Population density estimation using regression and area-to-point residual Kriging
File(s)
Journal
International Journal of Geographical Information Science
Date Issued
March 2008
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.