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Basic Concepts

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Subnational Population Estimates

Part of the book series: The Springer Series on Demographic Methods and Population Analysis ((PSDE,volume 31))

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Abstract

Creating, interpreting, and evaluating population estimates involves demographic, geographic, and statistical methods and data. This chapter introduces the major demographic concepts of size, distribution, characteristics, and the components of population change along with geographic concepts including Geographic Information Systems (GIS), density, center of population, concentration and clustering, distance, accessibility, and spatial interaction. We conclude with material on descriptive and inferential statistics, and regression techniques.

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References

  • Aronoff, S. (1989). Geographic information systems: A management perspective. Ottawa: WDL Publications.

    Google Scholar 

  • Bachi, R. (1958). “Statistical analysis of geographic series.” Bulletin of the International Statistical Institute 32(2), 229–240.

    Google Scholar 

  • Belsley, D., Kuh, A. E. & Welsch, R.E. (1980). Regression diagnostics: Identifying influential data and sources of collinearity. New York: John Wiley & Sons.

    Book  Google Scholar 

  • Blalock, H. M. (1972). Social statistics. New York: McGraw Hill.

    Google Scholar 

  • Bryan, K. N. and R. George (2004). Geographic Information Systems. In J. S. Siegel, & D. A. Swanson (Eds.) Methods and Materials of Demography, Second Edition (pp. 733–750). New York: Elsevier Academic Press.

    Google Scholar 

  • Chatterjee, S. and Hadi, A. S. (2006). Regression analysis by example. New York: John Wiley & Sons.

    Book  Google Scholar 

  • Draper, N. and Smith, H. (1981). Applied regression analysis, second edition. New York: John Wiley & Sons.

    Google Scholar 

  • Espenshade, T. J. and Tayman, J. (1982). "Confidence intervals for post-censal population estimates." Demography 19(2), 191–210.

    Article  Google Scholar 

  • Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2000). Quantitative geography: Perspectives on spatial data analysis. London: Sage Publications.

    Google Scholar 

  • Gnedenko, B. V. (1989). The theory of probability, Fourth Edition. New York: Chelsea Publishing Company.

    Google Scholar 

  • Hahn, G. J. and Meeker, W. Q. (1991). Statistical intervals: A guide for practitioners. New York: John Wiley & Sons.

    Book  Google Scholar 

  • Hartley, H. and R. Sielken, Jr. 1975. A “Super-Population Viewpoint” for Finite Population Sampling. Biometrics 31 (2): 411–422

    Article  Google Scholar 

  • Henkel, R. E. (1976). Tests of significance. Beverly Hills: Sage Publications.

    Google Scholar 

  • Hobbs, F. B. (2004). Age and sex composition. In J. S. Siegel, & D. A. Swanson (Eds.) Methods and Materials of Demography, Second Edition (pp. 125–173). New York: Elsevier Academic Press.

    Google Scholar 

  • Hodges, K. (1995). An evaluation of geometric data retrieval methods. Paper presented at the annual meeting of the Population Association of America. San Francisco, CA.

    Google Scholar 

  • Hosmer, D. W. and Lemeshow, S. (1989). Applied logistic regression. New York: John Wiley & Sons.

    Google Scholar 

  • Ikeda, M. (2008). Developing guidelines based on CVs for when one-year estimates can be used instead of three-year estimates in the American Community Survey (ACS) for areas with populations of 65,000 or more. Washington, D.C: US Census Bureau, Statistical Research Division.

    Google Scholar 

  • Jaeger, R. M. (1983). Statistics: A spectator sport. Beverly Hills: Sage Publications.

    Google Scholar 

  • Kintner, H. J. and D. Swanson, D. A. (1993). “Measurement errors in census counts and estimates of inter-censal net migration.” Journal of Social and Economic Measurement 19 (2), 97–120.

    Google Scholar 

  • Kish, L. (1965). Survey Sampling. New York: John Wiley & Sons.

    Google Scholar 

  • Kraemer, H. C. and Thiemann, S. (1987). Statistical power analysis in research. Beverly Hills: Sage Publications.

