Spatial microsimulation for urban, regional and social policy analysis

1. Spatial microsimulation for urban, regional and social policy analysis Dimitris Ballas Centre for Computational Geography School of Geography University of Leeds

2. Outline • Traditional spatial modelling approaches to policy analysis and socio-economic impact assessment • What is microsimulation? • Spatial microsimulation for socio-economic impact assessment - modelling a plant closure in Leeds • Spatial microsimulation for social policy analysis • Simulating the city • Spatial microsimulation research agenda

3. Spatial modelling approaches to socio-economic impact assessment • Regional Keynesian multiplier analysis • Input-output models • Regional Econometric Models • Spatial Interaction Models (modelling TTW flows)

4. Spatial modelling approaches to socio-economic impact assessment ‘Which regions will suffer most? Which towns will be most affected?’ (Armstrong and Taylor, 1993:5) BUT regions and cities comprise of smaller areas, which differ considerably in population size, demographic structure, etc.

5. Spatial modelling approaches to socio-economic impact assessment ‘To move to better dynamic representations of urban processes suggests that individuals rather than groups or aggregates must form the elemental basis of these simulations’ (Batty, 1996:261) ‘Governments need to predict the outcomes of their actions and produce forecasts at the local level.’ (Openshaw, 1995: 60) • Which neighbourhoods will suffer/benefit most? • Which households will be most affected? • What will be the intra-region, intra-urban and intra-ward impact of a possible plant closure/development?

6. What is microsimulation? • A technique aiming at building large scale data sets • Modelling at the microscale • A means of modelling real life events by simulating the characteristics and actions of the individual units that make up the system where the events occur

7. What is spatial microsimulation? An example • sex by age by economic position (1991 UK Census SAS table 08) • level of qualifications by sex (1991 UK Census SAS table 84) • socio-economic group by economic position (1991 UK Census SAS table 92)

8. What is microsimulation? An example • p(xi ,S,A,Q,EP,SEG) given a set of constraints or known probabilities: • p(xi ,S,A,EP) • p(xi ,Q,S) • p(xi ,SEG,EP) IPF-based microsimulation, CO-based microsimulation

9. Microsimulation: tenure allocation procedure After Clarke, G. P. (1996) , Microsimulation: an introduction, in G.P. Clarke (ed.) , Microsimulation for Urban and Regional Policy Analysis, Pion, London.

10. Advantages and drawbacks of microsimulation Advantages • Data linkage • Spatial flexibility • Efficiency of storage • Ability to update and forecast Drawbacks • Difficulties in calibrating the model and validating the model outputs • Large requirements of computational power

11. Microsimulating the local labour force

12. SimLeeds: a spatial microsimulation model for Leeds • Object oriented framework • Households or individuals can be viewed as objects - e.g. a Household class describes the features of all households (e.g. age, sex and marital status of head of household, employment status, tenure, etc.) • Different approaches to the estimation of household attributes

13. SimLeeds microsimulated attributes (variables of micro-unit) Stage 1 Stage 2 Stage 3

14. Using SimLeeds for impact assessment - modelling a plant closure in Leeds

15. The hypothetical plant’s workforce structure

16. Journey to work to Seacroft

17. Spatial distribution of SIC3 Managerial and Technical workforce

18. Estimated spatial distribution of total income loss in Seacroft, Halton and Whinmoor

19. Estimated spatial distribution of Job Seekers Allowance (JSA) new recipients in Seacroft, Halton and Whinmoor

20. Estimate the change in the demand for groceries

21. Estimated spatial distribution of change of demand for Food & non-alcoholic drinks in Seacroft, Halton and Whinmoor

22. Modelling a change in income tax

23. Current estimated distribution of tax paid

24. Estimated spatial distribution of change in tax paid under scenario 1

25. Estimated spatial distribution of change in tax paid under scenario 2

26. Modelling the Income and Substitution effect • A substitution effect making leisure more attractive than work • An income effect, encouraging people to work more to make up the loss of income “Different taxes have different effects, and affect people at different levels of income or in different household circumstances in different ways.” (Hill and Bramley, 1986: 85)

27. Towards a microsimulation-based local multiplier impact analysis • Multiplier effects to different localities • Increase/Decrease of consumption of goods and possible changes of consumer preferences • Further employment and income effects (caused by increase/decrease in consumption etc.) • Third and fourth round local multiplier effects (further job/income gains/losses generated)

28. Modelling the budget changes • Use SimLeeds to model the government budget changes • Use SimLeeds to model the opposition’s proposals for pensioners • Use SimLeeds to formulate and evaluate new policies

29. Source: The Guardian, 22 March 2000

30. Source: The Guardian, 22 March 2000

31. Estimated spatial distribution of pensioner couples in Leeds

32. Estimated spatial distribution of pension increases under the Conservatives’ proposals.

33. Human systems modelling -RS and spatial microsimulation for the generation of population microdata • S = (ed,h,g, ed,h,x1,x2,…,xn) (1) • from: • M = (ed,h, x1,x2,…,xn) (2) • R = (ed,h,g) (3) • where: • S: estimated spatially disaggregated population microdata set at the house level. • M: microsimulation output - spatially disaggregated population microdata set • R: remotely sensed data set • ed: the Enumeration District location of each household • h: the housing type • g: the exact geographical co-ordinates of each house • x1…xn: socio-economic and demographic attributes

35. Remotely Sensed data Microsimulation model output

36. Microsimulation research agenda • Combining census data and remotely census data for the generation of population microdata • Analysis of electoral behaviour • Models such as SimLeeds can be linked to Virtual Decision Making Environments • Adaptive rule-based agents approaches • Dynamic microsimulation / event modelling • SimIreland • SimYork, SimBritain, SimWorld!