7th International Forum on Tourism Statistics
Stockholm, Sweden, 9-11 June 2004
Micro-Simulation Modelling of Domestic
Tourism Travel Patterns in Sweden
Anders Lundgren
Spatial Modelling Centre, Kiruna
Department of Social and Economic Geography, Umeå University,
Box 839, 981 28 Kiruna, URL:www.smc.kiruna.se
Tel.:+46 980 676 27, fax +46 980 67626, e-mail: anders.lundgren@smc.kiruna.se
Abstract
From a geographical point of view, tourism is basically about flows in a spatial
system linking together a place of origin and a destination and the impacts on these
destinations induced by tourism. Forecasting tourism flows requires reliable data. In
the Swedish context the available data source, the Swedish Tourist Database (TDBÅre marknadsafakta AB), contains individual attributes as age and income as well as
individual choices of tourist activities and hence, the database enables analysing
socio-economic patterns in relation to recreational activities at an individual level.
Here it is demonstrated how the TDB-data can be used as empirical input for a
tourism module integrated into SVERIGE, a geographical micro-simulation model of
the entire Swedish population. It is argued that this modelling on the micro-level
accounts for changes in population structure and geography to a far greater extent
than conventional models because of its focus on individual behaviour in relation to
individual socio-economic characteristics. Thus, population change is mirrored
directly in the resulting travel patterns.
This paper describes equations and calculations for SVERIGE’s tourism module and
presents examples of model runs.
Background
In tourism literature, it is almost a rule to mention the increasing importance of
tourism and tourism as a developing industry in the world (Ioannides & Debbage,
1998; Jansson, 1994; Page & Getz, 1997; Page, 1999; Roberts &Hall, 2001; Sharpley
& Sharpley, 1997; Sharpley & Telfer, 2002; Shaw & Williams, 1994). It is also
widely argued that measuring tourism demand is obstructed by lack of suitable data
(Hall & Page, 2002). This lack of data concerning leisure activities indicates that
leisure and recreation, within which tourism activities belong, is not regarded to be
that important outside the community of tourism and leisure stakeholders. There is
only fragmented information on what tourists actually do. There is a diffuse picture of
what the tourist industry is and it is sometimes questioned if there is such a thing as a
tourist industry (Smith, 2003). This makes planning, management and impact
assessment a difficult task. Furthermore, this also makes it more difficult for the
commercial enterprises claiming to belong to tourism industry to argue for their case.
In addition, lack of data makes it difficult to forecast tourism from a scientific point of
view. Forecasting is dependent on time series of data. Forecasting with structural
7th International Forum on Tourism Statistics
Stockholm, Sweden, 9-11 June 2004
models has the aim to explain how changes in society and the surrounding
environment affect tourism (Smith, 1995). Structural models cannot be applied if data
on tourism only contain information on impact and activities without concerning
socio-economic data on tourists.
The aim of this paper is to describe a method to use Tourism statistics, structural
models and microsimulation to simulate tourism flows. Focus is put upon tourism
demand with the resulting spatial travel patterns, socio-economic attributes of
individuals and choice of activity.
Swedish Tourism Statistics
There are many organisations that are involved in the analysis and collection of data
on tourism in Sweden. The most important ones that can be mentioned are Statistics
Sweden (SCB), the Swedish Tourist Authority, Åre Marknadsfakta, Swedish Ski
resort Association (SLAO), Swedish Campsite Managers (SCR), The Swedish
Institute for Transport and Communications Analysis (SIKA) and there are also
projects that has a specific lifespan aimed att collecting and analysing data for special
purposes.
The Swedish Tourist database (TDB) contains results from interviews with randomly
picked individuals living in Sweden. The company Åre Marknadsfakta who own the
database interviews two thousand persons every month. Data has been collected since
1989. The database contains socioeconomic data as age, education, income, number
of children, place of residence etc and thematic data for the trip as purpose of trip,
money spent on the trip, number of nights, destination and other variables. There are
different trip types: domestic, abroad, staying overnight, daytrip, work or leisure.
Aggregated analyses are made and statistics are presented yearly in different shapes
based on this data by the Swedish Tourist Authority among others.
