Improving Population Estimation with Neural Network Models

  • Zaiyong Tang
  • Caroline W. Leung
  • Kallol Bagchi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


Intercensal and postcensal population estimates are essential in federal, state, and local governments planning and resource allocation. Traditionally, linear regression based models are widely used for projecting population distributions in a given region. We constructed population projection models with various types of artificial neural networks. Using historical census data, we tested the performance of the neural network models against the ratio correlation regression model that we have used for the last 20 years. The results indicate that properly trained neural networks outperform the regression model in both model fitting and projection. Among the different neural network models we tested, the fuzzy logic based neural network performed the best.


Neural Network Model Hide Node Feedforward Neural Network Mean Absolute Percentage Error State Total 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zaiyong Tang
    • 1
  • Caroline W. Leung
    • 1
  • Kallol Bagchi
    • 2
  1. 1.College of Administration and BusinessLouisiana Tech UniversityRustonUSA
  2. 2.Dept. of Information & Decision SciencesUniversity of Texas at El PasoUSA

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