Spatial Analysis Methods and Practice
Describe – Explore – Explain through GIS

An introductory overview of spatial analysis and statistics through GIS, including worked examples and critical analysis of results.

George Grekousis (Author)

9781108498982, Cambridge University Press

Hardback, published 11 June 2020

532 pages
25.3 x 17.9 x 3 cm, 1.17 kg

'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

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.

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.

Subject Areas: Geographical information systems [GIS & remote sensing RGW], Regional geography [RGL], Maths for scientists [PDE], Environmental economics [KCN], Econometrics [KCH], Research methods: general [GPS]