Skip to main content
Log in

Spatial weighting improves accuracy in small-area demographic forecasts of urban census tract populations

  • Published:
Journal of Population Research Aims and scope Submit manuscript

Abstract

Existing research in small-area demographic forecasting suffers from two important limitations: (1) a paucity of studies that quantify patterns of error in either total or age/sex-specific estimates and (2) limited methodological innovation aimed specifically at improving the accuracy of such forecasts. This paper attempts to fill, in part, these gaps in existing research by presenting a comparative evaluation of the accuracy of standard and spatially-weighted Hamilton–Perry forecasts for urbanized census tracts within incorporated New Mexico municipalities. These comparative forecasts are constructed for a 10-year horizon (base 1 April 2000 and target 1 April 2010), then compared to the results of the 2010 Census in an ex post facto evaluation. Results are presented for the standard Hamilton–Perry forecasts as well as two sets that incorporate two common variants of spatial weights to improve forecast accuracy. Findings are discussed in the context of what is currently known about error in small-area demographic forecasts and with an eye toward continued innovations.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

Notes

  1. Mapping to specific street segments: see Drummond 1995, Karimi and Durcik 2004 for general explanations and Baker et al. 2012 for demographic examples.

References

  • Alba, R., Logan, J., & Stults, B. (2000). How segregated are middle-class African-Americans. Social Problems, 47(4), 543–558.

    Article  Google Scholar 

  • Armstrong, C. M., & Harris, M. (1949). A method of predicting school-age population. Albany: State University of New York, State Education Department.

    Google Scholar 

  • Baker, J., Alcantara, A., Ruan, X. M., Ruiz, D., & Crouse, N. (2014). Sub-county population estimates using administrative records: A municipal-level case study in New Mexico. In Nazrul Hoque & Lloyd Potter (Eds.), Emerging techniques in applied demography. New York: Springer.

    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 

  • Baker, J., Alcantara, A., Ruan, X. M., Watkins, K., & Vasan, S. (2013). A comparative evaluation of error and bias in census tract-level age/sex-specific population estimates: Component I (Net-migration) vs Component III (Hamilton–Perry). Population Research and Policy Review, 32, 919–942.

    Article  Google Scholar 

  • Baker, J., Ruan, X. M., Alcantara, A., Jones, T., Watkins, K., McDaniel, M., et al. (2008). Density-dependence in urban housing unit growth: An evaluation of the Pearl-Reed model for predicting housing unit stock at the census tract level. Journal of Economic and Social Measurement, 33, 155–163.

    Google Scholar 

  • Best, N., & Wakefield, J. (1999). Accounting for inaccuracies in population counts and case registration in cancer mapping studies. Journal of the Royal Statistical Society: Series A (Statistics in Society)., 162(3), 363–382.

    Article  Google Scholar 

  • Cai, Q. (2007). New techniques in small area population estimates by demographic characteristics. Population Research and Policy Review, 26, 203–218.

    Article  Google Scholar 

  • Cavanaugh, F. (1981). The Census Bureau’s 1980 Census Test of Population Estimates. In Small-area population estimatesmethods and their accuracy and new metropolitan area definitions and their impact on the private and public sector, Series GE-41, No. 7. Washington, DC: Government Planning Office.

  • Centers for Disease Control (CDC). (1999). National Program of Cancer Registries cancer surveillance system rationale and approach. Atlanta.

  • Chi, G., & Voss, P. (2011). Small-area population forecasting: Borrowing strength across space and time. Population, Space, and Place., 17, 505–520.

    Google Scholar 

  • Chi, G., & Zhu, J. (2008). Spatial regression models for demoraphic analysis. Population Research and Policy Review, 27, 17–42.

    Article  Google Scholar 

  • de Miguel, V., Garlappi, L., & Uppal, R. (2007). Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy. Journal of Finance., 22(5), 1915–1953.

    Google Scholar 

  • Dietzel, C., & Clarke, K. (2007). Research article. Toward optimal calibration of the SLEUTH land use change model. Transactions in GIS, 11(1), 29–45.

    Article  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 

  • Duncan, O., & Duncan, B. (1955). A methodological analysis of segregation indexes. American Sociological Review, 20(2), 210–217.

