Advertisement

GIS and modeling prerequisites

  • Arthur Getis
Scales in Geographic Space
Part of the Lecture Notes in Computer Science book series (LNCS, volume 716)

Abstract

In this paper structural elements are identified for the preparation of GIS-based data for use in econometric or statistical modeling. These elements include the need to know about the special characteristics of spatial data, such as map scale, spatial dependence, spatial variance heterogeneity and spatial trend heterogeneity, and the usual problems faced by modelers, such as nonspherical disturbances, stationarity of data, heteroscedasticity, and temporally and spatially autocorrelated disturbances. Detective work proceeds on the basis of the varying structures implied by the cross product statistic. These include measures of spatial differences, covariance, and interaction, and the exploratory data analysis functions included in the S-Plus statistical package.

Keywords

Geographic Information System Spatial Data Spatial Unit Exploratory Data Analysis Confirmatory Analysis 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    L. Anselin: Spatial Econometrics, Methods and Models. Dordrecht: Kluwer Academic (1988)Google Scholar
  2. 2.
    L. Anselin, A. Getis: Spatial statistical analysis and geographic information systems, Annals of Regional Science, 26, 19–33 (1992)Google Scholar
  3. 3.
    G. Arbia: Spatial Data Configuration in Statistical Analysis of Regional Economic and Related Problems. Dordrecht: Kluwer Academic (1989)Google Scholar
  4. 4.
    T. Breusch, A. Pagan: A simple test for heteroscedasticity and random coefficient variation, Econometrica, 47, 203–7 (1979)Google Scholar
  5. 5.
    N. Cressie: Statistics for Spatial Data, Wiley (1991)Google Scholar
  6. 6.
    J.C. Davis: Statistics and Data Analysis in Geology. New York: Wiley (1986)Google Scholar
  7. 7.
    S.A. Foster, W.L. Gorr: An adaptive filter for estimating spatially-varying parameters: Application to modeling police hours in response to calls for service, Management Science, 32, 878–889 (1986)Google Scholar
  8. 8.
    A. Getis: Spatial dependence and heterogenity in geographic information systems, in NCGIA publication edited by S. Fotheringham, P. Rogerson consisting of papers of I-14 Conference (1993 forthcoming)Google Scholar
  9. 9.
    A. Getis: Spatial interaction and spatial autocorrelation: a cross-product approach, Environment and Planning, A, 23, 1269–1277 (1991)Google Scholar
  10. 10.
    A. Getis, J.K. Ord: The analysis of spatial association by use of distance statistics, Geographical Analysis, 24, 189–206 (1992)Google Scholar
  11. 11.
    R. Haining: Spatial Data Analysis in the Social and Environmental Sciences. Cambridge: Cambridge University Press (1990)Google Scholar
  12. 12.
    S. Openshaw, P. Taylor: A million or so correlation coefficients: Three experiments on the modifiable areal unit problem. In N. Wrigley, R.J. Bennett (eds), Statistical Applications in the Spatial Sciences, pp. 127–144, London: Pion (1979)Google Scholar
  13. 13.
    J.N. Raynen An Introduction to Spectral Analysis. London: Pion (1971)Google Scholar
  14. 14.
    W. Tobler: Frame independent spatial analysis, in M.G. Goodchild and S. Gopal (Eds.), The Accuracy of Spatial Databases, pp. 115–122, London: Taylor & Francis (1989)Google Scholar
  15. 15.
    G. Wahba, J. Wendelberger: Some new mathematical methods for variational objective analysis using splines and cross validation, Monthly Weather Review, 108, 1122–1143 (1980)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Arthur Getis
    • 1
  1. 1.Department of GeographySan Diego State UniversitySan Diego

Personalised recommendations