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An introductory overview of spatial analysis and statistics through GIS, including worked examples and critical analysis of results.
List of contents
1. Think spatially: basic concepts of spatial analysis and space conceptualization; 2. Exploratory spatial data analysis tools and statistics; 3. Analyzing geographic distributions and point patterns; 4. Spatial autocorrelation; 5. Multivariate data in geography: data reduction and clustering; 6. Modeling relationships: regression and geographically weighted regression; 7. Spatial econometrics.
About the author
After completing his postdoctoral studies in the USA, George Grekousis now teaches geography-related courses as Associate Professor in China. His interdisciplinary research focuses on spatial analysis, geodemographics, and artificial intelligence. Dr Grekousis has been awarded several grants from well-known international bodies, and his research has been published in several leading journals, including Computers, Environment and Urban Systems, PLOS One, and Applied Geography.
Summary
This book presents an introductory overview of spatial data analysis methods and geoinformation analysis techniques. Each chapter introduces the related theory, explains how to interpret metrics outputs, and provides worked examples using ArcGIS and GeoDa. This is a valuable resource for graduate students and researchers analyzing geospatial data.
Additional text
'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
