The New Zealand Environment: Influences of
Bluespace on Health
Rosie Cooper
GEOG420 Research Project
ABSTRACT
In the field of human geography, the relationship between water and human health and
wellbeing has been relatively understudied. Interest in the health effects of the outdoors, such
as greenspace, has grown rapidly in recent years and this is the first New Zealand study to
focus on the salutogenic influences of exposure to bluespace. This dissertation aimed to
investigate whether a relationship was present between proximity to bluespace and health
throughout New Zealand. All-cause mortality rates were used as a broad, reliable data-set for
overall health, and were compared against the straight-line proximity to any area of general
bluespace and three bluespace types; coastline, lakes, and wide rivers. Results indicated that
a statistically significant positive relationship exists between mortality and coastline
proximity, suggesting, somewhat surprisingly, that mortality rates are higher closer to the
coast. A statistically significant negative relationship exists between mortality and wide river
proximity, indicating that as the distance from wide rivers is decreased the mortality rate is
reduced. Similarly, a statistically significant negative relationship existed for general
bluespace. No statistically significant relationship was found to exist between mortality and
lake proximity, signifying that proximity to lakes does not have an effect on mortality. Some
of these findings were contrary to international research, suggesting that bluespace and
health relationships may differ according to national and environmental context. The varied
findings highlight the strong need for future research in this field, which could ultimately
help to shape public health initiatives and improve health.
2
Table of Contents
Introduction ............................................................................................................................. 3
Bluespace and Health............................................................................................................. 5
Bluespace ........................................................................................................................................... 5
Perception and Preference ............................................................................................................. 5
Emotional Benefits........................................................................................................................... 7
Recreational Benefits ...................................................................................................................... 8
Direct Health Benefits..................................................................................................................... 9
Greenspace ........................................................................................................................................ 9
Research Question ............................................................................................................... 11
Null Hypotheses..............................................................................................................................11
Methods .................................................................................................................................. 12
Results .................................................................................................................................... 16
Lake Proximity ...............................................................................................................................17
Wide River Proximity ...................................................................................................................18
Coastal Proximity ..........................................................................................................................19
Bluespace Proximity ......................................................................................................................20
Discussion .............................................................................................................................. 23
Conclusion ............................................................................................................................. 30
Acknowledgements .............................................................................................................. 31
References .............................................................................................................................. 32
Appendices ........................................................................................................................... 38
Appendix A .....................................................................................................................................38
Appendix B .....................................................................................................................................39
Appendix C .....................................................................................................................................40
3
Introduction
There is ongoing and increasing interest in health inequalities (Ministry of Health,
2002; United Nations, 2009; World Health Organisation, 1998). Some of geography’s
earliest engagement with wellbeing was thirty years ago, with the work of Antoine
Bailly (1981), who expressed many of the problems that are still significant today
(Fleuret & Atkinson, 2007). The concept of wellbeing was somewhat understudied in
geography, however recent interest has grown, particularly through the fields of public
health, epidemiology and psychology, on the influential salutogenic properties of
social and physical environments (Mitchell & Popham, 2008). The increased
prominence of wellbeing in policy agendas and public health debates has begun to reemphasise the health benefits of nature, and academic disciplines such as landscape
architecture, environmental psychology and health geography have had an extended
interest in the remedial effects and conceptualisations of nature (Park et al., 2010). This
research aims to identify the relationship between bluespace and health, within a New
Zealand context.
The influence of landscape on health has been studied fairly extensively (Fumkin,
2001; Maller et al., 2006), therapeutic landscapes provide social, physical and spiritual
environments (Volker & Kistemann, 2011). Water is one of the most important
aesthetic landscape elements (Kaplan and Kaplan, 1989) and an attractive landscape
offers health and wellbeing for humans (Abraham et al., 2010). The relationship
between water and health has been the subject of some interest in recent decades; for
example it was addressed at an international healthcare conference in 1978 in Alma
Ata (WHO, 1978). Subsequently, the Ottawa Charter for Health Promotion of 1986
identified that health encompassed social and personal resources, as well as physical
capacities. The Charter was developed under the influence of Antonosvsky’s concept
of salutogenesis, which “considers individual and corporate resources for health,
wellbeing and quality of life as central requirements to prevent health risks and
potential illnesses” (Volker & Kistemann, 2011: 450). The salutogenetic concept
proposed by Antonosvsky was supported in the European Charter on Environment and
4
Health (1989) and the United Nations Conference for Environment and Development
(1992).
Many broader studies have shown that exposure to natural environments, such as
greenspace, has influential effects on health and health-related behaviours (Mitchell &
Popham, 2008). However, the relationship between bluespace and health is an area of
science that has sparked some recent interest (Volker & Kistemann, 2011; Wheeler et
al., 2012; White et al., 2010). Limited previous research has been carried out in this
field, resulting in a restricted and undeveloped depth of knowledge at present. As a
result of this, greenspace literature focusing on wellbeing has been used as a
framework to supplement and develop this research project. Greenspace is an area of
human geography that has been extensively studied and a wide variety of literature is
available in this field. The psychological and physical benefits of greenspace have been
proven, leading to curiosity about the potential similar effects that bluespace may
possess.
A renewed interest on place as a core theoretical and policy relevant determinant of
health has occurred, and area level intervention has become a key component of
strategies to reduce health inequalities (Pearce et al., 2006). As a result of recent
interest and limited information in this field, this dissertation aims to investigate the
effect of bluespace proximity on mortality. Previous bluespace and health literature
will be reviewed, with a specialised focus on perception and preference of bluespace,
emotional, recreational and direct health benefits of bluespace, and the parallel
relationships between greenspace and bluespace literature.
For the purpose of this research the classification of health is broad, it includes
mortality through to a broadened definition of wellbeing. Wellbeing is a multi
dimensional, dynamic term that varies over time, shaped by the discoursive
construction of policy makers and society (Volker & Kistemann, 2011). It is a
“complex measurable state of consciousness, which contains components like the
habitual, actual, individual and social wellbeing” (Volker & Kistemann, 2011: 450).
