Post on 31-Mar-2015
Landscape Quality Assessment
Dr Andrew LothianScenic Solutions
Flinders University Research Colloquium, 13 August, 2014
Dr Andrew Lothian, Scenic Solutions 2
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
Who is Andrew Lothian?
3
• 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
Flinders Ranges
S.A. CoastRiver MurrayBarossa & Eden Valleys
Dr Andrew Lothian, Scenic Solutions 4
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 5
How not to measure landscape quality• There have been many attempts to measure
landscape quality by recording all the physical features – land forms, land cover, land use, water, geology, etc, in the expectation that by analysing all of this data, the landscape quality would emerge.
• It never did!• The reason is that this process is a cognitive
activity involving analysis and thinking. • But landscape quality involves making
judgements about what we like – i.e. preferences. This is an affective process.
• 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 judge music by whether we like it, not by analysis of the instruments, score, etc.
Landscape character units defined and mapped
Scenic quality indicators mapped
Weightings applied Scores of attributes applied
Subjective judgements madeScenic quality comparisons
made
Scenic quality described and/or mapped
Dr Andrew Lothian, Scenic Solutions 6
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.
Gustav Fechner 1801 - 1887
Dr Andrew Lothian, Scenic Solutions 7
• 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.
Applying the affective paradigm
Dr Andrew Lothian, Scenic Solutions 8
1. Photograph region
2. Classify region’s landscape units
3. Select survey photographs
4. Identify & score landscape quality components
5. Prepare & implement Internet survey
6. Prepare data set and analyse results
7. Map region’s landscape quality
The method I use involves photographing
the area, classifying the area into units of
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.
Community Preferences Method
Dr Andrew Lothian, Scenic Solutions 9
Use of PhotographsAdvantages 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 on-site and photo assessments.
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
The principle is standardisation so that respondents judge the landscape, not
the photograph
10
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 landscape units. Lake District Landscape Typology
Chris Blandford Associates Dr Andrew Lothian, Scenic Solutions
Dr Andrew Lothian, Scenic Solutions 11
Dr Andrew Lothian, Scenic Solutions 12
Landscape componentsIn 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:
• 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 quality can be determined.
Scores: Stone walls & hedgerows 3.31, naturalness 2.54, land cover 3.57
Scores: Land cover 4.22, water 3.10, land form 4.11, diversity 3.90
Dr Andrew Lothian, Scenic Solutions
• 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
Acquiring the Data – Lake District
Routes travelled for photography
Progress in survey participation
1 4 7 10 13 16 19 22 25 280
5
10
15
20
25
30
Water Stonewalls Land form
Land cover Naturalness Diversity
Cultural
Days after launch
Resp
onse
s
1 4 7 10 13 16 19 22 25 28 310
50
100
150
200
250
300
350
400
450
500
550
Days after launch
Resp
onse
s
13
Dr Andrew Lothian, Scenic Solutions 14
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 non-
UK born• Analysis covered only UK-born• Comparison of ratings by non-UK born
included.
Number of completed surveys
Histogram of scene means
Data Number Mean SDRespondents 430 6.14 1.23Scenes 145 6.11 1.24
1 2 3 4 5 6 7 8 9 100
10
20
30
40
50
60
Rating range
Freq
uenc
y
0 50 100 150 200 250 300 350 400 450 5000
25
50
75
100
125
150
Participants
Num
ber o
f sce
nes r
ated
Dr Andrew Lothian, Scenic Solutions 15
Characteristics covered• Age• Gender• Education• Birthplace• Postcode• Familiarity• Residence
Respondent characteristics
18-24 25-44 45-64 ≥650
50
100
150
200
250
Age
Freq
ency
Male Female0
50
100
150
200
250
300
Freq
uenc
y
The respondents were generally middle aged, with many more males participating than females, and most were very well educated.
No qual.
GCSE, A, D
ip, Cert
Bachelor d
egrees
Masters
& PhDs0
50
100
150
200
250
300
Freq
uenc
y
Dr Andrew Lothian, Scenic Solutions 16
Comparison of respondents with UK population
Male Female0
10
20
30
40
50
60
70Survey UK
% o
f tot
al
18 - 24 25 - 44 45 - 64 65+0
10
20
30
40
50
SurveyUK
Age
% o
f tot
al
No qual. Level 1-3
Level 4-6
Level 70
10
20
30
40
50
60
Survey UK
Education level
% o
f tot
alCompared with the general UK population, the respondents were:
• Older • More males • Higher levels of education
The differences were statistically different.