Activities vs purposes
TDB contain a variable called “travel purpose” which gives the respondent 35
choices. It is possible to declare three purposes for each trip. However, these purposes
are actually a mixture of purposes and activities. It is for example possible to choose
between “Visiting Second Home” and “Peace and Quiet”. Visiting your own or
someone’s second home is an activity. To experience piece and quiet is a purpose you
can achieve by performing that activity. Looking at recreation data in different
countries, one can see that activities are strictly defined as something a person does
and not why it is done (Cushman, 1996). Activities are easier to connect to a place
since they depend on certain prerequisites. A purpose can be fulfilled in many ways in
many different places and is more difficult to attach to a place.
To be able to assign tourism activities to places the purposes in the data has to be
assigned to activities. In this case, all the purposes reported as the primary reason for
travel were checked against the secondary purpose for the trip. If for example a
majority of respondents stated that the secondary purpose was “Visiting second
home” when they stated “Experiencing Peace and Quiet” as the primary reason,
“Experience piece and quiet” was put together with the activity “Visiting second
homes”.
7th International Forum on Tourism Statistics
Stockholm, Sweden, 9-11 June 2004
In table 1 the classification of activities and purposes is shown. It was, however, not
possible to avoid using some purposes as activities. The purposes considered as
activities are still to some extent possible to assign to a physical environment. The
purposes are “Experiencing natural environment” and “Experiencing pleasure and
entertainment”. These purposes are, however, more likely to have a location outside
cities and in urban areas respectively. Thus, it is possible to regard them as activities.
Table 1. Purposes and activities put together into 10 activities.
1
Activity
Freq.
Visit friends and relatives
Great Events
Valid
Percen
t
5
Fishing
1056 1,1
43380 43,9
Hunting
323 0,3
1875
1,9
Total Fish/Hunt
1379 1,4
Shopping
898
0,9
Private matters/look for job
778
0,8
6
Outdoor life
1993 2,0
7
Natural environment
1307 1,3
Community with others
2586
Course and meeting – as leisure
assignment
1493
2,6
Visit attraction
776
0,8
Cultural environment
277 0,3
Visit parks
593
0,6
Adventure/excitement
188 0,2
Education/studies
467
0,5
Stimulation
616 0,6
Big City Environment
441
0,4
Total nature/culture
1772 2,4
Health service
236
0,2
See the country
875
0,9
Pleasure/Entertainment
5989 6,1
Cultural activity
Total Social bonds
(SBA)
411
0,4
Get some action
307 0,3
School trip
253 0,3
1,5
8
activities
54809 55.,5
Total Pleasure/entertainment 6549 6,7
2
3
Visit second home
13608 13,8
Piece and quiet
7381
Total Visit second home (VSH)
21214 21,3
Sun & bath
2613
7,5
9
Sports
2433 2,5
Golf
168 0,2
Total Sports/Golf
2601 2,7
2,6
10 Others
4
Skiing
2762
2,8
1523 1,5
Otheractivity
1097 1,1
Total Others
2620 2,6
Sample size
When 10 years of TDB data, from 1989 to 1999, is used the number of cases are
approximately 100 000 for domestic overnight trips. The relatively small number of
cases regarding some activities is obvious as can be seen in table 1. Sparsely
populated regions will get few observations since data is collected randomly. To split
the material for example into regions as municipalities is not recommendable for
regression analysis. Hence, data from just one year is not possible to use for
regression analysis as the number of cases would be to low. A remedy for this is to
use larger functional regions for people’s recreation activities and to use variables that
are not connected to specific municipalities.
7th International Forum on Tourism Statistics
Stockholm, Sweden, 9-11 June 2004
Micro Simulation Models
Microsimulation models (MSM) were used quite early (Orcutt, 1957 in Holm et al,
2002). This simulation methodology implies that all analysis departs from single
individuals and not as in many quantitative studies from spatial aggregates. These
individuals respond to changes in stimuli that can be changes in the environment or
the behaviour of other individuals within the model. One basic argument for a micro
simulation, time-geographic approach to social phenomena is that aggregation prior to
analysis and modelling distorts not only individual but also aggregate outcomes.