This research will refer to health as “a state of complete physical, mental and social
wellbeing and not merely the absence of disease or infirmity” in following with the
World Health Organisation (1948).
5
Bluespace and Health
Bluespace
For the purpose of this dissertation, bluespace is defined as significant areas of natural
water, including lakes, wide-river sections and coastline. Water is considered to be one
of the most important aesthetic elements of a landscape (Kaplan & Kaplan, 1989) and
the ocean is a fundamental element of the Earth, covering more than seventy percent of
its surface (Brand, 2007). Globally there is a phenomenal relationship between human
beings and the ocean, as twenty-three of the worlds largest cities are located on the
coast and over a third of the world’s population live along a slim fringe of coastal land
(Wheeler et al., 2012). In New Zealand the effects of bluespace is of particular
importance as nearly two thirds of the population live within five kilometres of the
ocean (Richardson et al., 2010).
Perception and Preference
Perceptions towards bluespace are expressed using one’s senses to produce feelings
and attitudes; usually water represents a natural resource vital for consumption by
human beings (Volker & Kistemann, 2011). The perceptions of bluespace vary from
this general perception, instead focusing on positivity, fascination and attractiveness
(Burmil et al., 1999). Important aspects of the sensual perception of bluespace are the
colour, clarity, sound, motion and context of water (Volker & Kistemann, 2011).
Aesthetic perceptions of water colour and clarity in New Zealand were researched by
Smith et al. (1995), who concluded that blue waters are preferred to yellow1 waters, but
yellow waters may be acceptable if they were perceived as ‘natural’. It was also
concluded that water appearance and bathing activity are closely linked, and preference
towards a particular site of water “was strongly related to their perception of visual
clarity, but less strongly related to actual clarity” (Smith et al., 1995: 43). People
admire the motion of water and resultant sounds, and the special nature of these
sounds. Sound produced by water ranges from calm low velocity flows, to vigorous,
roaring sounds, and the variety of these flows is of great importance (Burmil et al.,
1999; White et al., 2010). From an evolutionary perspective, humans attracted to
1
Water with a yellow/brown colouration, this can be a result of natural effects such as staining from
algae and humic staining from native forests in river catchments.
6
aquatic environments were more likely to survive because of the fresh water
availability, food, migration corridors and omega 3 fatty acids which aid brain
development and evolution (White et al., 2010). As a result of this humans have
evolved towards the reverence of water and preferences for aquatic environments.
Investigation into the effects of bluespace is an important area of research as these
natural areas of bluespace are associated with higher preferences and positive
subjective reactions (White et al., 2010). The context of bluespace is also important in
determining human perception. Visual rating of the water is increased if the area is
connected with naturalness (Smith et al., 1995), nature and scenery enhance the
experience gained and creates a more positive perception. Cleanliness and refreshment
are often associated with water, and these adjectives conjure emotions that relate to
regained energy, youth and health (Herzog, 1985; Burmil et al., 1999). The combined
experience of bluespace and surrounding natural scenery is considered to be “peaceful,
traditional, worth-preserving and preferable” (Yamashita, 2002: 9).
Perception factors are closely correlated with preference for living by bluespace
(Asakawa et al., 2004; Volker & Kistemann, 2011), and the presence of water features
in residential areas were perceived as the most influential environmental attribute in
eight towns in the Netherlands (Luttik, 2000). The value of a residential property is
influenced by both its physical and locational characteristics, “the analysis of views
should be placed in the wider context of the analysis of the impact of externalities on
property values“ (Bourassa, 2004: 1427). Historically, views were desirable for
strategic reasons; a dominating location allowed for the owner to be aware of possible
intruders. Today, views are sought for completely different reasons, with the majority
being aesthetic. Appleton (1975) explores the ‘prospect-refuge’ theory, suggesting that
humans are biologically programmed to have a preference for vantage points where it
is possible to see a large amount, but without necessarily being seen. Views are often
associated with an ease of access to nature, but the two effects need to be clearly
separated. Views are difficult to measure, however proximity of access to nature is
easier to identify (Bourassa, 2004). When considering a view, the type of view, scope
of the view, and distance to the water in the case of a water view should be taken into
account. Since the 1980s urban planning has produced a visible trend for the
preference of waterfront revitalisation, allowing access to bluespace, often at an
expensive price (Volker & Kistemann, 2011). Bourassa conducted a study of house
7
prices and views, for the Auckland region in 1996, concluding that although water
views have a strong positive impact on house values for houses near the coast, such
views are not the only type of aesthetic externality that is priced in the residential
property market. “Particularly attractive surrounding improvements add an average
premium of 27% to values, and structures of superior quality in the neighborhood
contribute an average premium of 37%” (Bourassa, 2004: 22). Increased house prices
bordering coastlines, rivers and lakes highlight the preference for living near areas of
bluespace. In the city of Emmen, in the Netherlands, house prices are ten percent
higher when water views are present (Luttik, 2000).
Emotional Benefits
Environment-behaviour research “examines the relationship between social and
behavioural patterns and the physical environment” (Ataov, 1998: 240) and the field of
environmental aesthetics explores the human reactions to the visual qualities of an
environment. The aesthetics of an environment “evoke feelings such as pleasure,
relaxation, excitement, and fear” (Ataov, 1998: 239). Appearances of neighbourhoods
and the surrounding built environments influence our emotional reactions and affective
responses. Each person creates unique evaluative responses to an environment because
factors such as personality, affective state, intentions and socio-cultural experiences
influence individual responses (Ataov, 1998). People produce emotions about an
environment based on their past experiences with the area, enabling them to identify
specific visual features or attributes in an environment that create pleasant feelings and
distinguish these from features that produce negative feelings (Ataov, 1998). Water can
provide a strong sense of place, emotional feelings and attachment that can in turn
influence wellbeing. Water symbolises purity and is expressed through human mental
and spiritual life (Volker & Kistemann, 2011). The mental association and immersion
with waterscapes can be explained using several aspects such as tranquillity, attention,
interest, fascination or compatibility (Herzog & Bosley, 1992; Ulrich, 1981; White et
al., 2010). In particular, studies have highlighted the strong emotional influence of
water, providing greater positive influences on emotional states in comparison to other
environments (Felsten, 2009). Individuals in a relaxed or happy emotional state prefer
water environments, compared to those that are stressed (Regan & Horn, 2005),
signifying that water is a favourite place for recreational activities and to expend spare
8
time, in addition to the benefits gained from reducing stress and providing restorative
effects (Volker & Kistemann, 2011).