Dr Andrew Lothian, Scenic Solutions 17
Similarity of ratingsThe respondents differed significantly from the UK population. Does this matter?
It would matter if preferences varied widely across age, gender & education.
But they don’t vary significantly.
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.
So regardless of their characteristics, people rated the scenes similarly.
18-2
4
25 -
44
45 -
64 65+
Mal
e
Fem
ale
No
qual
.
Leve
l 1-3
Leve
l 4-5
Leve
l 6
Leve
l 7-8
Age Gender Education
123456789
10
Mea
n ra
ting
18-2
4
25 -
44
45 -
64 65+
Mal
e
Fem
ale
No
qual
.
Leve
l 1-3
Leve
l 4-5
Leve
l 6
Leve
l 7-8
Age Gender Education
5.9
6
6.1
6.2
6.3
Mea
n ra
ting
Dr Andrew Lothian, Scenic Solutions
Respondent origins & familiarity• Many of the respondents came from the north-
west, 64% lived in Lancashire and Cumbria.• 57% lived in the Lake District• Familiarity increased ratings by as much as 14%
• Familiarity might breed contempt, but in respect of landscapes it has the opposite effect. This is due to “place attachment”.
Category Rating % increaseExtremely familiar 6.26 14.21Very familiar 6.03 9.98Somewhat familiar 5.99 9.25Visited but not familiar 6.10 11.25Never visited 5.48 100.00
Never v
isited
Visited but n
ot familia
r
Somewhat f
amiliar
Very familia
r
Extremely
familiar
5
5.2
5.4
5.6
5.8
6
6.2
6.4
Rati
ngs
18
Dr Andrew Lothian, Scenic Solutions 19
Overall ratings by landscape type
Landscape Scenes Mean
Mountains 22 7.05
Valleys with lakes 25 7.02
Rockfaces 10 6.81
Streams 4 6.47
Valleys without lakes 9 6.27
High fells 22 5.87
Low fells 11 5.66
Coast 3 5.56
Dense trees 5 5.24
Quarries 3 4.95
Pines 8 4.39
Plains 10 4.15
Mountains
Valleys with lakes
Rockfaces
Streams
Valleys w/o lakes
High fells
Low fells
Coast
Dense trees
Quarries
Pines
Plains
1 2 3 4 5 6 7 8 9 10Ratings
Dr Andrew Lothian, Scenic Solutions 20
Mountains
#122 8.36
#44 7.55
#141 6.51
#26 7.20
• 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
1 2 3 4 5123456789
10
Naturalness scores
Ratin
gs
1 2 3 4 5123456789
10
Diversity scores
Ratin
gs
y = 0.78x + 4.20, R² = 0.37 y = 0.86x + 4.43, R² = 0.48
1 2 3 4 5 6 7 8 9 100
1
2
3
4
Ratings
Freq
uenc
y
Histogram
Dr Andrew Lothian, Scenic Solutions 21
Rockfaces
#81 6.38
#99 6.91
#17 7.02
#111 6.02
1 2 3 4 5 6 7 8 9 100
1
2
3
Ratings
Fre
qu
en
cy
• 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 5123456789
10
Height score
Ratin
gs
1 2 3 4 5123456789
10
Steepness scoreRa
tings
y = -0.49x + 8.85, R² = 0.26y = 0.19x + 5.92, R² = 0.09
Dr Andrew Lothian, Scenic Solutions
High Fells
22
#28 7.14
#30 5.04
#77 4.39
#59 4.39
• 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
1 2 3 4 5 6 7 8 9 100
1
2
3
4
5
Ratings
Freq
uenc
y
1 2 3 4 5123456789
10
Diversity scores
Ratin
gs
1 2 3 4 5123456789
10
Naturalness scoresRa
tings
y = 1.47x + 2.51, R² = 0.46 y = 0.61x + 3.94, R² = 0.16
Dr Andrew Lothian, Scenic Solutions 23
Low fells
#5 5.50
#55 5.41
#100 5.85
#109 6.04
• 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
1 2 3 4 5 6 7 8 9 100
1
2
3
Ratings
Freq
uenc
y
1 2 3 4 5123456789
10
Scores of stone walls
Ratin
gs
