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

Christopher D. Lloyd*, Behnam Firoozi Nejad

*Corresponding author for this work

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)64-72
JournalComputers, Environment and Urban Systems
Volume48
Early online date05 Aug 2014
DOIs
Publication statusPublished - 01 Nov 2014

Keywords

  • Census
  • Population surface modelling
  • Spatial variation

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Ecological Modelling
  • Environmental Science(all)
  • Urban Studies

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