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4 - Spatial Autocorrelation

Published online by Cambridge University Press:  20 May 2020

George Grekousis
Affiliation:
Sun Yat-Sen University (SYSU), China
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Summary

This chapter deals with

  • Spatial autocorrelation and its importance to geographical problems

  • Global and local spatial autocorrelation techniques like Moran’s I, Getis-Ord G and Geary C

  • Tracing spatial clusters of high values (hot spots) or low values (cold spots)

  • Tracing spatial outliers

  • Optimized hot spot analysis

  • Interpreting the statistical significance of results

  • Incremental spatial autocorrelation used to define the appropriate scale of analysis

  • The multiple comparison problem and spatial dependence

  • Introducing Bonferroni correction and the false discovery rate

  • Spatiotemporal autocorrelation analysis using bivariate and differential Local Moran’s I index

  • Presenting examples of step-by-step solutions using ArcGIS and GeoDa

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

  • Distinguish between global and local spatial autocorrelation

  • Understand why spatial autocorrelation analysis is relevant to geographical analysis

  • Apply local and global indices of spatial autocorrelation like local Moran’s, Getis-Ord Gi and Gi

  • Use Moran’s I scatter plot to identify patterns

  • Identify if clustering of hot or cold spots exist

  • Identify and locate spatial outliers

  • Use Bivariate and Differential Local Moran’s I to identify if spatiotemporal autocorrelation exists and if changes cluster over time

  • Apply these tools using ArcGISand GeoDa

  • Interpret the results from both the statistical significance and spatial analysis standpoints

Type
Chapter
Information
Spatial Analysis Methods and Practice
Describe – Explore – Explain through GIS
, pp. 207 - 274
Publisher: Cambridge University Press
Print publication year: 2020

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