Getis and Ord (1992) introduced a statistic, *Gi*(d)*, that may be used to assess spatial dependence. Currently, we focus on a simple version of the standardized form of this statistic that was further elucidated by Ord and Getis (1995). In a typical exploratory geographic analysis, *Gi*(d)* is calculated for several different values of d in order to detect spatial clustering at different scales. It is clear that *Gi*(d)* is a local measure. As such, it is particularly helpful when applied to datasets for which global measures of spatial dependence, such as Moran’s *I* (Cliff and Ord, 1973), may fail to reveal the existence of important pockets of clustering.

We have developed a parallel algorithm – P-*Gi*(d)*. This parallel algorithm can solve larger-size problems than *Gi*(d)* sequential algorithms can handle while some super-linear speedups were achieved because memory requirements are significantly decreased by exploiting spatial data parallelism. Data-intenisve computing technologies are particularly suitable for supporting typical exploratory scenarios using the parallel algorithm.

**Project Team**: Marc Armstrong, Kate Cowles, Shaowen Wang