Skip to main content

Sub-County Population Estimates Using Administrative Records: A Municipal-Level Case Study in New Mexico

  • Chapter
  • First Online:
Emerging Techniques in Applied Demography

Part of the book series: Applied Demography Series ((ADS,volume 4))

  • 925 Accesses

Abstract

This chapter explores the possibility of using administrative records to produce sub-county, municipal-level population estimates. Geocoding of vital records data is combined with IRS summary statistics on filers and dependents at the zip-code level to produce two sets of vintage 2010 Component 1 estimates for all 103 municipalities within the State of New Mexico; one made with no remediation for incomplete geocoding and the other remediated for observed biases in geocoding experiments conducted at the zip-code level. These estimates are compared against the results of the 2010 Census using an ex-post-facto evaluation strategy and standard measures of error and bias. The performance of the non-remediated and remediated estimates are compared to a null model of holding the 2000 Census constant and to a vintage 2010 set of estimates produced by the U.S. Census Bureau using their distributive housing unit method (D-HUM). The results suggest that spatial remediation does little to improve accuracy at the municipal level, and although both sets of component estimates represented significant improvements over the Census 2000 constant estimates, neither out-performed the (D-HUM) procedure, which was considerably more accurate and less biased–especially within the most rapidlygrowing municipalities. While the production of the component method-based estimates might permit the estimation of sub-county components of change, the results of this research suggest that this potential improvement would come at the cost of overall accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Abraham, B., & Ledolter, J. (1983). Statistical methods for forecasting. New York: Wiley.

    Book  Google Scholar 

  • Alcantara, A. (1999). Assessment of IRS tax returns migration coverage in New Mexico. Paper presented the Estimates Methods Conference, U. S. Bureau of the Census, Suitland.

    Google Scholar 

  • Baer, W. C. (1990). Aging of the housing stock and components of inventory change. In D. Myers (Ed.), Housing demography: Linking demographic structure and housing markets (pp. 249–273). Madison: University of Wisconsin.

    Google Scholar 

  • Baker, J., Alcantara, A., Ruan, X. M., & Watkins, K. (2012).The impact of incomplete geocoding on small area population estimates. Journal of Population Research, 29, 91–112.

    Article  Google Scholar 

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

    Book  Google Scholar 

  • Brown, W. (2008). Changes to the housing unit stock: Loss of housing units. Presentation at the New York State Data Center Affiliate Meeting. May 15, 2008. New York: West Point.

    Google Scholar 

  • Bryan, T. (2004). Population estimates. In J. Siegel & D. Swanson (Ed.), The Methods and materials of demography. New York: Springer.

    Google Scholar 

  • Bryan, T., & George, M. V. (2004). GIS. In J. Siegel & D. Swanson (Ed.), The methods and materials of demography. New York: Springer.

    Google Scholar 

  • Cavanaugh, F. (1981). The Census Bureau’s 1980 census test of population estimates. In: Small-area population estimates-methods and their accuracy and new metropolitan area definitions and their impact on the private and public sector. Series GE-41, No. 7. Washington, D.C.: Government Planning Office.

    Google Scholar 

  • Drummond, W. J. (1995). Address matching: GIS technology for mapping human activity patterns. Journal of the American Planning Association, 61(2), 240–251.

    Article  Google Scholar 

  • Esparza, A., & Donelson, A. (2008). Colonias in Arizona and New Mexico: Border poverty and community development solutions. Tucson: University of Arizona Press.

    Google Scholar 

  • Fellegi, I. P. (1968). Coverage check of the 1961 census of population. Technical memorandum (Census Evaluation Series). No. 2, Dominion Bureau of Statistics.

    Google Scholar 

  • Fisher, P. F., & Langford, M. (1995). Modeling the errors in areal interpolation between zonal systems using monte carlo simulation. Environment and Planning A, 27, 212–214.

    Article  Google Scholar 

  • Flowerdrew, R., & Green, M. (1992). Developments in areal interpolation methods and GIS. The Annals of Regional Science, 26, 67–78.

