Micro-Simulation Modelling of Micro-Simulation Modelling of Domestic Tourism Domestic Tourism in in SwedenSweden

Anders Lundgren

Dep. Geography, Umeå University

KIRUNA

SVERIGE

System for Visualizing Economic and Regional

Influences Governing the Environment

SVERIGE is a MicrosimulationMicrosimulation model based on microdata on the Swedish population

Socioeconomic data for ALL individuals

Current ModulesFertility Education

Cohabitation and marriage

Employment and earnings

Mortality Immigration

Divorce Emigration

Leaving home

Migration

What is microsimulation?• Micro = individual level• Microsimulation model - Simulation

model that describes the behaviour of individual micro units and permits analysis of the impacts of the environment

• Laboratory ”world” for testing policies• SVERIGE is dynamic dynamic and and spatialspatial

- What happens if we increase immigration?

Experiment

Base case – 60 000 immigrants

80 000 immigrants

”National” Tourism module

• How many trips are done?

• What do they do?

• Where do people go?

• Exchange of tourists between LA- regions

Swedish Tourist Database

• Managed by Åre marknadsfakta

• 14 years

• 24 000 interviews every year

• Using 10 years

Number of cases

• Randomly collected

• Densely populated areas are well represented

Different categories of tourism

Domestic travel with at least one night away from home done at

leisure time

Limitations in data

To few observations to perform regressions analyses IF

you look at municipalities and each activity in TDB

LA-regions

Activity/purpose in TDB 

•Meeting friends and relatives•Visit second home•Piece and quiet/relaxation (Experience)•Pleasure and entertainment (Experience)•Community with others (Experience)•Skiing (Participate/be active in)•Sun&swimming (Participate/be active in)•Events (away 00-01)•Outdoor life (Participate/be active in)•Sports (Participate/be active in)•Course & meeting as leisure assignment•Cultural activity (Participate/be active in)•Cultural environment (Experience)•Rush and speed (Experience)•Seclusion (Experience)•Prophylaxis, health care (Participate/be active in)

•Natural environment (Experience)•Fishing (Participate/be active in)•Other activity (Participate/be active in)•Private matters/look for job (Experience)•See the country (Experience)•Buy things•Attraction (Visit, watch, listen to)•Stimulation (Experience)•Education/studies (Participate/be active in)•Urban environment (Experience)•Parks (Visit, watch, listen to)•Hunting (Participate/be active in)•Golf (Participate/be active in)•Adventure and excitement (Experience)•School trip (Experience)

Activity Freq. Valid Percent

5 Fishing 1056 1,1

1 Visit friends and relatives 43380 43,9 Hunting 323 0,3

Great Events 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

Community with others 2586 2,6

Course and meeting – as leisure assignment 1493 1,5

7 Natural environment 1307 1,3

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 8 Pleasure/Entertainment 5989 6,1

Cultural activity 411 0,4 Get some action 307 0,3

Total Social bonds activities (SBA) 54809 55.,5

School trip 253 0,3

Total Pleasure/entertainment 6549 6,7

2 Visit second home 13608 13,8

Piece and quiet 7381 7,5 9 Sports 2433 2,5

Total Visit second home (VSH) 21214 21,3 Golf 168 0,2

Total Sports/Golf 2601 2,7

3 Sun & bath 2613 2,6

10 Others 1523 1,5

4 Skiing 2762 2,8 Otheractivity 1097 1,1

Total Others 2620 2,6

Aggregated activities

A model for number of trips

The two key factors that make tourism possible is access to

money and leisure time Age group– divided into 5 groups

Income – household income

Gender

Education – university degree or not

If the individual has children at home or not

If the individual is single or not

Size of place of residence

A model for choice of activities

Age, gender, income and lifecycle are key factors that affect activity choice

Age group– divided into 5 groups

Income – household income

Gender

Education – university degree or not

If the individual has children at home or not

If the individual is single or not

Main residential region

Regression analysis

Poisson regression for the number of trips – most people make 1 trip per

month - 98% within 5 trips.

Multinomial logit regression for the choice between 10 activities

Choice of destination • Interaction model used for

destination choice

• Compare calculated and observed number of trips from TDB

• Interaction model estimated by using iteration and minimising misplaced flows

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bij

ckjki

kij DA

DAPM

Results

• Distance is important for the activities VSH and sun/bath – people prefer to do that close to home.

• Skiers and people who visits friends and relatives are less concerned about distance.

Number of trips

SBA VSH Sun bath Ski

Hunt fish

Out door

Nature env

Pleasure/Entertain

Sport 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

X 1000

Tourism Flows

Misplaced flows

SBA VSH SUN&BATH SKI Percentage misplaced flows

18,81 31,91 34,46 26,71

Number of cases in TDB 37 100 10 124 1 818 2 067

Percentageof trips - VFR

0 - 11 - 2.52.5 - 44 - 99 - 18

Percentageof trips - VFR

0 - 11 - 2.52.5 - 44 - 99 - 18

ObservedCalculated

SBA SBA

Social Bond Activity

Visit Second Home

Percentageof trips - VSH

0 - 0.50.5 - 11 - 22 - 33 - 8

Percentageof trips - VSH

0 - 0.50.5 - 11 - 22 - 33 - 8

ObservedCalculated

Sun and Bath

Percentageof trips - sun&bath

0 - 0.50.5 - 1.51.5 - 44 - 1211 - 25

Percentageof trips - sun&bath

0 - 0.50.5 - 1.51.5 - 44 - 1212 - 25

ObservedCalculated

Skiing

Percentageof trips - ski

0 - 0.50.5 - 1.51.5 - 2.52.5 - 1515 - 55

Percentageof trips - ski

0 - 0.50.5 - 1.51.5 - 2.54 - 915 - 55

ObservedCalculated

Problems

• Multinomial logit regression for destination choice failed

• ”Empty” LA-regions – no observations

• ”Holes” in the data – system missing

Data needs• More observations for individuals in

sparsely populated areas• Purpose NOT mixed with activity• ”Clean” the array of variables with

respect to desired information• What about immigrants?• Improve ”visit second home” and

”social bond” activities by using SCB-data

Future experiments

• Change in demand by running ”SVERIGE”– 50 years forward– divorce, labour market, migration

• Change supply – move central point of attraction

Summary

• We model the flow of tourists between LA-regions

• TDB explain choice of activities

• An interaction model calculates the choice of place

END

Questions?