Hi Amber,
I'm really sorry you're having trouble with the Incremental Spatial Autocorrelation sample script. At 10.1 the Incremental Spatial Autocorrelation tool will be part of ArcGIS, and we're working really hard to deal with some of the issues that have come up since the release of the sample script. For now, though, there are some things that you can do.
The most likely reason that you're having issues with memory is that at the distances that you're using to test for spatial autocorrelation many of the features have tens of thousands of neighbors. Ideally, you want to use distances that give your features no more than maybe a hundred, a couple hundred, maybe even 1000 neighbors...but no feature should ever have 100,000 neighbors. A good way to see if this is your problem is to run the Generate Spatial Weights Matrix tool for some of your largest distance increments. The tool will tell you the maximum number of neighbors that any feature has. If you are seeing huge numbers there, then that is likely to be your problem with Incremental Spatial Autocorrelation. The solution is to lower the distances that you're testing so that each feature has a more reasonable number of neighbors.
One thing that you may be running into is that the distance at which each feature has at least one neighbor is large, maybe because of outliers (a couple of features that are really far away from all of the other features). A good option is to create a selection set that does not include the outliers and use just those features to figure out a good beginning and increment distance...and ultimately you would run Incremental Spatial Autocorrelation on just the selection set (without the outliers). After you find a peak and choose a threshold distance, you can then use the Generate Spatial Weights Matrix tool to create a weights matrix that uses a threshold distance that you choose, but then you can also choose a minimum number of neighbors. What that will do is for the majority of the features it will use the distance band you created, but for the outliers it will use the minimum distance (since that distance band may be too small for them to have any neighbors). That way you can use a threshold distance that makes sense for the majority of your features, but still include the outliers in your analysis.
