04387nam a22006015i 4500 978-981-97-2546-5 DE-He213 20251030112633.0 cr nn 008mamaa 240716s2024 si | s |||| 0|eng d 9789819725465 978-981-97-2546-5 10.1007/978-981-97-2546-5 doi GF1-900 PSAF bicssc SCI026000 bisacsh PSAF thema Altieri, Linda. author. (orcid)0000-0001-5667-3157 https://orcid.org/0000-0001-5667-3157 aut http://id.loc.gov/vocabulary/relators/aut 80716 Entropy Measures for Environmental Data [electronic resource] : Description, Sampling and Inference for Data with Dependence Structures / by Linda Altieri, Daniela Cocchi. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XX, 156 p. 35 illus., 23 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Advances in Geographical and Environmental Sciences, 2198-3550 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. Human ecology Study and teaching. 69359 Statistics . 65982 Physical geography. 66716 Ecology . 68629 Biodiversity. 70778 Environmental Studies. 69361 Statistics. 65982 Physical Geography. 66716 Ecology. 68629 Biodiversity. 70778 Cocchi, Daniela. author. aut http://id.loc.gov/vocabulary/relators/aut 80717 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819725458 Printed edition: 9789819725472 Printed edition: 9789819725489 Advances in Geographical and Environmental Sciences, 2198-3550 74433 https://doi.org/10.1007/978-981-97-2546-5 ZDB-2-EES ZDB-2-SXEE 61666 61666