y = -0.26x+ 6.79, R² = 0.142 = isolated, 3 = scattered, 4 = scat-dense, 5 = dense
1 2 3 4 5123456789
10
Tree spacingRa
tings
Dr Andrew Lothian, Scenic Solutions 24
#11 5.88
#120 6.93
#57 6.19
#63 6.18
• 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
1 2 3 4 5 6 7 8 9 100
1
2
3
Ratings
Freq
uenc
y
Valleys without lakes
1 2 3 4 5123456789
10
Land cover scores
Ratin
gs
y = 0.54x + 4.45, R² = 0.44
1 2 3 4 5123456789
10
Naturalness scoresRa
tings
y = 0.80x + 4.00, R² = 0.36
Dr Andrew Lothian, Scenic Solutions 25
Valleys with lakes
#16 8.12
#38 7.34
#89 7.59
#136 7.47
• 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
1 2 3 4 5 6 7 8 9 100
1
2
3
4
5
6
7
Ratings
Freq
uenc
y
Glimpse
Small
Moderate La
rge6.0
6.5
7.0
7.5
Area of water visible in scene
Mea
n ra
ting
1 2 3 4 5123456789
10
Naturalness scores
Ratin
gs
y = 1.20x + 2.98, R² = 0.40
Influence of water on ratings
Dr Andrew Lothian, Scenic Solutions 26
0 20 40 60 80 100 120 1401
2
3
4
5
Area of water in photo (cm-1)
Wat
er s
core
0 10 20 30 40 50 60123456789
10
Water as % land
Ratin
gs
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.
Dr Andrew Lothian, Scenic Solutions 27
River Murray Study
1
2
3
4
5
6
7
8
9
10
1 2 3 4 5Water score
Rating
A similar finding was made in the study of the River Murray.
Scenes without water rated 4.43 but the presence of even a small glimpse of water (score 1) raised this to 5.78.
The difference in ratings between a glimpse and extensive water was only 1 unit.
Water score Rating
1 5.78
2 6.03
3 6.28
4 6.53
5 6.78 Water score 1.05, Rating 6.08
Dr Andrew Lothian, Scenic Solutions
Plains
28
#18 3.74
#64 4.05
#107 4.74
#75 3.89
• 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.
1 2 3 4 5 6 7 8 9 100
1
2
Ratings
Freq
uenc
y
1 2 3 4 5123456789
10
Abundance of land cover
Ratin
gs
y = 0.44x + 2.77, R² = 0.53
1 2 3 4 5123456789
10
Diversity scoresRa
tings
y = 1.45x + 1.40, R² = 0.60
Dr Andrew Lothian, Scenic Solutions 29
Components vs components
1 2 3 4 51
2
3
4
5
Cultural scores
Ston
e w
all s
core
s
Revised cultural
y = 0.75x + 0.64, R² = 0.37
1 2 3 4 51
2
3
4
5
Diversity scores
Land
form
sco
res
y = 0.79x + 1.09, R² = 0.40
1 2 3 4 51
2
3
4
5
Land form scoresN
atur
alne
ss s
core
s
y = 0.48x + 1.59, R² = 0.33
Landscape components were scored on a 1 – 5 scale.
Comparing the scores of one component with another brings out some interesting relationships.
Dr Andrew Lothian, Scenic Solutions 30
1 2 3 4 5123456789
10
Land form scores
Ratin
gs
1 2 3 4 5123456789
10
Diversity score
Ratin
gs
Components vs ratings
y = 1.29x + 1.93, R² = 0.78
1 2 3 4 5123456789
10
Naturalness score
Ratin
gs
y = 1.14x + 2.52, R² = 0.43
Score Rating1 3.612 5.053 6.504 7.955 9.40
y = 1.45x + 2.16, R²= 0.63
1 2 3 4 5123456789
10
Cultural scores
Ratin
gsy = 0.19x + 5.78, R = 0.01
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.
Comparing ratings with scores shows their influence
Dr Andrew Lothian, Scenic Solutions 31
Barossa Study
The Barossa study made an interesting discovery through comparing factor scores with scenic ratings.
It might be thought that the vines enhance scenic quality but this is not so, they actually reduce it.
