The Application of Local Indicators for Categorical Data
Channel: Recording Archaeology
Date: 26/11/2020

Spatial autocorrelation or spatial dependency describes how the value of spatial data varies as the distance between the data increases or decreases. Archaeological research has long been relying on this property of spatial data to explore landscape and intra-site patterns, and dif-ferent  statistical  methods  (Moran’s  I,  Getis-Ord  Gi)  are  routinely  used  to  analyse  spatial  au-tocorrelation  of  continuous  numerical  values.  On  the  other  hand,  the  statistical  analysis  of  spatial autocorrelation for categorical data has largely been neglected in archaeology. Cross-K and cross-L functions are increasingly used for this purpose, but their application is limited to spatial point patterns. For the analysis of other type of spatial data, like grids or regions, Join-Count Statistics is widely employed in other research fields, but to the best of my knowledge it has never been used in archaeology. One of the reasons might be related to the absence of a solid method to calculate local statistics, namely the spatial dependency of each spatial unit in the study area. Recent research carried out by Barry Boots and Michal Pietrzack et al. have provided new statistical tools to calculate local indicators of autocorrelation for categorical data (LICD). Strengths and weaknesses of these tools will be explored in this paper, to assess their applicability to archaeological contexts. These methods will then be implemented in R and tested in two archaeological case-studies at landscape and intra-site scale.


Francesco Carrer