The role of geographic information science in applied geography

  • Arthur Getis
Part of the GeoJournal Library book series (GEJL, volume 77)


Applied geography has undergone remarkable changes in the last 20 years. Powerful new technologies have emerged that greatly improve the ability to collect, store, manage, view, analyze, and utilize information regarding the critical issues of our time. These technologies include geographic information systems (GIS), global positioning systems (GPS), satellite-base remote sensing, and a great variety of remarkable software that allows for the analysis of the compelling problems. The issues include globalization, global warming, pollution, security, crime, public health, transportation, energy supplies, and population growth. Geographic Information Science (GIScience) has given rise to an essentially multidisciplinary approach to applied problems. No single person is expert in all of these areas. It is necessary to emphasize coordination and collaboration and to find the bridges that reduce the barriers between disciplines. In this chapter we briefly discuss the new technologies and the way in which they are being used to solve the critical issues. We then make suggestions for an applied geography future vis-à-vis the geographic information sciences.


Geographic Information System Spatial Autocorrelation Spatial Data West Nile Virus Spatial Association 
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.


  1. Anselin, L. (1988) Spatial econometrics: methods and models. Dordrecht: Kluwer.CrossRefGoogle Scholar
  2. Anselin, L. (1994) Local indicators of spatial association - LISA. Geographical Analysis27:93–115CrossRefGoogle Scholar
  3. Anselin, L. and Rey, S. (1991) Properties of tests for spatial dependence in linear regression models. Geographical Analysis23: 112–31.CrossRefGoogle Scholar
  4. ArcNews ESRI, 380 New York Street, Redlands CA 92373–8100Google Scholar
  5. Bailey, T. C. and Gatrell, A. C. (1995) Interactive Spatial Data Analysis. Essex: Longman Scientific and Technical.Google Scholar
  6. Bateman, I. J., Lovett, A. A., and Brainard, J. S. (2003) Applied Environmental Economics: A GIS Approach to Cost-benefit Analysis. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  7. Batty, M. and Longley, P. (1994) Fractal Cities. London: Academic Press.Google Scholar
  8. Birkin, M. et al. (1996) Intelligent GIS: Location Decisions and Strategic Planning. New York: John Wiley & Sons.Google Scholar
  9. Boots, B. and Getis, A. (1988) Point Pattern Analysis. Newbury Park, CA: Sage.Google Scholar
  10. Briggs, D. J., Forer, P., Jarup, L., and Stern, R., eds. (2002) GIS for Emergency Preparedness and Health Risk Reduction. Dordrecht: Kluwer.Google Scholar
  11. Clark, P. J. and Evans, F. C. (1954) Distances to nearest neighbor as a measure of spatial relationships in populations. Science121: 397–8.CrossRefGoogle Scholar
  12. Cressie, N. (1991) Statistics for Spatial Data. Chichester: John Wiley.Google Scholar
  13. Cromley, E. and McLafferty, S. (2002) GIS and Public Health. Guilford Press.Google Scholar
  14. Diggle, P. J. (1983) Statistical Analysis of Spatial Point Patterns. London: Academic Press.Google Scholar
  15. Getis, A. (1984) Interaction modeling using second-order analysis. Environment and Planning A16, 173–183.CrossRefGoogle Scholar
  16. Getis, A. (1995) Spatial filtering in a regression framework: experiments on regional inequality, government expenditures, and urban crime. In Anselin, L. and Florax, R. J. G. M. (eds.) NewDirections in Spatial Econometrics. Berlin: Springer: 172–88.CrossRefGoogle Scholar
  17. Getis, A. and Ord, J. K. (1992) The analysis of spatial association by use of distance statistics. Geographical Analysis24: 189–206.CrossRefGoogle Scholar
  18. Getis, A. and Griffith, D. A. (2002) Comparative spatial filtering in regression analysis, Geographical Analysis, 34, 2, 130–140.Google Scholar
  19. Goodchild, M. F. and Janelle, D. G. (2003) Spatially Integrated Social Science: Examples in Best Practice, Oxford: Oxford University Press.Google Scholar
  20. Greene, R. W (2002) Confronting Catastrophe: A GIS Handbook. Redlands: ESRI Press.Google Scholar
  21. Griffith, D. A. (1988) Advanced Spatial Statistics: Special Topics in the Exploration of Quantitative Spatial Data Series. Dordrecht: Kluwer.Google Scholar
  22. Griffith, D. A. (1996) Spatial autocorrelation and eigenfunctions of the geographic weights matrix accompanying geo-referenced data. The Canadian Geographer40: 351–67.CrossRefGoogle Scholar
  23. Haining, R. (1990) Spatial Dta Analysis in the Social and Environmental Sciences. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  24. Heuvelink, G., Goodchild, M. F. and Heuvelink, B. M. (1998) Error Propagation In Environmental Modelling With GIS. London: Taylor & Francis.Google Scholar
  25. Hubert, L. J. (1979) Matching models in the analysis of cross-classifications. Psychometrika44: 21–41.CrossRefGoogle Scholar
  26. Hunsaker, C. T., Goodchild, M. F., Friedl, M. A., and Case, T. J. (eds.) (2001) Spatial Uncertainty in Ecology: Implications for Remote Sensing and Gis Applications. Berlin: Springer-Verlag.Google Scholar
  27. J. P. Jones, III, and Casetti, E. (1992) Applications of the Expansion Method. New York: Routledge.Google Scholar
  28. Lang, L. (1999) Transportation GIS. Redlands: ESRI Press.Google Scholar
  29. Longley, P., Goodchild, M. F., Maguire, D. J., and Rhind, D. W (eds.) (1999) Geographical Information Systems (two volumes). New York: John Wiley.Google Scholar
  30. Malczewski, J. (1999) GIS and Multicriteria Decision Analysis. New York. John Wiley.Google Scholar
  31. Mantel, N. (1967) The detection of disease clustering and a generalized regression approach. Cancer Research27: 209–20.Google Scholar
  32. Okabe, A., Boots, B., and Sugihara, K. (1992) Spatial Tesselations: Concepts and Applications of Voronoi Diagrams. New York, John Wiley.Google Scholar
  33. Ord, J. K. and Getis, A. (1993) Local spatial autocorrelation statistics: distributional issues and an application. Geographical Analysis 27: 286–306.CrossRefGoogle Scholar
  34. Paelinck, J. and Klaassen, L. (1979) Spatial Econometrics. Farnborough: Saxon House.Google Scholar
  35. Ripley, B. D. (1981) Spatial statistics. New York, John Wiley.CrossRefGoogle Scholar
  36. Skidmore, A. and Prins, H. (2002) Environmental Modelling With GIS and Remote Sensing. 2nd ed., London: Taylor & Francis.CrossRefGoogle Scholar
  37. Wescott, K., and Brandon, R. J. (eds.) (2000) Practical Applications of GIS for Archaeologists: A Predictive Modeling Toolkit. London: Taylor & Francis.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2004

Authors and Affiliations

  • Arthur Getis
    • 1
  1. 1.San Diego State UniversityUSA

Personalised recommendations