Samantha Cockings is a research assistant in the Department of Geography at the University of Durham.
Parameterization and Visualization of the Errors in Areal Interpolation
Article first published online: 3 SEP 2010
1997 The Ohio State University
Volume 29, Issue 4, pages 314–328, October 1997
How to Cite
Cockings, S., Fisher, P. F. and Longford, M. (1997), Parameterization and Visualization of the Errors in Areal Interpolation. Geographical Analysis, 29: 314–328. doi: 10.1111/j.1538-4632.1997.tb00967.x
- Issue published online: 3 SEP 2010
- Article first published online: 3 SEP 2010
Areal interpolation involves the transfer of data from one zonation of a region to another, where the two zonations of space are geographically incompatible. By its very nature this process is fraught with errors. However, only recently have there been specific attempts to quantify these errors. Fisher and Langford (1995) employed Monte Carlo simulation methods, based on modifiable areal units, to compare the errors resulting from selected areal interpolation techniques. This paper builds on their work by parameterizing and visualizing the errors resulting from the areal weighting and dasymetric methods of areal interpolation. It provides the basis for further research by developing the methodology to produce predictive models of the errors in areal interpolation. Random aggregation techniques are employed to generate multiple sets of source zones and interpolation takes place from these units onto a fixed set of randomly generated target zones. Analysis takes place at the polygon, or target zone level, which enables detailed analysis of the error distributions, basic visualization of the spatial nature of the errors and predictive modeling of the errors based on parameters of the target zones. Correlation and regression analysis revealed that errors from the areal weighting technique were related to the geometric parameters of the target zones. The dasymetric errors, however, demonstrated more association with the population or attribute characteristics of the zones. The perimeter, total population, and population density of the target zones were shown to be the strongest predictive parameters.