Recreational Benefits
Nature helps to stimulate us to be more active, and water can provide the basis for
recreation. Coastal areas, beaches and inland water bodies are particularly important in
inspiring recreational activity (Depledge, 2009). Regular contact with these
environments can diminish health inequalities by offering three major health benefits;
reduced stress levels, increased physical activity and a greater sense of community
(Depledge, 2009). Stress is a process by which “an individual responds
psychologically, physiologically, and often with behaviours, to a situation that
challenges or threatens wellbeing” (Ulrich et al., 1999:202). Human recuperation has
been suggested to be faster and more complete when individuals are exposed to natural
environments, rather than urban environments, and stress levels are also reduced when
exposed to natural environments compared to urban environments (Ulrich et al., 1999).
Recreational activity around bluespace is more common when waterfront activities are
aided by access routes to encourage movement, and other facilities are present, such as
benches to provide small respites (Volker & Kistemann, 2011). Recreational
experiences can be separated into four catergories; “kinetic recreational experiences,
situation-based recreational experiences, harvest experiences, and substitution or
aesthetic experiences” (Volker & Kistemann, 2011: 455). Kinetic recreational
experiences include activities with a high degree of motion on the water, such as
sailing, canoeing and jet–skiing, or activities on the water edge including jogging and
cycling (Yamashita, 2002). Situation based recreational experiences “refer to one
location at the water, which is visited several times for experiences such as swimming,
playing in the water, social interactions, or walking” (Volker & Kistemann, 2011:
455). Harvest recreational experiences describe activities such as fishing.
Contemplative or aesthetic recreational experiences include the perception of the views
and sounds of the bluespace, and passive exploration of the water (Smith et al., 1995;
White et al. 2010). Kinetic and situation-based recreational experiences are well known
for their positive health benefits, including the prevention of cardiovascular illnesses,
obesity and cancer (Bell et al., 2008), as well as anxiety and depression. Recreational
benefits of harvest and contemplative experiences are often immeasurable, as they are
experienced rather than scientifically measurable.
9
Direct Health Benefits
Bluespace has been recognised as providing direct health benefits (Volker &
Kistemann, 2011), the majority of this has been through research of therapeutic
landscapes. The concept of therapeutic landscapes provides a framework to assess the
health benefits of blue space. It identifies the salutogenic health effects of a landscape,
covering all elements of the landscape. However it has been noted that a therapeutic
landscape is not necessarily beneficial to all individuals, and should be considered
instead as a ‘potentially therapeutic landscape’ (Conradson, 2005; Volker &
Kistemann, 2011). The restorative scenery of Lourdes, with its flowing spring, is
described as a remedial core, and water is perceived to be responsible for cures and is
dedicated to healing (Gesler, 1996). The holy wells in Ireland are described as “sites of
indigenous health” and “a piece of micro-landscape of healing and wellbeing” (Foley,
2011:477), children are bathed in the wells as a representation for health promotion in
early life (Volker & Kistemann, 2011). Other research has stated the relationship
between water and health, with views of water being potentially beneficial for health
(Burmil, 1999) and water bodies being correlated with a high quality of life
(Ogunseitan, 2005).
Greenspace
The influence of greenspace on health has been studied extensively and there may be
parallel relationships between greenspace and health, and the influence of bluespace on
health. Previous literature has suggested that socioeconomic status may be a
determinant of greenspace proximity. Greenspace literature has shown a relationship
between proximity to greenspace and health (Groenewegen et al., 2006; Richardson et
al., 2010). Groenewegen et al. (2006) examined the effects of greenspace in the
Netherlands and concluded that individuals of a lower socioeconomic status are less
able to move to areas that have higher proportions of greenspace. This was similar to
the studies of Richardson et al. (2010: 2) who were concerned that “locational access to
health promoting community resources, such as greenspace, is lower in
socioeconomically deprived areas”, which could be a contributing factor to lower
levels of physical activity in deprived communities. Richardson et al. (2010) found that
in New Zealand total greenspace availability fell with increasing socioeconomic
deprivation.
10
Many studies found that there was a positive association between the size of the
greenspace and health (Guite et al., 2006; Maas et al., 2006; Mitchell et al., 2007).
Mitchell et al. (2007: 681) concluded that a “higher proportion of greenspace in an area
is associated with better health” although they identified that the association depends
on the degree of urbanity and level of social deprivation in an area. They also noted
that the quality and quantity of greenspace was significant in determining health
benefits. The strength of this relationship was tested in the Netherlands by Maas et al.
(2006), their findings show that the percentage of greenspace in an area is correlated
with a higher level of general health for individuals in the region. Numerous other
studies have found a link between the size of greenspace and health, such as Guite et
al. (2006: 1118) who concluded that there are “modest cross sectional associations
between elements of the physical environment and psychological health”. After
classifying the England population into groups according to income deprivation and
exposure to greenspace, Mitchell and Popham (2008) found that those living in the
greenest regions had an overall higher level of health. A contrasting article by
Richardson et al. (2010: 1) found that, contrary to expectations, there was “no evidence
that greenspace influenced cardiovascular disease mortality in New Zealand,
suggesting that greenspace and health relationships may vary according to national,
societal or environmental context”. They also concluded that greenspace variation
might have lesser relevance for the health of individuals in New Zealand as greenspace
is abundant and there is less social and spatial variation in its availability, compared to
other contexts. The information extracted from previous literature can form the basis of
research on bluespace and health.
11
Research Question
1. Within a New Zealand context, does the proximity of bluespace influence all cause
mortality?
