Spatial Data Analysis

TL;DR

This chapter introduces spatial econometrics techniques for analyzing spatial data, emphasizing models that account for spatial dependence, with practical applications like London house prices to uncover spatial patterns.

Contribution

It provides a comprehensive overview of spatial econometric models and interpretation guidance, enhancing analysis of spatial dependence in social science data.

Findings

Spatial models improve understanding of spatial dependence.

Application to London house prices reveals hidden spatial patterns.

Spatial econometrics addresses endogeneity in spatial data.

Abstract

This handbook chapter provides an essential introduction to the field of spatial econometrics, offering a comprehensive overview of techniques and methodologies for analysing spatial data in the social sciences. Spatial econometrics addresses the unique challenges posed by spatially dependent observations, where spatial relationships among data points can be of substantive interest or can significantly impact statistical analyses. The chapter begins by exploring the fundamental concepts of spatial dependence and spatial autocorrelation, and highlighting their implications for traditional econometric models. It then introduces a range of spatial econometric models, particularly spatial lag, spatial error, spatial lag of X, and spatial Durbin models, illustrating how these models accommodate spatial relationships and yield accurate and insightful results about the underlying spatial processes. The chapter provides an intuitive guide on how to interpret those different models. A practical example on London house prices demonstrates the application of spatial econometrics, emphasising its relevance in uncovering hidden spatial patterns, addressing endogeneity, and providing robust estimates in the presence of spatial dependence.