It is the presence of trees around the vineyards that enhance scenic quality.
1 2 3 4 51
2
3
4
5
6
7
8
9
10
Vines factor score
Rat
ing
scal
e
1 2 3 4 51
2
3
4
5
6
7
8
9
10
Tree score
Rat
ing
of
scen
es w
ith
vin
es
Dr Andrew Lothian, Scenic Solutions 32
Comparison scenes – with & without features
With poles
Without poles Diff. %
3.13 4.31 1.18 37.703.02 4.06 1.04 34.444.02 5.88 1.86 46.272.92 4.73 1.81 61.993.27 4.75 1.47 45.00
2.92
4.73
Powerlines Colour
With colour
Without colour Diff. %
6.65 5.67 0.98 14.74
6.394 6.385 0.009 0.14
5.79 4.84 0.95 16.41
6.28 5.63 0.64 10.25
4.05
3.74
5.79
4.84
With sheep
Without sheep Diff. %
6.47 5.88 0.59 9.12
5.5 4.87 0.63 11.45
4.05 3.74 0.31 7.65
5.34 4.83 0.51 9.55
Sheep
Dr Andrew Lothian, Scenic Solutions33
Stone walls & hedgerows
5.50
4.83
With walls
Without walls Diff. %
5.50 4.83 0.67 12.186.97 6.72 0.25 3.595.40 4.89 0.51 9.444.31 4.05 0.26 6.03
5.55 5.12 0.43 7.75
8.31
8.00
Snow Summer Diff. %7.30 6.29 1.01 13.848.31 8.00 0.31 3.736.85 6.83 0.02 0.90
7.49 7.04 0.45 5.95
Seasonal change Water
With water
Without water Diff. %
6.51 6.24 0.27 4.157.34 6.06 1.28 17.447.48 6.93 0.55 7.30
7.11 6.41 0.70 9.85
7.34
6.06
Dr Andrew Lothian, Scenic Solutions 34
5.02 4.80
7.14 7.17
With treesWithout
trees Difference%
difference4.8 5.02 0.22 4.58
6.76 7.14 0.38 5.627.17 7.14 -0.03 -0.425.04 5.12 0.08 1.59
5.94 6.11 0.16 2.84
Trees were inserted into 4 scenes to assess the effect of revegetating the fells on the landscape.
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.
Or they prefer the fells to be bare rather than vegetated.
Trees
5.12 5.04
Dr Andrew Lothian, Scenic Solutions 35
MappingMapping 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 4Pines 4Low fells 5Rivers 6Valleys without lakes 6Valleys with lakes 6/7High rounded fells 5High steep (≥30%) fells 6High fells with rockfaces 6Mountains (≥700 m – 850 m) 7Mountains ≥ 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 (darkest red).
Dr Andrew Lothian, Scenic Solutions 36
Landscape quality ratings
Unrated (towns)0.5%
Rating 421.4%
Rating 563.2%
Rating 610.4%
Rating 74.6% Rating 8
0.3%
Dr Andrew Lothian, Scenic Solutions 37
Why do we like what we like?
What generates the appeal of landscapes? – why do we like what we like?
Hierarchy of influences – innate individual
Most landscape theory is based on evolutionary perspective – what we like is survival enhancing. We like what aids our survival as a species.
This might explain our preference for water but doesn’t explain liking for the sea which we cannot drink. Or survival in mountains .
DEMOGRAPHIC Individual
Indi
FAMILIARITY
Regional
CULTURESociety
INNATEAll people
It may however explain preferences for scattered trees – like African savannah - rather than dense trees which can hide predators & be difficult to climb.
Dearden’s Pyramid of Influences
Dr Andrew Lothian, Scenic Solutions 38
Restorative benefits of viewing nature2012 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%).”
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.
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 39
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.
Dr Andrew Lothian, Scenic Solutions 40
ApplicationsPossible 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.
Dr Andrew Lothian, Scenic Solutions 41
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.
Dr Andrew Lothian, Scenic Solutions 42
Dr Andrew LothianDirector, Scenic Solutions
PO Box 3158, Unley, Adelaide South Australia, 5061, AUSTRALIA
Mobile: 0439 872 226Phone/fax: (618) 8272 2213
Email: lothian.andrew@gmail.com Internet: www.scenicsolutions.com.au