Surface models and the spatial structure of population variables: Exploring smoothing effects using Northern Ireland grid square data

• Use of ancillary data produces more accurate gridded population estimates from area data than does a smoothing approach.
• Using ancillary data and smoothing in combination produces the most accurate gridded estimates.
• Smoothing has a bigger impact on gridded estimates for smaller source zones than for larger source zones.
• Smoothing is likely to provide greater gains when a variable is more spatially continuous.

Where areal units used to report population counts from Censuses and other sources are incompatible, direct comparison of counts is not possible. To enable such comparisons, a wide variety of areal interpolation and surface modelling approaches have been developed to reallocate counts from one zonal system to another or to a regular grid. The particular characteristics of individual variables, representing population sub-groups, mean that the most accurate results for each sub-group may be obtained using quite different approaches, or different model parameters. This paper seeks to assess how the degree of smoothing associated with population surface modelling relates to the accuracy of predictions made using two variables in Northern Ireland – the number of Catholics and persons with a limiting long term illness (LLTI). The study makes use of counts for 2001 released for output areas (OAs) and wards to generate population grids with 100 m square cells. The accuracy of the predictions is then systematically assessed using counts released for 100 m grid cells as an additional output from the 2001 Census. The results show that the amount of smoothing and the spatial structure of the variables are related to the prediction errors and this suggests that use of information on the spatial structure of variables is likely to provide benefits, in terms of accuracy of population reallocations, over common areal weighting approaches.