Null Hypotheses
1) A statistically significant relationship between proximity to lakes and all cause
mortality does not exist.
2) A statistically significant relationship between proximity to wide rivers and all
cause mortality does not exist.
3) A statistically significant relationship between proximity to the coastline and all
cause mortality does not exist.
4) A statistically significant relationship between proximity to any type of
bluespace and all cause mortality does not exist.
12
Methods
A qualitative analysis of existing studies relevant to bluespace and health was carried
out. In following with the methods used by Volker and Kistemann (2011), a critical
realist approach was used to integrate qualitative and quantitative studies. The
approach “states that a mechanism cannot be disproved by the identification of a
missing recognition” (Volker & Kistemann, 2011: 450). To identify relevant articles,
electronic databases (including PubMed, Web of Science and Science Direct), relevant
journals, reference lists of earlier research papers, and relevant organisations were
searched. Keyword and phrase searches related to health, wellbeing, bluespace and
landscapes. Different words for bluespace were used in order to find maximum results;
water, rivers, lakes, aquatic environments, coast and bluespace. After gathering
selected studies, the articles and papers were filtered to exclude papers covering
irrelevant topics, such as those covering the pathogenetic impact of water on individual
human health, and those dealing with water policy and nature conservation. The
relevant articles were read, and important information was extracted and used to build
this research.
Analysis with a Geographic Information System (GIS) was carried out to find the
proximity of bluespace in relation to place of residence. The adoption of a GIS
framework enabled the integration of data that had been collected in different ways,
and the use of a GIS allowed bluespace proximity for different areas to be calculated
(Pearce et al. 2006). To prepare a dataset for analysis, age-sex-standardised mortality
rates2 were calculated for each census area unit3 (CAU) in New Zealand, resulting in
2
Age-sex standardised mortality rates are a method of adjusting the crude rate to eliminate the effect of
differences in population age and gender structures when comparing crude rates for different geographic
regions and different population sub-groups. The adjustments were undertaken for the comparison of
populations against a standard population.
3
Census area units are aggregations of meshblocks, they are non-administrative areas that define, or
aggregate to define, regional councils, territorial authorities, urban areas and statistical areas. Census
area units normally contain a population of about 3,000-5,000 people and usually coincide with suburbs
(Statistics New Zealand, 2006a).
13
36 categories4 per CAU. New Zealand 2005-2007 mortality data and 2006 Census
population counts (Statistics New Zealand, 2006a) were used to achieve this. Areas of
New Zealand coastline, wide rivers and lakes were retrieved online5 (koordinates.com,
2010), and imported into ArcMap10. Coastline was defined as the ‘mean high water
mark around North Island, South Island and Stewart Island’, wide rivers were
classified as ‘a natural, flowing body of water emptying into an ocean, lake or other
body of water and usually fed along it's course by converging tributaries’, and lakes
were defined as ‘any standing body of fresh inland water” (koordinates.com, 2010).
The ‘wide river’ data set was selected to represent significant areas of bluespace that
are highly visible and more likely to be usable, compared to the ‘all river and stream
centrelines’ data set which included many minute rivers and streams that are barely
visible and insignificant for this research.
CAUs (koordinates.com, 2010) were the identifying scale of mortalities, and in
accordance, CAU centroids were used to provide an average data point location for
each CAU. Using the GIS Software ArcMap10, CAU centroid points were laid over
bluespace shapefiles and the nearest distance to each type of bluespace (coastline, wide
rivers and lakes) was calculated using the ‘near’ proximity tool. The closest distance
from each centroid to any type of bluespace was also calculated. The data was exported
to an Excel workbook and imported into Statistics Software SPSS, allowing for the
relationships between mortality and proximity to bluespace to be investigated.
Graphs were produced in Microsoft Excel to identify the functions of the age and
deprivation data, as a linear regression model is not always appropriate to represent the
spread of data. Scatter graphs of age and sex data versus mortality rates were formed,
allowing the best model (linear, functional, exponential or quadratic) for each variable
to be found. For both models the quadratic model was the strongest model to represent
the data. The quadratic models for age versus log-mortality and deprivation versus logmortality both had a statistically significant p-value (p<0.001), indicating that they
were the correct models to describe and explain the data relationships. This was
4
Each category represented a five-year age group and gender. For example, category 1 was males aged
0-4 years, category 2 was females aged 0-4 years, category 3 was males aged 5-9 years, category 4 was
females aged 5-9 years, and so on.
5
The koordinates.com shapefiles were derived from the Land Information New Zealand 1:50,000 New
Zealand Topographic database.
14
predictable, as mortality rates are higher in newborns and significantly higher for the
elderly. Mortality rates are also higher amongst deprived communities due to a lack of
expenditure on healthcare, less emphasis on a healthy lifestyle and reduced living
conditions, and mortality rates are also higher in the most affluent areas as a result of
conditions such as obesity leading to cardiovascular disease. To account for the
quadratic curve, the age and deprivation data was squared to become quadratic, and
produced two new columns in the dataset.
Diagnostic tests were performed to test if the quadratic data models chosen were
correct. To test for model misspecification Cook’s distance test, the residual test and
the predicted value of response test were performed. The Cook’s distance tests did not
indicate any unusual values as the values ranged from between 0 and 0.002 (Appendix
A). The values are all less than one signifying that none of the data points have an
undue influence on the fit of the quadratic model, and therefore the quadratic model is
appropriate. To test for residual autocorrelation and normality, the residuals were
plotted using Q-Q Plots in SPSS. They formed a near straight line (Appendix B),
indicating that my choice of quadratic model was appropriate to represent the data.
Residual values were then plotted against mean predicted values, using curve
estimation in SPSS. This produced a clustered cloud of data points, symmetrical
around the zero line (Appendix C), indicating that the quadratic model chosen was
appropriate and that the variance of the residuals is constant.
The mortality rate was then logged, using the logit function in SPSS in order to
transform the variable so it can fit to a straight line; the new variable was inserted as a
new column in the dataset.
