Spatial econometrics in an age of CyberGIScience

In this article, we focus on the evolution of the technology that lies at the basis of implementing spatial econometric methods into software tools. We review the changing methodological emphases and their implications for data structures and computational infrastructure required for estimation and inference. We review the evolution of software solutions, starting with SpaceStat and GeoDa and moving on to the PySAL open source library of spatial analytical functions (Rey and Anselin 2010, PySAL: a Python library of spatial analytical methods. In: M.M. Fischer and A. Getis, eds. Handbook of applied spatial analysis. Berlin: Springer, 175-193.). We compare these approaches with other software solutions, such as the R spatial analytical routines and recently released Stata functionality for spatial econometrics. We follow the review with a discussion of requirements and challenges encountered when moving these software tools into a CyberGIScience framework. We focus on the efficient data structures, the need for metadata and provenance tracking, as well as high-performance computing requirements. We close with the outline of a vision for a 'spatial econometrics workbench' as a core component of cyberinfrastructure for GIScience.