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A public Cloud-based China＇s Landslide Inventory Database（Cs LID）： development, zone, and spatiotemporal analysis for significant historical events, 1949-2011

ISSN号：1007-6735
期刊名称：《上海理工大学学报》
时间：0
分类：TP311.13[自动化与计算机技术—计算机软件与理论;自动化与计算机技术—计算机科学与技术] P642.22[天文地球—工程地质学;天文地球—地质矿产勘探;天文地球—地质学]
作者机构：[1]Institute of Urban Study, Shanghai Normal University, Shanghai 200234, China, [2]Center for Spatial Information Science and Sustainable Development Applications, Tongji University, Shanghai 200092, China, [3]College of Surveying and Geo-Informatics, Tongji University, Shanghai 200234, China, [4]School of Civil Engineering and Environmental Sciences, University of Oklahoma, OK, Norman 73072, USA, [5]Advanced Radar Research Center, University of Oklahoma, Norman, OK, Norman 73072, USA, [6]State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, China, [7]College of Architectural Engineering, Civil Engineering and Environment, Ningbo University, Ningbo 315211, China, [8]Department of Geography, Shanghai Normal University, Shanghai 200234, China, [9]Engineering Center of SHMEC for Space Information and GNSS, East China Normal University, Shanghai 200062, China, [10]Applied Hydrometeorological Research Institute, Nanjing University of Information Science & Technology, Nanjing 210044, China, [11]Department of Stomatology, Shanghai Tenth People's Hospital of Tongji University, Shanghai 200072, China
相关基金：funded by National Natural Science Foundation （Grant No. 41501458）; National Natural Science Foundation （Grant No. 41201380）; National Basic Research Program of China：（Grant No. 2013CB733204）; Key Laboratory of Mining Spatial Information Technology of NASMG （KLM201309）; Science Program of Shanghai Normal University （SK201525）; sponsored by Shanghai Gaofeng ＆ Gaoyuan Project for University Academic Program Development, project 2013LASW-A09, project SKHL1310; the Center of Spatial Information Science and Sustainable Development Applications, Tongji University, Shanghai, China

作者： LI Wei-yue[1,2], LIU Chun[2,3], HONG Yang[4,5], ZHANG Xin-hua[6], WAN Zhan-ming[4,5], Manabendra SAHARIA[4,5], SUN Wei-wei[7], YAO Dong-jing[8], CHEN Wen[9], CHEN Sheng[4,5], YANG Xiu-qin[10], YUE Jing[11]

关键词：滑坡灾害, 历史事件, 数据库, 开发区, 中国, 时空分析, CS, 政务公开, Landslide Inventory Zone Distribution Cloud computing

中文摘要：

Landslide inventory plays an important role in recording landslide events and showing their temporal-spatial distribution. This paper describes the development, visualization, and analysis of a China’s Landslide Inventory Database(Cs LID) by utilizing Google’s public cloud computing platform. Firstly, Cs LID(Landslide Inventory Database) compiles a total of 1221 historical landslide events spanning the years 1949-2011 from relevant data sources. Secondly, the Cs LID is further broken down into six zones for characterizing landslide cause-effect, spatiotemporal distribution, fatalities, and socioeconomic impacts based on the geological environment and terrain. The results show that among all the six zones, zone V, located in Qinba and Southwest Mountainous Area is the most active landslide hotspot with the highest landslide hazard in China. Additionally, the Google public cloud computing platform enables the Cs LID to be easily accessible, visually interactive, and with the capability of allowing new data input to dynamically augment the database. This work developed a cyber-landslide inventory and used it to analyze the landslide temporal-spatial distribution in China.更多还原

英文摘要：

Landslide inventory plays an important role in recording landslide events and showing their temporal-spatial distribution. This paper describes the development, visualization, and analysis of a China＇s Landslide Inventory Database（Cs LID） by utilizing Google＇s public cloud computing platform. Firstly, Cs LID（Landslide Inventory Database） compiles a total of 1221 historical landslide events spanning the years 1949-2011 from relevant data sources. Secondly, the Cs LID is further broken down into six zones for characterizing landslide cause-effect, spatiotemporal distribution, fatalities, and socioeconomic impacts based on the geological environment and terrain. The results show that among all the six zones, zone V, located in Qinba and Southwest Mountainous Area is the most active landslide hotspot with the highest landslide hazard in China. Additionally, the Google public cloud computing platform enables the Cs LID to be easily accessible, visually interactive, and with the capability of allowing new data input to dynamically augment the database. This work developed a cyber-landslide inventory and used it to analyze the landslide temporal-spatial distribution in China.

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