Demographic projections are essential in helping understand future service provision. I use a synthetic population of your area for demographic modelling, which means I use the ABS Census and Survey data to create an individual and household level population that represents the towns in your council area, while maintaining the confidentiality of the citizens in your area.
I then project this synthetic population forward, using birth and death rates from the ABS, and migration rates from the latest Census. This provides a base file of what areas will look like in the future.
I can use the ABS geographies to derive population estimates from smallest villages to largest cities.
The power of this demographic modelling is not just in identifying what services are going to need to be provided into the future, but being able to conduct what if analysis. The projections require certain assumptions, for example, a base level of migration. What if analysis allows me to change this base level of migration, in consultation with the client, to be able to say: What if migration to an area was higher than it has been historically? How many people would there be in 2030? How much income from rates would be received? How many primary school students would be there? Would there be enough to support a new school? All these are important questions that can be answered with demographic modelling and what if analysis.
Examples of my work
Harding, Vidyattama, Tanton (2011), “Demographic change and the needs-based planning of government services: projecting small area populations using spatial microsimulation”, Journal of Population Research, 28: 203. https://doi.org/10.1007/s12546-011-9061-6
Tanton, Vidyattama, McNamara, Vu and Harding (2009), “Old, Single and Poor: Using Microsimulation and Microdata to Analyse Poverty and the Impact of Policy Change among Older Australians”, Economic Papers: A journal of applied economics and policy, 28: 102-120. doi:10.1111/j.1759-3441.2009.00022.x
Namazi-Rad, Tanton, Steel, Mokhtarian, Das (2017), “An unconstrained statistical matching algorithm for combining individual and household level geo-specific census and survey data”, Computers, Environment and Urban Systems, Volume 63, Pages 3-14, https://doi.org/10.1016/j.compenvurbsys.2016.11.003.