• DocumentCode
    2708753
  • Title

    GIS-based analysis of flood disaster risk in LECZ of China and population exposure

  • Author

    Liu, Jianli ; Wen, Jiahong ; Yang, Kai ; Shang, Zhaoyi ; Zhang, Haiying

  • Author_Institution
    Dept. of Environ. Sci., East China Normal Univ., Shanghai, China
  • fYear
    2011
  • fDate
    24-26 June 2011
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    China´s Low Elevation Coastal Zone (LECZ) is prone to flood, where the population exposure, death risk and economic loss risk are very high. We apply an ArcGIS environment to combine the gridded data of the world population (GPW) and flood disaster risk data from Hotspots and Chinese administrative division to analyze the spatial distributions of flood occurrence frequency, death risk, economic loss risk and the population exposure characteristics at the provincial level in East China´s coastal lowlands. The spatial patterns of the flood risk and population exposure in the provincial administrative regions are presented, the prone disaster areas and the related risk factors, social and economic vulnerabilities were identified. This work provides basic information for understanding and mitigating the flood risk in LECZ.
  • Keywords
    data analysis; disasters; floods; geographic information systems; risk analysis; ArcGIS environment; China low elevation coastal zone; East China coastal lowland zone; GIS-based technique; death risk analysis; economic loss risk analysis; flood disaster risk data; flood occurrence frequency; population exposure characteristics; provincial administrative region; spatial distribution; world population gridded data analysis; Cities and towns; Data mining; Economics; Floods; Frequency conversion; Rivers; Sea measurements; China; Geographic Information System (GIS); flood disaster; low elevation coastal zones (LECZ); population exposure;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Geoinformatics, 2011 19th International Conference on
  • Conference_Location
    Shanghai
  • ISSN
    2161-024X
  • Print_ISBN
    978-1-61284-849-5
  • Type

    conf

  • DOI
    10.1109/GeoInformatics.2011.5980841
  • Filename
    5980841