    Article  Google Scholar 

  • Gilboa, S. M. (2006). Comparison of residential geocoding methods in a population-based study of air quality and birth defects. Environmental Research, 101, 256–262.

    Article  Google Scholar 

  • Goldberg, D. W., Wilson, J. P., & Knoblock, C. A. (2007). From text to geographic coordinates: The current state of geocoding. URISA Journal, 19(1), 33–46.

    Google Scholar 

  • Harper, G., Coleman, C., & Devine, J. (2003). Evaluation of 2000 subcounty population estimates. Population Estimates Branch, Population Division. U.S. Census Bureau.

    Google Scholar 

  • Hogan, H. (1992). The 1990 post-enumeration survey: An overview. The American Statistician, 46, 261–269.

    Google Scholar 

  • Hogan, H. (1993). The 1990 post-enumeration survey: Operations and results. Journal of the American Statistical Association, 88, 1047–1060.

    Article  Google Scholar 

  • Hogan, H. (2003). The accuracy and coverage evaluation: Theory and design. Survey Methodology, 29(2), 129–138.

    Google Scholar 

  • Hoque, N. (2010). An evaluation of small area population estimates produced by component method II, ratio correlation, and housing unit methods for 1990. The Open Demography Journal, 3, 18–30.

    Article  Google Scholar 

  • Horvitz, D. G., & Thompson, D. J. (1952). A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.

    Article  Google Scholar 

  • Jarosz, B. (2008). Using assessor parcel data to maintain housing unit counts for small area population estimates. In S. Murdock & D. Swanson (Ed.), Applied demography in the 21st century (pp. 89–101). New York: Springer.

    Chapter  Google Scholar 

  • Keyfitz, N., & Caswell, H. (2005). Applied mathematical demography (2nd ed.). New York: Springer.

    Google Scholar 

  • Leslie, P. (1945). The use of matrices in certain population mathematics. Biometrika, 33(3), 183–212.

    Article  Google Scholar 

  • Leslie, P. (1948). Some further notes on the use of matrices in population mathematics. Biometrika, 35(3–4), 213–245.

    Article  Google Scholar 

  • Levinson, N. (1947). The wiener RMS (root mean square) error criteria for filter design and prediction. Journal of Mathematical Physics, 25, 261–278.

    Google Scholar 

  • Lotka, A. J. (1956). Elements of mathematical biology. New York: Dover.

    Google Scholar 

  • Neter, J., Kutner, M., Wasserman, M., & Nachtshem, C. (1999). Applied linear statistical models (4th ed.). New York: McGraw-Hill.

    Google Scholar 

  • Oliver, M. N. (2005). Geographic bias related to geocoding in epidemiologic Studies. International Journal of Health Geographics, 4(29), Online.

    Google Scholar 

  • Popoff, C., & Judson, D. (2004). Selected general methods. In J. S. Siegel & D. Swanson (Ed.), The methods and materials of demography.(2nd ed., pp. 644–675). New York: Springer.

    Google Scholar 

  • Preston, S., Hueverline, P., & Guillot, M. (2003). Demography: Measuring and modeling population processes. New York: McGraw Hill.

    Google Scholar 

  • Sadahiro, Y. (2000). Accuracy of count data transferred through the areal weighting interpolation method. International Journal of Geographical Information Science, 14, 25–50.

    Article  Google Scholar 

  • Shyrock, H., & Siegel, J. (1980). The methods and materials of demography (Vol. II). Washington, D.C.: U.S. Bureau of the Census.

    Google Scholar 

  • Simpson, L. (2002). Geography conversion tables: A framework for conversion of data between geographical units. International Journal of Population Geography, 8, 69–82.

    Article  Google Scholar 

  • Smith, S. (1987). Tests of forecast accuracy and bias for county population projections. Journal of the American Statistical Association, 82(400), 991–1003.

