JpGU-AGU Joint Meeting 2017

講演情報

[EJ] 口頭発表

セッション記号 M (領域外・複数領域) » M-ZZ その他

[M-ZZ41] [EJ] リスクコミュニケーションの未来-科学情報を社会にどう伝えるか

2017年5月20日(土) 10:45 〜 12:15 301B (国際会議場 3F)

コンビーナ:平田 直(東京大学地震研究所)、Schorlemmer Danijel(GFZ German Research Centre for Geosciences)、木村 玲欧(兵庫県立大学)、大友 章司(甲南女子大学)、座長:平田 直(東京大学地震研究所)、座長:木村 玲欧(兵庫県立大学環境人間学部)、座長:Schorlemmer Danijel(GFZ German Research Centre for Geosciences)、座長:大友 章司(甲南女子大学人間科学部心理学科)

10:45 〜 11:00

[MZZ41-01] Global Dynamic Exposure and the OpenBuildingMap - Communicating Risk and Involving Communities

*Danijel Schorlemmer1,5Thomas Beutin1Naoshi Hirata2Ken Xiansheng Hao3Max Wyss4Fabrice Cotton1Karsten Prehn1 (1.GFZ German Research Centre for Geosciences、2.Earthquake Research Institute, University of Tokyo, Japan、3.National Research Institute for Earth Science and Disaster Prevention, Tsukuba, Japan、4.International Centre for Earth Simulation Foundation, Geneva, Switzerland、5.University of Southern California, Los Angeles, USA)

キーワード:Seismic Hazard and Risk, Exposure, Citizen Science, Big Data

Detailed understanding of local risk factors regarding natural catastrophes requires in-depth characterization of the local exposure. Current exposure capture techniques have to find the balance between resolution and coverage. We aim at bridging this gap by employing a crowd-sourced approach to exposure capturing, focusing on risk related to earthquake hazard. OpenStreetMap (OSM), the rich and constantly growing geographical database, is an ideal foundation for this task. More than 3.5 billion geographical nodes, more than 200 million building footprints (growing by ~100'000 per day), and a plethora of information about school, hospital, and other critical facilities allows us to exploit this dataset for risk-related computations.

We are combining the strengths of crowd-sourced data collection with the knowledge of experts in extracting the most information from these data. Besides relying on the very active OpenStreetMap community and the Humanitarian OpenStreetMap Team, which are collecting building information at high pace, we are providing a tailored building capture tool for mobile devices. This tool is facilitating simple and fast building property capturing for OpenStreetMap by any person or interested community. With our OpenBuildingMap system, we are harvesting this dataset by processing every building in near-realtime. We are collecting exposure and vulnerability indicators from explicitly provided data (e.g. hospital locations), implicitly provided data (e.g. building shapes and positions), and semantically derived data, i.e. interpretation applying expert knowledge. The expert knowledge is needed to translate the simple building properties as captured by OpenStreetMap users into vulnerability and exposure indicators and subsequently into building classifications as defined in the Building Taxonomy 2.0 developed by the Global Earthquake Model (GEM) and the European Macroseismic Scale (EMS98). With this approach, we increase the resolution of existing exposure models from aggregated exposure information to building-by-building vulnerability.

We report on our method, on the software development for the mobile application and the server-side analysis system, and on the OpenBuildingMap (www.openbuildingmap.org), our global Tile Map Service focusing on building properties. The free/open framework we provide can be used on commodity hardware for local to regional exposure capturing, for stakeholders in disaster management and mitigation for communicating risk, and for communities to understand their risk.