Landscape Quality
Assessment
Flinders University Research
Colloquium, 13 August, 2014
Dr Andrew Lothian
Scenic Solutions
Scope
•
•
•
•
•
•
•
Why measure landscape quality?
How to measure landscape quality
Acquiring the data
Respondents
Overall findings
Mapping
Lessons & Applications
• The presentation focuses on the study of the
Lake District in England but also draws on
other studies conducted in South Australia
Dr Andrew Lothian, Scenic Solutions
2
Who is Andrew Lothian?
• I worked in environmental policy in SA Government for
many years in Australia. Lectured at Flinders in policy.
• Long interest in how to quantify landscape aesthetics.
• During 1990s, undertook PhD in landscape quality
assessment at the University of Adelaide.
• Since then I have conducted 10 consultancy studies on
landscape quality & visual impact assessment of
developments including wind farms.
www.scenicsolutions.com.au
Barossa & Eden Valleys
River Murray
Dr Andrew Lothian, Scenic Solutions
Flinders Ranges
S.A. Coast
3
Why measure landscape quality?
• Unlike biophysical assets, landscape aesthetics is a qualitative asset, as
perceived by people.
• The European Landscape Convention defines landscape as “an area, as
perceived by people, whose character is the result of the action and
interaction of natural and/or human factors.”
• Landscape quality is the human subjective aesthetic response to the
physical landscape.
• Beautiful landscapes attract millions of tourists throughout the world to
areas such as the Swiss Alps, the Canadian Rockies, the Italian lakes and
Amalfi coast. The Lake District in England attracts 20 million visitors
annually. Australia’s Great Barrier Reef, Kakadu and the Kimberlies,
Uluru and Kangaroo Island attract many overseas visitors. They come to
see the wild and natural landscapes, not the cities. Many World Heritage
areas are outstanding landscapes.
• Exposure to natural landscapes provides significant health and
restorative benefits.
• Views of attractive landscapes adds significant value to properties.
Dr Andrew Lothian, Scenic Solutions
4
How not to measure landscape quality
• There have been many attempts to measure
landscape quality by recording all the physical
Landscape character units
features – land forms, land cover, land use,
defined and mapped
water, geology, etc, in the expectation that by
analysing all of this data, the landscape
quality would emerge.
Scenic quality
indicators mapped
• It never did!
• The reason is that this process is a cognitive
activity involving analysis and thinking.
Weightings applied
Scores of attributes applied
• But landscape quality involves making
Subjective judgements made
judgements about what we like – i.e.
Scenic quality comparisons
preferences. This is an affective process.
made
• Example: We know whether or not we like
chocolate by tasting it, not by analysing its
content, origin, colour etc. These can inform
us but do not define its taste. Similarly we
Scenic quality described and/or
judge music by whether we like it, not by
mapped
analysis of the instruments,
score, etc.
Dr Andrew Lothian, Scenic Solutions
5
Psychophysics – basis for measuring landscape
quality
• Preferences are our likes and dislikes and are based on
affect, not cognition.
• The dictionary define aesthetics as “things perceptible
by the senses as opposed to things thinkable or
immaterial.”
• This clearly differentiates thinking from the senses.
• Researchers fell into the trap of assuming cognition
was the same as affect.
• They are completely different.
• IN the 19th century, Gustav Fechner, a German
physicist, developed psychophysics – the science of
measuring the brain’s interpretation of information
from the senses (sight, sound, smell, taste, touch).
• Over recent decades, psychologists have applied its
methods to measuring human landscape preferences.
Dr Andrew Lothian, Scenic Solutions
Gustav Fechner
1801 - 1887
6
Applying the affective paradigm
• Only by applying the affective paradigm can the attractiveness of a
landscape be determined.
• Attractiveness is determined by measuring preferences.
• As it relies on preferences it is a subjective quality but preferences can
be analysed objectively.
