Spatial Analysis Methods and Practice
eBook - PDF

Spatial Analysis Methods and Practice

Describe – Explore – Explain through GIS

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

Spatial Analysis Methods and Practice

Describe – Explore – Explain through GIS

About this book

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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Yes, you can access Spatial Analysis Methods and Practice by George Grekousis in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Civil Engineering. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Half-title
  3. Reviews
  4. Title page
  5. Copyright information
  6. Contents
  7. Preface
  8. 1 Think Spatially: Basic Concepts of Spatial Analysis and Space Conceptualization
  9. 2 Exploratory Spatial Data Analysis Tools and Statistics
  10. 3 Analyzing Geographic Distributions and Point Patterns
  11. 4 Spatial Autocorrelation
  12. 5 Multivariate Data in Geography: Data Reduction and Clustering
  13. 6 Modeling Relationships: Regression and Geographically Weighted Regression
  14. 7 Spatial Econometrics
  15. References
  16. Index