caa a22 4500 467922268 CHVBK 20180406152920.0 cr unu---uuuuu 170328e20060901xx s 000 0 eng 10.1007/s00477-006-0035-8 doi (NATIONALLICENCE)springer-10.1007/s00477-006-0035-8 Combining categorical and continuous spatial information within the Bayesian maximum entropy paradigm [Elektronische Daten] [M.A. Wibrin, P. Bogaert, D. Fasbender] Due to the fast pace increasing availability and diversity of information sources in environmental sciences, there is a real need of sound statistical mapping techniques for using them jointly inside a unique theoretical framework. As these information sources may vary both with respect to their nature (continuous vs. categorical or qualitative), their spatial density as well as their intrinsic quality (soft vs. hard data), the design of such techniques is a challenging issue. In this paper, an efficient method for combining spatially non-exhaustive categorical and continuous data in a mapping context is proposed, based on the Bayesian maximum entropy paradigm. This approach relies first on the definition of a mixed random field, that can account for a stochastic link between categorical and continuous random fields through the use of a cross-covariance function. When incorporating general knowledge about the first- and second-order moments of these fields, it is shown that, under mild hypotheses, their joint distribution can be expressed as a mixture of conditional Gaussian prior distributions, with parameters estimation that can be obtained from entropy maximization. A posterior distribution that incorporates the various (soft or hard) continuous and categorical data at hand can then be obtained by a straightforward conditionalization step. The use and potential of the method is illustrated by the way of a simulated case study. A comparison with few common geostatistical methods in some limit cases also emphasizes their similarities and differences, both from the theoretical and practical viewpoints. As expected, adding categorical information may significantly improve the spatial prediction of a continuous variable, making this approach powerful and very promising. Springer-Verlag, 2006 Bayesian maximum entropy nationallicence Geostatistics nationallicence Kriging nationallicence Random field nationallicence Categorical data nationallicence Soft data nationallicence Wibrin M.A. Unité d'Environnemétrie et de Géomatique, UCL/AGRO/MILA/ENGE, Université Catholique de Louvain, Croix du Sud 2/16, 1348, Louvain-la-Neuve, Belgium aut Bogaert P. Unité d'Environnemétrie et de Géomatique, UCL/AGRO/MILA/ENGE, Université Catholique de Louvain, Croix du Sud 2/16, 1348, Louvain-la-Neuve, Belgium aut Fasbender D. Unité d'Environnemétrie et de Géomatique, UCL/AGRO/MILA/ENGE, Université Catholique de Louvain, Croix du Sud 2/16, 1348, Louvain-la-Neuve, Belgium aut Stochastic Environmental Research and Risk Assessment Springer-Verlag 20/6(2006-09-01), 423-433 1436-3240 20:6<423 2006 20 477 https://doi.org/10.1007/s00477-006-0035-8 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s00477-006-0035-8 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Wibrin M.A. Unité d'Environnemétrie et de Géomatique, UCL/AGRO/MILA/ENGE, Université Catholique de Louvain, Croix du Sud 2/16, 1348, Louvain-la-Neuve, Belgium aut NATIONALLICENCE 700 1- Bogaert P. Unité d'Environnemétrie et de Géomatique, UCL/AGRO/MILA/ENGE, Université Catholique de Louvain, Croix du Sud 2/16, 1348, Louvain-la-Neuve, Belgium aut NATIONALLICENCE 700 1- Fasbender D. Unité d'Environnemétrie et de Géomatique, UCL/AGRO/MILA/ENGE, Université Catholique de Louvain, Croix du Sud 2/16, 1348, Louvain-la-Neuve, Belgium aut NATIONALLICENCE 773 0- Stochastic Environmental Research and Risk Assessment Springer-Verlag 20/6(2006-09-01), 423-433 1436-3240 20:6<423 2006 20 477 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer