Assessing the performance of control charts for detecting previously unexplored shift types in high density spatial data

As the collection and use of high-density (HD) spatial datasets has increased, the Statistical Process Control research community has strived to develop effective and efficient control charting techniques for these datasets. In general, these research efforts propose new control charting techniques and evaluate their abilities to detect different shift types. However, these works typically considered only conventional shift types, such as mean and variance shifts, which only account for a portion of the shift types that can manifest themselves in HD spatial datasets. In essence, advanced mathematical approaches are being developed for use with state-of-the-art measurement systems but assess their performance with traditional shift types developed for univariate statistics. This may hinder the effectiveness of these approaches in practice, as real-world systems may experience shift types other than (or in addition to) those addressed in the literature. The goal of this paper is to understand the ability of previously proposed control charting techniques to detect these unexplored shift types. This goal is accomplished through a simulation study that considers five different control charting techniques, identified from both the spatial statistics and spatial scan statistics literatures. The performances of these control charts are assessed against previously unexplored HD spatial dataset shift types. The results indicate that many control charting approaches were highly sensitive to variety of shift types. This suggests significant promise in the use of these approaches in systems that are susceptible to a wide variety of shift types, including shift types they were not specifically designed to detect.