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.
Emerging class of context-aware mobile applications, such as Google Now and Foursquare require continuous location sensing to deliver different location-aware services. Existing research, in finding location at higher abstraction, use GPS and WiFi location interfaces to discover places, which result in high power consumption. These interfaces are also not available on all feature phones that are in majority in developing countries. In this paper, we present a framework PlaceMap that discovers different places and routes, solely using GSM information, i.e., Cell ID. PlaceMap stores and manages all the discovered places and routes, which are used to build spatio-temporal mobility profiles for the users. PlaceMap provides algorithms that can complement GSM-based place discovery with an initial WiFi-based training to increase accuracy. We performed a comprehensive offline evaluation of PlaceMap algorithms on two large real-world diverse datasets, self-collected dataset of 62 participants for 4 weeks in India and MDC dataset of 38 participants for 45 weeks in Switzerland. We found that PlaceMap is able to discover up to 81 of the places correctly as compared to GPS. To corroborate the potential of PlaceMap in real-world, we deployed a life-logging application for a small set of 18 participants and observed similar place discovery accuracy.