Global Cyclone Mortality Risks and DistributionEntry ID: CIESIN_CHRR_NDH_CYCLONE_MRD
Abstract: Global Cyclone Mortality Risks and Distribution is a 2.5 minute grid of global cyclone mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provide a baseline estimation of population per grid cell from which to estimate potential mortality loss. Mortality loss estimates per hazard event are calculated using regional, hazard-specific mortality records of the Emergency Events ... Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of cyclone hazard are obtained from the Global Cyclone Hazard Frequency and Distribution dataset. In order to more accurately reflect the confidence associated with the data and procedures, the potential mortality estimate range is classified into deciles, 10 classes of an approximately equal number of grid cells, providing a relative estimate of cyclone-based mortality risks. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).
The data in American Standard Code for Information Interchange (ASCII) and dBASE (DBF) formats and a map in Portable Document Format (PDF) and Portable Network Graphics (PNG) formats are available from the NASA Socioeconomic Data and Applications Center (SEDAC).
Purpose: To provide a means of assessing global cyclone mortality risks and distribution.
(Click for Interactive Map)
Data Set Citation
Dataset Originator/Creator: Center for Hazards and Risk Research (CHRR)/Columbia University, Center for International Earth Science Information Network (CIESIN)/Columbia University, and International Bank for Reconstruction and Development/The World Bank
Dataset Title: Global Cyclone Mortality Risks and Distribution
Dataset Release Date: 2005
Dataset Release Place: Palisades, NY
Dataset Publisher: Center for Hazards and Risk Research (CHRR)/Columbia University
Data Presentation Form: raster, mapOnline Resource: http://sedac.ciesin.columbia.edu/data/set/ndh-cyclone-mortality-ris...
Start Date: 2000-01-01Stop Date: 2000-01-01
Latitude Resolution: 0.0417 Decimal degrees
Longitude Resolution: 0.0417 Decimal degrees
Horizontal Resolution Range: 1 km - < 10 km or approximately .01 degree - < .09 degree
ATMOSPHERE > ATMOSPHERIC PHENOMENA > CYCLONES
ATMOSPHERE > ATMOSPHERIC PHENOMENA > HURRICANES
ATMOSPHERE > ATMOSPHERIC PRESSURE > ANTICYCLONES/CYCLONES
ATMOSPHERE > ATMOSPHERIC WINDS > SURFACE WINDS
ATMOSPHERE > ATMOSPHERIC WINDS > UPPER LEVEL WINDS
DATA ANALYSIS AND VISUALIZATION > GEOGRAPHIC INFORMATION SYSTEMS
ENVIRONMENTAL ADVISORIES > WEATHER/CLIMATE ADVISORIES > SEVERE WEATHER
Quality The records of the Emergency Events Database (EM-DAT), collected over a 20 year period from 1981 to 2000, provide regional, hazard-specific mortality and economic loss rates. A crude estimation of the global cyclone hazard mortality is developed using the EM-DAT regional mortality rates, population distributions from Gridded Population of the World, Version 3 (GPWv3), and frequency/distribution ... data from Global Cyclone Hazard Frequency and Distribution. To better reflect the confidence associated with the result, mortality figures are classified into deciles, 10 classes of an approximately equal number of grid cells of increasing mortality risk (item Value).
Building upon a methodology developed by Sachs et al. (2003), a Gross Domestic Product (GDP) value (US$, 2000, purchase power parity adjusted (PPP)) is estimated for each grid cell. The process begins by determining the contribution of each subnational unit to national GDP using data of varied origin. The ratio of the subnational production to the national GDP is the contribution ratio. To ensure uniformity between countries, these contribution ratios are utilized with published World Bank estimates of GDP.
Once a standardized version of subnational GDP has been calculated, this value is further divided by the total population within the subnational unit. This subnational, per-person GDP value is multiplied by the grid cell population density to determine a GDP value for the grid cell. The GDP values presented in this dataset (item Gdpvalue) are not projections of impacted GDP, but rather the estimates of GDP that serve as a baseline for estimating hazard impacts. Furthermore, Gdpvalue is indicative of the GDP associated with each of the hazard risk deciles and not the individual grid cell.
Estimating the agricultural GDP (item Agvalue) follows a process similar to GDP. The amount of agricultural GDP is derived at the subnational unit using available data of various origins.
Access Constraints None
Use Constraints The Trustees of Columbia University in the City of New York, Center for Hazards
and Risk Research (CHRR), and International Bank for Reconstruction and
... Development/The World Bank hold the copyright of this dataset. Users are
prohibited from any commercial, non-free resale, or redistribution without
explicit written permission from CHRR, CIESIN, and The World Bank. Users should
acknowledge CHRR, CIESIN, and The World Bank as the source used in the creation
of any reports, publications, new datasets, derived products, or services
resulting from the use of this dataset. CHRR, CIESIN, and The World Bank also
request reprints of any publications and notification of any redistribution
Data Set Progress
Jones, J. M., and M. Widmann, 2003: Instrument-and tree-ring estimates of the Antarctic Oscillation. J. Climate, 16, 3511-3524.
Marshall, G. J., 2003: Trends in the Southern Annular Mode from observations and reanalyses. J. Climate, 16, 4134-4143.
Jones, J. M., and M. Widmann, 2004: Atmospheric science - Early peak in Antarctic Oscillation index. Nature, 432, 290-291.
Marshall, G. J., 2007: Half-century seasonal relationships between the Southern Annular Mode and Antarctic temperatures. Int. J. Climatol., 27, 373-383.
Visbeck, M., 2009: A station-based Southern Annular Mode index from 1884 to 2005. J. Climate, 22, 940-950.
Jones, J. M., R. L. Fogt, M. Widmann, G. J. Marshall, P. D. Jones, and M. Visbeck, 2009: Historical SAM Variability. Part I: Century length seasonal reconstructions. J. Climate, in press.
Fogt, R. L., J. Perlwitz, A. J. Monaghan, D. H. Bromwich, J. M. Jones, and G. J. Marshall, 2009: Historical SAM Variability. Part II: 20th century variability and trends from reconstructions, observations, and the IPCC AR4 Models. J. Climate, revised.
Creation and Review Dates
DIF Creation Date: 2010-10-14
Last DIF Revision Date: 2012-02-15