2004 · 234 citations · 8 references
ComputingInformation TechnologyGeographic Information ScienceMobile ComputingMobile SystemsIndoor LocalizationLocation AnalysisMobile Human-computer InteractionMobile SensingLocation AwarenessLocation SensingLocation-aware Information ExtractionMobile Location-aware SystemsPersonal ReflectionPlace Semantics
Location‑aware systems increasingly rely on coordinates or landmarks, yet users prefer higher‑level place concepts that carry semantic meaning, such as work or social venues, making coordinate‑based definitions tedious. The study aims to enable devices to make smarter decisions by leveraging place information. The authors propose an algorithm that extracts significant places from coordinate traces and evaluate it using real data from Place Lab, a WiFi‑based location system. The algorithm was evaluated on real data from Place Lab, demonstrating its ability to identify significant places.
Location-aware systems are proliferating on a variety of platforms from laptops to cell phones. Locations are expressed in two principal ways: coordinates and landmarks. However, users are often more interested in "places" rather than locations. A place is a locale that is important to an individual user and carries important semantic meanings such as being a place where one works, lives, plays, meets socially with others, etc. Our devices can make more intelligent decisions on how to behave when they have this higher level information. For example, a cell phone can switch to a silent mode when the user is in a quiet place (e.g., a movie theater, a lecture hall, or a place where one meets socially with others). It would be tedious to define this in terms of coordinates. In this paper, we describe an algorithm for extracting significant places from a trace of coordinates, and evaluate the algorithm with real data collected using Place Lab [14], a coordinate-based location system that uses a database of locations for WiFi hotspots.
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