The Annals of Regional Science

, Volume 26, Issue 1, pp 67–78

Developments in areal interpolation methods and GIS

  • Robin Flowerdew
  • Mick Green


This paper is a review and extension of the authors' research project on areal interpolation. It is concerned with problems arising when a region is divided into different sets of zones for different purposes, and data available for one set of zones (source zones) are needed for a different set (target zones). Standard approaches are based on the assumption that source zone data are evenly distributed within each zone, but our approach allows additional information about the target zones to be taken into account so that more accurate target zone estimates can be derived. The method used is based on the EM algorithm. Most of the work reported so far (e.g. Flowerdew and Green 1989) has been concerned with count data whose distribution can be modelled using a Poisson assumption. Such data are frequently encountered in censuses and surveys. Other types of data are more appropriately regarded as having continuous distributions. This paper is primarily concerned with areal interpolation of normally distributed data. A method is developed suitable for such data and is applied to house price data for Preston, Lancashire, starting with mean house prices in 1990 for local government wards and estimating mean house prices for postcode sectors.


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  1. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B 39:1–38Google Scholar
  2. Flowerdew R, Green M (1989) Statistical methods for inference between incompatible zonal systems. In: Goodchild M, Gopal S (eds) Accuracy of spatial databases. Taylor & Francis, London, pp 239–247Google Scholar
  3. Flowerdew R, Green M (1991) Data integration: statistical methods for transferring data between zonal systems. In: Masser I, Blakemore M (eds) Handling geographical information. Longman, London, pp 38–54Google Scholar
  4. Flowerdew R, Green M, Kehris E (1991) Using areal interpolation methods in geographical information systems. Papers Reg Sci 70:303–315Google Scholar
  5. Goodchild M, Lam NS-N (1980) Areal interpolation: a variant of the traditional spatial problem. Geo-Process 1:297–312Google Scholar
  6. Green M (1990) Statistical models for areal interpolation. In: Harts J, Ottens HFL, Scholten HJ (eds) EGIS '90 Proceedings, vol 1. EGIS Foundation, Utrecht, pp 392–399Google Scholar
  7. Manchester Computing Centre (1990) Post Office Central Postcode Directory (POSTZON file). CMS 628, Manchester Computing CentreGoogle Scholar
  8. Tobler WR (1979) Smooth pycnophylactic interpolation for geographical regions. J Am Statistical Assoc 74:519–530Google Scholar

Copyright information

© Springer-Verlag 1992

Authors and Affiliations

  • Robin Flowerdew
    • 1
  • Mick Green
    • 2
  1. 1.North West Regional Research LaboratoryLancaster UniversityLancasterUK
  2. 2.Centre for Applied StatisticsLancaster UniversityLancasterUK

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