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

Effects of missing data and locational errors on spatial concentration measures based on Ripley’s K-function. Spatial Economic Analysis. Measures based on Ripley’s K-function are the preferred tools to test the concentration of individual agents in an economic space. In many empirical cases, however, the datasets contain different inaccuracies due to missing data or uncertainty about the location of the agents. Little is known thus far about the effects of these inaccuracies on the K-function. This paper sheds 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 the effect of data imperfections. © 2017 Regional Studies Association.