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Titlebook: Entropy Measures for Environmental Data; Description, Samplin Linda Altieri,Daniela Cocchi Book 2024 The Editor(s) (if applicable) and The

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發(fā)表于 2025-3-21 16:38:46 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Entropy Measures for Environmental Data
副標題Description, Samplin
編輯Linda Altieri,Daniela Cocchi
視頻videohttp://file.papertrans.cn/321/320660/320660.mp4
概述Covers both theoretical and practical aspects of entropy measures.Is the first book to deal with spatial entropy estimation for complex data.Provides examples and tutorials to make results understanda
叢書名稱Advances in Geographical and Environmental Sciences
圖書封面Titlebook: Entropy Measures for Environmental Data; Description, Samplin Linda Altieri,Daniela Cocchi Book 2024 The Editor(s) (if applicable) and The
描述.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 approach that 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 suppo
出版日期Book 2024
關鍵詞Spatial Entropy; Environmental Data; Entropy Estimation; Spatial Sampling; Co-occurrence Entropy; Categor
版次1
doihttps://doi.org/10.1007/978-981-97-2546-5
isbn_softcover978-981-97-2548-9
isbn_ebook978-981-97-2546-5Series ISSN 2198-3542 Series E-ISSN 2198-3550
issn_series 2198-3542
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 23:16:46 | 只看該作者
Spatial Entropy Measures, the main approaches available in the literature for the consideration of the spatial dimension in entropy indices. In particular, Sect.?. contains Batty’s approach to spatial entropy for geographical data, and its extension for the inclusion of a neighbourhood system. Section?. outlines the other m
板凳
發(fā)表于 2025-3-22 03:13:59 | 只看該作者
地板
發(fā)表于 2025-3-22 07:54:23 | 只看該作者
Entropy Estimation,dependence structures. The idea is that the available observations are a (temporary) picture of an underlying and unknown process that governs the actual diversity of the system. Therefore, data are considered as a sample extracted from such process, and used for understanding what the latent hetero
5#
發(fā)表于 2025-3-22 10:13:05 | 只看該作者
Linda Altieri,Daniela CocchiCovers both theoretical and practical aspects of entropy measures.Is the first book to deal with spatial entropy estimation for complex data.Provides examples and tutorials to make results understanda
6#
發(fā)表于 2025-3-22 15:02:35 | 只看該作者
Advances in Geographical and Environmental Scienceshttp://image.papertrans.cn/f/image/320660.jpg
7#
發(fā)表于 2025-3-22 17:55:43 | 只看該作者
https://doi.org/10.1057/9781403983589iples of entropy, initially introduced in Information Theory: we focus on the original entropy formula for categorical variables, and on its decomposition, that contains the potential for the extension to spatial entropy. The second Section focuses on environmental studies and provides some necessar
8#
發(fā)表于 2025-3-22 23:07:58 | 只看該作者
9#
發(fā)表于 2025-3-23 03:56:06 | 只看該作者
Tshilidzi Marwala,Monica Lagaziod environmental data. Techniques are borrowed from the branch of spatial sampling, a collection of methods for extracting subsets of observations from a population, where the spatial location of occurrences is considered relevant for estimating the target characteristics, such as the mean or total o
10#
發(fā)表于 2025-3-23 07:51:50 | 只看該作者
https://doi.org/10.1007/978-3-319-78229-4dependence structures. The idea is that the available observations are a (temporary) picture of an underlying and unknown process that governs the actual diversity of the system. Therefore, data are considered as a sample extracted from such process, and used for understanding what the latent hetero
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