Common elements in research methodologies are:
• Selection of scenes for rating.
• Rating scale – e.g. 1 to 10.
• Rating instrument – i.e. a means for showing scenes with a rating scale.
• Participants who rate the scenes – a sufficient number of raters for
statistical analysis. They should be disinterested in the subject – i.e. have
no stake in the outcome.
Dr Andrew Lothian, Scenic Solutions
7
Community Preferences
Method
1. Photograph region
2. Classify region’s landscape units
The method I use involves photographing
3. Select survey photographs
the area, classifying the area into units of
4. Identify & score landscape
quality components
similar landscape characteristics,
selecting photographs representative of
these characteristics, rating of the
photographs, analysing the results, and
using the understanding gained to map
the landscape quality.
5. Prepare & implement Internet
survey
6. Prepare data set and analyse
results
7. Map region’s landscape quality
Dr Andrew Lothian, Scenic Solutions
8
Use of Photographs
Advantages of photographs:
• Avoids transporting large groups
of people through large region.
• Enables widely separated
locations to be assessed on
comparable basis.
• Can cover seasonal changes.
• Can assess visual impact of
hypothetical developments.
Many studies have shown that
photographs will provide similar
ratings as field assessments
providing certain criteria are met.
A meta-analysis of studies found
a correlation of 0.86 between onsite and photo assessments.
The principle is standardisation so that
respondents judge the landscape, not
the photograph
Criteria for photographs
•
•
•
•
•
•
Standardised horizontal format
50 mm focal length (digital equivalent)
Colour
Non-artistic composition
Sunny cloud-free conditions (ideal)
Avoid strong side lighting of early
morning or evening
• Good lateral & foreground context to
scenes
• Avoid distracting and transitory
features including people
Dr Andrew Lothian, Scenic Solutions
9
Landscape Units
•
•
•
Areas of similar characteristics
e.g. land form, land cover, land
use, water, texture, colour – as
shown in the map.
Simple classification of Lake
District:
– Coastal estuaries, marshes
and beaches
– Plains
– Low fells
– Valleys without lakes
– Valleys with lakes
– High fells
– High mountains
Base the selection of
photographs on sampling the Lake District Landscape Typology
landscape units.
Chris Blandford Associates
Dr Andrew Lothian, Scenic Solutions
10
Dr Andrew Lothian, Scenic Solutions
11
Landscape components
In addition to having photographs rated for
landscape quality, a small group scored the
scenes for a range of components that might
contribute to landscape quality.
1 – 5 scale used to score the visual significance of
the component in each scene.
For the Lake District, components covered:
•
•
•
•
•
•
•
Scores: Stone walls & hedgerows 3.31,
naturalness 2.54, land cover 3.57
Water
Land forms
Land cover – shrubs and trees
Naturalness – absence of human influence
Diversity – total busyness of the scene
Cultural elements – artificial features
Stone walls & hedgerows
By combining these scores with the ratings the
strength of their contribution to landscape
Scores: Land cover 4.22, water 3.10,
quality can be determined.
land form 4.11, diversity 3.9012
Dr Andrew Lothian, Scenic Solutions
Acquiring the Data – Lake District
• Photography March, June and July, 2013
covering winter, spring & summer
• Over 4000 photographs
• 145 photos selected and Internet survey
prepared in August
• 1500 invitations emailed to potential
participants
550
30
500
25
Responses
450
400
Responses
350
300
20
15
10
Routes travelled for photography
250
5
200
0
150
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
100
Days after launch
50
0
1 3 5 7 9 11 13 15 17 1921 23 25 27 29 31
Days after launch
Water
Stonewalls
Land form
Land cover
Naturalness
Diversity
Dr Andrew Lothian, Scenic
Solutions
Cultural
Progress in survey participation
13
150
Survey data
540 responses
314 rated all 145 scenes, 73%
34 rated 0 scenes
4 displayed strategic bias – mostly 10s
Net 430 UK-born respondents & 72 nonUK born
• Analysis covered only UK-born
• Comparison of ratings by non-UK born
included.