    Article  Google Scholar 

  • Smith, S., & Cody, S. (2013). Making the housing unit method work: An evaluation of 2010 population estimates in florida. Population Research and Policy Review, 32(2), 221–242.

    Article  Google Scholar 

  • Smith, S., & Lewis, B. (1983). Some new techniques for applying the housing unit method of local population estimation: Further evidence. Demography, 20(3), 407–413.

    Article  Google Scholar 

  • Smith, S., & Mandell, M. (1984). A comparison of population estimation methods: Housing unit versus component II, ratio correlation, and administrative records. Journal of the American Statistical Association, 79(386), 282–289.

    Article  Google Scholar 

  • Smith, S., & Sincich, T. (1990). The relationship between length of the base period and population forecast errors. Journal of the American Statistical Association, 85(410), 367–375.

    Article  Google Scholar 

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

    Google Scholar 

  • Swanson, D., & McKibben, J. (2010). New directions in the development of population estimates in the United States. Population Research and Policy Review, 29(6), 797–818.

    Article  Google Scholar 

  • Swanson, D., & Pol, L. (2005). Contemporary developments in applied demography within the United States. Journal of Applied Sociology, 21(2), 26–56.

    Google Scholar 

  • Sykes, M. (1969). Some stochastic versions of the matrix model for population dynamics. Journal of the American Statistical Association, 64, 111–130.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Tayman, J., Schafer, E., & Carter, L. (1998). The role of population size in the determination and prediction of population forecast errors: An evaluation using confidence intervals for subcounty areas. Population Research and Policy Review, 17(1), 1–20.

    Article  Google Scholar 

  • Tobler, W. R. (1979). Smooth pycnophylactic interpolation for geographical regions. Journal of the American Statistical Association, 74, 519–530.

    Article  Google Scholar 

  • United Nations. (1983). Manual X. New York: UN Population Division.

    Google Scholar 

  • Voss, P. R., Long, D. D., & Hammer, R. B. (1999). When census geography doesn’t work: Using ancillary information to improve the spatial interpolation of demographic data. Center for Demography and Ecology, University of Wisconsin, Madison. Working Paper No. 99–26.

    Google Scholar 

  • Zandbergen, P. (2009). Geocoding quality and implications for spatial analysis. The Geography Compass, 3(2), 647–680.

    Article  Google Scholar 

  • Zandbergen, P., & Ignizio, D. (2010).Comparison of dyasymetric mapping techniques for small area population estimates. Cartography and Geographic Information Science, 37(3), 199–214.

    Article  Google Scholar 

Download references

Acknowledgements

This research was funded in part by support from the U.S. Census Bureau, RFQ 10-002245RT (Services to Support 2010 Postcensal Estimates Program). The paper was greatly improved by the comments of Howard Hogan, Chief Demographer of the U.S. Census Bureau, and Paul Zandbergen, Associate Professor of Geography at the University of New Mexico. The paper also benefitted greatly from the comments of two anonymous referees. Ongoing suggestions from colleagues including Stan Smith, David Swanson, Nazrul Hoque, Mohammed Shahidullah, Eddie Hunsinger, Jan Vink, Webb Sprague, Qian Tsai, Jeff Hardcastle, RisaProehl, Matt Hesser, Susan Strate, Nicholas Nagle, Joe Francis, Warren Brown, Ken Hodges and others were valued. Ongoing and numerous comments, insights, and suggestions on population estimates research were provided by members of the Federal State Cooperative Program on Population Estimates and by Census Bureau staff especially including Jason Devine, Victoria Velkoff, Tiffany Yowell, and Rodger Johnson.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jack Baker .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Baker, J., Alcántara, A., Ruan, X., Ruiz, D., Crouse, N. (2015). Sub-County Population Estimates Using Administrative Records: A Municipal-Level Case Study in New Mexico. In: Hoque, M., B. Potter, L. (eds) Emerging Techniques in Applied Demography. Applied Demography Series, vol 4. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8990-5_6

Download citation

Publish with us

Policies and ethics