In a nutshell, stay-points are locations that a person has stopped for some amount of time. Previous work depends mainly on stay-point identification methods using experimentally fine tuned threshold values. These behave well on their experimental datasets but may exhibit reduced performance on other datasets. In this work, we demonstrate the potential of a geometry-based method for stay-point extraction. This is accomplished by transforming the user's trajectory path to a two-dimensional discrete time series curve that in turn transforms the stay-points to the local minima of the first derivative of this curve. To demonstrate the soundness of the proposed method, we evaluated it on raw, noisy trajectory data acquired over the period of 28 different days using four different techniques. The results demonstrate, among others, that given a good trajectory tracking technique, we can identify correctly 86 to 98 of the stay-points.
@cite_4 propose a gradient-based visit extractor algorithm. This algorithm works as follows: insert all points into a buffer until the user has moved more than some threshold (computed via the gradient) or the time difference between the first point and the last point in the buffer is greater than another time threshold; then if there is some duration between the first and last points in the buffer, then a stay-point is identified. The threshold for the gradient, that controls the distance the user moved, depends on two parameters, which are experimentally tuned per dataset.
@cite_4
2199718034
Harnessing the latent knowledge present in geospatial trajectories allows for the potential to revolutionise our understanding of behaviour. This paper discusses one component of such analysis, namely the extraction of significant locations. Specifically, we: (i) present the Gradient-based Visit Extractor (GVE) algorithm capable of extracting periods of low mobility from geospatial data, while maintaining resilience to noise, and addressing the drawbacks of existing techniques, (ii) provide a comprehensive analysis of the properties of these visits and consequent locations, extracted through clustering, and (iii) demonstrate the applicability of GVE to the problem of visit extraction with respect to representative use-cases.