Population density estimation using regression and area-to-point residual Kriging
Journal
International Journal of Geographical Information Science
Date Issued
March 2008
DOI
10.1080/13658810701492225
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-topoint
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.
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-topoint
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.