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

2. QMSS2, Leeds, 02-09/07/09 Outline Introduction Approaches of modelling social systems An application for Leeds Model description Initial result analysis Model improvement Summary

3. 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

4. QMSS2, Leeds, 02-09/07/09 Social Systems are “messy” boundaries large and complex (Moss, 2000) Approaches of modelling social systems

5. QMSS2, Leeds, 02-09/07/09 Individual Based Models (IBM): MSM (Microsimulation Model) CA (Cellular Automata) ABM (Agent Based Model) Approaches of modelling social systems

6. QMSS2, Leeds, 02-09/07/09 MSM Approaches of modelling social systems + t = 1, 2, …, t = n

7. QMSS2, Leeds, 02-09/07/09 MSM: Static vs Dynamic Approaches of modelling social systems Type of MSMcharacteristicsAgeing technique Entity Interactivity TimePopulation Change Impact of previous step on the next StaticDeterministic / Stochastic Static ageingNoNo time element/ stocks of entities updates No DynamicStochasticDynamic ageing PossibleChange process and events built in Yes

8. QMSS2, Leeds, 02-09/07/09 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

9. QMSS2, Leeds, 02-09/07/09 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

10. QMSS2, Leeds, 02-09/07/09 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.

11. QMSS2, Leeds, 02-09/07/09 CA source : http://www.bitstorm.org/gameoflife/ http://www.bitstorm.org/gameoflife/

12. QMSS2, Leeds, 02-09/07/09 ABM

13. QMSS2, Leeds, 02-09/07/09 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

14. QMSS2, Leeds, 02-09/07/09 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

15. QMSS2, Leeds, 02-09/07/09 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

16. QMSS2, Leeds, 02-09/07/09 Demographic processes in the MSM 6 modularised processes :  simple processes  complex processes  individuals and households

17. QMSS2, Leeds, 02-09/07/09 Initial Results: Leeds population change

18. QMSS2, Leeds, 02-09/07/09 Initial Results: small area variation

19. QMSS2, Leeds, 02-09/07/09 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

20. QMSS2, Leeds, 02-09/07/09 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).

21. QMSS2, Leeds, 02-09/07/09 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

22. QMSS2, Leeds, 02-09/07/09 Observed Predicted Comparison of Results: Pure MSM

23. QMSS2, Leeds, 02-09/07/09 Comparison of Results: MSM with ABM Observed Predicted

24. QMSS2, Leeds, 02-09/07/09 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

25. QMSS2, Leeds, 02-09/07/09 Thank you! B.Wu@Leeds.ac.uk