Reflections on spatial autocorrelation

By Arthur Getis. 2007

338 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1a43de029c93b2de509297d8e673f619ffebc4af

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In Economics and Econometrics

Citations in the corpus

Economic growth and pollutant emissions in China: a spatial econometric analysis (2014, methodology): “We use Moran’s I, one of the most widely used spatial autocorrelation statistics (Anselin 1995; Getis 2007; Ord and Getis 1995).”
Modeling Micro-Level Crime Location Choice: Application of the Discrete Choice Framework to Crime at Places (2010, neutral): “…that ‘‘everything is related to everything else, but near things are more related than distant things’’ (Tobler 1970: 236), and thus nearby places are more similar to each other than distance places, a relation that is known as spatial autocorrelation (Dubin 1998; Getis 2007; Legendre 1993).”
Geographically Weighted Colocation Quotients: Specification and Application (2014, background): “T here has been considerable statistical analysis ofspatial autocorrelation by geographers, statisticians, and others who have interests in the analysis of data with apparent patterns in their locations of observation (Griffith 1987; Getis 2007).”
A non-parametric spatial independence test using symbolic entropy (2010, background): “To sum up, there is a strong consensus about the importance of this question (Getis 2007), which already forms a routine part of any spatial Econometric application.”
A History of the Concept of Spatial Autocorrelation: A Geographer's Perspective (2008, methodology): “Some of what is contained in this paper was first described in an essay I wrote for the Regional Science and Urban Economics journal (Getis 2007).”
Energy requirements of consumption: Urban form, climatic and socio-economic factors, rebounds and their policy implications (2013, background): “Moran's I index is commonly estimated in order to address this issue, where the residuals of a regression analysis are tested to check if the regression model properly captures all spatial effects (Getis, 2007).”