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020 _a9789819725465
_9978-981-97-2546-5
024 7 _a10.1007/978-981-97-2546-5
_2doi
050 4 _aGF1-900
072 7 _aPSAF
_2bicssc
072 7 _aSCI026000
_2bisacsh
072 7 _aPSAF
_2thema
100 1 _aAltieri, Linda.
_eauthor.
_0(orcid)0000-0001-5667-3157
_1https://orcid.org/0000-0001-5667-3157
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980716
245 1 0 _aEntropy Measures for Environmental Data
_h[electronic resource] :
_bDescription, Sampling and Inference for Data with Dependence Structures /
_cby Linda Altieri, Daniela Cocchi.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXX, 156 p. 35 illus., 23 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdvances in Geographical and Environmental Sciences,
_x2198-3550
505 0 _aIntroduction -- Spatial Entropy Measures -- Entropy-based Spatial Sampling -- Entropy Estimation -- Conclusions and Final Discussion.
520 _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.
650 0 _aHuman ecology
_xStudy and teaching.
_969359
650 0 _aStatistics .
_965982
650 0 _aPhysical geography.
_966716
650 0 _aEcology .
_968629
650 0 _aBiodiversity.
_970778
650 1 4 _aEnvironmental Studies.
_969361
650 2 4 _aStatistics.
_965982
650 2 4 _aPhysical Geography.
_966716
650 2 4 _aEcology.
_968629
650 2 4 _aBiodiversity.
_970778
700 1 _aCocchi, Daniela.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980717
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819725458
776 0 8 _iPrinted edition:
_z9789819725472
776 0 8 _iPrinted edition:
_z9789819725489
830 0 _aAdvances in Geographical and Environmental Sciences,
_x2198-3550
_974433
856 4 0 _uhttps://doi.org/10.1007/978-981-97-2546-5
912 _aZDB-2-EES
912 _aZDB-2-SXEE
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