Shin'ichi KONOMI Associate Professor (2010-)
Further information available at http://www.csis.u-tokyo.ac.jp/~konomi/
Digital Field Sampling
This research develops a novel data collection technique that allows scientists and others to efficiently capture and utilize dynamic, realtime spatial information such as flows of humans and goods, and patterns of environmental change. One of the goals of this research is to develop a basic Digital Field Sampling architecture that allows users to collect useful realtime data automatically by using ubiquitous smart phones. Another complementary goal is to develop a smart, interactive pervasive computing system that can capture the "why" information (e.g., people's motivation, satisfaction, opinion, and emotion) from humans in a timely and efficient manner, along with the "when" and "where" data.
Collaboratory for Spatial Information Science
To increase efficiency and inspire creativity in scientific work, this research explores a Computer-Supported Cooperative Work (CSCW) environment, called Spatial Information Science Collaboratory (Collaborative Laboratory), to support interdisciplinary collaboration among scientists who use relevant spatial data in their research. While acknowledging the importance of understanding current scientific processes and practices, this research also seeks to enable novel collaboration patterns. In this context, information extraction and presentation techniques are being developed to support the awareness of geographical relevance of research as well as social proximity and community overlap among researchers.
Human-Centric and Data-Centric Urban Computing
An increasing amount of spatial data are being collected from various data sources including stationary and mobile sensors in a city, thereby creating an opportunity to design a city-scale Ubiquitous Computing (or Urban Computing) system that serves citizens and increases urban quality of life. From a broad, human-centric perspective, this research aims at contributing to the body of interdisciplinary knowledge that provides a grounding for designing and evaluating Urban Computing systems, and develops smart systems and models that embed the process of data collection, context recognition, information filtering, analysis and sensemaking, knowledge extraction, and knowledge fostering into urban living environments so as to support decision making, learning, collaboration, consensus building, and so on.