MSM can contain both deterministic and stochastic relations. In a stochastic or
probabilistic model all individuals of the same type do not respond exactly the same
way to the same stimuli and this can be represented by a stochastic function.
MSM has however not been used extensively to model tourism. There is one example
where microsimulation was used to model tourist expenditure (Brouwer, 1997). It is
mostly used in the exploration of tax and benefits systems. In Sweden SESIM has
been used (Ministry of Finance, 2001) and in the US CORSIM (Caldwell, 1996) is
used in basic research but also for policy analysis.
In Sweden there is systematic information about social behaviour in longitudinal databases produced by Statistic Sweden. These data are used for a MSM of the Swedish
population called SVERIGE at the Spatial Modelling Centre in Kiruna (SMC). This
data is used for estimating the probabilities for certain events to occur stratified for
individuals with certain sets of characteristics. Then, these probabilities are employed
to simulate the lives of all individuals living in Sweden. The individuals in the model
are born, enter school, move from home, obtain work, build families with other
individuals in the model, migrate and so on. This is useful for predicting the
population in single municipalities or regions and at the same time paying regard to
the population changes in the rest of the country (Holm et al, 2002). One idea with
SVERIGE is to create an artificial laboratory enabling systematic evaluations of, for
example, changed structural conditions or various policy options before implementing
them in reality.
Calculating number of trips, choice of activities and choice of
destination
The strategy is to first calculate the probability for the number of trips for each
individual. After that, the probabilities for the choices of activities for each trip are
calculated. Finally, the individuals will be distributed on destinations according to the
chosen activity and place of residence.
Variables
The two key factors that make tourism possible is access to money and leisure time
(Graham, 2001). There are also gender and social constraints that varies during the
life trajectory of an individual (Hall& Page, 2002). An individual’s current life cycle
could be either a barrier or a springboard for participating in tourism
(Shaw&Williams, 1994). A family with children and only one person employed is
more likely to have both less time and money for travels compared to a single person
with high income. Variables in the data that refer to the individual life cycle are age,
7th International Forum on Tourism Statistics
Stockholm, Sweden, 9-11 June 2004
income, number of children, marital status and gender. It is argued that motivation to
travel is to find something that is different from the ordinary life (Hall&Page, 2002)
or to have more of something, for example better skiing or better climate (warmer)
(Jansson, 1996). A person that lives near a skiing resort will probably not go for over
night trips to ski unless it is to a destination with much higher qualities and maybe
abroad. Someone living in a cold climate is attracted to spend his/hers vacation in a
warmer climate. This indicates that geography and place of origin matters. The
resolution in the data does not allow for regional models where individuals from each
municipality would have had their own model for each activity. In order to take this
resolution matter in regard, the main region (Riks-region) is used as a regional
variable. Sweden has 7 Riks-regions and this divides the population in 7 geographical
regions. Originally, the database does not contain local labour market regions (LAregions). Municipalities and destinations have been converted into LA-regions to
match SVERIGE. The variables used in the regression analyses are chosen for their
significance for an individual’s access to money and leisure time. The variables used
for calculating how many trips each individual is likely to perform are:
Age group– divided into 5 groups
Income – household income
Gender
Education – university degree or not
If the individual have children at home or not
If the indivudal are single or not
Size of place of residence
7th International Forum on Tourism Statistics
Stockholm, Sweden, 9-11 June 2004
Statistical method
A Poisson regression analysis was made to calculate a model for the number of trips
that each individual would make during a month. Poisson regression is used since the
distribution of trips approximately follows the Poisson distribution. It has been widely
used in studies to model the number of recreational trips (Ozuna and Gomaz, 1995,
Lundevaller, 2002). Most travellers make 1 trip every month. 98 % of all cases are
covered by a maximum of 5 trips per month. This means that individuals can have a
maximum of 60 activity choices per year. Some individuals will of course not travel.
The parameter estimates for the regression is shown in table 2.
Table 2. Parameter estimates from Poisson regression for number of trips
Coefficients
Estimate
Signif.
