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2 results

  • 1 - Think Spatially

  • George Grekousis
  • Book:
    Spatial Analysis Methods and Practice
    Published online:
    20 May 2020
    Print publication:
    11 June 2020, pp 1-58

  • Summary

    This chapter

    • Presents the basic concepts, terms and definitions pertaining to spatial analysis

    • Introduces a spatial analysis workflow that follows a Describe–Explore–Explain structure

    • Presents in detail the reasons why spatial data are special, namely spatial autocorrelation, scale, the modifiable area unit problem, spatial heterogeneity, the edge effects and the ecological fallacy

    • Explains why conceptualization of spatial relationships is extremely important in spatial analysis

    • Presents the approaches used to conceptualize spatial relationships

    • Explains how distance, contiguity/adjacency, neighborhood, proximity polygons and space–time window are used in space conceptualization

    • Defines the spatial weights matrix, which is essential to almost every spatial statistic/ technique

    • Introduces the real-world project along with the related dataset to be worked throughout the book

    After a thorough study of the theory and lab sections, you will be able to

    • Implement a comprehensive workflow when you conduct spatial analysis

    • Distinguish spatial from nonspatial data

    • Understand why spatial data should be treated with new methods (e.g., spatial statistics)

    • Understand the importance of applying conceptualization methods according to the problem at hand

    • Understand essential terms for conducting spatial analysis as for example distance, contiguity/adjacency, neighborhood, proximity polygons and space–time

    • Describe the spatial analysis process to be adopted for solving the real-world project of this book

    • Presents the project’s data with ArcGIS and GeoDa

Spatial Analysis Methods and Practice
  • Describe – Explore – Explain through GIS
  • George Grekousis
  • Published online:
    20 May 2020
    Print publication:
    11 June 2020
  • 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.