Volume 29 - Article 22 | Pages 579–616
Validation of spatially allocated small area estimates for 1880 Census demography
By Matt Ruther, Galen Maclaurin, Stefan Leyk, Barbara Buttenfield, Nicholas Nagle
References
Assunção, R.M., Schmertmann, C.P., Potter, J.E., and Cavenaghi, S.M. (2005). Empirical Bayes estimation of demographic schedules for small areas. Demography 42(3): 537-558.
Ballas, D., Clarke, G., Dorling, D., Eyre, H, Thomas, B., and Rossiter, D. (2005). SimBritain: A spatial microsimulation approach to population dynamics. Population, Space and Place 11(1): 13-34.
Beckman, R.J., Baggerly, K.A., and McKay, M.D. (1996). Creating synthetic baseline populations. Transportation Research Part A 30(6): 415-429.
Bogue, D.J. (1951). State economic areas. Washington: U.S. Bureau of the Census.
Demšar, U., Harris, P., Brunsdon, C., Fotheringham, A.S., and McLoone, S. (2013). Principal component analysis on spatial data: An overview. Annals of the Association of American Geographers 103(1): 106-128.
Goeken, R., Nguyen, C., Ruggles, S., and Sargent, W. (2003). The 1880 U.S. population database. Historical Methods 36(1): 27-34.
Hermes, K. and Poulsen, M. (2012). A review of current methods to generate synthetic spatial microdata using reweighting and future directions. Computer, Environment and Urban Systems 36(4): 281-290.
Johnston, R.J. and Pattie, C.J. (1993). Entropy-maximizing and the iterative proportional fitting procedure. Professional Geographer 45(3): 317-322.
Jolliffe, I.T. (2002). Principal component analysis (2nd edition). Berlin: Springer Verlag.
Kaiser, H.F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement 20(1): 141-151.
Leyk, S., Buttenfield, B.P., and Nagle, N. (2013). Modeling ambiguity in Census microdata allocations to improve demographic small area estimates. Transactions in Geographic Information Science 17(3): 406-425.
Leyk, S., Nagle, N., and Buttenfield, B.P. (2013). Maximum entropy dasymetric modeling for demographic small area estimation. Geographical Analysis 45(3): 285-306.
Logan, J.R., Jindrich, J., Shin, H., and Zhang, W. (2011). Mapping America in 1880: The urban transition historical GIS project. Historical Methods: A Journal of Quantitative and Interdisciplinary History 44(1): 49-60.
Malouf, R. (2002). A comparison of algorithms for maximum entropy parameter estimation. In: Proceedings of the sixth conference on natural language learning (CoNLL-2002). New Brunswick, NJ: 49-55.
Massey, D.S. and Denton, N.A. (1988). The dimensions of residential segregation. Social Forces 67(2): 281-315.
Melhuish, T., Blake, M., and Day, S. (2002). An evaluation of synthetic household populations for Census Collection Districts created using optimization techniques. Australasian Journal of Regional Studies 8(3): 369-387.
Mrozinski, Jr., R.D. and Cromley, R.G. (1999). Single – and doubly – constrained methods of areal interpolation for vector-based GIS. Transactions in GIS 3(3): 285-301.
Nagle, N.N., Buttenfield, B.P. , Leyk, S., and Spielman, S.E. (2012). An uncertainty-informed penalized maximum entropy dasymetric model. Paper presented at the 7th International Conference on Geographic Information Science (GIScience 2012), Columbus, OH, September 18-21, 2012.
Nagle, N.N., Buttenfield, B.P., Leyk, S., and Spielman, S.E. (2013, forthcoming). Dasymetric modeling and uncertainty. Annals of the Association of American Geographers .
National Research Council (2007). Using the American Community Survey: Benefits and challenges. In: Citro, C.F. and Kalton, G. (eds.). Panel on the Functionality and Usability of Data from the American Community Survey. Washington, DC: The National Academies Press, Committee on National Statistics, Division of Behavioral and Social Sciences and Education.
Phillips, S.J., Anderson, R.P., and Schapire, R.E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling 190(3-4): 231-259.
Ruggles, S., Alexander, J.T., Genadek, K., Goeken, R., Schroeder, M.B., and Sobek, M. (2010). Integrated public use microdata series: Version 5.0 [machine-readable database]. Minneapolis: University of Minnesota.
Smith, D.M., Clarke, G.P., and Harland, K. (2009). Improving the synthetic data generation process in spatial microsimulation models. Environment and Planning A 41(5): 1251-1268.
Tanton, R., Vidyattama, Y., Nepal, B., and McNamara, J. (2011). Small area estimation using a reweighting algorithm. Journal of the Royal Statistical Society, Series A 174(4): 931-951.
Voas, D. and Williamson, P. (2001). Evaluating goodness-of-fit measures for synthetic microdata. Geographical & Environmental Modelling 5(2): 177-200.
Williamson, P., Birkin, M., and Rees, P.H. (1998). The estimation of population microdata by using data from small area statistics and samples of anonymised records. Environment and Planning A 30(5): 785-816.