I was doing some geostatistical analysis (variogram+kriging) for a "presence only" type data in a species distribution modeling context. Since, we know that when estimating the (empirical) variogram, the attribute is basically assumed to be a realization of continuous random variables (although an attribute can occur in counts too). If the attribute is just the presence, and no sub-categories then all the values at all positions will be same (say 1, if we denote a presence by 1). Hence the variogram can not be calculated, not even the indicator variogram. One of the approaches to deal with such data pseudo absences or background data were generated using some algorithm e.g. Maxent etc (see e.g. [1, 2]). The pseudo absences are generated taking many factors into account and stacked/combined with actual data. This is equivalent to merely generating random positions (x, y coordinates) and giving them an absence status (say 0s). The new data is now a binary data with two categories, presence 1 and absences 0.
How many absences (as compared to actual data) should be generated and how to decide it?
Tomislav Hengl, Henk Sierdsema, Andreja Radović, Arta Dilo, Spatial prediction of species’ distributions from occurrence-only records: combining point pattern analysis, ENFA and regression-kriging, Ecological Modelling, Volume 220, Issue 24, 24 December 2009, Pages 3499-3511.