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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Tang, Z., Leung, C.W., Bagchi, K. (2006). Improving Population Estimation with Neural Network Models. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_172
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DOI: https://doi.org/10.1007/11760191_172
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34482-7
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