Abstract
Population Projection is the numerical outcome of a specific set of assumptions about future population changes. It is indispensable to the planning of sites as almost all successive planning activities such as the identification of land and housing supply, the release of land, the planning and construction of social and physical infrastructure are population related. This paper proposes a new hierarchical framework based on Hidden Markov Model (HMM), called HMM-Bin framework, for use in long-term population projection. Analyses of various existing suburbs indicate it outperforms traditional Cohort Component model and simple HMM in terms of less data dependency, output flexibility and long-term projection accuracy.
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© 2007 Springer-Verlag Berlin Heidelberg
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Jiang, B., Jin, H., Liu, N., Quirk, M., Searle, B. (2007). A HMM-Based Hierarchical Framework for Long-Term Population Projection of Small Areas. In: Orgun, M.A., Thornton, J. (eds) AI 2007: Advances in Artificial Intelligence. AI 2007. Lecture Notes in Computer Science(), vol 4830. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76928-6_77
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DOI: https://doi.org/10.1007/978-3-540-76928-6_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76926-2
Online ISBN: 978-3-540-76928-6
eBook Packages: Computer ScienceComputer Science (R0)