Integrating attribute and space characteristics in choropleth display and spatial data mining

International Journal of Geographical Information Science

Volumn 14, Issue 7, 2000, Pages 649-667

  Murray, Alan T ^a   Shyy, Tung Kai ^b  

^a OHIO STATE UNIVERSITY   (United States)

^b UNIVERSITY OF QUEENSLAND   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

CRIME; DATA PROCESSING; GIS; SPATIAL DATA;

AUSTRALIA; BRISBANE; QUEENSLAND;

EID: 0033775847     PISSN: 13658816     EISSN: 13623087     Source Type: Journal    
DOI: 10.1080/136588100424954     Document Type: Article

References (31)

1. FISHER, W. D., 1958. On grouping for maximum homogeneity. Journal of the American Statistical Association, 53:789–798.
2. MONMONIER, M. S., 1972. Contiguity-biased class-interval selection:a method for simplifying patterns on statistical maps. Geographical Review, 62:203–228.
3. JENKS, G. F., and CASPALL, F. C., 1971. Error on choroplethic maps:definition, measurement, reduction. Annals of the Association of American Geographers, 61:217–244.
4. ESTTVILL-CASTRO, V., and MURRAY, A. T., Mining spatial data via clustering. In Proceedings of 8th International Symposium on Spatial Data Handling. Edited by:Poiker, T., and Chrisman, N., pp. 522–532. Vancouver:International Geographical Union.
5. JENKS, G. F., 1963. Generalization in statistical mapping. Annals of the Association of American Geographers, 53:15–26.
6. EVANS, I. S., 1977. The selection of class intervals. Transactions of the Institute of British Geographers, 2:98–124.
7. GRIFFITH, D. A., and AMRHEIN, C. G., 1997. Multivariate Statistical Analysis for Geographers, New Jersey:Prentice-Hall.
8. KAUFMAN, L., and ROUSSEEUW, P., 1990. Finding Groups in Data:An Introduction to Cluster Analysis, New York:John Wiley & Sons.
9. ANSELIN, L., and BAO, S., 1997. “ Exploratory spatial data analysis linking SpaceStat and Arc View ”. In Recent Developments in Spatial Analysis, Edited by:Fischer, M., and Getis, A., 35–59. Berlin:Springer-Verlag.
10. CLARKE, K. C., 1995. Analytical and Computer Cartography, 2nd, New York:Prentice Hall.
11. COHON, J. L., 1978. Multiobjective Programming and Planning, New York:Academic Press.
12. COULSON, M. R. C., 1987. In the matter of class intervals for choropleth maps:with particular reference to the work of George Jenks. Cartographica, 24:16–39.
13. CROMLEY, E. K., and CROMLEY, R. G., An analysis of alternative classification schemes for medical atlas mapping. European Journal of Cancer, 32A1551–1559.
14. CROMLEY, R. G., 1995. Classed versus unclassed choropleth maps:a question of how many classes. Cartographica, 32:15–27.
15. CROMLEY, R. G., 1996. A comparison of optimal classification strategies for choroplethic displays of spatially aggregated data. International Journal of Geographical Information Systems, 10:405–424.
16. DECLERCQ, F. A. N., Choropleth map accuracy and the number of class intervals. In Proceedings of t he 17th International Cartographic Conference. Barcelona, Spain. Vol. 1, pp. 918–922.
17. DENT, B. D., 1999. Cartography:Thematic Map Design, 5th, New York:WCB/ McGraw-Hill.
18. MONMONIER, M. S., 1973. Analogs between class-interval selection and location allocation models. The Canadian Cartographer, 10:123–131.
19. MONMONIER, M. S., 1976. Modifying objective functions and constraints for maximizing visual correspondence of choroplethic maps. The Canadian Cartographer, 13:21–34.
20. ESTTVILL-CASTRO, V., and HOULE, M., Robust clustering of large geo-referenced data sets. In 3rd Pacific-Asia Conference on Knowledge Discovery and Data Mining. Edited by:Zhong, N., and Zhow, L., pp. 327–337. New York:Springer-Verlag.
21. MURRAY, A. T., 1999. Spatial analysis using clustering methods:evaluating the use of central point and median approaches. Journal of Geographical Systems, 1:367–383.
22. MURRAY, A. T., 2000. Spatial characteristics and comparisons of interaction and median clustering models. Geographical Analysis, 32:1–19.
23. MURRAY, A. T., and CHURCH, R. L., 1996. Applying simulated annealing to location planning models. Journal of Heuristics, 2:49–71.
24. MURRAY, A. T., and ESTTVILL-CASTRO, V., 1998. Cluster discovery techniques for exploratory spatial data analysis. International Journal of Geographical Information Science, 12:431–443.
25. MURRAY, A. T., and GERRARD, R. A., 1997. Capacitated service and regional constraints in location-allocation modeling. Location Science, 5:103–118.
26. NG, R., and HAN, J., Efficient and effective clustering methods for spatial data mining. In Proceedings of the 20th Conference on Very Large Data Bases (VLDB). Edited by:Bocca, J., Jarke, M., and Zaniolo, C., pp. 144–155.
27. ROBINSON, A. H., MORRISON, J. L., MUEHRCKE, P. C., KIMERLING, A. J., and GUPTTLL, S. C., 1995. Elements of Cartography, 6th, New York:Wiley & Sons.
28. SLOCUM, T. A., 1999. Thematic Cartography and Visualization, New Jersey:Prentice Hall.
29. SMTTH, R. S., 1986. Comparing traditional methods for selecting class intervals on choropleth maps. Professional Geographer, 38:62–67.
30. SOLANKI, R. S., 1991. Generating the noninferior set in mixed integer biobjective linear programs:an application to a location problem. Computers and Operations Research, 18:1–15.
31. WISE, S., HAINING, R., and MA, J., 1997. “ Reionalisation tools for the exploratory spatial analysis of health data ”. In Recent Developments in Spatial Analysis, Edited by:Fischer, M., and Getis, A., 83–100. Berlin:Springer-Verlag.