I have two datasets that I would like to analyze together. The first dataset has bird nest locations in neighborhoods (nest). The second dataset has bird feeder locations in neighborhoods (food). At each X,Y location of food I also have information about the amount of food (extent of the resource). Each of these are point shapefiles in ArcGIS.
I would like to create a smoothed surface (food raster) of food so I can extract a food index value for the location of each nest in my dataset. The food index should depend on the nearness of a birdfeeder to that nest, and the extent of food at that birdfeeder.
I used Getis Ord Gi* to generate zscores for each of my food locations (weighting each location based on extent of resource), and then used these values in IDW analysis. However, the Getis Ord Gi* zscores are both positive and negative because they compare the food value at each location to the mean food value at all of the feeders within the neighborhood. The result is that some of my feeders turn out to be "coldspots" in the resulting IDW raster.
What I'd like is a food raster where all of the food locations are "hot" (to varying degrees dependent on extent) and unmeasured locations are by definition "colder" than these (because I know there are not feeders there).
Does anyone know of a way to do this (e.g. constrain the Zscores so they are all positive or otherwise specify that the measured points are all "hot")?
Or do I need to use an alternate analysis altogether?
I have tested KDE on these data as well but would prefer IDW if possible because multiple fields of KDE are chosen at the "whim" of the researcher. I think I may have a difficult time defending choices of bandwidth, etc. with my datasets.
