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Spatial Analysis Methods and Practice
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Book description

This is an introductory textbook on spatial analysis and spatial statistics through GIS. Each chapter presents methods and metrics, explains how to interpret results, and provides worked examples. Topics include: describing and mapping data through exploratory spatial data analysis; analyzing geographic distributions and point patterns; spatial autocorrelation; spatial clustering; geographically weighted regression and OLS regression; and spatial econometrics. The worked examples link theory to practice through a single real-world case study, with software and illustrated guidance. Exercises are solved twice: first through ArcGIS, and then GeoDa. Through a simple methodological framework the book describes the dataset, explores spatial relations and associations, and builds models. Results are critically interpreted, and the advantages and pitfalls of using various spatial analysis methods are discussed. This is a valuable resource for graduate students and researchers analyzing geospatial data through a spatial analysis lens, including those using GIS in the environmental sciences, geography, and social sciences.


‘… An excellent course text for students of GIS, spatial statistics, quantitative geography, and ecology … Essential reading for beginning students as well as those who wish to refresh their knowledge with respect to newer tools such as geographically weighted regression and spatial econometrics … introduces spatial analysis to those with very little training in statistics while at the same time developing applications using standard software for spatial analysis based on the ArcGIS and Geoda software systems. An excellent primer for anyone following a full course in spatial analysis. Spatial analysis is a tough subject to teach but Grekousis guides the reader through the basic ideas about understanding how correlations define our geographic world, introducing the full range of spatial tools and models.’

Michael Batty - Centre for Advanced Spatial Analysis (CASA), University College London

‘… the perfect introduction to the emerging field of spatial data science. It is clearly written, with realistic and carefully worked out examples and based on a sound pedagogical approach.’

Luc Anselin - Director, Center for Spatial Data Science, University of Chicago, and creator of the GeoDa software

‘Highly valuable and timely book for multidisciplinary professionals and students who aim to work with spatial problems but do not yet have the tools to study and solve these. The book provides an excellent introduction to the concepts and tools to think and analyse spatially, complemented by practical, realistic examples of how to apply this knowledge. The book has sufficient depth and rigor to allow students at all levels to learn for themselves and reach a good comprehension of a wide variety of aspects within this scientific domain.’

Walter T. de Vries - Technical University of Munich

‘… an excellent blend of key theoretical concepts and applications. It covers a wide range of spatial topics and concepts while progressively building up in difficulty. The engaging examples, demonstrative code and laboratory follow-up exercises make this book suitable for both self-learners and traditional academic settings. Highly recommended.’

Giorgos Mountrakis - State University of New York

‘A much welcomed and timely addition to the bookshelf of practitioners interested in the quantitative analysis of geographical data. The book offers a clear and concise exposition to basic and advanced methods and tools of spatial analysis, solidifying understanding through worked real-world case studies based on state-of-the-art commercial (ArcGIS) and public-domain (GeoDA) software. Definitely a book to be routinely used as a reference on the practical implementation of key analytical methods by people employing geographical data across a wide spectrum of disciplines.’

Phaedon Kyriakidis - Cyprus University of Technology

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