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.
Original language | English (US) |
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Pages (from-to) | 431-447 |
Number of pages | 17 |
Journal | International Journal of Geographical Information Science |
Volume | 22 |
Issue number | 4 |
DOIs | |
State | Published - Apr 2008 |
Externally published | Yes |
Keywords
- Areal interpolation
- Dasymetric mapping
- Geostatistics
- Kriging
- Population surface
ASJC Scopus subject areas
- Information Systems
- Geography, Planning and Development
- Library and Information Sciences