Improving population estimation with neural network models

Authors:
Zaiyong Tang;Caroline W. Leung;Kallol Bagchi
Affiliations:
College of Administration and Business, Louisiana Tech University, Ruston, LA;College of Administration and Business, Louisiana Tech University, Ruston, LA;Dept. of Information & Decision Sciences, University of Texas at El Paso, TX
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

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Abstract

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.