QMSS2, Leeds, 02-09/07/09 Dynamic population model and an application for Leeds B.M.Wu School of Geography University of Leeds.

QMSS2, Leeds, 02-09/07/09

Dynamic population model and an application for Leeds

B.M.Wu

School of GeographyUniversity of Leeds

QMSS2, Leeds, 02-09/07/09

Outline

IntroductionApproaches of modelling social systemsAn application for LeedsModel descriptionInitial result analysisModel improvementSummary

QMSS2, Leeds, 02-09/07/09

To understand:

various social modelling approaches

individual based models

dynamic MSM

how a typical dynamic MSM is structured

how a typical dynamic MSM works

importance of data for a MSM

alternative modelling approaches that may compliment MSM

Learning objectives

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Social Systems are “messy”

boundaries large and complex (Moss, 2000)

Approaches of modelling social systems

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Individual Based Models (IBM):

MSM (Microsimulation Model) CA (Cellular Automata) ABM (Agent Based Model)

Approaches of modelling social systems

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MSM

Approaches of modelling social systems

+

t = 1, 2, … , t = n

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MSM: Static vs Dynamic

Approaches of modelling social systems

Type of MSM characteristics Ageing technique

EntityInteractivity

Time PopulationChange

Impact of previous step on the next

Static Deterministic / Stochastic

Static ageing No No time element/ stocks of entities updates

No No

Dynamic Stochastic Dynamic ageing

Possible Change process and events built in

Yes Yes

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Advantages of Static MSM:

quicker to run

simpler to develop and understand

lower costs: computing resources, skills and development time

often with very detailed programme simulations

Approaches of modelling social systems

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Advantages of Dynamic MSM:

more details

better representation of population ageing, especially in long term, as it accounts interim changes in economic and demographic trends

generally accepted more realistic representation of micro population unit changes

Approaches of modelling social systems

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MSM: Spatial and non-Spatial

Approaches of modelling social systems

“One can not be at two places at the same time.”(Hägerstrand, 1967)

“Means are to be employed somewhere.” (De Man, 1998)

People have to live in a local area and they are affected by local environment.

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CA

source: http://www.bitstorm.org/gameoflife/

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ABM

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An application of Leeds: modelling objectives

Modelling objectives:

To develop a complete representation of the Leeds population at a fine spatial scale

To produce rich, detailed and robust forecasts of the future population of Leeds

To investigate scenarios which relate demographics to service provision

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Modelling Description Dynamic representation of key demographic events /transactions in a geographically identified population Macrosimulation and microsimulation models (MSM) are alternative ways of realising the processes (van Imhoff and Post, 1998) We use a spatial MSM of the population and its dynamics, but the structure parallels the macro multi-state cohort-component (MSCC) projection model An MSM depends on good data on the important transitions experienced by individuals We experimented with an Agent Based Model(ABM) for a sub-population, students, where empirical data on migration has often proved problematic

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What does that mean? Scale

Leeds population:760,000 Each individual has about 60 individual variables +

20 household variables + area variables

Various probabilities/rates eg: localised single year of age based mortality probabilities

Movement, interaction and behaviour

Distinctive behaviours from various population groups in different demographic processes

Interdependency of household and individual variables in different demographic processes

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Demographic processes in the MSM

6 modularised processes :

simple processes

complex processes

individuals and households

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Initial Results: Leeds population change

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Initial Results: small area variation

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Characteristics of student migrants

Students are highly mobile during their studies in the universities

Mostly only move around the area close to the universities where they study, NOT in the suburban areas

Most of them will leave the city once they finish their study, NOT growing old in the suburban areas

Due to the replenishment of the student population each year, the population of the small areas where university student stay tends to remain younger than other areas

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ABM

An alternative approach that models individuals as agents through their interactions with each other and the environment that they live in.

It is very flexible to introduce heterogeneous agents with distinctive behaviours through their built-in rules

It is useful in modelling features of the population where knowledge and data is lacking (Billari et al. , 2002).

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ABM experiments: Student Migrants We recognise the following groups:

•First year undergraduates•Other undergraduates•Master students•Doctoral students

We apply the following general rules:•Each group is allowed set years to stay in the area •Students prefer to stay with their fellow students•Students stay close to their university of study, subject to housing availability•They don’t “do” marriage and fertility

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Observed Predicted

Comparison of Results: Pure MSM

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Comparison of Results: MSM with ABM

Observed Predicted

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We have discussed the difficulty in modelling the social systems and various modelling approaches.

IBM provide detailed info at individual level and MSM is an important social modelling approach, especially in assisting public policy development and planning.

Dynamic MSM provides a more realistic reflection of the studied system than static MSM.

Typical dynamic MSM structure and functions.

MSM depends on quality data and may be strengthened by complementary techniques such as ABM where there is a knowledge gap.

Summary

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Thank [email protected]