A HMM-based hierarchical framework for long-term population projection of small areas

  • Authors:

  • Bin Jiang;Huidong Jin;Nianjun Liu;Mike Quirk;Ben Searle

  • Affiliations:

  • The Australian National University, Canberra, Australia;The Australian National University, Canberra, Australia and NICTA, Canberra Lab, ACT, Australia;The Australian National University, Canberra, Australia and NICTA, Canberra Lab, ACT, Australia;ACT Planning and Land Authority;ACT Planning and Land Authority and Geoscience Australia

  • Venue:
  • AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

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.