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  • Netherlands Red Cross
    Updated August 15, 2017 | Dataset date: Jul 14, 2017
    A crude version of the INFORM risk-framework is applied to Enumeration Areas (which is unofficial, but is deeper than admin-3), in Southern Malawi. This is done specifically for area selection regarding the ECHO2 project in 3 TA's: Mwambo (Zomba district), Makhwira (Chikwawa district) and Ndamera (Nsanje district). Scope Enumeration areas are retrieved from http://www.masdap.mw/layers/geonode%3Aeas_bnd. These are used, because we want to prioritize on a deeper level than Traditional Authority (admin-3) level, and there are no other official boundaries available. The dataset in principle data for the whole of Malawi, but contains 4 filters, which can be applied, which are the following: Filter_south: this filters out only the South of Malawi, for which the drough and flood analysis has been carried out (see details below). Filter_district: contains all EA's from the 3 pre-identified districts Zomba, Chikwawa and Nsanje. Filter_TA: contains all EA's from the 3 pre-identified TAs Mwambo, Makhwira and Ndamera. Filter_GVH: there are also 44 Group Village Heads pre-identified for the project. As these GVH's are points on a map, all EA's are selected here which have a GVH within their boundaries or very close to their boundaries. INFORM risk-framework The INFORM framework (http://www.inform-index.org/) is applied to assess risk per community, which is considered the main criteria for prioritization within the project. Because of low data availability we apply a crude version for now, with only some important indicators of the framework actually used. Since we feel that these indicators (see below) still constitute together a current good assessment of risk, and we want to stimulate the use and acceptance of the INFORM-framework, we choose to use it anyway. The INFORM risk-score consists of 3 main components: hazards, vulnerability and coping capacity. Hazard: For hazard we focus - in line with the ECHO2 project - on floods and droughts only. Analysis has been carried out (see more details below), to determine flood and drought risk on a scale from 0-10 with a resolution of 250meter grid cells. This has subsequently been aggregated to Enumeration Areas, by taking a population-weighted average. Thereby taking into account where people actually live within the Enumeration Areas. (Population data source: Worldpop: http://www.worldpop.org.uk/data/summary/?doi=10.5258/SOTON/WP00155) Vulnerability: Vulnerability is operationalized here through poverty incidence. Poverty rate (living below $1.25/day) is retrieved from Worldpop (http://www.worldpop.org.uk/data/summary/?doi=10.5258/SOTON/WP00157) and again transformed from a 1km resolution grid to Enumeration Areas through a population-weighted average. Lack of Coping capacity: Coping capacity is measured through traveltimes to various facilities, namely traveltime to nearest hospistal, traveltime to nearest trading centre and traveltime to nearest secondary school. Together these are all proxies of being near/far to facilities, and thereby an indicator of having higher/lower coping capacity. See https://510.global/developing-and-field-testing-a-remoteness-indicator-in-malawi/ for more information on how these traveltimes were calculated and validated. Use All features are stored in a CSV, but can easily be joined to the geographic shapefile to make maps on EACODE. Flood and Drought calculations Drought layer The drought risk map was created by analyzing rainfall data in the past 20 years using standard precipitation index (SPI) , which is a widely used index in drought analysis. Based on SPI6 values for the period October-march, which is the main rainy season in Malawi. Each pixel is classified to drought or no drought for each year based on SPI6 values, drought year if SPI value for a pixel is less than -1. Next, relative frequency is calculated, the number of times drought has occurred in the considered 20 year period. This frequency is then converted to probability of drought occurrence in a given year. We validated our analysis by comparing NDVI values for the drought year against long term average values. Flood layer To identify flood moments in Malawi Landsat imagery was studied (1984-2017). Floods were clearly evidenced in 9 dates. For the clearest and most representative layers the mNDWI (modified Normalized Water Index) was calculated. The index mNDWI (McFeeters 1996; Xu 2006) for Landsat bands is calculated as follows: (b2GREEN-b7MIRSWIR/b2GREEN+b7MIRSWIR). In this variation of the index the higher values are the wettest. A threshold was applied to the mNDWI to separate flood from non-flood or water from non-water pixels. The resulting layers were aggregated and the final stretched from 0-10, where 0 are the pixels where no flood is expected while pixels with 10 are where most frequent flood has been evidenced and therefore expected. The largest flood was observed in 2015, as the scenes were cloudy the flood extent was manually interpreted from several scenes. The evidenced flood dates are: 29 Feb. 1988 low flood, 19 march 1989, 17 march 1997, Feb 1998, March 1999 low flood, 2001 since February 16 until end of April, 2007 17 February since early Feb., 2008 Feb. medium flood, 2015 January – March. The water bodies in this layer are not represented and have a value of 0 like the rest of land where flood is absent.
  • OCHA Philippines
    Updated June 28, 2017 | Dataset date: Sep 1, 2010
    This datasets contains a collection of pre-disaster indicators for the Philippines.
    • XLSX
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    • This dataset updates: Never
  • HDX
    Updated January 20, 2017 | Dataset date: Jan 1, 1990-Jan 1, 2015
    [Source: United Nations Department of Economic and Social Affairs] The maternal mortality ratio (MMR) is the ratio of the number of maternal deaths during a given time period per 100,000 live births during the same time-period.
