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In the Earth Sciences, the concept of fractals and scale invariance is well-recognized in many natural objects. However, the use of fractals for spatial and temporal analyses of natural hazards has been less used (and accepted) in the Earth Sciences. This book brings together twelve contributions that emphasize the role of fractal analyses in natural hazard research, including landslides, wildfires, floods, catastrophic rock fractures and earthquakes. A wide variety of spatial and temporal fractal-related approaches and techniques are applied to 'natural' data, experimental data, and computer simulations. These approaches include probabilistic hazard analysis, cellular-automata models, spatial analyses, temporal variability, prediction, and self-organizing behaviour. The main aims of this volume are to present current research on fractal analyses as applied to natural hazards, and to stimulate the curiosity of advanced Earth Science students and researchers in the use of fractals analyses for the better understanding of natural hazards.
This handbook focuses on the enormous literature applying statistical methodology and modelling to environmental and ecological processes. The 21st century statistics community has become increasingly interdisciplinary, bringing a large collection of modern tools to all areas of application in environmental processes. In addition, the environmental community has substantially increased its scope of data collection including observational data, satellite-derived data, and computer model output. The resultant impact in this latter community has been substantial; no longer are simple regression and analysis of variance methods adequate. The contribution of this handbook is to assemble a state-of-the-art view of this interface. Features: An internationally regarded editorial team. A distinguished collection of contributors. A thoroughly contemporary treatment of a substantial interdisciplinary interface. Written to engage both statisticians as well as quantitative environmental researchers. 34 chapters covering methodology, ecological processes, environmental exposure, and statistical methods in climate science.
by Peter J. Roussopoulos, Director, Southern Research Station The world and its ecosystems are repeatedly punctuated by natural disturbances, and human societies must learn to manage this reality Often severe and unp- dictable, dynamic natural forces disrupt human welfare and alter the structure and composition of natural systems Over the past century, land management ag- cies within the United States have relied on science to improve the sustainable management of natural resources Forest economics research can help advance this scientifc basis by integrating knowledge of forest disturbance processes with their economic causes and consequences As the twenty-frst century unfolds, people increasingly seek the goods and services provided by forest ecosystems, not only for wood supply, clean water, and leisure pursuits, but also to establish residential communities that are removed from the hustle and bustle of urban life As vividly demonstrated during the past few years, Santa Ana winds can blow wildfres down from the mountains of California, incinerating homes as readily as vegetation in the canyons below Hurricanes can fatten large swaths of forest land, while associated foods create havoc for urban and rural residents alike Less dramatic, but more insidious, trees and forest stands are succumbing to exotic insects and diseases, causing economic losses to private property values (including timber) as well as scenic and recreation values As human demands on public and private forests expand, science-based solutions need to be identifed so that social needs can be balanced with the vagaries of forest disturbance processes
The fourth edition of An Introduction to Statistical Problem Solving in Geography continues its standing as the definitive introduction to statistics and quantitative analysis in geography. Assuming no reader background in statistics, the authors lay out the proper role of statistical analysis and methods in human and physical geography. They delve into the calculation of descriptive summaries and graphics to explain geographic patterns and use inferential statistics (parametric and nonparametric) to test for differences (t-tests, ANOVA), relationships (regression and correlation), and spatial statistics (point and area patterns, spatial autocorrelation). This edition introduces more advanced topics, including logistic regression, two-factor ANOVA, and spatial estimation (inverse distance weighting, Kriging). Many chapters also include thought-provoking discussions of statistical concepts as they relate to the COVID-19 pandemic. Maintaining an exploratory and investigative approach throughout, the authors provide readers with real-world geographic issues and more than 50 map examples. Concepts are explained clearly and narratively without oversimplification. Each chapter concludes with a list of major goals and objectives. An epilogue offers over 150 open-ended geographic situations, inviting students to apply their new statistical skills to solve problems currently affecting our world.
An overview of results and methods, written for graduates and researchers in physics, mathematics, biology, sociology, finance, medicine and engineering.
What is a natural forest disturbance? How well do we understand natural forest disturbances and how might we emulate them in forest management? What role does emulation play in forest management? Representing a range of geographic perspectives from across Canada and the United States, this book looks at the escalating public debate on the viability of natural disturbance emulation for sustaining forest landscapes from the perspective of policymakers, forestry professionals, academics, and conservationists. This book provides a scientific foundation for justifying the use of and a solid framework for examining the ambiguities inherent in emulating natural forest landscape disturbance. It acknowledges the divergent expectations that practitioners face and offers a balanced view of the promises and challenges associated with applying this emerging forest management paradigm. The first section examines foundational concepts, addressing questions of what emulation involves and what ecological reasoning substantiates it. These include a broad overview, a detailed review of emerging forest management paradigms and their global context, and an examination of the ecological premise for emulating natural disturbance. This section also explores the current understanding of natural disturbance regimes, including the two most prevalent in North America: fire and insects. The second section uses case studies from a wide geographical range to address the characterization of natural disturbances and the development of applied templates for their emulation through forest management. The emphasis on fire regimes in this section reflects the greater focus that has traditionally been placed on understanding and managing fire, compared with other forms of disturbance, and utilizes several viewpoints to address the lessons learned from historical disturbance patterns. Reflecting on current thinking in the field, immediate challenges, and potential directions, the final section moves deeper into the issues of practical applications by exploring the expectations for and feasibility of emulating natural disturbance through forest management.
This book collects important advances in methodology and data analysis for directional statistics. It is the companion book of the more theoretical treatment presented in Modern Directional Statistics (CRC Press, 2017). The field of directional statistics has received a lot of attention due to demands from disciplines such as life sciences or machine learning, the availability of massive data sets requiring adapted statistical techniques, and technological advances. This book covers important progress in bioinformatics, biology, astrophysics, oceanography, environmental sciences, earth sciences, machine learning and social sciences.