Neuware - Spatial Autocorrelation: A Fundamental Property of Geospatial Phenomena offers a state-of-the-art exploration of one of the most pivotal spatial analysis concepts. Beginning with foundational theories and clear definitions, it sets out the concepts and basic theory of spatial autocorrelation through elaborated conceptualizations and practical examples. In-depth case studies reveal the pervasive influence of spatial patterns in scientific inquiry while anticipating emerging research and innovative techniques. It offers practical methods for quantifying spatial autocorrelation, complete with step-by-step instructions and real-world examples.Spatial Autocorrelation equips graduate students, researchers, and professionals with the knowledge and tools to confidently comprehend, navigate, and apply spatial analysis in their respective domains, making it an ideal companion for technical reference books, and a vital addition to the libraries of any discipline utilizing spatial analysis.

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Bibliographic Details

Title: Spatial Autocorrelation
Publisher: Elsevier Inc Aug 2025
Publication Date: 2025
Language: English
ISBN 10: 0443417431
ISBN 13: 9780443417436
Binding: Taschenbuch
Condition: Neu
Dimensions: 229 millimeters width by 152 millimeters height by 18 millimeters depth
Weight: 457 grams

About this title

Synopsis

Spatial Autocorrelation: A Fundamental Property of Geospatial Phenomena offers a state-of-the-art exploration of one of the most pivotal spatial analysis concepts. Beginning with foundational theories and clear definitions, it sets out the concepts and basic theory of spatial autocorrelation through elaborated conceptualizations and practical examples. In-depth case studies reveal the pervasive influence of spatial patterns in scientific inquiry while anticipating emerging research and innovative techniques. It offers practical methods for quantifying spatial autocorrelation, complete with step-by-step instructions and real-world examples.

Spatial Autocorrelation equips graduate students, researchers, and professionals with the knowledge and tools to confidently comprehend, navigate, and apply spatial analysis in their respective domains, making it an ideal companion for technical reference books, and a vital addition to the libraries of any discipline utilizing spatial analysis.

Explores a fundamental geospatial concept via a blend of multidisciplinary topics
Provides an educational focus in a conceptually friendly manner
Offers a progressive, iterative layout, spanning basic to advanced concepts
Emphasizes novel as well as benchmark empirical and simulation examples
Covers spatial autocorrelation in various disciplines, being nascent in some of them

About the Authors

Dr. Daniel A. Griffith is an Ashbel Smith Professor Emeritus of Geospatial Information Sciences at
the University of Texas at Dallas, United States; a past affiliated Professor in the College of Public
Health at the University of South Florida, United States; and an Adjunct Professor in the Department
of Resource Economics and Environmental Sociology at the University of Alberta, Canada. He
specializes in spatial statistics, quantitative-urban-economic geography, and urban public health.

Dr. Bin Li is a Professor at Central Michigan University, United States, where he previously
served as the Chair of the Department of Geography and Environmental Studies. Prior to this role, he was a faculty member at the University of Miami. He specializes in Geographic Information
Science, with extensive research and teaching experience in spatial statistics, geographic information
science, and cartography.

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