The Analysis of Spatial Association by Use of Distance Statistics

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Empirical analysis of free-floating carsharing usage: The Munich and Berlin case (2015, methodology): “This toolbox performs the Getis-Ord Gi⁄-test (Getis and Ord, 1992; Ord and Getis, 1995) that computes a z-score for each cell by considering all cells within a (predefined) search radius.”
Crime Mapping: Spatial and Temporal Challenges (2009, methodology): “Local Moran’s I (and similar statistics such as the Getis and Ord Gi , see Getis and Ord 1992) provides a mechanism to make inferences about a population from a sample.”
Using spatial statistics to identify emerging hot spots of forest loss (2017, background): “Hot spots are locations where observed patterns are not likely the result of random processes or of subjective cartographic design decisions; they represent places where there are underlying spatial processes at work (Getis and Ord 1992).”
Valuing biodiversity enhancement in New Zealand's planted forests: Socioeconomic and spatial determinants of willingness-to-pay (2014, methodology): “Johnston identified the presence of WTP hotspots in a stated CE framework by applying the Getis–Ord statistic (Getis and Ord, 1992).”