Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/8652
Title: | Response to ‘Comments on “Combining Spatial Transition Probabilities for Stochastic Simulation of Categorical Fields” with Communications on Some Issues Related to Markov Chain Geostatistics | Authors: | Cao, Guofeng Kyriakidis, Phaedon Goodchild, Michael F. |
Major Field of Science: | Engineering and Technology | Field Category: | Environmental Engineering | Keywords: | Categorical data;Transition probability;Geostatistics;Conditional independence;Markov random field | Issue Date: | 27-Sep-2012 | Source: | International Journal of Geographical Information Science, 2012, vol. 26, no. 10, pp. 1741-1750 | Volume: | 26 | Issue: | 10 | Start page: | 1741 | End page: | 1750 | Abstract: | Li and Zhang (2012b, Comments on ‘Combining spatial transition probabilities for stochastic simulation of categorical fields’ with communications on some issues related to Markov chain geostatics) raised a series of comments on our recent paper (Cao, G., Kyriakidis, P.C., and Goodchild, M.F., 2011. Combining spatial transition probabilities for stochastic simulation of categorical fields. International Journal of Geographical Information Science, 25 (11), 1773–1791), which include a notation error in the model equation provided for the Markov chain random field (MCRF) or spatial Markov chain model (SMC), originally proposed by Li (2007b, Markov chain random fields for estimation of categorical variables. Mathematical Geology, 39 (3), 321–335), and followed by Allard et al. (2011, An efficient maximum entropy approach for categorical variable prediction. European Journal of Soil Science, 62, 381–393) about the misinterpretation of MCRF (or SMC) as a simplified form of the Bayesian maximum entropy (BME)-based approach, the so-called Markovian-type categorical prediction (MCP) (Allard, D., D'Or, D., and Froideveaux, R., 2009. Estimating and simulating spatial categorical data using an efficient maximum entropy approach. Avignon: Unite Biostatisque et Processus Spatiaux Institute National de la Recherche Agronomique. Technical Report No. 37; Allard, D., D'Or, D., and Froideveaux, R., 2011. An efficient maximum entropy approach for categorical variable prediction. European Journal of Soil Science, 62, 381–393). Li and Zhang (2012b, Comments on ‘Combining spatial transition probabilities for stochastic simulation of categorial fields’ with communication on some issues related to Markov chain geostatistics. International Journal of Geographical Information Science) also raised concerns regarding several statements Cao et al. (2011, Combining spatial transition probabilities for stochastic simulation of categorical fields. International Journal of Geographical Information Science, 25 (11), 1773–1791) had made, which mainly include connections between permanence of ratios and conditional independence, connections between MCRF and Bayesian networks and transiograms as spatial continuity measures. In this response, all of the comments and concerns will be addressed, while also communicating with Li and other colleagues on general topics in Markov chain geostatistics. | URI: | https://hdl.handle.net/20.500.14279/8652 | ISSN: | 13623087 | DOI: | 10.1080/13658816.2012.717630 | Rights: | © Informa UK Limited, an Informa Group Company | Type: | Article | Affiliation : | University of Illinois at Urbana-Champaign University of California University of Aegean |
Appears in Collections: | Άρθρα/Articles |
CORE Recommender
SCOPUSTM
Citations
3
checked on Nov 9, 2023
WEB OF SCIENCETM
Citations
10
3
Last Week
0
0
Last month
0
0
checked on Oct 29, 2023
Page view(s)
337
Last Week
0
0
Last month
5
5
checked on Nov 6, 2024
Google ScholarTM
Check
Altmetric
Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.