Jim Burt

Position title: Professor Emeritus of Geography

Email: jeburt@wisc.edu




Ph.D., Geography, UCLA, 1980


Physical geography, Climatology, Quantitative methods, and Geovisualization


Geography 120: Global Physical Environments
Geography 127: Physical Systems of the Environment
Geography 321: Climatology
Geography 360: Quantitative Methods in Geographical Analysis
Geography 560: Advanced Quantitative Methods
Geography 575: Advanced Computer Cartography


Quinn, T., A-X Zhu, and J.E. Burt 2005: “Effects of detailed soil information on watershed modeling across different models scales.” International Journal of Applied Earth Observation and Geoinformation, 7, 324-338.

Li, W., C. Zhang, J.E. Burt, and A-X. Zhu, 2005:, “A Markov chain-based probability vector approach for modeling spatial uncertainties of soil classes.” Soil Science Society of America Journal, 69, 1931-1942.

Burt, J.E., A-X Zhu, M. Harrower, 2005. “Depicting fuzzy soil class uncertainty using perception-based color models,” Proceedings of the 11th World Congress of International Fuzzy Systems Association (IFSA2005): Fuzzy Logic, Soft Computing and Computational Intelligence, July 28-31, 2005, Beijing, China, pp. 112-117.

X. Shi, A-X Zhu, J.E. Burt, F. Qi, and D. Simonson, 2004: “A case-based reasoning approach to fuzzy soil mapping,” Soil Science Society of America Journal, 68, 885-894.

W. Li, C. Zhang, J.E. Burt, A-X Zhu, and J. Feyen, 2004: “Two-dimensional Markov chain for simulating spatial distribution of soil types,” Soil Science Society of America Journal, 68, 1479-1490.

Zhu, A-X, Hudson B, Burt, J.E., Lubich K, Simonson D., 2001: “Soil mapping using GIS, expert knowledge, and fuzzy logic.” Soil Science Society of American Journal 65 (5): 1463-1472.

Books Understanding Weather and Climate, 3rd edition. E. Aguado and J.E. Burt. Upper Saddle River, NJ: Prentice-Hall, 2004. Elementary Statistics for Geographers, 2nd edition. J. E. Burt and G. Barber. New York: Guilford Press, 1996.


Co-principal investigator for SoLIM (Soil Land Inference Model) for soil mapping based on recent developments in geographic information science (GISc), artificial intelligence (AI), and information representation theory.