Spatial statistics and composite indicators: a review of existing case studies and open research issues on spatial composite indicators

Recent research advances in spatial statistic on spatial autocorrelation help to better understand the spatial dependence between spatial units. Understanding this kind of relationships helps to earn better insights on how phenomena are distribute along space and why in a particular location they feature a particular value. In the last decade or so, spatial statistic techniques have been used by several scholars to study the spatial distribution of composite indicators, introducing a new point of view in the study of composite indicators that allows to earn more knowledge with respect to the indicator’s value only; in addition to this, spatial analysis was proven to show, for a particular location, the dependency of the composite from one of its sub-factor. This contribution presents a state of the art review of most recent advances in spatial statistics applied to composite indicators as an early contribution towards a more robust definition and application of spatial composite indicators.