Hervey Gibson
COGENT STRATEGIES INTERNATIONAL LTD
Nearly everyone is a tourist in the forest
Scenery and
nature most
frequently cited
by visitors from
ALL countries
What drives destination choices?
• Less empirical
–
–
–
–
Psychological needs analysis
Sequential choice models Sociological theory
Expert opinion
• More evidence‐based
– Travel trade modelling
– Visitor stated preferences on destination characteristics (not usually comparative)
– Micro‐observation and visitor characteristics
– Revealed preference: where visitors (and investors) actually go
This paper
• Macro/meso revealed preference study of tourism in Scotland
• Modelling aspect of a 2007‐2009 report commissioned by Forest Research
• Other aspects included – literature review of forest tourism studies
– two dozen case studies of tourism businesses engaged with the forest resource
Conceptual framework
Comparative
Advantages
Competitive
Advantages
(resource
endowments)
(resource
deployment)
* Human resources
* Audit & inventory
* Physical resources
* Capital resources
* Historical and
cultural resources
* Size of economy
* Growth and
development
QUALIFYING & AMPLIFYING DETERMINANTS
Location Safety/Security Cost/Value
Interdependencies Awareness/Image Carrying Capacity
DESTINATION POLICY, PLANNING & DEVELOPMENT
System
Definition
Philosophy/
Values
Vision
Positioning/
Branding
Development
Competitive/
Collaborative
Analysis
Monitoring &
Evaluation
Audit
DESTINATION MANAGEMENT
Organization Marketing
Quality
Finance
Human
of
&
Information/
Resource
Service/
Management Venture
Research
Experience
Capital
Crisis
Visitor
Resource
Management Stewardship Management
CORE RESOURCES & ATTRACTORS
Physiography
and Climate
Culture & History
Mix of Activities
Special Events
Entertainment
Superstructure
Market Ties
SUPPORTING FACTORS & RESOURCES
Infrastructure
DCmodel(v13).ppt – © RITCHIE & CROUCH, APRIL 2003
Accessibility
Facilitating Resources
Hospitality
Enterprise
Political Will
GLOBAL (MACRO ) ENVIRONMENT
* Infrastructure
and tourism
superstructure
* Maintenance
COMPETITIVE (MICRO) ENVIRO NMENT
* Knowledge resources
Ref: y/industries/ttt/tourism/CrouchRirchieDestCompmodelv14.ppt
* Efficiency
* Effectiveness
Datasets – dependent variables
• Length of trips, number of trips, spend
–
–
–
–
International Passenger Survey
UK Tourism Survey
Scottish Recreation Survey
visitScotland accommodation register (number of beds)
unusually
• all broken down by Council Area
NB: visitor data as often presented has been ‘massaged’ to remove information content, so it is not much use for analysis
An operational model of destination choice
CORE
DEMAND
VISITOR
SOCIETY
CHARACTERISTICS
& CIRCUMSTANCES
Population Density
CULTURE
ACCESS
Location, distance,
transport facilities
ACCOMMODATION
DESTINATION
CHARACTERISTICS
HISTORY
PHYSIOGRAPHY
ACTIVITIES/
FACILITIES
CURRENT
DEMAND
OPEN
SPACE
COASTS
MOUNTAINS
WOODLANDS
accessible/non-
LOCHS
Datasets – independent variables
•
•
•
•
•
•
•
•
•
•
Distance from origin/entry
Urban density
Rural sparsity
Munro/m2
Coastal dummy
Island Dummy
Coast length
Lochs %
Forest
Access to forest
Role of distance
A schematic model
20
18
16
14
Attractors
12
10
Availability/distance
trade-off
8
6
4
2
Distance/availability trade-off
300
290
280
270
260
U
250
240
230
220
210
200
190
180
170
160
150
140
130
120
110
100
90
80
70
60
50
40
P
20
10
Availability limited
0
Below threshold
T
30
Miles (round trip)
0
Above
maximum
Source: cogentsi
Ref: z/Data/tourism/ScRS/distance regn
Overall, long distances make tourism less likely
Ratio of actual trips to birpoiportional trips between regions
8
7
6
Scotland to Scotland
5
Source: UKTS and DREAM®trade
Ref:z/imndustries/ttt/tourism/ ukgravitymodelupdate/chart 5
