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Alternative Distance Metrics for Enhanced Reliability of Spatial Regression Analysis of Health Data

  • Stefania Bertazzon
  • Scott Olson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5072)

Abstract

We present a spatial autoregressive model (SAR) to investigate the relationship between the incidence of heart disease and a pool of selected socio-economic factors in Calgary (Canada). Our goal is to provide decision makers with a reliable model, which can guide locational decisions to address current disease occurrence and mitigate its future occurrence and severity. To this end, the applied model rests on a quantitative definition of neighbourhood relationships in the city of Calgary. Our proposition is that such relationships, usually described by Euclidean distance, can be more effectively described by alternative distance metrics. The use of the most appropriate metric can improve the regression model by reducing the uncertainty of its estimates, ultimately providing a more reliable analytical tool for management and policy decision making.

Keywords

West Nile Virus Census Tract Spatial Dependence Geographically Weight Regression Distance Metrics 
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.

References

  1. 1.
    Cliff, D., Ord, J.K.: Spatial Processes. Models and Applications. Pion, London (1981)zbMATHGoogle Scholar
  2. 2.
    Griffith, D.A., Amrhein, C.G.: Statistical Analysis for Geographers. Prentice-Hall, Englewood Cliffs (1991)Google Scholar
  3. 3.
    Ahlbom, A., Norell, S.: Introduction to Modern Epidemiology. Epidemiology Resources Incorporated (1984)Google Scholar
  4. 4.
    Ghali, W.A., Knudtson, M.L.: Overview of the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease. Canadian Journal of Cardiology 16(10), 1225–1230 (2000)Google Scholar
  5. 5.
    Anselin, L.: Under the Hood. Issues in the Specification and Interpretation of Spatial Regression Models. Agricultural Economics, 27(3), 247–267 (2002)CrossRefGoogle Scholar
  6. 6.
    Cressie, N.: Statistics for Spatial Data. Wiley, New York (1993)Google Scholar
  7. 7.
    Fotheringham, A.S., Brundson, C., Charlton, M.: Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley, Chichester (2002)Google Scholar
  8. 8.
    Openshaw, S., Alvanides, S.: Applying geocomputation to the analysis of spatial distributions. In: Longley, P.A., Goodchild, M.F., Maguire, D.J., Rhind, D.W. (eds.) Geographical Information Systems: Principles and Technical issues, vol. 1, pp. 267–282 (1999)Google Scholar
  9. 9.
    Getis, A., Aldstadt, J.: Constructing the Spatial Weights Matrix Using a Local Statistic. Geographical Analysis 36, 90–104 (2004)CrossRefGoogle Scholar
  10. 10.
    Bertazzon, S.: A definition of contiguity for spatial regression analysis in GISc: Conceptual and computational aspects of spatial dependence. Rivista Geografica Italiana 2(CX), 247–280 (2003)Google Scholar
  11. 11.
    Bailey, T., Gatrell, A.: Interactive Spatial Data Analysis. Wiley, New York (1995)Google Scholar
  12. 12.
    Haggett, P., Cliff, A.D., Frey, A.: Locational Analysis in Human Geography. Edward Arnold, London (1977)Google Scholar
  13. 13.
    Krause, E.F.: Taxicab geometry. Addison-Wesley, Menlo Park, California (1975)Google Scholar
  14. 14.
    Anselin, L.: SpaceStat tutorial. Regional Research Institute. West Virginia University. Morgantown, West Virginia (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Stefania Bertazzon
    • 1
  • Scott Olson
    • 1
  1. 1.Department of GeographyUniversity of CalgaryCalgaryCanada

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