Data
100
75
50
25
0
0
100
200
300
Participants
400
500
Number of completed surveys
60
50
Frequency
•
•
•
•
•
Number of scenes rated
125
40
Number
Mean
SD
Respondents
430
6.14
1.23
20
Scenes
145
6.11
1.24
10
30
0
1
Dr Andrew Lothian, Scenic Solutions
2
3
4 5 6 7
Rating range
8
9 10
Histogram of scene means 14
300
The respondents were generally
middle aged, with many more
males participating than
females, and most were very
well educated.
200
150
100
50
0
Male
Female
300
250
250
Frequency
200
Freqency
Characteristics covered
• Age
• Gender
• Education
• Birthplace
• Postcode
• Familiarity
• Residence
250
Frequency
Respondent characteristics
150
100
200
150
100
50
0
50
0
18-24 25-44 45-64 ≥65
Age
Dr Andrew Lothian, Scenic Solutions
15
Comparison of respondents with UK population
50
Compared with the general UK population,
the respondents were:
% of total
•
•
•
Older
More males
Higher levels of education
30
20
10
0
The differences were statistically different.
30
20
10
0
25 - 44 45 - 64
Age
Survey
UK
Survey
60
65+
UK
50
% of total
% of total
40
18 - 24
70
60
50
Survey
UK
40
40
30
20
10
No qual. Level 1-3 Level 4-6Dr Andrew
Level 7 Lothian, Scenic Solutions 0
Education level
Male
Female
16
Age
Gender
Level 7-8
Level 6
Level 4-5
Level 1-3
No qual.
Female
Male
65+
45 - 64
It would matter if preferences varied
widely across age, gender &
education.
25 - 44
18-24
The respondents differed
significantly from the UK population.
Does this matter?
Mean rating
Similarity of ratings
10
9
8
7
6
5
4
3
2
1
Education
But they don’t vary significantly.
6.2
6.1
6
Education
17
Level 7-8
Level 6
Level 4-5
Level 1-3
Female
Male
Gender
No qual.
So regardless of their characteristics,
Age
people rated the scenes similarly.
Dr Andrew Lothian, Scenic Solutions
65+
45 - 64
25 - 44
5.9
18-24
The top graph compares the average
preferences on a 1 – 10 scale,
indicating their similarity. The
bottom graph exaggerates the scale
to show the differences. The range is
only 0.32 or +/- 0.16.
Mean rating
6.3
Respondent origins & familiarity
6.4
6.2
• Many of the respondents came from the northwest, 64% lived in Lancashire and Cumbria.
• 57% lived in the Lake District
• Familiarity increased ratings by as much as 14%
Ratings
6
5.8
5.6
5.4
5.2
5
Category
Extremely familiar
Very familiar
Somewhat familiar
Visited but not familiar
Never visited
Rating
6.26
6.03
5.99
6.10
5.48
% increase
14.21
9.98
9.25
11.25
100.00
• Familiarity might breed contempt, but in
respect of landscapes it has the opposite effect.
This is due to “place attachment”.
