scores (dependent variable) (Model 1, β¼ �0.09, p¼0.455). However, higher visibility of blue space was significantly asso- ciated with lower psychological distress (β¼ �0.28, po0.001) (Model 2). This suggests that for a 10% increase in visibility of blue space, a 0.28 lower K10 score could be expected.

In both models, personal income exhibited a significant, in- dependent association with K10 scores, with higher income earners exhibiting lower levels of distress. Neighbourhood housing quality was negatively, significantly associated with K10 scores, whereby better quality housing was associated with lower levels of distress. Population density and crime rate were also significant

in both models (pr0.05), yet effects were weak (βo0.001). When assessing green space visibility by distance bands

(Table 4), none of these measures were independently associated with K10 scores. However, personal income, neighbourhood housing quality, population density and crime rate were all sig- nificantly associated with k10 scores.

In the validation model (Table 5, Model 7), we found that personal income and age were significantly associated with tooth loss, in the expected directions. Importantly, blue space visibility was not significantly associated with tooth loss.

4. Discussion

Increased visibility of blue space was significantly associated with lower psychological distress after controlling for covariates, in our sample of adults in the capital city of New Zealand. Yet, tooth loss was not – as theorised. Our findings suggest that re- sidents living in neighbourhoods with increased views of blue spaces (predominantly oceanic) had lower distress levels. This finding is in accordance with qualitative studies which demon- strated visible waterscapes evoke positive emotions (White et al., 2010; Herzog, 1985; Ulrich, 1981; Völker and Kistemann, 2011). Blue space may be of more importance in regards to psychological distress reduction, a notion that is supported by (Ulrich, 1981; Ashbullby et al., 2013) who found scenes of blue space may have a stronger influence on psychological distress than views of green space. Richardson et al., (2010) offer theoretical support that blue

Table 2 Survey participant characteristics, by average level of psychological distress (K10 scores).

Variable N K10, mean (sd)

Total study population 442 5.8 (4.9)

Sex Female 260 6.1 (5.2) Male 182 5.5 (4.5)

Age 15–44 yr 243 6.4 (5.1) 45–65 yr 146 4.8 (4.3) 65þ yr 53 6.0 (5.3)

Ethnicity Māori 42 8.9 (7.5) Non-Māori 400 5.5 (4.5)

Personal income $0–$40,000 152 6.8 (5.1) $41,000–$70,000 95 5.4 (4.1) $71,000þ 108 4.2 (3.3) Missing 87 6.7 (6.5)

K10 scores are values between 0 and 40 indicative of psychological distress, where higher values represent increased distress. Scores above 6 represent a moderate likelihood of a mental health disorder.

D. Nutsford et al. / Health & Place 39 (2016) 70–78 75

space may be of more significance in New Zealand than green space due to the country's island geography. Other work in New Zealand has highlighted how island spaces offer unique settings for the therapeutic benefits of blue space (Kearns et al., 2014).

Also, it is possible that visibility of blue space is simply a better representation of visibility of ‘natural’ environments than green space, especially in urban settings where sports fields and open parks fall under the category of green space. Since almost all of the blue space in Wellington is oceanic, it is unclear whether these results would be replicated if the blue space was fresh water. If the type of water is irrelevant, similar findings could potentially be evaluated on large fresh water bodies, such as the North American Great Lakes. If the type of water is salient, this may relate not only to the visibility of the ocean, but the other sensory stimuli related to the ocean, including the sound of waves and the smell of air passing over the ocean. While this study was not able to assess these questions, these could be fruitful areas of future investiga- tion. Studies similar to this one could also confirm whether in- creased visibility of blue space could promote mental well-being and reduce stress in other cities.

Levels of visible green space were not associated with psy- chological distress. The quality or composition of the vegetation within green spaces was not assessed in this work. It is possible that native or high quality vegetation, for example, evokes differ- ent responses from a viewer, compared to a sports fields.

Table 3 Results from models showing the association between 10 percentiles of visible green sp

Outcome¼K10 score Model 1: Visibility of green space

Variables β p 95% CI

All visible green space �0.09 0.455 �0.32 All visible blue space Sex �0.51 0.324 �1.51 Age �0.06 0.461 �0.23 Income �0.99 0.003 �1.70 Neighbourhood deprivation 0.12 0.428 �0.18 Neighbourhood housing quality �0.45 0.002 �0.73 Population density o �0.001 0.003 �0.005 Crime rate o0.001 0.047 o0.001

Note: Items in bolded font indicate po0.05.

Wellington City also has a significant amount of greenery in backyard gardens which were not included in measures of green space. It is possible that residents across the city are exposed to varying levels of greenery within their immediate neighbourhood. It is also possible that too much localised green space may have less beneficial connotations, as suggested in other research such as being intrusive, creating a feeling of crowdedness or reduce light and airflow (Kuo et al., 1998). Still, our findings are inconsistent with a number of other qualitative studies which found improved moods to be associated with green space immediately visible and in close proximity (Kearney, 2006; Moore, 1981; Kaplan, 2001).

