Colloquium Presentation on Context-Aware Movement Analysis (Dr. Mohammad Sharif)

we cordially invite everybody interested to our next open GIScience colloquium talk

Context-Aware Movement Analysis: An Application to Similarity Search of Trajectories

Dr. Mohammad Sharif
Department of Geographic Information Systems, Faculty of Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran

Time and date: Mon, January 22, 2:15 pm
Venue: INF 348, Room 015, Department of Geography, Heidelberg University

Studying movement in geographic information science (GIScience) has received attention in recent years because it plays a crucial role in understanding and modeling various spatial activities and processes. In reality, movement of an object is embedded in context and is highly affected by both internal and external contexts. The former is any factor that is related to the object’s characteristic, state, and condition, while the latter is dedicated to the environmental conditions during the move. Such consequential influence has created new paradigms for context-aware movement data mining and analysis. Among the potential movement analysis research, studying moving point objects (MPOs) and measuring the similarities between their trajectories have been of interest recently because it can be the basis for understanding objects’ behaviors, extracting their movement patterns, and predicting their future movement trends. Despite such importance, less attention has been paid to contextualizing similarity search of trajectories, so far. In this research, after providing a new definition and a taxonomy for context in movement analysis, a series of distance functions have been developed for assessing the similarities of trajectories, by including not only the spatial footprints of MPOs but also a notion of their internal and external contexts. In other words, the degree of similarity between two trajectories not only is related to the spatial and temporal closeness of trajectories but also is highly associated with the commonalities in the contexts that they share. The effectiveness of the developed methods have been examined in several experiments on real datasets, i.e., commercial airplanes’, pedestrians’, and cyclists’ trajectories, in separate study areas, while accounting the internal and external context information during the movement. The results of these implementations demonstrate the significance of incorporating contextual information in movement studies, as movement is highly affected by context in both positive and negative manners.