George Grekousis

Spatial Analysis Methods and Practice (eBook, ePUB)

Describe - Explore - Explain through GIS

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 11. Juni 2020
• Englisch
• ISBN-13: 9781108585507
• Artikelnr.: 68440700

Autorenporträt

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.

Inhaltsangabe

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.