Skip to main content
SpringerLink
Log in

Small-Area Population Forecasting: A Geographically Weighted Regression Approach

  • Chapter
  • First Online:
The Frontiers of Applied Demography

Part of the book series: Applied Demography Series ((ADS,volume 9))

Abstract

The regression approach for small-area population forecasting is increasingly used in urban planning and emerging research in climate change and infrastructure systems in response to disasters because the regression approach can not only provide population projections but also estimate the relationships between population change and possible driving factors. In this research, we use the geographically weighted regression (GWR) method to estimate relationships between population change and a variety of driving factors and consider possible spatial variations of the relationships for small-area population forecasting using 1990–2010 data at the minor civil division level in Wisconsin, USA. The results indicate that the GWR method provides an elegant estimation of the relationships between population change and its driving factors, but it underperforms traditional extrapolation projections. The findings have important implications about the need for more accurate population projections versus more accurate estimation of the relationships between population and its driving factors.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
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

Notes

  1. 1.

    A projection includes one or more assumptions, while a forecast is a projection that is most likely to occur based on judgments. However, we use “projection” and “forecast” interchangeably in this chapter.

References

  • Agarwal, C., Green, G. M., Grove, J. M., Evans, T. P., & Schweik, C. M. (2002). A review and assessment of land-use change models: Dynamics of space, time, and human choice (General technical report NE-297). Newtown Square: USDA Forest Service Northeastern Research Station and Indiana University Center for the Study of Institutions, Populations, and Environmental Change.

    Google Scholar 

  • Agresti, A., & Finlay, B. (2009). Statistical methods for the social sciences. Upper Saddle River: Pearson Prentice Hall.

    Google Scholar 

  • Ahlburg, D. A. (1987). The impact of population growth on economic growth in developing nations: The evidence from macroeconomic-demographic models. In D. G. Johnson & R. D. Lee (Eds.), Population growth and economic development: Issues and evidence (pp. 492–522). Madison: University of Wisconsin Press.

    Google Scholar 

  • Alho, J. M. (1997). Scenarios, uncertainty and conditional forecasts of the world population. Journal of the Royal Statistical Society: Series A (Statistics in Society), 160(1), 71–85.

    Article  Google Scholar 

  • Ali, K., Partridge, M. D., & Olfert, M. R. (2007). Can geographically weighted regressions improve regional analysis and policy making? International Regional Science Review, 30(3), 300–329.

    Article  Google Scholar 

  • Anselin, L. (2001). Spatial econometrics. In B. Baltagi (Ed.), Companion to econometrics (pp. 310–330). Oxford: Blackwell.

    Google Scholar 

  • Armstrong, J. S. (2001). Extrapolation of time-series and cross-sectional data. In J. S. Armstrong (Ed.), Principles of forecasting: A handbook for researchers and practitioners (pp. 217–244). Boston: Kluwer.

    Chapter  Google Scholar 

  • Armstrong, J. S., Adya, M., & Collopy, F. (2001). Rule-based forecasting: Using judgment in time-series extrapolation. In J. S. Armstrong (Ed.), Principles of forecasting: A handbook for researchers and practitioners (pp. 259–282). Boston: Kluwer.

    Chapter  Google Scholar 

  • Chi, G. (2009). Can knowledge improve population forecasts at subcounty levels? Demography, 46(2), 405–427. doi:10.1353/dem.0.0059.

    Article  Google Scholar 

  • Chi, G., & Marcouiller, D. W. (2011). Isolating the effect of natural amenities on population change at the local level. Regional Studies, 45(4), 491–505.

    Article  Google Scholar 

  • Chi, G., & Voss, P. R. (2011). Small‐area population forecasting: Borrowing strength across space and time. Population, Space and Place, 17(5), 505–520.

    Article  Google Scholar 

  • Chow, G. C. (1960). Tests of equality between sets of coefficients in two linear regressions. Econometrica, 28(3), 591–605.

