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

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

Initial Thinking Still some way to go…

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

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

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

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?

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

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)

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

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

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

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

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

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

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

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

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

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. Robert.tanton@canberra.edu.au