    Article  Google Scholar 

  • Fabricant, R., & Weinman, J. (1972). Forecasting first grade public school enrollment by neighborhood. Demography, 9(4), 625–634.

    Article  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.

  • Frotheringham, A., Brundson, C., & Charlton, M. (2002). Geographically weighted regression: The analysis of spatially-varyiugn relationships. West Sussex: Wiley.

    Google Scholar 

  • George, M. V. (2004). Population projections. In J. Siegel & D. Swanson (Eds.), The methods and materials of demography. New York: Springer.

    Google Scholar 

  • Getis, A. (2009). Spatial weight matrices. Geographical Analysis, 41(4), 404–410.

    Article  Google Scholar 

  • Getis, A., & Aldstadt, J. (2004). Constructing the spatial weights matrix using a local statistic. Geographical Analysis, 36(2), 90–104.

    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 

  • Haining, R. (2003). Spatial data analysis: Theory and practice. New York: Cambridge University Press.

  • Hamilton, C., & Perry, J. (1962). A short method for projecting population by age from one decennial census to another. Social Forces, 41(2), 163–170.

    Article  Google Scholar 

  • Harris, R., Sleight, P., & Webber, R. (2005). Geodemographics, GIS, and neighborhood targeting. New York: Wiley.

    Google Scholar 

  • Herold, M., Goldstein, N., & Clark, K. C. (2003). The spatiotemporal form of urban growth: Measurement, analysis, and modeling. Remote Sensing of Environment, 86, 286–302.

    Article  Google Scholar 

  • Hogan, H. (1992). The 1990 post-enumeration survey: An overview. The American Statistician, 46(4), 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, J., & Tchernis, R. (2004). Bayesian factor analysis for spatially-correlated data, with application to summarizing area-level material deprivation from census data. Journal of the American Statistical Association, 99(466), 314–324.

  • 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 

  • Hund, L., Chen, J., Krieger, N., & Coull, B. (2012). A geostatistical approach to large-scale disease mapping with temporal misalignment. Biometrics, 68(3), 849–858.

    Article  Google Scholar 

  • Karimi, H. A., & Durcik, M. (2004). Evaluation of uncertainties associated with geocoding techniques. Computer-aided Civil and Infrastructural Engineering, 19, 170–185.

    Article  Google Scholar 

  • Keyfitz, N. (1981). The limits of population forecasting. Population and Development Review, 7(4), 579–593.

    Article  Google Scholar 

  • Kuldorff, M. (1997). A spatial scan statistic. Communication in Statistics: Theory and Methods, 26, 1481–1496.

    Article  Google Scholar 

  • Kuldorff, M. (1999). An isotonic spatial scan statistic for Geographical Disease Surveillance. Journal of the National Institute of Public Health., 48, 94–101.

    Google Scholar 

  • Landis, J., & Zhang, M. (1998). The second generation of the California urban futures model: Part 2, Specification and calibration results of the land use change submodel. Environment and Planning B., 25, 795–842.

    Article  Google Scholar 

  • Le Sage, J., & Pace, K. R. (2004). Models for spatially-dependent missing data. Journal of Real Estate Finance and Economics, 29(2), 233–254.

    Article  Google Scholar 

  • Leach, D. (1981). Re-evaluation of the logistic curve for human populations. Journal of the Royal Statistical Society, 144, 94–103.

    Article  Google Scholar 

  • Legare, J. (1972). Methods for measuring school performance through cohort analysis. Demography, 9(4), 617–624.

    Article  Google Scholar 

  • Long, J. (1995). Complexity, accuracy, and the utility of official population projections. Mathematical Population Studies, 5(3), 203–216.

    Article  Google Scholar 

  • Markowitz, H. M. (1952). Portfolio selection. Journal of Finance., 7, 77–91.

    Google Scholar 

  • Massey, D., & Denton, N. (1985). Spatial assimilation as a socioeconomic outcome. American Sociological Review, 50(1), 94–106.

    Article  Google Scholar 

  • McKibben, J. (1996). The impact of policy changes on forecasting for school districts. Population Research and Policy Review, 15(5–6), 527–536.