(Intercept)
-1.494
0,00000
Agegroup 45-59
0.1801
0,00000
Agegroup 30-44
0.1078
0,00000
Agegroup 15-29
0.3303
0,00000
Agegroup 0-14
0.5052
0,00000
Income – low
0.00001183
0.99910
Income – medium
0.09618
0,00000
Gender 1 = man
0.05654
0,00000
University degree
0.3562
0,00000
Have children
-0.1426
0,00000
Single
0.02437
0.00864
Big city (St-holm G0.3702
0,00000
burg, Malmoe)
City > 50 000
0.4148
0,00000
Town 5 – 50 000
0.2508
0,00000
Village 500 – 5 000
0.1256
0,00001
Since the individual will choose among ten activities, multinomial logistic regression
is an appropriate technique for calculating models for these probabilities (Lee et.al,
2002). It allows a simulation of the individuals choosing between these 10 activities
having all alternatives regarded when the choice is made. The variables used for the
analysis of activity choice are:
Age group – divided into 5 groups
Income – household income
Gender
Education – university degree or not
If the individual have children at home or not
If the indivudal are single or not
main residential region
Table 3 shows the parameter estimates from the calculation for 4 of the 10 activities.
7th International Forum on Tourism Statistics
Stockholm, Sweden, 9-11 June 2004
Table 3. Parameter estimates from the multinomial logit regression. Results from 4 out of 10 dependent
variables.
SBA
Intercept
Agegroup
30-44
Agegroup
45-59
Agegroup
60-74
University
degree
Income medium
Income high
B
Sig.
Exp(B)
1,6170
0,0409
0,0455
0,5926
1,0466
-0,1311
0,0994
0,8771
0,0230
0,7900
1,0233
0,3348
0,0000
1,3976
0,0320
0,6573
1,0325
VSH
B
Sig.
-0,0850
0,9201
Exp(B)
0,6947
0,0000
2,0031
0,9025
0,0000
2,4656
0,7835
0,0000
2,1891
0,3115
0,0000
1,3654
0,2513
0,0010
1,2857
-0,4472
0,0000
0,6394
Intercept
Agegroup
30-44
Agegroup
45-59
Agegroup
60-74
University
degree
Income medium
Income high
-0,1078
0,3088
0,8978
Single
-0,0916
0,2038
0,9125
Single
0,4989
0,0000
1,6470
Children
Gender
1=male
City > 50
000
Town 5 –
50 000
Village 500
– 5 000
-0,3270
0,0000
0,7211
-0,2790
0,0007
0,7566
-0,3572
0,0000
0,6996
-0,1775
0,0027
0,8374
-0,0096
0,8307
0,9905
-0,0318
0,4990
0,9687
-0,1129
0,1741
0,8933
-0,2975
0,0007
0,7427
-0,2107
0,0487
0,8100
Children
Gender
1=male
City > 50
000
Town 5 –
50 000
Village 500
– 5 000
-0,4079
0,0003
0,6650
Rural
Riksreg
Sthlm
East midSweden
South-east
and islands
Southern
Sweden
West
Sweden
Northern
MidSweden
-0,2165
0,1539
0,8053
-0,9046
0,0000
0,4047
1,4814
0,0602
4,3992
1,1101
0,1882
3,0346
1,7408
0,0271
5,7020
0,9376
0,2662
2,5538
1,6473
0,0368
5,1929
0,9065
0,2832
2,4757
1,6389
0,0377
5,1493
0,8651
0,3055
2,3752
1,6283
0,0387
5,0953
0,9209
0,2746
2,5116
1,6297
0,0406
5,1025
Rural
Riksreg
Sthlm
East midSweden
South-east
and islands
Southern
Sweden
West
Sweden
Northern
MidSweden
0,7551
0,3755
2,1279
7th International Forum on Tourism Statistics
Stockholm, Sweden, 9-11 June 2004
-cont. Table 3
SUN/BATH
Intercept
Agegroup
30-44
Agegroup
45-59
Agegroup
60-74
University
degree
Income medium
Income high
Single
B
Sig.
Exp(B)
SKIING
0,1603
0,1728
1,1739
-0,2866
0,0139
0,7508
-0,9162
0,0000
0,4001
0,1729
0,0437
1,1887
0,1892
0,0719
1,2082
-0,6210
0,0001
0,5374
Intercept
Agegroup
30-44
Agegroup
45-59
Agegroup
60-74
University
degree
Income medium
Income high
-15,2641 0,9893
B
Sig.
