Spatial data analysis

By Tobias Ruttenauer. ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems, 2024

189 citations recorded by the source corpus.

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.

View Paper: https://www.semanticscholar.org/paper/28dc1a03820c12eca42272a624bf4f0d55143309

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Citations in the corpus

• Interpolation of Missing Temperature Data at Meteorological Stations Using P-BSHADE (2013, background): “Spatial lag and spatial error models take spatial autocorrelation into account (Fischer and Wang 2011).”