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Demographic Forecasting for Local Governments in Queensland, Australia – Difficult, But Effective

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The Frontiers of Applied Demography

Part of the book series: Applied Demography Series ((ADS,volume 9))

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Abstract

In Australia, demographic projections are available at national, state and local level through Census, Bureau of Statistics and variety of institutes, however, demographic projections for smaller areas of local government/County/Shire/Council/Municipality are often developed by the Local government authority.

In Queensland, Australia, basic demographic information on dwellings and population can easily be extracted from various Council resources. The availability of databases on Property tax rates and property development status as well as technological advances like Queensland Globe, Open-Data, Google Street-view, regular aerial view updates and the like have made validating demographic forecasts feasible. It has become an effective desktop exercise to model land use across the local government area. Broader statistics provide information on trends in dwellings and population at local government level. Physical constraints are mapped and shared by various government agencies. Land uses are guided by local governments via planning schemes and development codes. Through a “attractor and detractor” analysis, projections for next 15 years are provided in quinquennial cycles. Considering all of the above inputs, it is possible to project what is achievable for a given Council area down to the lot level. Basic forecasts on land use, dwellings & population can be developed efficiently for local governments in Queensland because of data availability and consistent structure and functioning of local governments. Forecasts can be regularly updated depending on the size of local government, although the task is relatively less complex and often more accurate for smaller Councils. This chapter describes forecast methods based on government agency data, methods that have been regularly applied over a number of years by various Councils.

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Notes

  1. 1.

    “Open Knowledge” is a worldwide non-profit network of people passionate about openness, using advocacy, technology and training to unlock information and enable people to work with it to create and share knowledge.

    Open knowledge is what open-data becomes when it is useful, usable and used.

    The key features of openness are:

    • Availability and access: the data must be available as a whole and at no more than a reasonable reproduction cost, preferably by downloading over the internet. The data must also be available in a convenient and modifiable form.

    • Reuse and redistribution: the data must be provided under terms that permit reuse and redistribution including the intermixing with other datasets. The data must be machine-readable.

    • Universal participation: everyone must be able to use, reuse and redistribute — there should be no discrimination against fields of endeavour or against persons or groups. For example, ‘non-commercial’ restrictions that would prevent ‘commercial’ use, or restrictions of use for certain purposes (e.g. only in education), are not allowed.

  2. 2.

    AURIN:

    • Brings together and streamlines access to more than 1,000 datasets previously difficult, time consuming or costly to obtain

    • Provides the online capability to combine data at various levels of scale from multiple sources

    • Delivers online access to open source e-research tools to interrogate, model and visualise data.

    • Analytical capabilities include statistical and spatial modelling, planning and simulation tools, graphing and mapping routines (including 2D and 3D visualisation).

    • Provides the network to facilitate collaboration, partnerships and knowledge sharing across academia, all levels of government, and the private sector.

    Where needed, AURIN provides merit-based securitised access protocols to interrogate unit record data, maintaining privacy and confidentiality.

    The AURIN Workbench is the collective term for the applications and services that the AURIN project has delivered. Most data and services are delivered through the ‘Portal’, a flagship application, but other tools and capability are made available through other websites; in many cases, these sites are open access and do not require the AAF login that the Portal does.

  3. 3.

    Brian Lister Planning approach

    Brian Lister as a consultant worked with number of companies and local governments to develop a small area growth projection model for predicting population and employment at the lot level. This methodology has been automated by developing model suitable for individual local government. Over the years, methodology has evolved by resolving the shortcomings known through implementation of the model.

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Acknowledgements

The author is grateful to a number of people for comments and suggestions, including David A. Swanson and Ashish G.Shah.

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Correspondence to Kanan M. Saraiya .

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Saraiya, K.M. (2017). Demographic Forecasting for Local Governments in Queensland, Australia – Difficult, But Effective. In: Swanson, D. (eds) The Frontiers of Applied Demography. Applied Demography Series, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-319-43329-5_17

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  • DOI: https://doi.org/10.1007/978-3-319-43329-5_17

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