    Article  Google Scholar 

  • Cutter, S. L., & Smith, M. M. (2009). Fleeing from the hurricane’s wrath: Evacuation and the two Americas. Environment: Science and Policy for Sustainable Development, 51(2), 26–36. doi:10.3200/envt.51.2.26-36.

    Article  Google Scholar 

  • Deal, B., Pallathucheril, V. G., Sun, Z., Terstriep, J., & Hartel, W. (2005). LEAM technical document: Overview of the LEAM approach. Urbana: Department of Urban and Regional Planning, University of Illinois at Urbana-Champaign.

    Google Scholar 

  • Espenshade, T. J., & Tayman, J. M. (1982). Confidence intervals for postcensal state population estimates. Demography, 19(2), 191–210.

    Article  Google Scholar 

  • Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically weighted regression. West Sussex: Wiley.

    Google Scholar 

  • Fraser, L. K., Clarke, G. P., Cade, J. E., & Edwards, K. L. (2012). Fast food and obesity: A spatial analysis in a large United Kingdom population of children aged 13–15. American Journal of Preventive Medicine, 42(5), e77–e85. doi:10.1016/j.amepre.2012.02.007.

    Article  Google Scholar 

  • Ginsberg, J., Mohebbi, M. H., Patel, R. S., Smolinski, M. S., Brillian, L., & Brammer, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012–1014. doi:10.1038/nature07634.

    Article  Google Scholar 

  • Hunt, D., & Simmons, D. (1993). Theory and application of an integrated land-use and transport modeling framework. Environment and Planning B, 20, 221–244.

    Article  Google Scholar 

  • Keyfitz, N. (1982). Can knowledge improve forecasts? Population and Development Review, 8(4), 729–751.

    Article  Google Scholar 

  • Longley, P. A., & Tobon, C. (2004). Spatial dependence and heterogeneity in patterns of hardship: An intra-urban analysis. Annals of the Association of American Geographers, 94(3), 503–519.

    Article  Google Scholar 

  • Lutz, W., & Goldstein, J. R. (Guest Eds.). (2004). How to deal with uncertainty in population forecasting. International Statistical Review, 72(1 and 2), 1–106.

    Google Scholar 

  • Lutz, W., & Striessnig, E. (2015). Demographic aspects of climate change mitigation and adaptation. Population Studies: A Journal of Demography, 69(S1), S69–S76. doi:10.1080/00324728.2014.969929.

    Article  Google Scholar 

  • Matthews, S. A., & Yang, T. C. (2012). Mapping the results of local statistics: Using geographically weighted regression. Demographic Research, 26, 151–166.

    Article  Google Scholar 

  • Meadows, D. H., Meadows, D. L., Randers, J., & Behrens, W. I. (1972). The limits to growth. New York: Potomac Associations.

    Google Scholar 

  • Meadows, D. H., Meadows, D. L., & Randers, J. (1992). Beyond the limits: Confronting global collapse, envisioning a sustainable future. London: Earthscan.

    Google Scholar 

  • Mennis, J. L., & Jordan, L. (2005). The distribution of environmental equity: Exploring spatial nonstationarity in multivariate models of air toxic releases. Annals of the Association of American Geographers, 95(2), 249–268.

    Article  Google Scholar 

  • O’Neill, B. C. (2004). Conditional probabilistic population projections: An application to climate change. International Statistical Review, 72(2), 167–184.

    Article  Google Scholar 

  • Riahi, K., & Nakicenovic, N. (Eds.). (2007). Greenhouse gases—Integrated assessment, technological forecasting and social change, Special Issue, 74(7), 873–886.

    Google Scholar 

  • Sanderson, W. C. (1998). Knowledge can improve forecasts: a review of selected socioeconomic population projection models. Population and Development Review 24(Suppl), 88–117.

