Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/83650
Title: Entropy Based Modelling for Estimating Demographic Trends
Authors: Monterola, Christopher
Li, Guoqi
Zhao, Daxuan
Xu, Yi
Kuo, Shyh-Hao
Xu, Hai-Yan
Hu, Nan
Zhao, Guangshe
Keywords: Computer Science and Engineering
Issue Date: 2015
Source: Li, G., Zhao, D., Xu, Y., Kuo, S.-H., Xu, H.-Y., Hu, N., et al. (2015). Entropy Based Modelling for Estimating Demographic Trends. PLoS ONE, 10(9), e0137324-.
Series/Report no.: PLoS ONE
Abstract: In this paper, an entropy-based method is proposed to forecast the demographical changes of countries. We formulate the estimation of future demographical profiles as a constrained optimization problem, anchored on the empirically validated assumption that the entropy of age distribution is increasing in time. The procedure of the proposed method involves three stages, namely: 1) Prediction of the age distribution of a country’s population based on an “age-structured population model”; 2) Estimation the age distribution of each individual household size with an entropy-based formulation based on an “individual household size model”; and 3) Estimation the number of each household size based on a “total household size model”. The last stage is achieved by projecting the age distribution of the country’s population (obtained in stage 1) onto the age distributions of individual household sizes (obtained in stage 2). The effectiveness of the proposed method is demonstrated by feeding real world data, and it is general and versatile enough to be extended to other time dependent demographic variables.
URI: https://hdl.handle.net/10356/83650
http://hdl.handle.net/10220/39134
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0137324
Schools: School of Computer Engineering 
Rights: © 2015 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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