
 
Hi,
 
I have a dataset of insect samplings from vegetable fields and I want to reveal if some hotspots exist in the region. I am experiencing difficulties how to handle data in a statistically correct way since there are some questions on the sampling.
 
Data collection points are randomly distributed according to Moran's I test (I:0.06, p:0.26) that applied with a minimum threshold value allowing all sampling locations has at least one neighbor.
 
Samplings were made on month basis but, not periodically. For each point, sampling frequency varies between 12 and 24, for a total of 24 months period, so least visited points were sampled at least every two months, while others visited sometimes monthly or sometimes bimonthly, some were sampled every month. This is due to high number of sampling locations and limited labor. But samplings were made to cover the whole study region every month, so if field_A was not sampled this month, neighboring field_B is certainly sampled.
 
Now I am looking for advise on a statistically correct and acceptable way to homogenize these non-uniformities to explore hot spots, if it is possible at all.
 
Regards,
 
Alp 		 	   		  
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