    Google Scholar 

  • Sanderson, W. C., Scherbov, S., O’Neill, B. C., & Lutz, W. (2004). Conditional probabilistic population forecasting. International Statistical Review, 72(2), 157–166.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Smith, S. K., Tayman, J. M., & Swanson, D. A. (2013). A practitioner’s guide to state and local projections. Dordrecht: Springer.

    Book  Google Scholar 

  • Swanson, D. A. (2004). Advancing methodological knowledge within state and local demography: A case study. Population Research and Policy Review, 23(August), 379–398.

    Google Scholar 

  • Swanson, D. A., & Beck, D. (1994). A new short-term county population projection method. Journal of Economic and Social Measurement, 20(1), 25–50.

    Google Scholar 

  • Swanson, D. A., & Tayman, J. M. (2012). Subnational population estimates. Dordrecht: Springer.

    Book  Google Scholar 

  • Swanson, D. A., & Tayman, J. M. (2014). Measuring uncertainty in population forecasts: A new approach. In Marsili M., & Capacci G (Eds.), Proceedings of the 6th EUROSTAT/UNECE work session on demographic projections. Rome: Italian National Institute of Statistics.

    Google Scholar 

  • Swanson, D. A., Schlottman, A., & Schmidt, R. (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 

  • Tobler, W. (1970). A Computer movie simulating urban growth in the Detroit region. Economic Geography, 46, 234–240.

    Article  Google Scholar 

  • U.S. Global Change Research Program (USGCRP). (2015). Towards scenarios of U.S. demographic change: Workshop report. Washington, DC: USGCRP. Available at http://www.globalchange.gov/sites/globalchange/files/USGCRP_Workshop_Report-Towards_Scenarios_of_US_Demographic_Change.pdf as of September 2015

  • Wilson, T., & Rees, P. (2005). Recent developments in population projection methodology: A review. Population, Space and Place, 11, 337–360.

    Article  Google Scholar 

  • Wood, N. J., Burton, C. G., & Cutter, S. L. (2009). Community variations in social vulnerability to Cascadia-related tsunamis in the U.S. Pacific Northwest. Natural Hazards, 52(2), 369–389. doi:10.1007/s11069-009-9376-1.

    Article  Google Scholar 

  • Yang, T. C., & Matthews, S. A. (2012). Understanding the non-stationary associations between distrust of the health care system, health conditions, and self-rated health in the elderly: A geographically weighted regression approach. Health and Place, 18(3), 576–585. doi:10.1016/j.healthplace.2012.01.007.

    Article  Google Scholar 

  • Yu, D. L., Wei, Y. D., & Wu, C. (2007). Modeling spatial dimensions of housing prices in Milwaukee, WI. Environment and Planning B, 34, 1085–1102. doi:10.1068/b32119.

    Article  Google Scholar 

Download references

Acknowledgements

We thank Annelise Hagedorn and Xuan Zhou for assistance in data collection and cleanup. Appreciation is extended to David Swanson and Jacques Poot for providing comments on earlier drafts of this chapter. This research was supported by the National Science Foundation (Award # 1541136).

Author information

Authors and Affiliations

  1. Department of Agricultural Economics, Sociology, and Education, The Pennsylavania State University, State College, PA, USA

    Guangqing Chi & Donghui Wang

  2. Computational and Spatial Analysis Core, Population Research Institute and Social Science Research Institute, The Pennsylvania State University, State College, PA, USA

    Guangqing Chi & Donghui Wang

Authors
  1. Guangqing Chi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Donghui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guangqing Chi .

Editor information

Editors and Affiliations

  1. Department of Sociology, University of California at Riverside, Riverside, California, USA

    David A. Swanson

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Chi, G., Wang, D. (2017). Small-Area Population Forecasting: A Geographically Weighted Regression Approach. In: Swanson, D. (eds) The Frontiers of Applied Demography. Applied Demography Series, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-319-43329-5_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-43329-5_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-43327-1

  • Online ISBN: 978-3-319-43329-5

  • eBook Packages: Social SciencesSocial Sciences (R0)

Publish with us

Policies and ethics

Search

Navigation