Spatial auto correlation would be one preferred statistics and ArcGIS can be used to do spatial mapping. Article of Papadopoulos et al. 2003. Spatial Autocorrelation Analysis of a Ceratitis capitata (Diptera: Tephritidae) Adult Population in a Mixed Deciduous Fruit Orchard in Northern Greece. Environmental Entomology, 32(2):319-326 .

Alemany et al. 2006. Changes in the spatial and temporal population density of the Mediterranean fruit fly (Diptera: Tephritidae) in a citrus orchard. Spanish Journal of Agricultural Research. 4(2):161-166.

Hope this would help 

Since your response is a count, it sounds like a good starting point would be a Poisson regression via a glm or glmm ...or, potentially, a negative bionomial regression (if your count distribution is overdispersed). Use the glmm if you want to account for random effects in one of your predictors.

It's super duper easy to do in R; the glm() function is built into the base package.

> mydata fit summary(fit) 

#before you run the glm above, it is probably a good idea to check and make sure you variables are being read properly before doing the glm.  For example, if you had a dummy variable (0/1) that you wanted to be a factor:

> sapply(mydata, class)

> mydata