1. Using spatial microsimulation in a spatial decision support system
Special session on Perspectives on Population Change and Impacts
Robert Tanton
15 November2014

2. Authors
Robert Tanton, NATSEM, Institute for Governance and Policy Analysis, University of Canberra
Yogi Vidyattama, NATSEM, Institute for Governance and Policy Analysis, University of Canberra

3. Initial Thinking
Still some way to go…

4. Structure The Problem One solution Spatial Microsimulation
Planning for population growth
One solution
Spatial Decision Support Systems
Spatial Microsimulation
How to incorporate spatial microsimulation into an SDSS
Advantages and Disadvantages of this approach
Conclusions

5. The Problem Population growth with constraints
Demographic models based on births, deaths and migration
No constraints
Land
Food
Energy
Water
How inform future planning?

6. Example of the ACT Land-Locked Area to the South Areas can’t expand
national park
Areas can’t expand
Hills and ridges
Commonwealth land
Lakes

7. Where will all these people go?
Example of the ACT
Using a cohort-component method, ACT population predicted to grow from 390,000 people (2014) to 682,000 people (2054)
Where will all these people go?

8. One Solution Spatial Decision Support System
Provide data to support planner’s decision making process
Spatial planning with constraints

9. What is an SDSS?
To provide data to support the decision making process and allow the decision makers to resolve semi-structured or ill- structured spatial decision problems.
Final solution not given by program due to complexity - provides scenarios to help decision makers plan
“What would happen if birth rates increased by X?”
Population would increase by Y, water needed would increase by W, etc
(Chakroun and Benie, 2005)

10. Spatial Microsimulation
Small area estimation technique that derives a synthetic dataset for each small area
Synthetic dataset for small area based on real data from survey or completely synthetic
Use survey data with reweighting or selection
Use completely synthetic if don’t have survey with information required
Provides unit record data for all small areas
Use this for cross-tabulations, projections, imputing more data, etc

11. Using spatial microsimulation dataset in a SDSS
Spatial microsimulation provides records for every individual in a small area
This can then be used as a base file for an SDSS
Constraints and imputation can be added

12. Incorporating spatial microsimulation into an SDSS
Demographics (Spatial Microsimulation Base FIle)
Land Use
Economic (Income, House Prices)
Infrastructure
Environment
Employment (Industry)
Livability
These are all different models – could be programmed separately
Arrows show interaction back to base file – could also be interaction between modules
Interaction will require automatic running of models

13. How?
Create synthetic people/households for each small area as a base (2014)
Constrain by Income – assign people to areas they can afford
Assign to a physical house in the area
Grow areas using cohort component method (Births/Deaths)
Assign births to families
If assigned to a house that is too small, move them
Assign deaths randomly based on Age
If assigned to a house that is too large, move a proportion of them
Allocate migration based on Incomes, Work, etc

14. How? Impute water use, heat use, etc, using current variables
Modelling interactions?
Increased population leads to more congestion and lower livability which leads to lower population?
Iterative method – keep running until stable solution
All models need to be connected automatically

15. How?
We have detail on every resident and house in an area – can be as complex as we want

16. Advantages Flexibility Realism Scenario modelling – ‘What If’
Like SimCity

17. Disadvantages Data intensive Complexity Modelling interactions?
Explaining to users?
Modelling interactions?

18. Conclusions
Spatial Microsimulation can provide spatial base data for an SDSS and could be used with other models to constrain growth in areas
The final model would be complex but would provide realistic scenario modelling for planners
Interactions between models could be incorporated

19. References
Birkin, M., Turner, A., Wu, B., Townsend, P., Arshad, J., & Xu, J. (2009). MoSeS: A Grid-Enabled Spatial Decision Support System. Social Science Computer Review, 27(4), 493–508.
Tanton, R., & Edwards, K. L. (Eds) (2013). Spatial Microsimulation: A Reference Guide for Users. Springer Netherlands.