Effects of missing data and locational errors on spatial concentration measures based on Ripley’s K-function

measures based on Ripley's k function are the tools of election to test the concentration of individual agents in the economic space. In many empirical cases, however, the datasets contain differemt inaccuracies due to missing data or to uncertainty about the location of agents. Little is known, so far, on the effects of these inaccuracies on the K-.function. This paper aims at shedding light on on the problem through a theoretical analysis supported by Monte ACrlo experiments. The results show that pattern of Clustering or inhibition may be observed not as genuine phenomena, but only as the effect of data imperfection