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Entropy measures for environmental data = description, sampling and inference for data with dependence structures /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Entropy measures for environmental data/ by Linda Altieri, Daniela Cocchi.
其他題名:	description, sampling and inference for data with dependence structures /
作者:	Altieri, Linda.
其他作者:	Cocchi, Daniela.
出版者:	Singapore :Springer Nature Singapore : : 2024.,
面頁冊數:	xx, 156 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Biodiversity. -
電子資源:	https://doi.org/10.1007/978-981-97-2546-5
ISBN:	9789819725465

Entropy measures for environmental data = description, sampling and inference for data with dependence structures /
Altieri, Linda.

Entropy measures for environmental datadescription, sampling and inference for data with dependence structures /[electronic resource] :by Linda Altieri, Daniela Cocchi. - Singapore :Springer Nature Singapore :2024. - xx, 156 p. :ill. (some col.), digital ;24 cm. - Advances in geographical and environmental sciences,2198-3550. - Advances in geographical and environmental sciences..

Introduction -- Spatial Entropy Measures -- Entropy-based Spatial Sampling -- Entropy Estimation -- Conclusions and Final Discussion.

This 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.

ISBN: 9789819725465

Standard No.: 10.1007/978-981-97-2546-5doiSubjects--Topical Terms:

593899
Biodiversity.

LC Class. No.: GE45.D37

Dewey Class. No.: 363.700285

Entropy measures for environmental data = description, sampling and inference for data with dependence structures /
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