Once the quadratic models were fitted, a generalized linear regression model was used
to find out if the data was significant, and socioeconomic status was controlled for with
New Zealand Deprivation Index data6. The response factor was mortality rate, and
covariate factors included age, deprivation, gender and distance to bluespace. The
6
The New Zealand Deprivation index is based on ten categories and ranges on a scale from 1 – 10,
where 1 represents the areas with the least deprived scores and 10 the areas with the most deprived
scores. The scores include nine variables; people on the benefit, people living below an income
threshold, people not living in their own homes, people in a single parent family, people that are
unemployed, people without qualifications, people living in households below a bedroom occupancy
threshold, people with no access to a telephone and people with no access to a car. It divides New
Zealand into tenths of the distribution of the first principal component scores and is based on areas rather
than individual people (Ministry of Health, 2007).
15
regression was carried out four times, using proximity to rivers, lakes, coastline, and
any bluespace, as the four differing factors. The confidence levels used were 95%, so if
the p-value was less than 0.05, the null hypothesis is rejected and the findings are
statistically significant.
16
Results
The results showed that 73% of New Zealanders live within 10 km of the coastline,
highlighting the close proximity and abundance of bluespace in New Zealand, with the
mean distance to bluespace 2.4 km (Table 1). The minimum distances of all bluespace
types was zero, or extremely close to zero. This is because some CAUs had lakes, wide
rivers or coastline running through the centroid, resulting in a distance of zero.
The maximum distance from a census area centroid to the coastline is Dunstan, Central
Otago, with a distance of 116.4 km. The greatest distance from a census area centroid
to a lake is White Island, with a distance of 50.0 km. However, the population of White
Island is zero, so the greatest distance of a mainland census area unit centroid to a lake
is Te Kaha, in the Opotiki District, with a distance of 18.4 km. The greatest distance
from a census area centroid to a wide river is again White Island, with a distance of
49.8 km. However, the greatest mainland distance to a wide river is the Kaikoura
Township, with a distance of 28.9 km7.
The standard deviations of all bluespace types, with the exception of coastline, were
low (Table 1), indicating that the data points were all relatively close to the mean. The
high standard deviation of coastline proximity indicates that the data points are spread
out over a large range of values. This signifies that the coastal proximity data had
much greater variation than other bluespace measures as a result of the coastline
bordering New Zealand. Comparatively, lakes and wide rivers have a more even
distribution across New Zealand. This is not surprising, as coastline is only on the
coast but rivers and lakes are located everywhere.
7
Even though Kaikoura has several small rivers located in the region, these were not classified in my
study as ‘wide rivers’ and were not included in the study.
17
Table 1: Descriptive statistics of bluespace proximity data by bluespace type.
The minimum mortality rate was 0.00, and was a result of CAUs with zero population
(inlets, bays, islands, harbours, marinas, ports and estuaries). The highest mortality rate
was in Strowan, Christchurch, for males aged 85-89. There was a population count of
20 in this category, and the number of deaths for this age-sex group from 2005 – 2007
was 59, resulting in an average deaths per year of 19.67 (Statistics New Zealand,
2006a). The mortality rate was therefore extremely high for this group, 98%, when
calculated with these data sets.
Lake Proximity
The results showed that nearly one third (32%) of New Zealanders live within one
kilometre of a lake. This is a very close proximity for many New Zealanders, and the
data set representing lakes from koordinates.com can be partly responsible for this.
Any standing body of fresh inland water was mapped, resulting in a large amount of
minor lakes throughout the country being represented, and in turn influencing the lake
proximity data. The results of the model fitting (Table 2) indicate that the proximity to
lakes was associated with lower mortality (log-odds ratio of -0.003), but the effect was
not statistically significant (p=0.389). Therefore the null hypothesis for the relationship
between lake proximity and wellbeing cannot be rejected, and it can be concluded that
proximity to lakes does not have a significant association with mortality. This suggests
that no relationship does exist between residential proximity to lakes and mortality.
18
Table 2: Generalised Linear Regression Model of lake proximity.
Parameter Estimates
Parameter
B
Std.
95% Wald Confidence
Error
Interval
Lower
Upper
Hypothesis Test
Wald Chi-
df
Sig.
Square
(Intercept)
-4.81
0.0241
-4.858
-4.763
39759.585
1
0.000
age
-0.088
0.0038
-0.095
-0.08
525.616
1
0.000
nzdep
0.131
0.0073
0.117
0.145
323.914
1
0.000
sex
0.76
0.0093
0.742
0.778
6664.175
1
0.000
age2
0.01
0.0002
0.01
0.011
2718.734
1
0.000
nzdep2
-0.011
0.0006
-0.012
-0.01
300.352
1
0.000
lake
-0.003
0.0032
-0.009
0.004
0.742
1
0.389
(Scale)
1.257a
0.0074
1.242
1.271
Wide River Proximity
The results showed that approximately one sixth (16%) of New Zealanders live less
than one kilometre away from a wide river. The generalised linear model for river
proximity produced a statistically significant result (Table 3). Proximity to rivers was
associated with lower mortality (log-odds ratio of -0.027), and the effect was
statistically significant (p<0.001). The null hypothesis can be rejected, and it can be
concluded that an increased proximity to rivers has a significant negative association
with mortality. By reducing the distance lived from a wide river, there is an effect of a
lowered mortality rate; this indicates that a relationship does exist between residential
proximity to wide rivers and mortality. Therefore, for every additional kilometre
towards a wide river, the odds of mortality are decreased by 2.7%.
19
Table 3: Generalised Linear Regression Model of wide river proximity.
Parameter Estimates
Parameter
B
Std.
95% Wald
Error
Confidence Interval
Lower
Upper
Hypothesis Test
Wald Chi-
df
Sig.
