Population‐density estimation using regression and area‐to‐point residual kriging

Abstract

Census population data are associated with several analytical and cartographic problems. Regression models using remote‐sensing covariates have been examined to estimate urban population density, but the performance may not be satisfactory. This paper describes a kriging‐based areal interpolation method, namely area‐to‐point residual kriging, which can be used to disaggregate the residuals remaining from regression. Compared with conventional cokriging, the area‐to‐point residual kriging is much simpler in that only a semivariogram model for the point residuals is required, as opposed to a set of auto‐ and cross‐semivariogram models involving the dependent variable and all the covariates. In addition, area‐to‐point residual kriging explicitly accounts for any scale differences between source data and target values. The method is illustrated by disaggregating population from census units to the land‐use zones within them. Comparative results for regression with and without area‐to‐point residual kriging show that area‐to‐point residual kriging can substantially improve interpolation accuracy.

Keywords:

• Areal interpolation
• Dasymetric mapping
• Kriging
• Geostatistics
• Population surface

Acknowledgements

The authors thank Melissa Kelly at University of California, Santa Barbara for sharing the land‐use map created from visual interpretation of aerial photos. This research is supported by the National Geospatial‐Intelligence Agency under the award ‘Strategic Enhancement of NGA's Geographic Information Science Infrastructure’.