This study may differ for a host of reasons. As this study cap- tured visibility at the neighbourhood or small area level and ap- plied quantities to individuals, measures of greenery are likely to be less representative for each individual within a neighbourhood. Additionally, the majority of the qualitative studies relied on comparisons in mental health for those viewing green spaces and those viewing starkly contrasted urbanised spaces (i.e. open pas- ture views versus building facades and prison courtyards). Our study takes advantage of a real life, city scenario. Furthermore, our study did not measure individual orientations that may influence use inclinations (i.e., self-identification with nature) or the possi- ble dynamic nature of the relationship between exposure to visible green space and mental health over time (with changing life cir- cumstances), which were limitations identified in a recent review of the green space and health research (Bell et al., 2014). Studies in other cities could help confirm whether the findings of this study are generalizable.

There are a number of limitations to this study. Due to the cross-sectional nature of this study, it is unknown whether people who have lower distress levels choose to live in areas with higher levels of blue space visibility or the reverse. Additionally, this study did not account for the length of residence in the neigh- bourhood and its possible influence on the relationship between visibility of natural environments and psychological distress. It can be posited that the effects of visible natural environments on psychological distress and well-being are not instantaneous and that prolonged visual exposure might lead to the theorised ben- efits. As a result of issues relating to data confidentiality, this study was not able to use the exact home address locations for study participants for measuring visibility of natural environments, ra- ther the population-weighted centroid of small areas were used (meshblocks). Likewise, some continuous demographic variables for participants such as income and age were supplied as ordinal groupings to conform to Ministry of Health confidentiality re- quirements, leading to a loss of precision. In addition, the study did not include private green spaces such as backyard gardens, which have been found to be important for distress reduction (Stigsdotter and Grahn (2004)). Nor did this study separately

ace and visible blue space and psychological distress.

Model 2: Visibility of blue space

β p 95% CI

0.14 �0.28 o0.001 �0.41 �0.15

0.50 �0.40 0.412 �1.37 0.56 0.10 �0.04 0.592 �0.20 0.12 �0.34 �0.89 0.006 �1.53 �0.25 0.43 0.21 0.079 �0.02 0.45 �0.17 �0.33 0.021 �0.61 �0.05 o �0.001 o �0.001 0.002 o �0.001 o �0.001 o0.001 o0.001 0.023 o0.001 o0.001

Table 4 Results from models showing the association between visible green space (i) within 300 m, (ii) at distances 300 m to 3 km, (iii) at distances 3–6 km, and (iv) at distances 6– 15 km, and psychological distress.

Outcome¼K10 score Model 3: Visibility of green space (o300 m) Model 4: Visibility of green space (300 m to 3 km)

Variables β p 95% CI β p 95% CI

Green space o300 m 0.09 0.299 �0.08 0.26 Green space 300 m to 3 km 0.11 0.239 �0.08 0.31 Sex �0.47 0.363 �1.48 0.54 �0.49 0.340 �1.48 0.51 Age �0.05 0.540 �0.22 0.12 �0.07 0.429 �0.23 0.10 Income �1.05 0.002 �1.70 �0.40 �1.01 0.003 �1.66 �0.25 Neighbourhood deprivation 0.18 0.163 �0.07 0.43 0.25 0.099 �0.05 0.55 Neighbourhood housing quality �0.49 0.003 �0.80 �0.17 �0.37 0.010 �0.65 �0.09 Population density o �0.001 0.006 o �0.001 o �0.001 o �0.001 0.004 o �0.001 o �0.001 Crime rate o0.001 0.008 o0.001 o0.001 o0.001 0.006 o0.001 o0.001

Outcome¼K10 Score Model 5: Visibility of green space (3–6 km) Model 6: Visibility of green space (6–15 km)

Variables β p 95% CI β p 95% CI

Green space 3–6 km �0.13 0.096 �0.29 0.02 Green space 6–15 km 0.57 �0.05 0.591 �0.22 0.13 Sex �0.42 0.406 �1.41 0.57 �0.48 0.340 �1.48 0.51 Age �0.06 0.492 �0.22 0.11 �0.06 0.461 �0.23 0.10 Income �0.99 0.003 �1.65 �0.34 �0.99 0.004 �1.66 �0.32 Neighbourhood deprivation 0.12 0.373 �0.14 0.38 0.17 0.157 �0.07 0.41 Neighbourhood housing quality �0.47 0.001 �0.75 �0.18 �0.39 0.019 �0.72 �0.07 Population density o �0.001 0.004 o �0.001 o �0.001 o �0.001 0.006 o �0.001 o �0.001 Crime rate o0.001 0.045 o-0.001 o0.001 o0.001 0.014 o0.001 o0.001

Note: All items in bolded font indicate po0.05.

Table 5 Results from regression model showing the association between visible blue space and missing teeth.

