Spatial analysis by distance indices: an alternative local clustering index for studying spatial patterns

1. The spatial analysis by distance indices (SADIE) methodology for data analysis is valuable for quantifying spatial patterns of organisms in terms of patches and gaps. Previous research showed that the calculation of the local clustering indices, key SADIE statistics, does not adequately adjust for the absolute location or the magnitude of the counts. 2. We present a new definition of a local clustering index, which overcomes the problem associated with the original cluster indices related to sampling position and count size. The new index is calculated without breaking the link between the observed count and its original position and quantifies the contribution of an observed count at this particular position to the local gaps or patches for the observed pattern relative to the expected under the assumption of spatial independence amongst observed counts. Randomisation-based testing for statistical significance of an individual local clustering index follows naturally from the definition of the new index. 3. New indices, calculated for several simulated and observed data sets, showed that the original indices overestimated the number of points (sites, locations) contributing to the gaps / patches in a spatial grid. Results indicate that the significance (or interpretation) of individual local clustering indices cannot be made based on its magnitude only and needs to be supported by statistical testing. 4. The newly developed index will provide a valuable tool for quantifying the local pattern and testing for its significance and enhance the value of SADIE methodology in analysing spatial patterns. It can also be used in conjunction with other approaches that test for global clustering.