Exp(B)
-1,1750
0,3433
0,2420
0,0317
1,2738
-0,2686
0,0162
0,7645
-1,3129
0,0000
0,2690
0,3704
0,0000
1,4483
0,0957
0,3514
1,1004
0,0134
0,9235
1,0135
0,2724
0,0130
1,3131
Single
0,2815
0,0069
1,3252
Children
0,3881
Gender
1=male
-0,2190
City > 50
000
0,1140
Town 5 –
50 000
0,3707
Village 500
– 5 000
0,4114
0,0003
1,4741
0,5141
0,9338
0,0077
0,8033
0,7063
0,9706
0,0984
1,1208
0,8382
1,0128
0,0035
1,4487
0,7517
0,9639
0,0088
1,5089
Children
-0,0685
Gender
1=male
-0,0298
City > 50
000
0,0127
Town 5 –
50 000
-0,0367
Village 500
– 5 000
0,1349
0,3748
1,1444
Rural
Riksreg
Sthlm
East midSweden
South-east
and islands
Southern
Sweden
West
Sweden
Northern
MidSweden
0,1943
0,3886
1,2144
0,0137
0,9515
1,0138
14,5727
0,9897 2132170,95
1,2831
0,2990
3,6078
14,7267
0,9896 2487198,96
1,0247
0,4068
2,7864
14,9233
0,9895 3027537,84
0,1510
0,9031
1,1630
14,5061
0,9898 1994961,90
0,2156
0,8618
1,2406
14,6686
0,9897 2346984,33
0,7411
0,5486
2,0983
14,4361
0,9898 1860086,97
Rural
Riksreg
Sthlm
East midSweden
South-east
and islands
Southern
Sweden
West
Sweden
Northern
MidSweden
0,6975
0,5760
2,0088
Calculation
In order to generate trips and activity choices the independent variables of individuals
where used to calculate individual probabilities for number of trips and choice of
activity for each individual. The number of trips and choice of activity was then
randomly distributed according to the estimated probabilities. The result was then
compared with TDB.
Probabilities based on the Poisson regression model for number of trips were
calculated using this equation:
p( y) 
e ( f ) * ( f y )
(1)
y!
p(y) = probability for y number of trips
f = an individuals value based on his/her attributes and the parameter estimates
exp(intercept+b*variable)
y = number of trips
7th International Forum on Tourism Statistics
Stockholm, Sweden, 9-11 June 2004
The equation for each choice of the 10 activities was put into SPSS to calculate the
probabilities and the outcomes for each individual by using this equation:
e ( fk )
pk 
(2)
1   e ( fk )
pk = probability to chose activity k
fk = result from calculations using the parameter estimates from multinomial logit
regression for activity k
The individuals represent a 3% sample from the Swedish population. To make the
manual calculation easier all individuals that travelled were allowed to make 12
choices. That is one per month. For the individuals that got 2 trips per month the
result from equation 2 was doubled. Individuals with 3 trips where tripled and so on
up to 5 trips. The number of individuals with more than 5 trips was included with
those with 5 trips because they represented less than 2% of the sample. This way the
number of calculations were reduced compared to repeating the calculations for the
individuals that got more than one trip. After this the activity choices performed by
the individuals where aggregated per LA-region and summarised per activity. In table
6 each activity is summarised for 10 LA-regions.
