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010   $a9789819725465
024 7 $a10.1007/978-981-97-2546-5
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035   $a(Au-PeEL)EBL31529366
035   $a(DE-He213)978-981-97-2546-5
035   $a(OCoLC)1446476587
035   $a(EXLCZ)9932970882000041
100   $a20240716d2024      u|         0
101 0 $aeng
135   $aur|||||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aEntropy Measures for Environmental Data $eDescription, Sampling and Inference for Data with Dependence Structures /$fby Linda Altieri, Daniela Cocchi
205   $a1st ed. 2024.
210  1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024.
215   $a1 online resource (172 pages)
225 1 $aAdvances in Geographical and Environmental Sciences,$x2198-3550
311 08$a9789819725458 
327   $aIntroduction -- Spatial Entropy Measures -- Entropy-based Spatial Sampling -- Entropy Estimation -- Conclusions and Final Discussion.
330   $aThis book shows how to successfully adapt entropy measures to the complexity of environmental data. It also provides a unified framework that covers all main entropy and spatial entropy measures in the literature, with suggestions for their potential use in the analysis of environmental data such as biodiversity, land use and other phenomena occurring over space or time, or both. First, recent literature reviews about including spatial information in traditional entropy measures are presented, highlighting the advantages and disadvantages of past approaches and the difference in interpretation of their proposals. A consistent notation applicable to all approaches is introduced, and the authors? own proposal is presented. Second, the use of entropy in spatial sampling is focused on, and a method with an outstanding performance when data show a negative or complex spatial correlation is proposed. The last part of the book covers estimating entropy and proposes a model-based approachthat differs from all existing estimators, working with data presenting any departure from independence: presence of covariates, temporal or spatial correlation, or both. The theoretical parts are supported by environmental examples covering point data about biodiversity and lattice data about land use. Moreover, a practical section is provided for all parts of the book; in particular, the R package SpatEntropy covers not only the authors? novel proposals, but also all the main entropy and spatial entropy indices available in the literature. R codes are supplemented to reproduce all the examples. This book is a valuable resource for students and researchers in applied sciences where the use of entropy measures is of interest and where data present dependence on space, time or covariates, such as geography, ecology, biology and landscape analysis.
410  0$aAdvances in Geographical and Environmental Sciences,$x2198-3550
606   $aHuman ecology$xStudy and teaching
606   $aStatistics
606   $aPhysical geography
606   $aEcology
606   $aBiodiversity
606   $aEnvironmental Studies
606   $aStatistics
606   $aPhysical Geography
606   $aEcology
606   $aBiodiversity
615  0$aHuman ecology$xStudy and teaching.
615  0$aStatistics.
615  0$aPhysical geography.
615  0$aEcology.
615  0$aBiodiversity.
615 14$aEnvironmental Studies.
615 24$aStatistics.
615 24$aPhysical Geography.
615 24$aEcology.
615 24$aBiodiversity.
676   $a333.707
700   $aAltieri$b Linda$01749725
701   $aCocchi$b Daniela$088941
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
912   $a9910874660803321
996   $aEntropy Measures for Environmental Data$94183989
997   $aUNINA