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

遗失数据和区位误差对于根据雷普力K函数的空间集中测量之影响。Spatial Economic Analysis. 基于雷普利K函数的测量，是检验个别行动者在经济空间中的集中度时偏好的工具。但在诸多经验案例中，却因遗失的数据或行动者地点的不确定性，导致数据集包含了各种不精确性。这些不精确性对K函数的影响至今却所知甚少。本文透过由蒙地卡罗实验所支持的理论分析，对此一问题提出洞见。研究结果显示，集群或抑制的模式，或许不应被视为真实的现象，而仅是数据不完美的影响。

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.