1. No category

Publications
Related to the development and applications of Markov chain spatial statistics /geostatistics
(i.e., the MCRF approach)
Major papers
1. Li, W., C. Zhang, M.R. Willig, D.K. Dey, G. Wang, and L. You. 2015. Bayesian Markov chain
random field cosimulation for improving land cover classification accuracy. Mathematical
Geosciences, 47(2): 123-148.
2. Li, W., C. Zhang, D. K. Dey, and M. R. Willig. 2013. Updating categorical soil maps using
limited survey data by Bayesian Markov chain cosimulation. The Scientific World Journal (Soil
science section), vol. 2013, Article ID 587284, doi:10.1155/2013/587284.
3. Li, W., C. Zhang and D.K. Dey, 2012. Modeling experimental cross transiograms of neighboring
landscape categories with the gamma distribution. International Journal of Geographical
Information Science, 26(4): 599-620.
4. Li, W., C. Zhang, D.K. Dey, and S. Wang, 2010. Estimating threshold-exceeding probability
maps of continuous environmental variables with Markov chain random fields. Stochastic
Environmental Research and Risk Assessment, 24(8): 1113-1126.
5. Li, W. and C. Zhang, 2010. Linear interpolation and joint model fitting of experimental
transiograms for Markov chain simulation of categorical spatial variables. International Journal
of Geographical Information Science, 24(6): 821-839.
6. Li, W., and C. Zhang, 2008. A single-chain-based multidimensional Markov chain model for
subsurface characterization. Environmental and Ecological Statistics, 15(2):157-174.
7. Li, W., and C. Zhang, 2007. A random-path Markov chain algorithm for simulating categorical
soil variables from random point samples. Soil Science Society of America Journal, 71(3): 656668.
8. Li, W. 2007b. Transiograms for characterizing spatial variability of soil classes. Soil Science
Society of America Journal, 71(3): 881-893.
9. Li, W. 2007a. Markov chain random fields for estimation of categorical variables. Mathematical
Geology, 39(3): 321-335.
Other publications
1. Li, W. and C. Zhang. 2013. Some further clarification on Markov chain random fields and
transiograms. International Journal of Geographical Information Science, 27(3): 423-430.
2. Li, W. and C. Zhang, 2012. Comments on ‘Combining spatial transition probabilities for
stochastic simulation of categorical fields’ with communications on some issues related to
Markov chain geostatistics. International Journal of Geographical Information Science, 26(10):
1725–1739.
3. Li, W. and C. Zhang, 2012. Comments on ‘An efficient maximum entropy approach for
categorical variable prediction’ by D. Allard, D. D’Or & R. Froidevaux. European Journal of Soil
Science, 63(1): 120-124.
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4. Li, W. and C. Zhang, 2011. A Markov chain geostatistical framework for land-cover
classification with uncertainty assessment based on expert-interpreted pixels from remotely
sensed imagery. IEEE Transactions on Geoscience and Remote Sensing, 49(8): 2983-2992.
5. Li, W. and C. Zhang, 2010. Simulating spatial distribution of clay layer occurrence depth in
alluvial soils with a Markov chain geostatistical approach. Environmetrics, 21(1): 21–32.
6. Li, W. and C. Zhang, 2009. Markov chain analysis. International Encyclopedia of Human
Geography, 6: 455-460.
7. Zhang, C, and W. Li, 2008. Regional-scale modeling of the spatial distribution of surface and
subsurface textural classes in alluvial soils using Markov chain geostatistics. Soil Use and
Management, 24(3): 263-272.
8. Zhang, C. and W. Li, 2008. A comparative study of nonlinear Markov chain models in
conditional simulation of categorical variables from regular samples. Stochastic Environmental
Research and Risk Assessment, 22(2): 217-230.
9. Li, W. 2007c. A fixed-path Markov chain algorithm for conditional simulation of discrete spatial
variables. Mathematical Geology, 39(2): 159-176.
10. Zhang, C. and W. Li, 2007. Comparing a fixed-path Markov chain geostatistical algorithm with
sequential indicator simulation in categorical variable simulation from regular samples.
GIScience & Remote Sensing, 44(3): 251-266.
11. Li, W. 2006. Transiogram: A spatial relationship measure for categorical data. International
Journal of Geographical Information Science, 20(6): 693-699.
12. Li, W. and C. Zhang. 2013. Updating categorical soil map with limited survey data by Bayesian
Markov chain co-simulation. GeoComputation 2013 Extended Abstracts, May 23-25, Wuhan
University, China. http://www.geocomputation.org/2013/papers/55.pdf.
13. Li, W. and C. Zhang. 2012. A Bayesian Markov chain approach for land use classification based
on expert interpretation and auxiliary data. GIScience 2012, Sept. 19-21, Columbus, Ohio.
http://www.giscience.org/proceedings/abstracts/giscience2012_paper_137.pdf.
14. Li, W. and C. Zhang, 2008. Mapping the probabilities of soil clay layer thickness exceeding some
threshold values with Markov chain geostatistics. In: T.J. Cova, H.J. Miller, K. Beard, A.U. Frank,
M.F. Goodchild (eds.) GIScience 2008, Park City, UT, Sept 23-26, 2008, pp. 270-274.
15. Li, W., and C. Zhang, 2007. The nonlinear Markov chain geostatistics. In: IAMG 2007,
Geomathematics and GIS Analysis of Resources, Environment, and Hazards, IAMG 2007 Annul
Conference. Aug. 26-31, Beijing, China. pp. 573-578.
16. Li, W., and C. Zhang, 2007. A middle-insertion algorithm for Markov chain simulation of soil
layering. In: ACMGIS 2007, 15th ACM International Symposium on Advances in Geographic
Information Systems, Nov. 7-9, 2007, Seattle, USA. pp. 328-331.
17. Li, W. and C. Zhang. 2006. Visualizing spatial uncertainty of multinomial classes in area-class
mapping (online article). AutoCarto 2006, June 26-28, Vancouver, Washington, 13 pages.
http://www.cartogis.org/docs/proceedings/2006/li_zhang.pdf.
18. Li, W. and C. Zhang. 2005. Transiograms for characterizing soil spatial variability (extended
abstract). GeoComputation 2005. University of Michigan, Aug. 1-3, 2005, Ann Arbor, Michigan.
http://www.geocomputation.org/2005/LiW.pdf.
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Earlier publications
In modifying the coupled Markov chain model for mapping categorical variables
1. Li, W., and C. Zhang, 2006. A generalized Markov chain approach for conditional simulation of
categorical variables from grid samples. Transactions in GIS, 10(4): 651-669.
2. Li, W. and C. Zhang, 2005. Application of transiograms to Markov chain modeling and spatial
uncertainty assessment of land cover classes. GIScience & Remote Sensing, 42(4): 297-319.
3. Zhang, C. and W. Li, 2005. Markov chain modeling of multinomial land-cover classes. GIScience
& Remote Sensing, 42(1): 1-18.
4. Li, W., C. Zhang, J.E. Burt and A. Zhu, 2005. A Markov chain-based probability vector approach
for modeling spatial uncertainties of soil classes. Soil Science Society of America Journal, 69(6):
1931-1942.
5. Li, W., C. Zhang, J.E. Burt, A. Zhu and J. Feyen, 2004. Two-dimensional Markov chain
simulation of soil type spatial distribution. Soil Science Society of America Journal, 68(5): 14791490.
6. Zhang, C., and W. Li. 2004. Predictive area class mapping of multinomial land-cover categories
using Markov chains. pp. 239-242. In: GIScience 2004 - The Third International Conference on
Geographic Information Science, Maryland.
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