Spatial Regression

Spatial Regression is a statistical modeling technique that accounts for spatial relationships and dependencies in data when analyzing relationships between variables. Unlike traditional regression, it incorporates the spatial structure of data to address issues like spatial autocorrelation and spatial heterogeneity.

This method recognizes that observations in geographic space are often not independent, violating assumptions of classical regression analysis. Spatial regression models explicitly incorporate spatial effects, providing more accurate parameter estimates and better understanding of how relationships between variables vary across space.

Practical Applications

• Housing price modeling considering neighborhood effects and spillovers
• Agricultural yield prediction accounting for spatial soil variations
• Crime rate analysis incorporating spatial spillover effects
• Economic development modeling with regional interaction effects
• Environmental impact assessment considering spatial dependencies

Related Terms

• Spatial Econometrics
• Geographically Weighted Regression
• Spatial Lag Model
• Spatial Error Model
• Spatial Heterogeneity