A Comparison of Methods for Spatial Interpolation across Different Spatial Scales

Spatially distributed estimates of population provide commonly used demand surfaces in support of spatial planning. In many countries, spatially detailed population estimates in small areas are not available. For such cases a number of interpolation methods have been proposed to redistribute summary population totals over small areas to estimate locally nuanced demand surfaces. Population allocations to small areas are commonly validated by comparing the estimates with some known values for those areas. This paper explores different interpolation methods applied at different spatial scales in locations where the validation of estimated surfaces is possible in order to suggest appropriate interpolation parameters for locations where it is not. The results show binary dasymetric mapping applied at medium scales provide the best estimates of population, among the methods, areal weighting the worst at all scales and pycnophylactic interpolation shows significant improvement on areal weighting at all scales. This paper provides a comprehensive evaluation of these techniques, using different scales of input data and residual mappings to compare and evaluate the spatial distribution of errors in the estimated surfaces. The application of such methods for estimating spatially distributed demand population values in different types of spatial data analysis and in locations where validation data do not exist are discussed.

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