    Google Scholar 

  • Kreitz, M. S., Doherty, T., & Rindsfuser, G. (2002). Collection of spatial behavior data and their use in activity scheduling models. Paper presented at the annual Meeting of the Transportation Research Board. Washington, DC

    Google Scholar 

  • Langley, R. (1970). Practical statistic: Simply explained. New York: Dover Publications.

    Google Scholar 

  • Lehmann, E. L. (1986). Testing statistical hypothesis. New York: John Wily & Sons.

    Google Scholar 

  • Levin, R. S. and Rubin, D. S. (1998). Statistics for management, Seventh Edition. Englewood Cliffs: Prentice-Hall.

    Google Scholar 

  • Los Angeles County Department of Public Health (2010). Life expectancy in Los Angeles County: How long do we live and why? A cities and communities report. Los Angeles, CA.

    Google Scholar 

  • Lowry, I. S. (1964). A model of metropolis. Santa Monica: The Rand Corporation.

    Google Scholar 

  • Martin, J. A., Hamilton, B. E., Sutton, P. D., et al. (2010). Births: Final data for 2007. National Vital Statistics Report, Vol. 58, No. 24. Hyattsville: National Center for Health Statistics.

    Google Scholar 

  • McKibben, J. N. (2004). Racial and ethnic composition. In J. S. Siegel, & D. A. Swanson (Eds.) Methods and Materials of Demography, Second Edition (pp. 175–189). New York: Elsevier Academic Press.

    Google Scholar 

  • National Oceanic and Atmospheric Association, US Census Bureau, & American Congress on Surveying and Mapping. (2004). Center of population project. Washington, DC (http://www.ngs.noaa.gov/INFO/COP/).

  • O’Hare, W. P., Pollard, K. M. & Ritualo, A. (2004). Educational and economic characteristics. In J. S. Siegel, & D. A. Swanson (Eds.) Methods and Materials of Demography, Second Edition (pp.211–215). New York: Elsevier Academic Press.

    Google Scholar 

  • Perz, S. G. (2004). Population Change. In J. S. Siegel, & D. A. Swanson (Eds.) Methods and Materials of Demography, Second Edition (pp. 253–263). New York: Elsevier Academic Press.

    Google Scholar 

  • Plane, D. A. (2004). Population distribution-Geographic areas. In J. S. Siegel, & D. A. Swanson (Eds.) Methods and Materials of Demography, Second Edition (pp. 81–104). New York: Elsevier Academic Press.

    Google Scholar 

  • Putman, S. H. (1983). Integrated urban models. London: Pion Limited.

    Google Scholar 

  • Putman, S. H. (1994). Integrated transportation and land use models: An overview of progress with DRAM and EMPAL, with suggestions for further research. Paper presented at the annual meeting of the Transportation Research Board. Washington, DC.

    Google Scholar 

  • Robinson, J. G., B. Ahmed, P. Das Gupta, and K. Woodrow. 1993. Estimates of Population Coverage in the 1990 United States Census Based on Demographic Analysis. Journal of the American Statistical System 88: 1061–1071.

    Article  Google Scholar 

  • Rodrigue, J. P., Comtois, C. & Slack, B. (2009). The geography of transport systems. New York: Routledge.

    Google Scholar 

  • Sampath, S. 2005. Sampling Theory and Methods. Harrow, England: Alpha Science International Ltd.

    Google Scholar 

  • SANDAG (2010a). “Subregional and Major Statistical Areas in San Diego County.” San Diego, CA: San Diego Association of Governments (http://gis.sandag.org/boundary/viewer.htm).

  • SANDAG (2010b). “2050 Regional Growth Forecast.” San Diego, CA: San Diego Association of Governments (http://profilewarehouse.sandag.org/profiles/fcst/reg999fcst.pdf)

  • Sharkova, I. V. (2000). With or without GIS? Evaluating accuracy, timeliness, and costs of population estimates for user-defined areas. Paper presented at the annual meeting of the Population Association of America. Los Angeles, CA.

    Google Scholar 

  • Smith, S. K., J. Tayman, J., & Swanson, D. A. (2001). State and local population projections: Methodology and analysis. New York: Kluwer Academic/Plenum Publishers.

