In the five years since the publication of the first edition of Spatial Analysis: Statistics, Visualization, and Computational Methods, many new developments have taken shape regarding the implementation of new tools and methods for spatial analysis with R. The use and growth of artificial intelligence, machine learning and deep learning algorithms with a spatial perspective, and the interdisciplinary use of spatial analysis are all covered in this second edition along with traditional statistical methods and algorithms to provide a concept-based problem-solving learning approach to mastering practical spatial analysis. Spatial Analysis with R: Statistics, Visualization, and Computational Methods, Second Edition provides a balance between concepts and practicums of spatial statistics with a comprehensive coverage of the most important approaches to understand spatial data, analyze spatial relationships and patterns, and predict spatial processes. New in the Second Edition: <ul> <li>Includes new practical exercises and worked-out examples using R</li> <li>Presents a wide range of hands-on spatial analysis worktables and lab exercises</li> <li>All chapters are revised and include new illustrations of different concepts using data from environmental and social sciences</li> <li>Expanded material on spatiotemporal methods, visual analytics methods, data science, and computational methods</li> <li>Explains big data, data management, and data mining</li> </ul> This second edition of an established textbook, with new datasets, insights, excellent illustrations, and numerous examples with R, is perfect for senior undergraduate and first-year graduate students in geography and the geosciences.

Spatial Analysis with R: Statistics, Visualization, and Computational Methods

Science & Math

Oyana, Tonny J.

Earth Sciences

CRC-Press

In the five years since the publication of the first edition of Spatial Analysis: Statistics, Visualization, and Computational Methods, many new developments have taken shape regarding the implementation of new tools and methods for spatial analysis with R. The use and growth of artificial intelligence, machine learning and deep learning algorithms with a spatial perspective, and the interdisciplinary use of spatial analysis are all covered in this second edition along with traditional statistical methods and algorithms to provide a concept-based problem-solving learning approach to mastering practical spatial analysis. Spatial Analysis with R: Statistics, Visualization, and Computational Methods, Second Edition, provides a balance between concepts and practicums of spatial statistics with a comprehensive coverage of the most important approaches to understand spatial data, analyze spatial relationships and patterns, and predict spatial processes. New in the Second Edition: <ul> <li>Includes new practical exercises and worked out examples using R</li> <li>Presents a wide range of hands-on spatial analysis work tables and lab exercises</li> <li>All chapters are revised and include new illustrations of different concepts using data from environmental and social sciences</li> <li>Expanded material on spatiotemporal methods, visual analytics methods, data science, and computational methods</li> <li>Explains big data, data management, and data mining</li> </ul> This second edition of an established textbook, with new datasets, insights, excellent illustrations, and numerous examples with R, is perfect for senior undergraduate and first year graduate students in geography and the geosciences.

Spatial Analysis with R

An introductory text for the next generation of geospatial analysts and data scientists, Spatial Analysis: Statistics, Visualization, and Computational Methods focuses on the fundamentals of spatial analysis using traditional, contemporary, and computational methods. Outlining both non-spatial and spatial statistical concepts, the authors present practical applications of geospatial data tools, techniques, and strategies in geographic studies. They offer a problem-based learning (PBL) approach to spatial analysis―containing hands-on problem-sets that can be worked out in MS Excel or ArcGIS―as well as detailed illustrations and numerous case studies. The book enables readers to: <ul>
<li>Identify types and characterize non-spatial and spatial data</li> <li>Demonstrate their competence to explore, visualize, summarize, analyze, optimize, and clearly present statistical data and results</li> <li>Construct testable hypotheses that require inferential statistical analysis</li> <li>Process spatial data, extract explanatory variables, conduct statistical tests, and explain results</li> <li>Understand and interpret spatial data summaries and statistical tests</li> </ul>
Spatial Analysis: Statistics, Visualization, and Computational Methods incorporates traditional statistical methods, spatial statistics, visualization, and computational methods and algorithms to provide a concept-based problem-solving learning approach to mastering practical spatial analysis. Topics covered include: spatial descriptive methods, hypothesis testing, spatial regression, hot spot analysis, geostatistics, spatial modeling, and data science.

Spatial Analysis: Statistics, Visualization, and Computational Methods

Margai, Florence

See the best price to sell, buy, or rent books by Oyana, Tonny J.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Oyana, Tonny J.

See the best price to sell, buy, or rent books by Oyana, Tonny J.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Spatial Analysis with R: Statistics, Visualization, and Computational Methods

Spatial Analysis with R

Spatial Analysis: Statistics, Visualization, and Computational Methods