Classifying Space and Analysing the Consequences: Spatial Analysis of Health Data

  • Robert Haining
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


The paper classifies the main forms of spatial analysis and briefly reviews advances in spatial analysis over the last twenty years. The paper then considers three problem areas using health data for illustration: (i) how to construct a spatial framework for analysis; (ii) how to construct reliable area rates that recognize the spatial distribution of areas and their variable populations; (iii) how to test for relationships between variables when rates vary in reliability and there may be spatial autocorrelation in the unexplained variation. The paper briefly considers the potentially important role of geographic information systems in the field of spatial data analysis.


Geographic Information System Spatial Autocorrelation Spatial Analysis Spatial Data Royal Statistical Society 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. ANSELIN, L. (1988): Spatial Econometrics: Methods and Models. Dordrecht, Kluwer Academic Publishers.Google Scholar
  2. BARTLETT, M. (1966): Stochastic Processes. Cambridge University Press.Google Scholar
  3. BESAG, J.E. (1974): Spatial interaction and the statistical analysis of lattice systems. Journal, Royal Statistical Society, B36, 192–236.Google Scholar
  4. CARSTAIRS, V. (1981): Small area analysis and health service research. Community Medicine, 3(2): 131–139. Google Scholar
  5. CLAYTON, D. and KALDOR, J. (1987): Empirical Bayes estimates of age standardised relative risks for use in disease mapping. Biometrics 43, pp. 671–681.CrossRefGoogle Scholar
  6. CLIFF, A., HAGGETT, P., et al (1975): Elements of Spatial Structure. Cambridge University Press.Google Scholar
  7. CLIFF, A.D. and ORD, J.K. (1973): Spatial Autocorrelation. Pion, London.Google Scholar
  8. CLIFF, A.D and ORD, J.K. (1981): Spatial Processes: Models and Applications. London, Pion.Google Scholar
  9. CRESSIE, N. (1984): Towards resistant geostatistics. In G. Verly et al (eds) Geo-statistics for Natural Resources Characterization, 21–44. Dordrecht, Reidel.Google Scholar
  10. CRESSIE, N. (1991): Statistics for Spatial Data, Wiley, New York.Google Scholar
  11. CRESSIE, N. and READ, T.R.C. (1989): Spatial data analysis of regional counts. Biometrical Journal, 6, 699–719.CrossRefGoogle Scholar
  12. DIGGLE, P. (1983): Statistical Analysis of Spatial Point Patterns. Academic Press, London.Google Scholar
  13. GEARY, R.C. (1954): The contiguity ratio and statistical mapping. The Incorporated Statistician, 5, 115–145.CrossRefGoogle Scholar
  14. GOODCHILD, M., HAINING, R.P. and WISE, S.M. (1992): Integrating Geographic Information Systems and Spatial Data Analysis: Problems and Possibilities. International Journal of Geographical Information Systems, 16, 407–24.CrossRefGoogle Scholar
  15. HAINING, R.P. (1993): Spatial data analysis in the social and environmental sciences. Cambridge University Press.Google Scholar
  16. HAINING, R.P. (1994): Diagnostics for regression modelling in spatial econometrics. Journal of Regional Science, 34, 324–341.CrossRefGoogle Scholar
  17. HAINING, R.P., WISE, S.M. and BLAKE, M. (1994): Constructing regions for small area analysis: health service delivery and colorectal cancer (Unpublished ms).Google Scholar
  18. HASLETT, J., BRADLEY, R., CRAIG, P.S. et ai (1991): Dynamic graphics for exploring spatial data with application to locating global and local anomalies American Statistician, 45, 234–242.CrossRefGoogle Scholar
  19. KENDALL, M.G. (1939): The geographical distribution of crop productivity in: England. Journal, Royal Statistical Society, 102, 21–48.CrossRefGoogle Scholar
  20. KRISHNA IYER, P.V.A. (1949): The first and second moments of some probability distributions on a lattice and their applications. Biometrika, 36, 135–41Google Scholar
  21. MARSHALL, R.J. (1991): A review of methods for the statistical anaiaysis of spatial patterns of disease. Journal, Royal Statistical Society, A, 154, 421–441.CrossRefGoogle Scholar
  22. MARTIN, R.J. (1992): Leverage, influence and residuals in regression models when observations are correlated. Communications in Statistics: Theory and Methods, 21, 1183–1212.CrossRefGoogle Scholar
  23. MORAN, P.A.P. (1948): The interpretation of statistical maps. Journal, Royal Statistical Society, B, 10, 243–51.Google Scholar
  24. OLIVER, M.A. and WEBSTER, R. (1989): A geostatistical basis for spatial weighting in multivariate classification. Mathematical Geology, 21, 15–35.CrossRefGoogle Scholar
  25. ORD, J.K. (1975): Estimation methods for models of spatial interaction. Journal, American Statistical Association, 70, 120–6.CrossRefGoogle Scholar
  26. POCOCK, S.J., COOK, D.G. and BERESFORD, S.A.A. (1981): Regressions of area mortality rates on explanatory variables: what weighting is appropriate? Applied Statistician 30, 286–96.CrossRefGoogle Scholar
  27. RIPLEY, B. (1977): Modelling Spatial Patterns (with discussion). Journal, Royal Statistical Society,B, 39, 172–212.Google Scholar
  28. RIPLEY, B. (1981): Spatial Statistics. New York, Wiley.CrossRefGoogle Scholar
  29. SEMPLE, R. and GREEN, M. (1984): Classification in Human Geography. In G. Gaile and C. Willmott (eds): Spatial Statistics and Models. Reidel, Dordrecht.Google Scholar
  30. STOYAN, D., KENDALL, W.S. and MECKE, J. (1989): Stochastic Geometry and its Applications. New York, Wiley.Google Scholar
  31. UPTON, G.J. and FINGLETON, B. (1985): Spatial Data Analysis by Example Volume 1: Point Pattern and Quantitative Data. New York, Wiley.Google Scholar
  32. WEISBERG, S. (1985): Applied Linear Regression. New York, Wiley.Google Scholar

Copyright information

© Springer-Verlag Berlin · Heidelberg 1996

Authors and Affiliations

  • Robert Haining
    • 1
  1. 1.Department of GeographyUniversity of SheffieldSheffieldUK

Personalised recommendations