Outcome¼missing teeth Model 7: Visibility of blue space

Variables β p 95% CI

All visible blue space �0.03 0.456 �0.05 0.11 Sex 0.37 0.217 �0.20 0.94 Age 0.32 o0.001 0.22 0.42 Income �0.41 0.037 �0.79 �0.03 Neighbourhood deprivation 0.08 0.244 �0.06 0.22 Neighbourhood housing quality �0.08 0.289 �0.23 0.07 Population density o �0.001 0.184 o �0.001 o0.001 Crime rate o0.001 0.649 o �0.001 o0.001

Note: All items in bolded font indicate po0.05.

D. Nutsford et al. / Health & Place 39 (2016) 70–7876

evaluate types or quality of greenery (e.g., native vegetation). There may, in fact, be differing effects of type or quality of green space on mental health, whereby more ‘natural’ green spaces and those with native, dense or lush vegetation may be more bene- ficial. The study only evaluated the influence of visibility of natural environments in residential settings and was unable to assess visibility in other settings, such as work, school or while travelling (along routes). Future studies could quantify green and blue space visibility in these other settings. If paired with data on time spent in each setting, a more precise measure of visual exposure to green and blue spaces could be usefully generated.

Importantly, because personal income and neighbourhood housing quality were significantly associated with distress in all models, there is likely residual confounding related to income over and above that measured in this study. Since housing prices tend to be higher when accompanied by views of the ocean (Hamilton and Morgan, 2010) (and since we observed significant associations

between personal income and distress), we included an indicator of home value as a covariate which attenuated all effects of greenspace on psychological distress. Still, even if the effect was halved when accounting for residual confounding, our findings would suggest that for a 20% increase in visibility of blue space, a 0.28 lower K10 score could be expected. Furthermore, our finding that exposure to higher levels of blue space did not exhibit an effect on tooth loss (while the effect of personal income remained) provides some evidence that the observed relationship between blue space and distress may not be purely a result of residual confounding. Further limitations include the lack of adjustment for other individual characteristics which likely influence mental health, including household composition (Simon, 2002), physical health status, and immigrant status (Beiser et al., 2002) and other neighbourhood characteristics such as social fragmentation (Ivory et al., 2011) and isolation (Pearson et al., 2013).

While other studies have used qualitative measures, such as photographical response analysis (White et al., 2010) and surveys (Velarde et al., 2007) to assess positive effects of natural environ- ments, this is the first identifiable study to quantitatively examine the relationship between visibility of blue and green spaces and distress. Testing both green and blue spaces overcomes many lim- itations of studies which either ignore blue space or treat it as a component of green space. This study separately assessed the ef- fects of visibility of green space and blue space on psychological distress. Future research improvements include a longitudinal study design which would allow more accurate temporal characterisation of exposure to visible natural environments and subsequent out- comes. Longitudinal data could potentially assist in investigating the effect of changes in exposure on distress levels, via residential mobility (Bell et al., 2014). Also, automated satellite image classifi- cation techniques could be used as a method to incorporate private and backyard green spaces as well as measures of streetscape and the techniques are already in place (De Ridder et al., 2004; Miller et al., 2009; Taylor et al., 2011). Satellite images could also assist in

D. Nutsford et al. / Health & Place 39 (2016) 70–78 77

differentiating between ‘natural’ native vegetation and other out- door green spaces. Inclusion of home address, work address and commuting patterns as well as amount of time spent in each setting could also improve the individual characterisation of exposure to visible blue and green spaces throughout the day. Finally, future studies using similar methodologies could help confirm whether increased visibility of blue space could promote mental well-being and reduce stress in other cities.

5. Conclusion

This study, in the capital city of New Zealand, identified an association between increased views of blue space and decreased psychological distress while adjusting for covariates. In addition to theoretical evidence that improved health can be gained in re- sponse to natural background environments within cities, this research offers encouragement to further our understanding of the relationship between visible blue spaces and mental well-being. Confirmation of these findings in other locations is important and would be of value to the urban health field. If confirmed, there may be opportunities to promote mental health through the designation of state housing and designate a proportion of af- fordable homes in locations with oceanic views or to designate some higher levels of multi-family high rise buildings to affordable ownership or creative urban design to maximise exposure to blue spaces.

Competing interests

None declared.

Acknowledgements

We thank the Ministry for the Environment, Land Information New Zealand and the Department of Conservation for access to their land use datasets. This research was carried out as part of the Geohealth Laboratory work programme at the University of Can- terbury, funded by the New Zealand Ministry of Health.

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  • Residential exposure to visible blue space (but not green space) associated with lower psychological distress in a...
    • Introduction
    • Methods
      • Ethical approval
      • Spatial data and the creation of the visibility measures
      • Health and demographic data
      • Statistical analyses
    • Results
      • Descriptive statistics
      • Regression results
    • Discussion
    • Conclusion
    • Competing interests
    • Acknowledgements
    • References