A gap-based dichotomization technique for outlier detection and its application to wildlife GPS data

Abstract Outliers in datasets are a concern for analysts as disturbances or signals of interest. Many techniques have been proposed for outlier detection. Some techniques label outliers as output, but others do not. The latter methods quantify data points to expose outliers but leaves their dichotomization as a task for analysts. We developed a technique to help analysts perform this task. Our technique uses value gaps between adjacent data pairs in a univariate dataset, where the data are sorted in ascending order of value. Its core process is to find the largest gap in the upper range of the dataset and remove the data above the gap as outliers; its supplementary process is to repeat the core process with the same dataset after removal. The analysts must decide when to stop this iteration. However, this iterative process leaves analysts with only a few reasonable options for the decision. We can apply this method to any dataset, such as a time series or multivariate dataset, if its data points are quantified to expose outliers in a ratio scale univariate dataset. We demonstrate how to implement this technique using wildlife GPS data and discuss the uniqueness and usefulness of the approach.