    Google Scholar 

  • Myers, J. K. (1954). Note on the homogeneity of census tracts: A methodological problem in urban ecological research. Social Forces, 32, 364–366.

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

  • Pace, K., & Gilly, O. R. (1997). Using the spatial configuration of data to improve estimation. The Journal of Real Estate Finance and Economics, 14(3), 330–340.

    Article  Google Scholar 

  • Patacchini, E., & Zenou, Y. (2007). Spatial dependence in local unemployment rates. Journal of Economic Geography, 7(2), 169–191.

    Article  Google Scholar 

  • Pflaumer, P. (1992). Forecasting US population totals with the Box-Jenkins approach. International Journal of Forecasting, 8, 329–338.

    Article  Google Scholar 

  • Schmid, C., & Shanley, F. (1952). Techniques of forecasting university enrollment. Tested empirically by deriving forecasts of enrollment for the University Of Washington. The Journal of Higher Education, 23(9), 483–488–502–503.

    Article  Google Scholar 

  • Schmitt, A., & Crosetti, A. (1954). Accuracy of the ratio-correlation method for estimating postcensal population. Land Economics, 30, 279–281.

    Article  Google Scholar 

  • se Can, A., & Megbolugbe, I. (1997). Spatial dependence in house price index construction. Journal of Real Estate Finance and Economics., 14(1–2), 203–222.

    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., & Shahidullah, M. (1995). An evaluation of projection errors for census tracts. Journal of the American Statistical Association, 90(429), 64–71.

    Article  Google Scholar 

  • Smith, S., & Sincich, T. (1992). 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 

  • Stoto, M. (1983). The accuracy of population projections. Journal of the American Statistical Association, 78(381), 13–20.

    Article  Google Scholar 

  • Swanson, D., Schlottman, A., & Schmidt, B. (2010). Forecasting the population of census tracts by age and sex: An example of the Hamilton–Perry method in action. Population Research and Policy Review, 29(1), 47–63.

    Article  Google Scholar 

  • Swanson, D., & Tayman, J. (2012). Subnational population estimates. New York: Springer.

    Book  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 

  • Vasan, S., Alcantara, A., Nefertari, N., Ruan, X. M., & Baker, J. (2014). Geography is destiny: Spatial correlations in poverty and educational attainment in a New Mexico School District. In Nazrul Hoque & Lloyd Potter (Eds.), Emerging techniques in applied demography. New York: Springer.

    Google Scholar 

  • Voss, P., & Kale, B. (1985). Refinements to small-area population projection models: Results of a test based on 128 Wisconsin communities. Presented at the Annual Meeting of the Population Association of America. 28–30 March.

  • 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.

  • Ward, D., Murray, A., & Phinn, S. (2000). A stochastically constrained cellular model of urban growth. Computers, Environment and Urban Systems, 24(6), 539–558.

    Article  Google Scholar 

  • White, H. R. (1954). Empirical study of selected methods of projecting state population. Journal of the American Statistical Association, 49, 480–498.

    Google Scholar 

  • Witmer, J. A., & Samuels, M. L. (1998). Statistics for the life sciences. New York: Sinauer.

    Google Scholar 

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

  • Zitter, M. (1954). Forecasting school enrollment for the United States and local areas. Journal of Teacher Education, 5(1), 53–63.

    Article  Google Scholar 

Download references

Acknowledgments

This manuscript has been greatly improved by the suggestions made by two anonymous referees as well as Dr. Elin Charles-Edwards, Associate Editor for the Journal of Population Research. This research was supported by an annual appropriation to Geospatial and Population Studies by the Legislature of the State of New Mexico to support the Census Data Dissemination and Demographic Analysis project. While we wish to acknowledge these contributions, any errors or omissions in either logic or content remain the responsibility of the authors.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jack Baker.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Baker, J., Alcántara, A., Ruan, X. et al. Spatial weighting improves accuracy in small-area demographic forecasts of urban census tract populations. J Pop Research 31, 345–359 (2014). https://doi.org/10.1007/s12546-014-9137-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12546-014-9137-1

Keywords

Navigation