A Cookie-Cutter Approach for Determining Places and Stays from Movement Data

Abstract

Technological progress with regard to various sensors and mobile devices is constant. In the field of movement data analysis in particular, this has led to new opportunities thanks to data sources such as Global Positioning Systems (GPS). In recent years, many research groups have developed new approaches for analysing this data. Most of these approaches are computationally intensive and unable to deliver results in a reasonable time when run on a mobile device. This paper presents a lightweight approach, called the "cookie-cutter", which follows an alternative path by using an Eulerian model to determine stays of individuals within reasonable computation time. The quality measures used in this work show that the approach is promising with regard to both accuracy and computing requirements.

A Framework for Evaluating Stay Detection Approaches

“…Examples for grid-/cell-based approaches can be found in [13,24,25]. Examples for stochastic approaches are discussed in [14,26,27].…”

“…In such cases, different standard pre-processing methods like filtering and/or smoothing are used. With respect to stay detection evaluation procedures, a pre-processing step has only been mentioned in [13].…”

“…Common measures used for evaluating spatial and/or temporal accuracy of stay detection approaches are recall, precision, and F-measure, as well as entropy and purity [8][9][10][11]13,30]. Only in some cases, additional measures are used.…”

“…In this context, the challenge was the identification of a suitable stay detection approach as such a standard selection process is currently missing. Usually, stay detection approaches are evaluated with ground truth data using different measures like recall, precision, and F-measure, as well as entropy and purity [8][9][10][11][12][13]. In some research additional measures like spatio-temporal accuracy, user's forgetfulness, or user assessment are applied [10,12,13].…”

“…Therefore, this paper proposes an evaluation framework for stay detection approaches taking into account the aforementioned aspects. As stochastic and grid-/cell-based approaches have already been investigated in previous work [13,14] spatio-temporal clustering approaches are now investigated here. The proposed framework is validated by comparing three different spatio-temporal clustering approaches using two different datasets, namely ground truth data as well as application data from a field trial conducted with older (partly cognitively impaired) people.…”