It is actually point data. I read each observation in with a lat and long coord. So there is a couple points on top of eachother in each of the 12 locations. There are a total of 255 points. I suppose the hotspot analysis would idenfity clusters if there were any. I will give that a try and see what results suggest.
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I have samples of individuals from various areas in South and Central America (12 samples, so 12 locations). Each sample has a different number of individuals with a measurement assigned to them. Each sample also has somewhere between 4-40 individuals. So there are coincident data at each location. I would like to see if there is spatial autocorrelation (clustering/dispersion) among the samples taking into account this measurement. I am using the Spatial Autocorrelation Moran's 1 tool. Having done something similar to this before, I felt each location could only have one value. In the past (using geostatistical analyst), it would just average the values at each location. So I went ahead and did that myself. Thus I had 12 locations, each with one data point. I ran the spatial autocorrelation tool, and there was nothing significant. I am doing it again without averaging the samples. Thus each location as between 4-40 data points. After running the tool again, it finds significant clustering. However, if there are 12 locations with a ton of data points, I feel this is due to sampleing bias. Is that right? Any suggestions on the best way to do this?
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