Projects

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Project HAYANO

Prepare for future disasters by performing an analysis of the shared data in conjunction with SPEEDI data and a simulation of the plume of radioactive iodine emitted by the nuclear reactor.

Mass Media Coverage MapVisualize the locations included/not included in the mass media by extracting the place names mentioned in TV broadcasts (and newspapers) just after the earthquake and mapping them in chronological sequence. This will be compared with information provided from the bottom up--such as the actual disaster status and regions that required aid--highlighting any differences between the two.

Project: Information Supply at Great Disaster for the Sufferer with Special Needs.

The aim of this project is to improve ways and means of providing information for the sufferer with special needs and their families in great disaster.

"The sufferer with special need" is include many kinds of the person with disabilities and the person from another countries.

However, during term of the workshop, we will focus on the sufferer with pervasive developmental disorders (PDD) and Attention Deficit Hyperactivity Disorder (ADHD) and their families. Because they have difficulties to adapt circumstances change. So we have to provide appropriate information for them and refuges stuff.

Our analysis are narrow area. Though, we believe that our results, process and methods are applicable for other needs.

Development of the Specific Keyword Extraction System

The aim of this project is to extract the specific keywords, which are closely related to the disaster, by focusing on the rate of increase in the frequency of occurrence of the substring, which consisting of one or two characters.

What kind of info people in Sendai had needed and what kind of info had distributed in Sendai after one week of the disaster?

Sendai is the hub of politics and the economy in northeast region of Japan.

The devastating earthquake and resulting tsunami hit Sendai but Sendai has not completed destroyed.

What kind of info people in Sendai had needed and what kind of info had distributed in Sendai after one week of the disaster? We would like to share with you what we learn by analyzing them and what we could do in case of the next disaster.

Spatiotemporal damaged area estimation with geo-referenced tweets

This project focuses on damaged area estimation using geo-referenced tweets. Understanding damaged are is one of the most important tasks in disaster response phase. Geo-referenced tweet data is employed as an indicator of spatial and temporal patterns of human activities.