Project

Discipline

Geographic Information Science; movement analysis; trajectory data; context-awareness; behavioral ecology; urban mobility

Lead
Lay summary
Moving objects are the building blocks of dynamic processes in natural and built environments. Technological advancements of localization and ICT technologies enable almost ubiquitous tracking of individuals, be it pedestrians using a smart phone, GPS-enabled cars or GPS-tagged animals. Overcoming the legacy of static cartography, Geographical Information Science (GIScience) is currently challenged with developing new tools allowing application scientist a profound understanding of the movement processes they study. Whereas movement analysis tools developed so far focus on geometric properties of movement paths, methods relating movement and its patterns to the geographical context embedding, and thereby enabling and limiting that movement, are lacking.The CAMA project therefore develops spatiotemporal analysis for Context-Aware Movement Analysis. 'How can the geographical context enabling and constraining the movement of individuals and groups be explicitly integrated into the context-aware analysis of such movement for a better understanding of the processes and behaviors producing that movement?' The project aims at a continuation of the successful integration of spatiotemporal analysis, data mining and computational geometry for extending the GIS toolbox with context-aware movement analysis tools.The project is grouped around two fundamentally different forms of movement, (A) unconstrained movement in Euclidean space, and (B) network-constrained movement. The interleaved research strands are further separated by their application fields, which are behavioral ecology (A), and human urban mobility (B). Both application fields are pivotal as they ground the methodological GIScience research in real world problems and provide essential case study data. Both tracks will feature four phases, (I) an initial literature review and research plan refinement phase, (II) the establishment of a firm conceptual basis, (III) the development of the actual context-aware analysis tools, and (IV) an evaluation phase.The new methods have high scientific significance and are expected to have considerable relevance and resonance not only in the field of GIScience but also in the two application fields behavioral ecology and urban mobility studies. A profound understanding of organismal movement is key to managing endangered species and habitats, control invasive species and pests, and will become even more relevant with recurring emergence of pandemic infectious diseases. Commercial mobile ICT applications and context-aware-computing will furthermore profit from the new methods by optimizing mobile applications for mobile users.

Direct link to Lay Summary

Last update: 21.02.2013

Active participation

Self-organised

Self-organised

Title	Year

Number	Title	Start	Funding scheme

We propose a research project developing new methods for the analysis the individual mobility. We aim for context-aware movement analysis that relates movement data to the underlying geographical context in which that movement is embedded. We request funding for two PhD students during 3 years.[Motivation]. Moving objects are the building blocks of dynamic processes in natural and built environments. Technological advancements of localization and ICT technologies enable almost ubiquitous tracking of individuals and hence lead to rapidly growing repositories of movement data. Overcoming the legacy of static cartography, Geographical Information Science (GIScience) is currently challenged with developing new tools allowing application scientist a profound understanding of the movement processes they study. Whereas movement analysis tools developed so far focus on geometric properties of movement trajectories, methods relating movement and its patterns to the geographical context embedding, and thereby enabling and limiting that movement, are lacking.[Objectives]. We therefore propose a project with two parallel PhD tracks that will develop context-aware movement analysis techniques. The central research question shared by the two tracks is: ‘How can the geographical context enabling and constraining the movement of individuals and groups be explicitly integrated into the context-aware analysis of such movement for a better understanding of the processes and behaviors producing that movement?’ Drawing on previously developed concepts and tools, the project aims at a continuation of the successful integration of spatiotemporal analysis, data mining and computational geometry for extending the GIS toolbox with context-aware movement analysis tools.[Research plan]. The two tracks correspond to two PhD positions requested, and are grouped around two fundamentally different forms of movement, (A) unconstrained movement in Euclidean space, and (B) network-constrained movement. The interleaved subprojects are further separated by their application fields, which are behavioral ecology (A), and human urban mobility (B). Both application fields are pivotal as they ground the methodological GIScience research in real world problems and provide essential case study data. Both tracks will feature four phases, (I) an initial literature review and research plan refinement phase, (II) the establishment of a firm conceptual basis, (III) the development of the actual context-aware analysis tools, and (IV) an evaluation phase. [Track A] will develop context-aware movement analysis tools for avian navigation research and behavioral ecology of primates. Track A will draw upon and extend established conceptual data models and structures for representing moving point objects moving in heterogeneous Euclidean spaces, and concepts interrelating movement and embedding spaces. New forms of movement patterns explicitly including the notion of context will be formalized and algorithms for their efficient detection will be developed. In a further attempt to find structure in movement data, methods will be developed allowing for a context-aware segmentation of meaningful subtrajectories. Once identified, context-aware similarity measures will be put forward suitable for subtrajectory clustering. Finally, in close collaboration with the application specialists, qualitative and quantitative evaluation will be used to assess if and to what degree the new methods improve the understanding of the respective movement processes.[Track B]. will be focused around a substantial data repository tracking couriers of a London courier company, and data gained from a self-tracking experiment captureing the project team’s mobility in and around Zurich. First, a conceptual framework will be established allowing for the modeling of objects moving in urban networks. This step includes the development of methods mapping context relevant to movement, having some spatiotemporal shape and extent, to the transportation networks. Methods from geographic information retrieval will be used to derive relevant events and processes (and their spatiotemporal footprints) relating to the urban spaces of London and Zurich form Internet sources. Again, the actual context-aware movement analysis tools are grouped around context-aware movement patterns and similarity measures for the network case. An evaluation phase concludes track B.The proposed research will be carried out at the Geographic Information Science Group at the Department of Geography, University of Zurich. The project will be led by main applicant Laube, with assistance from co-applicant Weibel. The project will furthermore benefit from substantial internal expertise in relevant adjacent areas (relevance in mobile and ubiquitous cartography, mobile ICT, geographic information retrieval) and an excellent international network, exemplified by the concurrent COST Action IC0903 MOVE chaired by Weibel.[Broader context of proposed research]. The new methods have high scientific significance and are expected to have considerable relevance and resonance not only in the field of GIScience but also in the two application fields behavioral ecology and urban mobility studies, as the lack of context-awareness has repeatedly been stressed in both fields. A profound understanding of organismal movement is key to managing endangered species and habitats, control invasive species and pests, and will become even more relevant with recurring emergence of pandemic infectious diseases. Commercial mobile ICT applications and context-aware-computing will furthermore profit from the new methods by optimizing mobile applications to the of mobile users.