    Google Scholar 

  • State of California. (2011). Population estimates and components of change by county, July 1, 1999–2010 with 2010 Census benchmark. Sacramento, CA.

    Google Scholar 

  • Stock, J. H. and Watson, M. H. (2003). Introduction to econometrics. Boston: Addison Wesley.

    Google Scholar 

  • Swanson, D. A. 2004. “Advancing Methodological Knowledge within State and Local Demography: A Case Study.” Population Research and Policy Review 23: 379–398.

    Article  Google Scholar 

  • Swanson, D. A. 2012. Learning Statistics: A Manual for Sociology Students. San Diego, CA: Cognella Academic Publishing.

    Google Scholar 

  • Swanson, D. A. and Siegel, J. S. (2004). Introduction. in J. S. Siegel, & D. A. Swanson (Eds.) Methods and Materials of Demography, Second Edition (pp. 1–8). New York: Elsevier Academic Press.

    Google Scholar 

  • Swanson, D. A. and Stephen, G. E. (2004). Glossary. In J. S. Siegel, & D. A. Swanson (Eds.) Methods and Materials of Demography, Second Edition (pp. 571–778). New York: Elsevier Academic Press.

    Google Scholar 

  • Swanson, D. A., Tayman, J., & Barr, C. F. (2000). “A note on the measurement of accuracy for subnational demographic estimates.” Demography 37(2), 193–201.

    Article  Google Scholar 

  • Swanson, D. A. (1989). “Confidence intervals for post-censal population estimates: A case study for local areas.” Survey Methodology 15, 271–280.

    Google Scholar 

  • Swanson, D. A. (2008). Measuring uncertainty in population data generated by the cohort-component method: A report on research in progress In S. H. Murdock & D. A. Swanson (Eds.) Applied Demography in the 21st Century (pp. 165–184). Dordrecht, Heidelberg, London, and New York: Springer.

    Google Scholar 

  • Tayman, J. (1999). Post-censal population estimates for census blocks: The San Diego experience. Paper presented at the US Bureau of the Census Population Estimates Conference. Washington, DC.

    Google Scholar 

  • Tayman, J. and Swanson. D. A. (1999). “On the validity of the MAPE as a measure of forecast accuracy.” Population Research and Policy Review 18(4), 299–322.

    Article  Google Scholar 

  • Tordella, S. J. (1987). Geometric data retrieval methods in commercial sector demography. In D. A. Swanson and J. W. Wicks (Eds.), Issues in Applied Demography: Proceedings of the 1986 National Conference (pp. 51–55). Bowling Green, OH.

    Google Scholar 

  • Van Leeuwen, N. (2007). Delineating population clusters by polygons and research of a grid approach. Nordic Forum for Geo-Statistics Seminar 2007. Helsinki, Finland.

    Google Scholar 

  • Warwick, D. P. and Lininger, C. A. (1975). The sample survey: Theory and practice. New York: McGraw Hill.

    Google Scholar 

  • Wegener, M. (1994). “Operation urban models: State of the art.” Journal of the American Planning Association 60, 17–30.

    Article  Google Scholar 

  • Wilmoth, J. (2004). Population size. In J. S. Siegel, & D. A. Swanson (Eds.) Methods and Materials of Demography, Second Edition (pp. 65–80). New York: Elsevier Academic Press.

    Google Scholar 

  • Winkler, R. L. and Hays, W. L. (1975). Statistics: Probability, inference and decision, Second Edition. New York: Holt, Rinehart, and Winston.

    Google Scholar 

  • Ziliak, S. T. and McCloskey, D. N. (2008). The cult of statistical significance: How the standard error costs us jobs, justice, and lives. Ann Arbor: The University of Michigan Press.

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of California Riverside, Riverside, CA, 92521, USA

    David A. Swanson

  2. Department of Economics, University of California San Diego, Gilman Drive 9500-0508, La Jolla, CA, 92093, USA

    Jeff Tayman

Authors
  1. David A. Swanson
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  2. Jeff Tayman
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Swanson, D.A., Tayman, J. (2012). Basic Concepts. In: Subnational Population Estimates. The Springer Series on Demographic Methods and Population Analysis, vol 31. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8954-0_2

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