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dc.contributor.author | Ameijeiras Alonso, José |
dc.contributor.author | Lagona, Francesco |
dc.contributor.author | Crujeiras Casais, Rosa María |
dc.date.accessioned | 2019-04-23T12:59:48Z |
dc.date.available | 2019-04-23T12:59:48Z |
dc.date.issued | 2019 |
dc.identifier.citation | Ameijeiras‐Alonso, J, Lagona, F, Ranalli, M, Crujeiras, RM. A circular nonhomogeneous hidden Markov field for the spatial segmentation of wildfire occurrences. Environmetrics. 2019; 30:e2501. https://doi.org/10.1002/env.2501 |
dc.identifier.uri | http://hdl.handle.net/10347/18672 |
dc.description | This is the pre-peer reviewed version of the following article: Ameijeiras‐Alonso, J, Lagona, F, Ranalli, M, Crujeiras, RM. A circular nonhomogeneous hidden Markov field for the spatial segmentation of wildfire occurrences. Environmetrics. 2019; 30:e2501. https://doi.org/10.1002/env.2501, which has been published in final form at https://doi.org/10.1002/env.2501. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions |
dc.description.abstract | Motivated by studies of wildfire seasonality, we propose a nonhomogeneous hidden Markov random field to model the spatial distribution of georeferenced fire occurrences during the year, by representing occurrence times as circular data. The model is based on a mixture of Kato–Jones circular densities, whose parameters vary across space according to a latent nonhomogeneous Potts model, modulated by georeferenced covariates. It allows us to segment fire occurrences according to a finite number of latent classes that represent the conditional distributions of the data under specific periods of the year, simultaneously accounting for unobserved heterogeneity and spatial autocorrelation. Further, it parsimoniously accommodates specific features of wildfire occurrence data such as multimodality, skewness, and kurtosis. Due to the numerical intractability of the likelihood function, estimation of the parameters is based on composite likelihood methods. It reduces to a computationally efficient expectation–maximization algorithm that iteratively alternates the maximization of a weighted composite likelihood function with weights updating. The proposal is illustrated in a study of wildfire occurrences in the Iberian Peninsula during a decade |
dc.description.sponsorship | Jose Ameijeiras‐Alonso and Rosa M. Crujeiras gratefully acknowledge the support of Project MTM2016‐76969‐P (Spanish State Research Agency, AEI), co‐funded by the European Regional Development Fund (ERDF), IAP network from Belgian Science Policy. Part of the research was carried out by Jose Ameijeiras‐Alonso during his visit to University of Roma Tre, supported by Grants BES‐2014‐071006 and EEBB‐I‐17‐12716 from the Spanish Ministry of Economy, Industry and Competitiveness. Francesco Lagona is supported by the 2015 PRIN supported project “Environmental processes and human activities: capturing their interactions via statistical methods”, funded by the Italian Ministry of Education, University and Scientific Research |
dc.language.iso | eng |
dc.publisher | Wiley |
dc.rights | © 2018 John Wiley & Sons, Ltd. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions |
dc.subject | Composite likelihood |
dc.subject | Fires |
dc.subject | Kato–Jones density |
dc.subject | Land use |
dc.subject | Markov random field |
dc.subject | Spatial circular data |
dc.title | A circular nonhomogeneous hidden Markov field for the spatial segmentation of wildfire occurrences |
dc.type | journal article |
dc.identifier.doi | 10.1002/env.2501 |
dc.relation.publisherversion | https://doi.org/10.1002/env.2501 |
dc.type.hasVersion | SMUR |
dc.identifier.essn | 1099-095X |
dc.rights.accessRights | open access |
dc.contributor.affiliation | Universidade de Santiago de Compostela. Departamento de Estatística, Análise Matemática e Optimización |
dc.description.peerreviewed | NON |
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