Dr Andrew Lothian, Scenic Solutions
18
Overall ratings by landscape type
Ratings
Landscape
Scenes
Mean
Mountains
22
7.05
Valleys with lakes
25
7.02
Valleys with lakes
Rockfaces
10
6.81
Rockfaces
Streams
4
6.47
Streams
Valleys without lakes
9
6.27
Valleys w/o lakes
High fells
22
5.87
High fells
Low fells
11
5.66
Low fells
Coast
3
5.56
Dense trees
5
5.24
Quarries
3
4.95
Pines
8
4.39
Plains
10
4.15
1 2 3 4 5 6 7 8 9 10
Mountains
Coast
Dense trees
Quarries
Pines
Plains
Dr Andrew Lothian, Scenic Solutions
19
4
Mountains
#141 6.51
1
0
1
2
3
4
Naturalness scores
5
Dr Andrew Lothian, Scenic Solutions
y = 0.78x + 4.20, R² = 0.37
2
3
4
5 6 7
Ratings
8
9
Histogram
10
9
8
7
6
5
4
3
2
1
1
#26 7.20
2
Ratings
#44 7.55
• 22 scenes
• Mean rating 7.05
• Range 5.43 to 8.36, a
wide range of 2.93
• Strong skew to higher
ratings – histogram
• Diversity & naturalness
have quite strong
influence on ratings
Ratings
#122 8.36
Frequency
3
10
9
8
7
6
5
4
3
2
1
1
2
3
4
Diversity scores
y = 0.86x + 4.43, R² = 0.48
5
20
10
3
Rockfaces
#17 7.02
Frequency
1
2
3
4
5 6 7
Ratings
8
9 10
10
9
8
7
6
5
4
3
2
1
10
9
8
7
6
5
4
3
2
1
1
2
3
4
Height score
5
y = 0.19x
+ 5.92,Lothian,
R² = 0.09
Dr Andrew
Scenic Solutions
#111 6.02
1
Ratings
#99 6.91
2
0
Ratings
#81 6.38
• 10 scenes
• Mean rating 6.81
• Range 5.73 to 7.73, a
moderate range of 2.00
• Strong skew to higher
ratings – histogram
• Surprisingly, neither
height or steepness
influenced ratings
1
2
3
4
Steepness score
y = -0.49x + 8.85, R² = 0.26
21
5
5
High Fells
#77 4.39
Frequency
2
1
0
1
10
9
8
7
6
5
4
3
2
1
1
2
3
4
Diversity scores
5
Dr Andrew Lothian, Scenic Solutions
#59 4.39
3
Ratings
#30 5.04
• 22 scenes
• Mean rating 5.87
• Range 3.85 to 7.39, a wide
range of 3.54
• Low to high ratings –
histogram
• Diversity & naturalness
have strong influence on
ratings
Ratings
#28 7.14
4
y = 1.47x + 2.51, R² = 0.46
2
3
4
5 6 7
Ratings
8
9
10
9
8
7
6
5
4
3
2
1
1
2
3
4
Naturalness scores
y = 0.61x + 3.94, R² = 0.16
5
22
10
3
Low fells
#100 5.85
Frequency
1
0
1
10
9
8
7
6
5
4
3
2
1
1
#109 6.04
2
Ratings
#55 5.41
Ratings
#5 5.50
• 11 scenes
• Mean rating 5.66
• Range 4.36 to 6.64, a wide
range of 2.28
• Middle rating – histogram
• For those low fells with
stone walls, their presence
actually decreased ratings
• Highest influence of tree
spacing on ratings was for
scattered trees
2
3
4
Scores of stone walls
5
Dr Andrew Lothian, Scenic Solutions
y = -0.26x+
6.79, R² = 0.14
2
3
4
5 6 7
Ratings
8
9
10
10
9
8
7
6
5
4
3
2
1
1
2
3
4
Tree spacing
2 = isolated, 3 = scattered, 23
4 = scat-dense, 5 = dense
5
3
Valleys without lakes
#57 6.19
Frequency
2
0
1
10
9
8
7
6
5
4
3
2
1
2
3
4
5 6 7
Ratings
8
9
10
10
9
8
7
6
5
4
3
2
1
1
2
3
4
Land cover scores
5
Dr Andrew Lothian, Scenic Solutions
#63 6.18
1
Ratings
#120 6.93
Ratings
#11 5.88
• 9 scenes
• Mean rating 6.27
• Range 5.55 to 6.93, a
narrow range of 1.38
• Middle to higher ratings –
histogram
• Land cover & naturalness
have moderate influence
on ratings
y = 0.54x + 4.45, R² = 0.44
1
2
3
4
Naturalness scores
y = 0.80x + 4.00, R² = 0.36
24
5
7