4
Wales to Wales
3
NW to Cumbria
2
Wales to Scotland
1
London
to London
0
0
100
200
300
400
500
600
700
Distance (one way, km) between origin and destination region
800
900
1000
Impact of distance on visitor flows
2.00
NE to NE
ln(actual flow/biproportional
flow)
Scotland to Scotland
1.50
ln(actual flow/biproportional flow)
Wales to Wales
Own region ('distance' based
on square root of area)
NW to Cumbria
1.00
SW to SW
Visits to Scotland
0.50
Wales to Scotland
Contiguous regions
Sco to NW
0.00
London to London
London to Scotland
-0.50
WM to EM
Scotland to London
-1.00
Source: UKTS and
DREAM®trade
Wales to EM
-1.50 Constant
All mainland points
4.42
-2.00 ‐13.06
Own‐region
10‐10.13
Contiguous region
To Scotland
‐5.05
Other mainland
‐7.49
Source: cogentsi estimates
t‐statistic Gravity on gravity coefficient
coefficient
‐0.81
‐13.96
0.50
‐0.28
‐1.12
‐0.80
R2
60%
df Ref:z/industries/ttt/tourism/
uk gravity model update
(res i dua l )
129
2.50
38%
10
100
‐1.84
8%
40
‐2.12 distance
36% in km
8 (one way, log scale)
‐7.54
45%
69
Ref: y/industries/tourism/ukgravitymodelupdate /regnsummary
1000
Density of attractions and distance very important for day visits
Ratio of recreation trip flows to biproportional
1000.00
100.00
10.00
Ratio
Islands
Own Council area
Contiguous council areas
Other
1.00
Source: Scottish Recn Survey
Ref z/data/touris/screcs/
OD 0307-0612/bipropcht3
0.10
0.01
1
10
100
Estimated Distance
1000
The distribution of travelling distance in the
Scottish Recreation Survey -stylised
1.8
1.6
1.4
Town
Seaside
Countryside
1
Source: ScRS and
cogentsi analysis
0.8
Ref
z/data/touris/SCRS
/distanceregn1/cha
rt 6
0.6
0.4
0.2
Distance (log scale)
1000
100
10
1
0
0.1
Frequency
1.2
Multiple regression results
• Two stage recursive model for overnight visitors
• Independent variables determine accommodation (NB therefore v important for investment)
• Independent variables plus accommodation determine visitors
– ‘Reduced form’ (ie without accommodation) also estimated
• ‘Residual (‘non‐distance’) model for ScRecSurv
– Overall explanatory power good
– Individual variables correct signs, but could be more precise
Key Forest results
• Forest is good for tourism
• Accessible forest is very good for tourism
• (Accessible forest is good for recreation: INaccessible forest is BAD for recreation)
Woodland‐attributed tourism revenues: geographical distribution across Scotland
West Dunbartonshire
1.1%
Stirling
6.7%
West Lothian
1.2%
Aberdeen City
4.5%
Aberdeenshire
2.8%
South Lanarkshire
1.0%
Angus
0.7%
South Ayrshire
6.5%
Argyll & Bute
11.3%
Shetland Islands
0.0%
Scottish Borders
2.4%
Renfrewshire
0.3%
Clackmannanshire
0.3%
Dumfries & Galloway
6.8%
Dundee City
0.6%
Perth & Kinross
5.8%
East Ayrshire
1.9%
Orkney Islands
0.0%
East Dunbartonshire
0.2%
North Lanarkshire
1.7%
East Lothian
0.5%
North Ayrshire
3.1%
East Renfrewshire
0.2%
Moray
2.2%
Midlothian
0.1%
Source: cogentsi
Ref: z/data/tourism/
final effects.xls/ Chart 1
Inverclyde
0.3%
Outer Hebrides
0.0%
Highland
19.1%
Fife
4.3%
Glasgow City
5.3%
Falkirk
0.6%
Edinburgh
8.4%
Percentage of total tourism revenues associated with woodland
0%
Aberdeen City
Aberdeenshire
Angus
Argyll & Bute
Clackmannanshire
Dumfries & Galloway
Dundee City
East Ayrshire
East Dunbartonshire
East Lothian
East Renfrewshire
Edinburgh
Outer Hebrides
Falkirk
Fife
Glasgow City
Highland
Inverclyde
Midlothian
Moray
North Ayrshire
North Lanarkshire
Orkney Islands
Perth & Kinross
Renfrewshire
Scottish Borders
Shetland Islands
South Ayrshire
South Lanarkshire
Stirling
West Dunbartonshire
West Lothian
SCOTLAND
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
UK visitors
Overseas v
Recreation
Source: this rep
Ref: Z:/data/Tourism
Final effects/Ch