Square
(Intercept)
-4.672
0.0244
-4.72
-4.624
36732.715
1
0.000
age
-0.088
0.0038
-0.095
-0.08
531.051
1
0.000
nzdep
0.125
0.0073
0.111
0.14
298.161
1
0.000
sex
0.76
0.0093
0.742
0.778
6714.009
1
0.000
age2
0.01
0.0002
0.01
0.011
2742.647
1
0.000
nzdep2
-0.011
0.0006
-0.012
-0.01
300.018
1
0.000
wide river
-0.027
0.0013
-0.029
-0.024
440.697
1
0.000
(Scale)
1.247a
0.0073
1.233
1.262
Coastal Proximity
Just over a quarter (28%) of New Zealanders live within one kilometre of the coast.
The generalised linear model for coastal proximity produced a statistically significant
effect (Table 4). Proximity to the coast was positively associated with higher mortality
(log-odds ratio of 0.002), and the effect was statistically significant (p<0.001). The null
hypothesis can be rejected, and we can therefore conclude that an increased proximity
to the coast has a significant positive association with mortality. By reducing the
distance lived from the coastline, there is an effect of an increased mortality rate; this
signifies that there is an evident relationship between coastal proximity and mortality.
Therefore, for every additional kilometre towards the coast, the odds of mortality are
increased by 0.2%.
20
Table 4: Generalised Linear Regression Model of coastal proximity.
Parameter Estimates
Parameter
B
Std.
95% Wald
Error
Confidence Interval
Lower
Upper
Hypothesis Test
Wald Chi-
df
Sig.
Square
(Intercept)
-4.836
0.0236
-4.883
-4.79
41845.109
1
0.000
age
-0.088
0.0038
-0.096
-0.081
529.797
1
0.000
nzdep
0.129
0.0073
0.115
0.144
314.167
1
0.000
sex
0.76
0.0093
0.742
0.778
6673.763
1
0.000
age2
0.01
0.0002
0.01
0.011
2733.169
1
0.000
nzdep2
-0.011
0.0006
-0.012
-0.01
288.99
1
0.000
coast
0.002
0.0002
0.001
0.002
62.731
1
0.000
(Scale)
1.255a
0.0074
1.241
1.27
Bluespace Proximity
The generalised linear regression model produced a statistically significant output
(Table 5) for overall bluespace proximity. Proximity to overall bluespace was
associated with lower mortality (log-odds ratio of -0.015), and the effect was
statistically significant (p<0.001). The null hypothesis can be rejected and it can be
concluded that increased proximity to overall bluespace has a statistically significant
negative association with mortality. By reducing the distance lived from general
bluespace, there is an effect of a lowered mortality rate; this indicates that a
relationship does exist between general bluespace proximity and mortality. Therefore,
for every additional kilometre towards general bluespace, the odds of mortality are
decreased by 1.5%.
21
Table 5: Generalised Linear Regression Model of overall bluespace proximity.
Parameter Estimates
Parameter
B
Std.
95% Wald
Error
Confidence Interval
Lower
Upper
Hypothesis Test
Wald Chi-
df
Sig.
Square
(Intercept)
-4.776
0.0238
-4.823
-4.729
40167.556
1
0.000
ageid
-0.088
0.0038
-0.095
-0.08
523.893
1
0.000
nzdep
0.131
0.0073
0.117
0.146
325.375
1
0.000
22
sex2
0.76
0.0093
0.742
0.778
6673.595
1
0.000
age2
0.01
0.0002
0.01
0.011
2714.079
1
0.000
Nzdep
-0.011
0.0006
-0.013
-0.01
308.137
1
0.000
Bluespace
-0.015
0.0016
-0.019
-0.012
92.272
1
0.000
(Scale)
1.255a
0.0074
1.24
1.269
Overall, the results showed that the influence of general bluespace, wide rivers and
coastline proximity has a statistically significant association with health. The coastal
association was positive, indicating that people who live closer to the coast, on
average, have an increased effect of mortality than those who live further away. The
general bluespace and river proximity was negative, indicating that people who live
closer to general bluespace and wide rivers, on average, have a decreased effect of
mortality than those who live further away. The effects were all relatively small, but a
significant relationship was evident. The proximity of lakes had no significant
association with health, and no effect was present.
23
Discussion
Bluespace plays an important role in the environment, influencing landscape
perception, preference and design. It is a major challenge to understand the diverse
individual perspectives of experiencing bluespace. This study adds to a small, but
growing research area, and largely agrees with previous research by confirming that
characteristics of the natural environment are related to wellbeing, regardless of age,
sex and deprivation.
The findings showed that living nearer to the coastline is associated with a very small
increase in mortality, despite controlling for socioeconomic status. This was surprising,
but after reflecting on other New Zealand research this seemed feasible. New Zealand
differs from other countries, in that the waterfront properties are not always the most
affluent areas. This unusual pattern between socioeconomic measures and location is
evident through local knowledge and supported by examining the location based
census information (Figure 1). In Christchurch the annual median household income is
NZ$$48,200, with a maximum median household income of $98,500 in Holmwood,
and a minimum of NZ$31,200 in Linwood8. Households in the eastern and
southeastern areas of Christchurch, and west along major arterial road corridors,
generally have lower incomes. In contrast households around the Port Hills and to the
northwest region of Christchurch have higher incomes. The waterfront areas9 have a
wide distribution of median incomes (Figure 1); for example, New Brighton has a low
median household income of NZ$34,200 and Waimairi Beach has a high median
household income of NZ$68,200.
8
The lowest median household income was NZ$30,600 in Riccarton West, although this area is
dominated by a high percentage of low income students. For the purpose of this study student dominated
areas were considered anomalies, and as a result of this Linwood was regarded as the lowest median
household income area in Christchurch.
9
Statistical areas where majority of the census area unit lies within 1km of the coastline.
24
Figure 1: Choropleth Map Showing Median Household Income per CAU,
Christchurch, New Zealand 2006.
25
The varied socioeconomic pattern in Christchurch is mirrored further north in
Wellington City. Wellington City has an average median household income of
NZ$74,200, notably higher than Christchurch. Waterfront areas have a wide range of
median household incomes, with a minimum median household income of $31,700 in
Haitaitai, to a maximum of $100,000 in Eastbourne.
