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Virtual Kyoto is a virtual time-space created on a computer for the purpose of investigating the past, present and future of the historical city of Kyoto. Using the cutting-edge technologies in GIS and VR, we built Virtual Kyoto which is 4DGIS that comprise a series of 3DGIS at different points in time. Our 3D city modeling begins with the present Kyoto and then goes back to the past, including those soon after and before the World War II, the Taisho and Meiji eras, pre-modern Edo era and finally back to Heian era when the city of Kyoto was founded in late 8th century. Creating Virtual Kyoto includes the following projects : a) Archiving geo-referenced materials such as current digital maps, old topographic maps, cadastral maps, aerial photos, picture maps, street photos, landscape paintings, archaeological sites data, and historical documents ; b) Creating a database of all existing buildings including machiyas (traditional town houses) , early modern buildings, shrines and temples including historical and cultural heritages ; c) Creating 3D VR models of the above buildings ; and d) Estimating and simulating land use and landscape changes over the study periods using aforementioned materials. Virtual Kyoto is an infrastructure to place various digitally archived materials associated with the city, and to disseminate Kyoto's subtle and sophisticated forms of cultures and arts to the world over the Internet. The web-based system provides user-friendly interface to explore historical materials of cultures and arts in the geographical context of Kyoto with its historical landscapes. Virtual Kyoto should play a valuable role in the assistance for urban landscape planning of Kyoto as well as sending rich information about Kyoto to the world through the Internet.

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With the goal of assessing participant attitudes and group activities in collaborative learning, this study presents models of participation styles based on co-occurrence patterns of nonverbal behaviors between conversational participants. First, we collected conversations among groups of three people in a collaborative learning situation, wherein each participant had a digital pen and wore a glasses-type eye tracker. We then divided the collected multimodal data into 0.1-second intervals. The discretized data were applied to an unsupervised method to find co-occurrence behavioral patterns. As a result, we discovered 122 multimodal behavioral motifs from more than 3,000 possible combinations of behaviors by three participants. Using the multimodal behavioral motifs as predictor variables, we created regression models for assessing participation styles. The multiple correlation coefficients ranged from 0.74 to 0.84, indicating a good fit between the models and the data. A correlation analysis also enabled us to identify a smaller set of behavioral motifs (fewer than 30) that are statistically significant as predictors of participation styles. These results show that automatically discovered combinations of multiple kinds of nonverbal information with high co-occurrence frequencies observed between multiple participants as well as for a single participant are useful in characterizing the participant's attitudes towards the conversation.