effects of missing data and locational errors on spatial concentration measures based on Ripley's k-function

Distance-based measures (developed as extensions of the basic Ripley’s K-function) are the tools of choice for model builders in testing the concentration of individual agents in the economic space. In many empirical cases, however, the datasets contain inaccuracies due to missing data or uncertainty about the locations of the agents. So far, little is known about the effects of these inaccuracies on the K-function. This paper aims at shedding light on the problem through a theoretical analysis supported by Monte Carlo experiments. The results show that patterns of clustering or inhibition may be observed not as genuine phenomena, but only as an artefact of data imperfections.