Matching of local government for cooperation agreement on disaster management

2023 Fiscal Year Final Research Report

• Project/Area Number: 19K04897
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 25010:Social systems engineering-related
• Research Institution: 防衛大学校(総合教育学群、人文社会科学群、応用科学群、電気情報学群及びシステム工学群)
• Principal Investigator: UKAI Takamori 防衛大学校(総合教育学群、人文社会科学群、応用科学群、電気情報学群及びシステム工学群), 電気情報学群, 講師 (20453540)
• Co-Investigator(Kenkyū-buntansha): 高嶋 隆太 東京理科大学, 創域理工学部経営システム工学科, 教授 (50401138) ; 廣井 悠 東京大学, 大学院工学系研究科(工学部), 教授 (50456141)
• Project Period (FY): 2019-04-01 – 2024-03-31
• Keywords: 自治体共助 / リスク / 相互応援協定

Outline of Final Research Achievements

Municipalities, which are closest to residents, take various measures to prepare for disasters. They also receive support from prefectural and other wide-area administrative agencies, the national government, and neighboring and remote municipalities as needed. Each municipality has a disaster support agreement with local governments and the private sector in preparation for a disaster.
When a disaster strikes, it is necessary to assess the damage in order to determine the type and amount of support required. As a basis for this, we conducted an SP survey on evacuation choice behavior in the event of a disaster, and estimated the utility of residents for the measures taken. In addition, we have studied the method of immediate damage prediction based on the contributions to digital data networks, and proposed a simple model using matching among multiple entities.

Free Research Field

都市解析

Academic Significance and Societal Importance of the Research Achievements

災害発生時の避難選択行動についてSP調査に基づいて把握することで，どこへどれだけの支援を必要とする人が存在するかを推定することができる．また，施策に対する住民の効用推定により，多数ある施策を効果的な組み合わせを求めることに寄与する．さらに，SNSに代表されるデジタルデータ・ネットワークへの投稿から被害を予測する手法は，効果的な支援を実施するための基盤となり得る．