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Micro-Simulation Modelling of Domestic Tourism in Sweden. KIRUNA. Anders Lundgren Dep. Geography, Umeå University. SVERIGE. S ystem for V isualizing E conomic and R egional I nfluences G overning the E nvironment

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Micro simulation modelling of domestic tourism in sweden
Micro-Simulation Modelling of Domestic Tourism in Sweden


Anders Lundgren

Dep. Geography, Umeå University


System for Visualizing Economic and Regional

Influences Governing the Environment

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

Socioeconomic data for ALL individuals

Current modules

Current Modules


What is microsimulation

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 and spatial

- What happens if we increase immigration?


80 000 immigrants

Base case – 60 000 immigrants

National tourism module

”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

Swedish Tourist Database

Managed by Åre marknadsfakta

14 years

24 000 interviews every year

Using 10 years

Number of cases

Number of cases

Randomly collected

Densely populated areas are well represented

Different categories of tourism

Different categories of tourism

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

Limitations in data

Limitations in data

To few observations to perform regressions analyses IF

you look at municipalities and each activity in TDB

Micro simulation modelling of domestic tourism in sweden

  • 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/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)

A model for number of trips

A model for number of trips

The two key factors that make tourism possible is access to money and leisure time

A model for choice of activities

A model for choice of activities

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

Regression analysis

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

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



X 1000

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.

Tourism flows

Tourism Flows

Misplaced flows



Multinomial logit regression for destination choice failed

”Empty” LA-regions – no observations

”Holes” in the data – system missing

Data needs

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

Future experiments

Change in demand by running ”SVERIGE”

50 years forward

divorce, labour market, migration

Change supply – move central point of attraction



We model the flow of tourists between LA-regions

TDB explain choice of activities

An interaction model calculates the choice of place

End questions