Place of origin and choice of destination
When people move or chose a place for vacation, distance and attraction of the
destination matters. A spatial interaction model or a gravity model can describe this
flow. The gravity model is a well-known structural forecasting model where
population often is used as a mass term or attraction (Smith, 2000). Interaction models
can be used to model flows between different locations (Wilson, 2000). The number
of trips to a destination will normally increase the nearer the place of origin and the
higher the attraction or gravity is. In the present case, the relative number of nights
spent in a region by individuals performing a specific activity will represent attraction
(A) at the destination (j). This can be obtained from TDB. In a production constrained
spatial interaction model the sum of the predicted outflows from any origin will equal
the known total outflows from that origin (Fotheringham, 2000). The proportion of
travel from an origin i to a destination j for individuals that has chosen the activity (k)
can thus be calculated like this:
M kij 
Pki Akjc Dijb
 kj Akjc Dijb
(3)
Mkij= number of travellers going to region j to perform activity k from region i
Pki = number of individuals that has chosen a specific activity k in region i
Akj = activity k’s attraction in destination j
Dij = distance between place of origin i and destination j
c = scale elasticity for attraction (estimated through iteration to fit each activity)
b = scale elasticity for distance (estimated through iteration to fit each activity)
7th International Forum on Tourism Statistics
Stockholm, Sweden, 9-11 June 2004
From TDB it is known how many individuals that visited different LA-regions
performing different activities. The factors c and b where estimated using this
information, equation 3, and iteration with Newtons method where the percentage of
wrongly distributed flows were minimised. For each activity the number of travellers
from each region was put into the equation and travellers were distributed among
destinations.
A multinomial logit model was also tested for the distribution of tourists but problems
occurred calculating probabilities when the difference between odds is large. This
method will be explored further. In table 4 the values for b and c are presented. The
importance of distance is apparently higher for the activities VSH and sun/bath. If
distance for these activities is increased it affects the number of travellers more
compared to the other activities strengthening social bonds (SBA) and skiing. Hence
skiers and people who visits friends and relatives are less concerned about distance.
Table 4. Estimated values for the interaction models.
Factor
c - attraction
b -distance
SBA
1,190
-0,927
VSH
SUNBATH
0,986
-1,536
1,204
-1,437
SKI
1,040
-1,104
Results and implementation in SVERIGE
There are no absolute figures on how many domestic overnight trips that are made in
Sweden (Holmström & Junkka, 2003). According to the Swedish tourism authority,
an estimation gave that 47 million overnight trips where done in Sweden 2002
(Turistdelegationen, 2003). As seen in table 5 approximately 40 million trips where
generated totally by using the multinomial logistic model and TDB data for the years
1989 to 1999. TDB has a bit more than 270 000 individuals compared with
approximately 9 million individuals in Sweden. The ratio is 32,4. This number is used
as weight to calculate the number of trips for the entire population. These were
distributed among the activities in a way that can be expected when comparing with
tourism statistics in Sweden as can be seen in table 1. In table 6 the distribution of
tourists on home regions is shown for 4 of the activities and 10 of the regions.
Table 5. Number of trips for individuals leaving their home region ( * 1000) that were generated by
using a multinomial logistic model .The result from the sample is multiplied by 32.4 to represent the
whole population. The trips are distributed on 10 activities.
Number of trips
LASun
Hunt Out
Nature Pleasure/ Sport
region SBA VSH bath Ski fish
door env
Entertain golf
Other Total
Sample 721
196 28
36
12
16
24
124
29
58
1 243
Sweden 23 358 6 364 920 1 151 373
519
762
4 005
940 1 895 40 288
7th International Forum on Tourism Statistics
Stockholm, Sweden, 9-11 June 2004
Table 6. The result from the multinomial logistic model for the distribution of tourists in 10 of 81 home
regions and for 4 of 10 activities. Numbers are weighted to reflect the whole population.
SBA
VSH
Sunbath
Ski
1
Stockholm
155 887
53 635
5 754
11 613
2
Uppsala
25 724
5 437
1 133
1 304
3
Nyköping
5 749
1 244
246
271
4
Katrineholm
3 985
826
192
189
5
Eskilstuna
8 904
2151
307
431
6
Linköping
22 897
4 945
915
1018
7
Norrköping
14 015
3 153
568
656
8
Värnamo
5 450
1 209
371
102
9
Jönköping
11 654
2 959
686
289
10 Nässjö
6 818
1 649
513
134
Figure 1, 2, 3 and 4 show the observed distribution of destination patterns and the
distribution generated with the interaction model, respectively. The selected activities
are social bond activities, visiting second home, skiing and sun & bath. It seems that
the interaction model do not distribute tourists to peripheral regions to the same extent
as the empirical data. In table 7 the percentage of misplaced flows is shown. This is
calculated comparing the observed flows with calculated flows. In table 4 it can be
seen that if distance is increased for skiing and sun & bath it affects the number of
travellers more compared to the other activities strengthening social bonds friends and
relatives (SBA) and visiting second home (VSH). Hence skiers do not go to far to ski.
