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This book is aimed directly at students of geography, particularly those who lack confidence in manipulating numbers. The aim is not to teach the mathematics behind statistical tests, but to focus on the logic, so that students can choose the most appropriate tests, apply them in the most convenient way and make sense of the results. Introductory chapters explain how to use statistical methods and then the tests are arranged according to the type of data that they require. Diagrams are used to guide students toward the most appropriate tests. The focus is on nonparametric methods that make very few assumptions and are appropriate for the kinds of data that many students will collect. Parametric methods, including Student’s t-tests, correlation and regression are also covered. Although aimed directly at geography students at senior undergraduate and graduate level, this book provides an accessible introduction to a wide range of statistical methods and will be of value to students and researchers in allied disciplines including Earth and environmental science, and the social sciences.
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.
Written for undergraduate geography majors and entry-level graduate students with limited backgrounds in statistical analysis and methods, McGrew and Monroe provide a comprehensive and understandable introduction to statistical methods in a problem-solving framework. Engaging examples and problems are drawn from a variety of topical areas in both human and physical geography and are fully integrated into the text. Without compromising statistical rigor or oversimplifying, the authors stress the importance of written narratives that explain each statistical technique. After introducing basic statistical concepts and terminology, the authors focus on nonspatial and spatial descriptive statistics. They transition to inferential problem solving, including probability, sampling, and estimation, before delving deeper into inferential statistics for geographic problem solving. The final chapters examine the related techniques of correlation and regression. A list of major goals and objectives is included at the end of each chapter, allowing students to monitor their own progress and mastery of geographic statistical materials. An epilogue, offering over 150 geographic situations, gives students a chance to figure out which statistical technique should be used for a particular situation.
"Covers a broad range of subjects that undergraduates in the discipline should be familiar and comfortable with upon graduation. From chapters on the scientific method and fundamental research concepts, to experimental design, sampling and statistical analysis, the text offers an excellent introduction to the key concepts of geographical research. The content is applicable for students at the beginning of their studies right through to planning and conducting dissertations. The book has also been of particular support in designing my level 1 and 2 tutorials which cover similar ground to several of the chapters." - Joseph Mallalieu, School of Geography, Leeds University "Montello and Sutton is one of the best texts I′ve used in seminars on research methodology. The text offers a clear balance of quantitative vs. qualitative and physical vs. human which I′ve found particularly valuable. The chapters on research ethics, scientific communication, information technologies and data visualization are excellent." - Kenneth E. Foote, Department of Geography, University of Colorado at Boulder This is a broad and integrative introduction to the conduct and interpretation of scientific research, covering both geography and environmental studies. Written for undergraduate and postgraduate students, it: Explains both the conceptual and the technical aspects of research, as well as all phases of the research process Combines approaches in physical geography and environmental science, human geography and human-environment relations, and geographic and environmental information techniques (such as GIS, cartography, and remote sensing) Combines natural and social scientific approaches common to subjects in geography and environmental studies Includes case studies of actual research projects to demonstrate the breadth of approaches taken It will be core reading for students studying scientific research methods in geography, environmental studies and related disciplines such as planning and earth science.
First published in 2004. This text is an essential guide to current research approaches in human geography, covering all aspects of undertaking a geography research project, from the selection of an appropriate topic through to the organisation and writing of the final report. Covering a wide range of contemporary research methods, the authors provide practical advice on how to actually undertake a project.
Geographical Weighted Regression (GWR) is a new local modelling technique for analysing spatial analysis. This technique allows local as opposed to global models of relationships to be measured and mapped. This is the first and only book on this technique, offering comprehensive coverage on this new 'hot' topic in spatial analysis. * Provides step-by-step examples of how to use the GWR model using data sets and examples on issues such as house price determinants, educational attainment levels and school performance statistics * Contains a broad discussion of and basic concepts on GWR through to ideas on statistical inference for GWR models * uniquely features accompanying author-written software that allows users to undertake sophisticated and complex forms of GWR within a user-friendly, Windows-based, front-end (see book for details).
This book contains a rich set of tools for nonparametric analyses, and the purpose of this text is to provide guidance to students and professional researchers on how R is used for nonparametric data analysis in the biological sciences: To introduce when nonparametric approaches to data analysis are appropriate To introduce the leading nonparametric tests commonly used in biostatistics and how R is used to generate appropriate statistics for each test To introduce common figures typically associated with nonparametric data analysis and how R is used to generate appropriate figures in support of each data set The book focuses on how R is used to distinguish between data that could be classified as nonparametric as opposed to data that could be classified as parametric, with both approaches to data classification covered extensively. Following an introductory lesson on nonparametric statistics for the biological sciences, the book is organized into eight self-contained lessons on various analyses and tests using R to broadly compare differences between data sets and statistical approach.
Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools originated as material to accompany the spatial analysis module of MSc programmes at University College London delivered by the principal author, Dr Mike de Smith. The project was discussed with Professors Longley and Goodchild. They kindly agreed to contribute to the contents of the Guide itself. As such, this Guide may be seen as a companion to the pioneering book on Geographic Information Systems and Science (now changed to Science and Systems) by Longley, Goodchild, Maguire and Rhind, particularly the chapters that deal with spatial analysis and modeling. Their participation has also facilitated links with broader “spatial literacy” and spatial analysis programmes. Notable amongst these are the GIS&T Body of Knowledge materials provided by the Association of American Geographers together with the spatial educational programmes provided through UCL and UCSB. The formats in which this Guide has been published have proved to be extremely popular, encouraging us to seek to improve and extend the material and associated resources further. Many academics and industry professionals have provided helpful comments on previous editions, and universities in several parts of the world have now developed courses which make use of the Guide and the accompanying resources. Workshops based on these materials have been run in Ireland, the USA, East Africa, Italy and Japan, and a Chinese version of the Guide (2nd ed.) has been published by the Publishing House of Electronics Industry, Beijing, PRC, www.phei.com.cn in 2009. A Chinese version of this 6th edition is due to be published in 2021 by Science Press.
Statistical Methods for Geography is the essential introduction for geography students looking to fully understand and apply key statistical concepts and techniques. Now in its fifth edition, this text is an accessible statistics ‘101’ focused on student learning, and includes definitions, examples, and exercises throughout. Fully integrated with online self-assessment exercises and video overviews, it explains everything required to get full credits for any undergraduate statistics module. The fifth edition of this bestselling text includes: · Coverage of descriptive statistics, probability, inferential statistics, hypothesis testing and sampling, variance, correlation, regression analysis, spatial patterns, spatial data reduction using factor analysis and cluster analysis. · New examples from physical geography and additional real-world examples. · Updated in-text and online exercises along with downloadable datasets. This is the only text you’ll need for undergraduate courses in statistical analysis, statistical methods, and quantitative geography.