Valleys with lakes
#38 7.34
5
Frequency
#16 8.12
• 25 scenes
• Mean rating 7.02
• Range 5.51 to 8.66, a wide
range of 3.15
• Mainly higher ratings –
histogram
• Even a glimpse of water
increased ratings
• Naturalness has a strong
influence on ratings
6
4
3
2
1
0
1
7.0
Ratings
#89 7.59
Mean rating
7.5
6.5
6.0
2
Dr Andrew Lothian, Scenic Solutions
#136 7.47
4
5 6 7
Ratings
8
9
10
10
9
8
7
6
5
4
3
2
1
1
Area of water visible in scene
3
2
3
4
Naturalness scores
y = 1.20x + 2.98, R² = 0.40
5
25
Influence of water on ratings
The scores of water in the scenes was
compared with the area of water as
measured on each photo. There was
a reasonable correlation (0.52) but
other factors were clearly involved in
determining the visual significance of
water in a scene
The area of water as a % of the non-sky
portion of each scene was measured
and related to the ratings. Surprisingly
this found virtually no relationship
between the percentage of the scene
that was water and the ratings, which
suggests that any amount of water,
small or large, increases ratings.
4
Ratings
Water score
5
3
2
1
0
50
100
150
10
9
8
7
6
5
4
3
2
1
Area of water in photo (cm-1)
Dr Andrew Lothian, Scenic Solutions
0
10
20 30 40 50
Water as % land
60
26
River Murray Study
10
A similar finding was made in the
study of the River Murray.
9
8
Scenes without water rated 4.43 but
the presence of even a small glimpse
of water (score 1) raised this to 5.78.
Rating
7
6
5
4
The difference in ratings between a
glimpse and extensive water was
only 1 unit.
3
2
1
1
2
3
Water score
4
5
Water score
Rating
1
5.78
2
6.03
3
6.28
4
6.53
5
Water score 1.05, Rating 6.08
6.78
Dr Andrew Lothian, Scenic Solutions
27
2
#107 4.74
10
9
8
7
6
5
4
3
2
1
1
#75 3.89
1
0
1
Ratings
#64 4.05
Ratings
#18 3.74
• 10 scenes
• Mean rating 4.15
• Range 3.11 to 5.77, a wide
range of 2.66
• Low to middle ratings –
histogram
• Abundance of land cover has
slight influence
• Plains are low in diversity
but it has a strong influence.
Frequency
Plains
2
3
4
5
Abundance of land cover
Dr Andrew Lothian, Scenic Solutions
y = 0.44x + 2.77, R² = 0.53
2
3
4
5 6 7
Ratings
8
9
10
10
9
8
7
6
5
4
3
2
1
1
2
3
4
Diversity scores
y = 1.45x + 1.40, R² = 0.60 28
5
Components vs components
4
Landscape components
were scored on a 1 – 5
scale.
3
2
5
1
4
2
3
4
Cultural scores
Revised cultural
y = 0.75x + 0.64, R² = 0.37
5
Comparing the scores of
one component with
another brings out some
interesting relationships.
3
2
5
1
1
2
3
4
Diversity scores
y = 0.79x + 1.09, R² = 0.40
5
Naturalness scores
1
Land form scores
Stone wall scores
5
4
3
2
1
1
Dr Andrew Lothian, Scenic Solutions
2
3
4
Land form scores
y = 0.48x + 1.59, R² = 0.33
5
29
Ratings
Comparing ratings with scores
shows their influence
1
2
3
4
Land form scores
5
Score
1
2
3
4
5
Rating
3.61
5.05
6.50
7.95
9.40
Ratings
Components vs ratings
10
9
8
7
6
5
4
3
2
1
10
9
8
7
6
5
4
3
2
1
1
2
3
4
Diversity score
y = 1.45x + 2.16, R²= 0.63
Cultural elements include
farming, sheep and cattle,
stone walls and hedgerows,
fields, narrow winding roads,
and farmhouses.
