Please use this identifier to cite or link to this item:
|Title:||Population density estimation using regression and area-to-point residual Kriging||Authors:||Liu, X. H.
Goodchild, Michael F.
|Keywords:||A real interpolation
|Issue Date:||Mar-2008||Publisher:||Taylor & Francis, Ltd||Source:||International Journal of Geographical Information Science, Volume 22, Issue 4, pages 431-447||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.||URI:||http://ktisis.cut.ac.cy/jspui/handle/10488/8675||ISSN:||1365-8816
|DOI:||10.1080/13658810701492225||Rights:||© Informa UK Limited, an Informa Group Company|
|Appears in Collections:||Άρθρα/Articles|
Show full item record
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.