Abstract
The need to combine spatial data representing sociodemographic information across incompatible spatial units is a common problem for demographers. A particular concern is computing small area trends when aggregation zone boundaries change during the trend interval. To that end, this study provides an example of dasymetric areal interpolation using the pre-classified land cover data available through the US Geological Survey’s National Land Cover Dataset (NLCD) program. Areal interpolation of population estimates is preferable to traditional reaggregation techniques, and the use of land cover data as a weighting factor in interpolated estimation has been shown in earlier studies to be highly accurate. In this study, the NLCD data set performs well and, because it requires no classification, it compares favorably with other land cover data sets for areal interpolation when considered on the basis of accuracy, precision and ease of use.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Information about the NOAA’s C-CAP program and its integration into the USGS NLCD effort can be found at http://www.csc.noaa.gov/crs/lca/ccap.html
Weighting regressions for areal interpolation should be fitted without intercepts, since areas with no inhabitable land cover are expected to have no population.
Our thanks to an anonymous reviewer for bringing this fact to our attention.
References
Bracken, I., & Martin, D. (1989). The generation of spatial population distributions from census centroid data. Environment and Planning A, 21, 537–543.
Cockings, S., Fisher, P., & Langford, M. (1997). Parameterization and visualization of the errors in areal interpolation. Geographical Analysis, 29(4), 314–328.
Eicher, C., & Brewer, C. (2001). Dasymetric mapping and areal interpolation: Implementation and evaluation. Cartography and Geographic Information Science, 28, 125–138.
Fisher, P., & Langford, M. (1995). Modeling the errors in areal interpolation between zonal systems by Monte Carlo simulation. Environment and Planning A, 27, 211–224.
Fisher, P., & Langford, M. (1996). Modeling sensitivity to accuracy in classified imagery. Professional Geographer, 48, 299–309.
Flowerdew, R., & Green, M. (1989). Statistical methods for inference between incompatible zone systems. In M. Goodchild, & S. Gopal (Eds.), The accuracy of spatial databases (pp. 239–247). London, England: Taylor and Francis.
Flowerdew, R., & Green, M. (1992). Developments in areal interpolation methods and GIS. Annals of Regional Science, 26, 67–78.
Goodchild, M., Anselin, L., & Deichmann, U. (1993). A framework for the areal interpolation of socioeconomic data. Environment and Planning A, 25, 383–397.
Goodchild, M., & Lam, N. (1980). Areal interpolation: A variant of the traditional spatial problem. Geo-Processing, 1, 297–312.
Gotway, C., & Young, L. (2002). Combining incompatible data. Journal of the American Statistical Association, 97, 632–648.
Jensen, J. R. (1996). Introductory digital image processing—A remote sensing perspective, 2nd ed. Upper Saddle River NJ: Prentice Hall.
Langford, M., Maguire, D., & Unwin, D. (1991). The areal interpolation problem: Estimating population using remote sensing in a GIS framework. In I. Masser, & M. Blakemore (Eds.), Handling geographic information: Methodology and potential applications (pp. 55–77). Harlow, Essex, England: Longman.
Langford, M., & Unwin, D. (1994). Generating and mapping population density surfaces within a geographical information system. Cartographic Journal, 31, 21–26.
Lillesand, T. M., & Kiefer, R. W. (1987). Remote sensing and image interpretation, 2nd ed. New York: John Wiley and Sons.
Longley, P. A., Goodchild, M. F., Maguire, D. J., & Rhind, D. W. (2005). Geographic information systems and science, 2nd ed. New York: John Wiley & Sons.
Mennis, J. (2003). Generating surface models of population using dasymetric mapping. The Professional Geographer, 55(1), 31–42.
Reibel, M. (2007). Geographic information systems and spatial data processing in demography: A review. Population Research and Policy Review, 26(5–6). doi: 10.1007/s11113-007-9046-5.
Reibel, M., & Agrawal, A. (2005). Land use weighted areal interpolation. Proceedings of the GIS Planet 2005 International Conference, Estoril, Portugal.
Reibel, M., & Bufalino, M. E. (2005). A test of street weighted areal interpolation using geographic information systems. Environment and Planning A, 37, 127–139.
Schowengerdt, R. A. (1997). Remote sensing: Models and methods for image processing, 2nd ed. San Diego CA: Academic Press.
Tobler, W. (1979). Smooth pycnophylactic interpolation for geographical regions. Journal of the American Statistical Association, 74, 519–536.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Reibel, M., Agrawal, A. Areal Interpolation of Population Counts Using Pre-classified Land Cover Data. Popul Res Policy Rev 26, 619–633 (2007). https://doi.org/10.1007/s11113-007-9050-9
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11113-007-9050-9