It indicates that cultural
elements had little influence on
ratings.
Ratings
10
9
8
7
6
5
4
3
2
1
2
3
4
Naturalness score
y = 1.14x + 2.52, R² = 0.43
Ratings
y = 1.29x + 1.93, R² = 0.78
1
5
10
9
8
7
6
5
4
3
2
1
1
5
2
3
4
Cultural scores
y = 0.19x + 5.78, R = 0.01
Dr Andrew Lothian, Scenic Solutions
30
5
Barossa Study
10
The Barossa study made an
interesting discovery through
comparing factor scores with
scenic ratings.
9
Rating scale
8
7
6
5
4
3
2
1
1
2
3
4
5
Vines factor score
It is the presence of trees around
the vineyards that enhance scenic
quality.
10
Rating of scenes with vines
It might be thought that the vines
enhance scenic quality but this is
not so, they actually reduce it.
9
8
7
6
5
4
3
2
1
1
2
3
Tree score
4
5
Dr Andrew Lothian, Scenic Solutions
31
Comparison scenes – with & without features
Powerlines
Sheep
Colour
4.73
5.79
4.05
2.92
4.84
3.74
With
poles
3.13
3.02
4.02
2.92
Without
poles
4.31
4.06
5.88
4.73
Diff.
1.18
1.04
1.86
1.81
%
37.70
34.44
46.27
61.99
3.27
4.75
1.47
45.00
With
colour
Without
colour
Diff.
6.65
5.67
6.394
5.79
%
With
sheep
Without
sheep
Diff.
%
0.98
14.74
6.47
5.88
0.59
9.12
6.385
0.009
0.14
5.5
4.87
0.63
11.45
4.84
0.95
16.41
4.05
3.74
0.31
7.65
5.34
4.83
0.51 329.55
Dr Andrew
Lothian, Scenic
6.28
5.63
0.64Solutions
10.25
Stone walls & hedgerows
Seasonal change
5.50
8.31
7.34
4.83
8.00
6.06
With
walls
5.50
6.97
5.40
4.31
Without
walls
4.83
6.72
4.89
4.05
Diff.
0.67
0.25
0.51
0.26
%
12.18
3.59
9.44
6.03
5.55
5.12
0.43
7.75
Snow Summer
7.30
6.29
8.31
8.00
6.85
6.83
Diff.
1.01
0.31
0.02
7.49
0.45 5.95
7.04
%
13.84
3.73
0.90
Dr Andrew Lothian, Scenic Solutions
Water
With Without
water water
6.51
6.24
7.34
6.06
7.48
6.93
Diff.
0.27
1.28
0.55
7.11
0.70 9.85
6.41
%
4.15
17.44
7.30
33
Trees
Trees were inserted into 4 scenes
to assess the effect of revegetating
the fells on the landscape.
5.02
4.80
3 were rated higher without the
trees & one was higher with the
trees.
Respondents may have rejected
trees on familiar fells. Or they
rejected the dense trees as
scattered trees received a positive
rating.
7.14
7.17
Or they prefer the fells to be bare
rather than vegetated.
Without
%
With trees trees
Difference difference
4.8
5.02
0.22
4.58
6.76
7.14
0.38
5.62
7.17
7.14
-0.03
-0.42
5.04
5.12
0.08
1.59
5.12
5.04
Dr Andrew Lothian, Scenic Solutions
5.94
6.11
0.16
2.84
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Mapping
Mapping proceeded area by area, 40 in
all, to build up the complete map. The
generic ratings that were derived from
the survey were applied to each area.