People that visit friends and relatives are less concerned about distance.
Table 7. The percentage of flows wrongly distributed.
SBA
VSH
SUN&BATH
Percentage
misplaced
18,81
31,91
34,46
flows
Number of
37 100
10 124
1 818
cases in TDB
SKI
26,71
2 067
7th International Forum on Tourism Statistics
Stockholm, Sweden, 9-11 June 2004
Calculated
Percentage
of trips - SBA
VFR
0-1
1 - 2.5
2.5 - 4
4-9
9 - 18
Observed
Percentage
of trips - VFR
SBA
0-1
1 - 2.5
2.5 - 4
4-9
9 - 18
Figure 1. Calculated and observed distribution of tourists that strengthen social bonds.
Calculated
Percentage
of trips - VSH
0 - 0.5
0.5 - 1
1-2
2-3
3-8
Observed
Percentage
of trips - VSH
0 - 0.5
0.5 - 1
1-2
2-3
3-8
Figure 2. Calculated and observed distribution of tourists that visit a second home
7th International Forum on Tourism Statistics
Stockholm, Sweden, 9-11 June 2004
Calculated
Observed
Percentage
of trips - sun&bath
0 - 0.5
0.5 - 1.5
1.5 - 4
4 - 12
11 - 25
Percentage
of trips - sun&bath
0 - 0.5
0.5 - 1.5
1.5 - 4
4 - 12
12 - 25
Figure 3. Calculated and observed distribution of tourists that do the sun&bath. activity
Calculated
Observed
Percentage
of trips - ski
0 - 0.5
0.5 - 1.5
1.5 - 2.5
4-9
15 - 55
Figure 4. Calculated and observed distribution of tourists that ski
Percentage
of trips - ski
0 - 0.5
0.5 - 1.5
1.5 - 2.5
2.5 - 15
15 - 55
7th International Forum on Tourism Statistics
Stockholm, Sweden, 9-11 June 2004
Conclusions
The MSM approach based on TDB can be performed in practice. The input to the
tourism module consists of socio economic attributes that are used in SVERIGE.
Results from experiments with SVERIGE resulting in population changes either
spatially or with respect to income distribution etc can be put forward into the tourism
module. In this run, population data from TDB was used but individual data from
SCB will be used in future runs. This enables simulation of the effect from changes in
the Swedish population on the size and direction of tourism flows. Furthermore, it
allows for analysing possible outcomes in the direction of tourism flows by changing
the environment with respect to the location of attractions. The percentage of
misplaced flows is quite high for VSH with respect to the number of cases. This could
indicate that this flow is not well explained by the TDB-data. In the data provided by
SCB that forms the base of SVERIGE, there is data on family bonds, migration,
second home ownership, and location of second homes that possibly can improve the
precision for VSH and possibly also for social bond activities.
All calculations were made in SPSS and Excel. This makes it complicated to handle a
lot of individuals, regions and activities. With a specially designed simulation engine
made for instance in C++, it would be possible to let individuals in the model act
more individually and for example use individual coordinates both for people and
attractions.
An interesting attempt would be to use a multinomial logit model for the distribution
of tourists to destinations and compare this result with the interaction model that was
used in this work. However, there are indication that this is difficult due to lack of
data in sparsely populated regions and the large difference in odds for destination
choices.
In order to improve the database for this kind of spatial modelling, emphasis should
be put on respondents declaring their place of residence and the destination. The
destination variable ought to be declared by municipality and not only the locality.
The variable “trip purpose” should be divided into two variables that distinguish
between activity and purpose. This would of course endanger to interrupt the
longitudinal sequence of data.
In sparsely populated areas the number of respondents are low and that brings a
limitation to analysing what these individuals actually do. A remedy for this could
possibly be that these areas gets a larger proportion of respondents.
7th International Forum on Tourism Statistics
Stockholm, Sweden, 9-11 June 2004
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Tourist database. Produced by Marknadsfakta Åre AB, Kurortsvägen 20, 830 13 Åre.