These income patterns are also evident throughout Auckland. Overseas, in countries
such as Australia, waterfront areas are considered to be highly desirable locations. In
the area of Bellingham, Western Australia, a full view of the ocean could increase
house prices by almost 60% (Benson et al., 1998). In Sydney, Australia, the median
weekly household income is A$1,447 (Australian Bureau of Statistics, 2011). The
waterfront areas10 all have relatively high median weekly household incomes; Balmain
has a median household weekly income of A$2,544, Lindfield has a median household
weekly income of A$2,558, and Rose Bay has a median weekly income of A$2,524.
These median weekly incomes are all much higher than the Sydney average, indicating
that waterfront areas are affluent and sought-after locations. Only 24.6% of Sydney’s
coastal suburbs have a median household income less than the Greater Sydney median
(Australian Bureau of Statistics, 2011), the majority of these lower income coastal
suburbs are located towards Botany Bay11. In New Zealand this percent is significantly
greater, with 42.9% of Christchurch coastal CAUs having a lower median household
income than the Christchurch City median (Statistics New Zealand, 2006b). This is
also observed in Wellington City, with 40.0% of coastal CAUs below the city’s
median, emphasizing the diverse socioeconomic coastal land patterns in New Zealand.
There may be a possible number of reasons why the results from this research differ
from international studies of bluespace. A lack of variation in exposure to bluespace
for New Zealanders may be present, as bluespace is extremely abundant with 73% of
New Zealanders living within ten kilometres of the ocean. One kilometre has been
considered as a relatively easy walking or cycling distance (Richardson et al., 2010),
and in New Zealand approximately one third of people live within this distance from a
lake, one quarter from the coast and one sixth from a wide river section. The average
10
Statistical areas adjacent to the coastline.
11
An industrial area hosting an airport and container port.
26
New Zealander lives just 2.4 km from any bluespace type. These proximity figures
contrast with other countries, for example, only 6.4% of the United States population
live within one kilometre of the coast (Inter-American Institute, 2010).
Another factor influencing the results could be the coastline to land-area ratio of New
Zealand. The coastline to land-area ratio for New Zealand is 56.5m/km2, this ratio is
comparatively larger than most other non-island countries. The United States has a
coastline to land area ratio of 2.2m/km2, China has a ratio of 1.5m/km2, Australia has a
ratio of 3.4m/km2 and all land-locked countries have a ratio of zero. The high ratio in
New Zealand could be a contributing factor towards the low influence of coastline
proximity as the coastline is highly accessible and therefore may not be a significant
health determinant.
Viewing New Zealand as an exception to international findings has also been
suggested in New Zealand greenspace literature. Richardson et al. (2010) investigated
the association between green space and cause-specific mortality in urban New
Zealand. Their results found that contrary to expectations, there was no evidence that
greenspace influenced cardiovascular mortality in New Zealand. As a result of this
they concluded that greenspace and health relationships might vary according to
national, societal or environmental contexts, and a lack of variation in exposure to
greenspace was present in New Zealand. They suggested the idea that greenspace
variation may have lesser relevance for New Zealand health issues, as greenspace is
more abundant in New Zealand compared to other countries, and social and spatial
variation is less varied compared to other contexts. Another national study by Witten at
al. (2008) also drew upon the conclusion that greenspace and health relationships in
New Zealand may differ from those found in other countries, as they found no
relationship between greenspace and Body Mass Index.
The descriptive findings for overall bluespace and wide rivers lends support to
previous claims (Volkner & Kistemann, 2011; Wheeler et al., 2012) that bluespace in
the physical environment may be an important influence upon individual wellbeing
levels and has many positive influences on human health. The results indicated that
living closer to general bluespace and wide rivers is associated with a decreased
mortality rate. This study and others (Maas et al., 2006; Mitchell & Popham, 2008;
Wheeler et al., 2012) indicate that access to natural blue and green environments may
27
play a part in reducing health inequalities. Evidence towards this concept is still
relatively limited although the issue is starting to become acknowledged in health
inequalities policy. Suggestions relating to greenspace and health have occurred at the
2010 Marmot Review for the UK Department of Health (Marmot, 2010) and
greenspace and bluespace importance were discussed as part of the Christchurch City
Council’s ‘Open Space Strategy’ as an area that can be used for recreation or public
health benefit (Christchurch City Council, 2011).
The results also indicated that living near lakes was not associated with an increased or
decreased mortality. It was surprising that this result was not significant for health, as
the proximity for wide rivers was statistically significant. The lake data set
(koordinates.com) mapped all New Zealand lakes to a high level of detail, including
many insignificant lakes with a small surface area. This resulted in most CAUs being
extremely close to a lake, and 99.8% of New Zealanders living within 10 km of a lake.
A possible consequence of the high detail mapping of small lakes could be that lake
proximity had no effect on mortality. By only including significant lakes in future
studies, for example over 1km2 surface area, lake proximity may have an affect on
mortality rates.
Many previous experimental studies in this discipline have used abstract images of
reality, portraying contrasting environments to investigate the preference for and
benefits of natural environments (Dramstad et al., 2006; Kaltenborn & Bjerke, 2002;
Van den Berg et al., 2003; White et al., 2010). These studies had varied results, for
example Van den Berg et al. (2003) found that the presence of water did not have a
reliable influence on environmental preference or restoration, although in contrast with
these findings, White et al. (2010) found that scenes containing water were associated
with higher preferences, greater positive effect and higher perceived restorativeness
ratings. An underlying factor for these mixed results could be due to the high variance
of diversity and individual perceptions and preferences (Volkner & Kistemann, 2011).
Experimental studies such as these only concentrate on photographs or videos that
contain a special focus on the environment, so the individual perspective may
disappear (Volkner & Kistemann, 2011). By experiencing an area of actual bluespace
and the surrounding environment, an individual receives a three-dimensional view, and
contributing sensory components of sound, smell and taste, whereas images only
provide a two-dimensional view of the bluespace and lack additional sensory
28
constituents. Understanding a landscape “cannot be known or understood simply from
publications, from maps, diagrams, photographs and descriptions, because these are
only representations, there can be no substitute for the human experience of place – of
being there” (Tilley, 2004: 218). The variety of research methods in this field is
extremely diverse, leading to mixed results. For the effects of bluespace proximity to
be understood at a greater depth further investigation is necessary. Despite some
significant results showing that bluespace has positive influences on human health,
research in this field is far from advanced.