Landscape
Rating
Plains
4
Pines
4
Low fells
5
Rivers
6
Valleys without lakes
6
Valleys with lakes
6/7
High rounded fells
5
High steep (≥30%) fells
6
High fells with rockfaces
6
Mountains (≥700 m – 850 m)
7
Mountains ≥ 850 m
8
The map shows the main rating to be 5
(yellow) with ribbons & areas of 6 (light
red - rivers, valleys without lakes, steep
fells). Many lakes and mountains from
700 – 850 m were 7 (darker red) and
inside those were small areas of 8
Dr Andrew Lothian, Scenic Solutions
(darkest red).
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Landscape quality ratings
Rating 7
4.6%
Rating 8
0.3%
Rating 6
10.4%
Unrated
(towns)
0.5%
Rating 4
21.4%
Rating 5
63.2%
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Why do we like what we like?
What generates the appeal of landscapes? –
why do we like what we like?
Hierarchy of influences – innate
DEMOGRAPHIC
Individual
individual
Most landscape theory is based on evolutionary
perspective – what we like is survival enhancing.
We like what aids our survival as a species.
Indi
FAMILIARITY
Regional
This might explain our preference for water but
doesn’t explain liking for the sea which we
cannot drink. Or survival in mountains .
CULTURE
Society
It may however explain preferences for
scattered trees – like African savannah rather than dense trees which can hide
predators & be difficult to climb.
INNATE
All people
Dearden’s Pyramid of Influences
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Restorative benefits of viewing nature
Studies from experiencing natural
environments:
• Reduced anger and violence
among residents of Chicago
apartments and reduced crime
in their neighbourhood
• Less fatigue and more rapid
recovery from fatigue
• Reduced blood pressure
• Lower heart rates and reduced
stress for students swotting for
exams
• Even viewing posters of natural
scenes is beneficial.
2012 Cumbria Visitor Survey found that
the top reasons for visiting the area was
because of the physical scenery and
landscape of the area (69%) followed by
the “atmospheric character of the area
being peaceful, relaxing, beautiful and
so on (54%).”
Intuitive understanding of the restorative benefits of viewing nature helps
explain the popularity of the Lake District which attracts 20 million visitors a
year. The landscape survey found that the naturalness correlated highly with
ratings, as did land form and diversity, both part of naturalness.
Dr Andrew Lothian, Scenic Solutions
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What is the economic value of Lake District
landscape?
A century ago, the Swiss landscape was
judged to be worth $200m/annum
2009 – 2012 visitation averaged 22.05
million visitor days .
Average expenditure of £980 million/year =
£44.44/visitor/day.
The area of the Lake District National Park
is 2219.68 km2
Annual expenditure = £441,505/km2 or
£4,415/ hectare.
Farmgate income £59m = £31,536/sq km or
£315/ha = 7% of its value for visitors.
Total: £473,041/sq km or £4,730/ hectare.
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Applications
Possible applications include:
1. Incorporating landscape quality provisions in policies and
planning to ensure its recognition, protection and
enhancement;
2. Defining scenic quality objectives for the management,
protection and enhancement of landscape quality in the
region;
3. Assisting in the definition and substantiation of nominations
of areas for World Heritage and National Park status;
4. Promoting the tourism and recreational opportunities of the
region;
5. Assisting in the selection of routes for transmission lines and
roads and for minimizing developmental impacts, e.g. wind
farms.
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Conclusions
• The project provides insights and
understanding of how the
community view the Lake District’s
scenic assets.
• Measuring and mapping the
landscape quality of the Lake
District is a first for the UK which
abandoned landscape quality
assessment decades ago.
• However the project demonstrates
that a robust and credible method
of measuring community
preferences is available.
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Dr Andrew Lothian
Director, Scenic Solutions
PO Box 3158, Unley, Adelaide
South Australia, 5061, AUSTRALIA
Mobile: 0439 872 226
Phone/fax: (618) 8272 2213
Email: lothian.andrew@gmail.com
Internet: www.scenicsolutions.com.au
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