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6 - Modeling Relationships

Regression and Geographically Weighted Regression

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

  • Linear regression notions and mathematical formations

  • The assumptions that a linear regression model is based on

  • Identifying linear relationships between a dependent variable and independent variables

  • Residual plots, leverage and influential points

  • Evaluate the degree (effect) to which one variable influences the positive or negative change of another variable

  • The geometrical interpretation of the intercept and the slope, and various metrics and tests used to assess the quality of the results

  • The interpretation of coefficients

  • Studying casual relationships (under specific conditions)

  • Building a predictive model by fitting a regression line to the data

  • Evaluating the correctness of the model

  • Model overfitting

  • Ordinary least squares (OLS)

  • Exploratory OLS

  • Geographically weighted regression (GWR)

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

  • Distinguish which regression method is more appropriate for the problem at hand and the data available

  • Interpret statistics and diagnostics used to evaluate a regression model

  • Interpret the outcomes of a regression model such as the coefficient estimates, the scatter plots, the statistics and the maps created

  • Identify if relationships, associations or linkages among dependent and independent variables exist

  • Analyze data through regression analysis in Matlab

  • Conduct exploratory regression analysis in ArcGIS

  • Conduct geographically weighted regression in ArcGIS

Type
Chapter
Information
Spatial Analysis Methods and Practice
Describe – Explore – Explain through GIS
, pp. 351 - 450
Publisher: Cambridge University Press
Print publication year: 2020

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