This study is subject to limitations. The cross sectional nature of the analysis means
that migration is not accounted for. However, the findings from this research can still
be used to build a hypothesis about the bluespace-health relationship. As identified by
Wheeler et al. (2012), a ‘healthy migrant effect’ may have occurred, whereby the
“healthiest (and wealthiest) proportion of the population are more mobile and
potentially more able (physically and financially) to move towards the desirable
environments including the coast” (Wheeler et al. 2012:3). An alternative hypothesis
may also be drawn from this study; the coastal and river locations may be the preferred
living environment for people who place a higher value on physical activity. It is also
possible that the found associations between bluespace proximity and health may be as
a result of residual confounding by one or more unaccounted population/area
characteristic that is associated with bluespace and health.
Components of the methodology may have influenced the relationship found between
living closer to the coastline and the associated increased mortality rate. Coastline
proximity was calculated by measuring the straight-line distance, although visibility
and accessibility of the ocean was not taken into account. Many people live within one
kilometre of the coastline, but this does not necessarily mean that they have a view of
the ocean or can easily access the ocean. Views may be veiled as a result of elements
of the natural and built environment, such as the topography of the land, urban
developments, vegetation, and residential housing. Accessibility may be limited or
complicated due to features of the land and network systems. For example, residents on
a cliff may have a close proximity to the ocean but ocean accessibility is not direct, or
alternatively residents may live within a close proximity to the ocean but accessibility
is complicated as a result of the network systems available in the area. Chen & Jim
(2010) found that the visibility of a bay view was perceived as an extremely desirable
29
attribute, and valued higher than accessibility and availability for use. A residential
view of Shenzhen Bay, China, increased house prices by 11.2%, and house prices
dropped by 0.7% with each increasing kilometre of distance away from the bay.
Bluespace visibility could be analysed by using the viewshed analysis ArcMap tool,
which determines the surface locations visible to an observer. The network-analyst
ArcMap tool can dynamically model realistic network conditions whilst providing
options for routing, travel directions, closest facility, service area, and locationallocation (ESRI, 2012). Incorporating the use of these tools into the methodology
would provide valuable information, and may help to explain further the positive
relationship between coastline proximity and mortality.
If this study were continued, it would be valuable to use the New Zealand 2006/2007
Health Survey data12 to supplement the all cause mortality data. The Health Survey
information would have provided data at all levels of impact, not just the extreme
outcome of death. This would have allowed for preventative measures to be studied at
a greater depth and would have stratified all impact levels. This would be useful in
influencing a wide array of mental and physical public health policies, and providing
information before the individual had passed away. It would have also been
constructive to separate the all-cause mortality data into different categories, such as
cardiovascular disease, suicide and cancerous diseases, to see if there were any
significant relationships. This field of study would benefit from increased research into
emotional and experiential response to bluespace, as this would assist the researchers
and practitioners analysing health effects.
12
The Health Survey included questions such as whether the individual had received treatment for
depression, bipolar, anxiety disorder, alcohol disorder and drug disorder. It also asks the individual to
rate their self-reported general health, vitality, social functioning, role-emotional, and mental health. The
survey also includes a New Zealand Index of Socioeconomic Deprivation for Individuals category (New
Zealand 2006/07 Health Survey, 2012).
30
Conclusion
In spite of the fact that water covers over two-thirds of the earth’s surface, solid
conclusions regarding the relationship between bluespace and human health and
wellbeing are still largely unknown. Initial research suggests that bluespace has a
salutogenic effect on human health and wellbeing, although, due to a variety of
research methodologies and diverse study locations, no conclusive conclusions can be
drawn. The findings showed that being within close proximity to areas of general
bluespace had a negative influence on mortality, for every additional kilometre towards
general bluespace, the odds of mortality are decreased by 1.5%. A similar relationship
was also found for wide rivers and mortality, as every additional kilometre towards a
wide river, decreased the odds of mortality by 2.7%. The variation in the nature of the
findings for the health effects of living within close proximity to the coastline and
lakes further suggest the research evidence is inconclusive and that that further
investigation is necessary. Being within close proximity to the coastline was found to
have a positive influence on mortality, for every additional kilometre towards the coast
the odds of mortality are increased by 0.2%. Lake proximity had no effect on mortality
rates, however by using a data set that only included lakes over 1km2 , a different
outcome may have been found. The results highlight the idea that bluespace in New
Zealand may not be as important a determinant of health as has been demonstrated in
other countries. As this field of research is built upon, it could provide valuable
information for public health and urban planning policy initiatives. New initiatives
would need to balance potential benefits of the close proximity to bluespace against
threats from overdeveloping waterfront locations, extreme events and climate change
impacts. By adhering to rational precautions, health inequalities may be reduced and
there is potential to achieve major health improvements.
31
Acknowledgements
I am sincerely and extremely grateful to my supervisor, Simon Kingham, for the
endless encouragement and direction throughout the writing of my dissertation. I
would also like to thank Christopher Bowie and Elena Moltchanova, for their guidance
with the statistical analysis of my data. Furthermore, I would like to show my gratitude
towards my parents, as this dissertation would also not have been possible without
their ongoing support and enthusiasm.
32
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Appendices
Appendix A
Cook’s distance output, showing maximum value of 0.002 indicating there are no
points with undue influence on the fit of the quadratic model.
N
Cook's Distance
64080
Valid N (listwise)
64080
Minimum
.000
Maximum
.002
Mean
.00002
Std. Deviation
.000046
39
Appendix B
Q-Q Plot of residuals showing a slight deviation from normality.
40
Appendix C
Predicted value versus residuals, showing a constant cloud formation of data points
along the zero line, indicating that the variance of the residuals is constant.