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OMLETH: Eine Plattform für ortsbezogenes mobiles Lernen an der ETH
Projektleiter: Prof. Dr. M. Raubal / Dr. P. Kiefer
Wissenschaftlicher Mitarbeiter: Christian Sailer
Förderung: Innovedum ETH Zürich
Das Projekt „OMLETH: Eine Plattform für ortsbezogenes mobiles Lernen an der ETH“ erhält finanzielle Unterstützung aus dem Innovedum-Fonds der ETH Zürich. OMLETH ist eine Kooperation zwischen der Professur für Geschichte des Städtebaus gta und der Professur für Geoinformations-Engineering.
Design, operating strategies and potential of a biogenic CHP swarm
Project Duration: 01.11.2012–31.12.2015
Project Leader: Prof. Dr. K. Boulouchos
Internal Researchers: Fabrizio Noembrini, Martin Raubal, René Buffat, Stefano Grassi, Turhan Demiray
External Researchers: Hal Turton, Kannan Ramachandran
Funding: Swiss public institutions
An increasing share of fluctuating renewable electricity production requires more flexibility in stabilising the electricity grid. Decentralised biogenic combined heat and power plants could play a significant role. The potential of this approach is analysed by spatial-temporal based modelling.
Characterizing human mobility from mobile phone usage
Project Duration: 01.09.2012–31.12.2013
Project Leader: Prof. Dr. M. Raubal
PhD Candidate: Yihong Yuan
Funding: Schweizerischer Nationalfond (SNF)
Our mobile information society depends increasingly on the use of Information and Communication Technologies (ICTs) such as mobile phones. People’s usage of these technologies impacts many aspects of their lives but the relationship between ICT and human activities is not fully known. An understanding of this relationship will help in predicting people’s mobility patterns and provide important guidelines for maintaining sustainable transportation, updating environmental policies, and designing early warning and emergency response systems.
The goal of this project is to develop a framework for extracting and characterizing human mobility patterns from georeferenced mobile phone datasets. We analyze the different types of information that can be stored in mobile phone datasets, and develop human mobility models and data mining methodologies for spatio-temporal knowledge discovery. These models provide the basis for investigating and quantifying the relationship between human physical travel, communication travel, and environmental structure. Our research also addresses issues of uncertainty, which arise from the natural variability of human mobility, the inaccuracy and imprecision of recorded trajectories, and the imperfection of the underlying models. In order to evaluate the developed models and the relationship between human mobility patterns, spatial structure, and mobile phone usage, we will utilize a large dataset of northeast China.
This research will enhance our understanding of the relationship between human mobility and ICT in general, and between human mobility patterns and mobile phone usage in particular. We will advance conventional geographic knowledge discovery by focusing on knowledge extraction from sparse datasets with low resolution and individual attributes. The case study from northeast China allows us to examine the influence of mobile phone usage in a highly populated and rapidly developing country.
The project contributes to both scientific advances and professional development. The application of advanced geographic knowledge discovery methods to mobile data is highly important in the age of instant access and extremely relevant in diverse fields, ranging from geography to transportation, planning, and economics. The results of our project can be directly utilized by makers of environmental and transportation policies in order to direct people to more sustainable behaviors, as well as private business people in the Location-Based Services market. Our dataset from China covers over 5 million people and is therefore an excellent case study for the examination of public policies by a strong central government.