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    • 100+ Downloads
    • This dataset updates: Every year
  • HDX
    Updated January 20, 2017 | Dataset date: Jan 1, 1966-Jan 1, 2014
    [Source: World Health Organization] Percentage of stunting (height-for-age less than -2 standard deviations of the WHO Child Growth Standards median) among children aged 0-5 years
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    • This dataset updates: Every year
  • HDX
    Updated January 20, 2017 | Dataset date: Jan 1, 1970-Jan 1, 2014
    [Source: World Health Organization] Percentage of overweight (weight-for-height above +2 standard deviations of the WHO Child Growth Standards median) among children aged 0-5 years
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    • This dataset updates: Every year
  • HDX
    Updated January 20, 2017 | Dataset date: Jan 1, 1966-Jan 1, 2014
    [Source: World Health Organization] Percentage of (weight-for-height less than -2 standard deviations of the WHO Child Growth Standards median) among children aged 0-5 years
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    • This dataset updates: Every year
  • HDX
    Updated January 20, 2017 | Dataset date: Jan 1, 1990-Jan 1, 2015
    [Source: United Nations Department of Economic and Social Affairs] The proportion of seats held by women in national parliaments is the number of seats held by women members in single or lower chambers of national parliaments, expressed as a percentage of all occupied seats.
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    • 70+ Downloads
    • This dataset updates: Every year
  • HDX
    Updated January 20, 2017 | Dataset date: Jan 1, 1950-Jan 1, 2005
    [Source: United Nations Department of Economic and Social Affairs] Probability of dying between birth and exact age 1. It is expressed as average annual deaths per 1,000 births
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    • 60+ Downloads
    • This dataset updates: Every year
  • HDX
    Updated January 20, 2017 | Dataset date: Jan 1, 1950-Jan 1, 2005
    [Source: United Nations Department of Economic and Social Affairs] Probability of dying between birth and exact age 5. It is expressed as average annual deaths per 1,000 births.
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    • 40+ Downloads
    • This dataset updates: Every year
  • HDX
    Updated January 20, 2017 | Dataset date: Jan 1, 1950-Jan 1, 2005
    [Source: United Nations Department of Economic and Social Affairs] Number of deaths over a given period. Refers to five-year periods running from 1 July to 30 June of the initial and final years.
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    • 60+ Downloads
    • This dataset updates: Every year
  • HDX
    Updated January 20, 2017 | Dataset date: Jan 1, 1950-Jan 1, 2005
    [Source: United Nations Department of Economic and Social Affairs] The average number of years of life expected by a hypothetical cohort of individuals who would be subject during all their lives to the mortality rates of a given period.
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    • 40+ Downloads
    • This dataset updates: Every year
  • HDX
    Updated January 20, 2017 | Dataset date: Jan 1, 2000-Jan 1, 2011
    [Source: United Nations Development Programme] This document is an extract of data compiled by automated extraction of data from a variety of online sources and manually compiled sources.
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  • HDX
    Updated January 20, 2017 | Dataset date: Jan 1, 2010-Jan 1, 2014
    [Source: United Nations Development Programme] Percentage of the population ages 15 and older who can, with understanding, both read and write a short simple statement on their everyday life.
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    • This dataset updates: Every year
  • HDX
    Updated January 20, 2017 | Dataset date: Jan 1, 1950-Jan 1, 2010
    [Source: United Nations Department of Economic and Social Affairs] Total Population - Both Sexes. De facto population in a country, area or region as of 1 July of the year indicated. Figures are presented in thousands.
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    • 200+ Downloads
    • This dataset updates: Every year
  • HDX
    Updated January 19, 2017 | Dataset date: Jan 1, 2003-Jan 1, 2011
    [Source: United Nations Office on Drugs and Crime] Number of sexual violence cases
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    • 70+ Downloads
    • This dataset updates: Every year
  • HDX
    Updated January 19, 2017 | Dataset date: Jan 1, 1960-Jan 1, 2012
    [Source: World Bank] Adult mortality rate is the probability of dying between the ages of 15 and 60--that is, the probability of a 15-year-old dying before reaching age 60, if subject to current age-specific mortality rates between those ages.
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    • 30+ Downloads
    • This dataset updates: Every year
  • HDX
    Updated January 19, 2017 | Dataset date: Jan 1, 1960-Jan 1, 2012
    [Source: World Bank] Adult mortality rate is the probability of dying between the ages of 15 and 60--that is, the probability of a 15-year-old dying before reaching age 60, if subject to current age-specific mortality rates between those ages.
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    • 30+ Downloads
    • This dataset updates: Every year
  • HDX
    Updated January 19, 2017 | Dataset date: Jan 1, 1990-Jan 1, 2012
    [Source: World Bank] Share of women employed in the nonagricultural sector is the share of female workers in the nonagricultural sector (industry and services), expressed as a percentage of total employment in the nonagricultural sector.
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    • This dataset updates: Every year
  • HDX
    Updated January 19, 2017 | Dataset date: Jan 1, 1990-Jan 1, 2013
    [Source: United Nations Department of Economic and Social Affairs] The proportion of 1 year-old children immunized against measles is the proportion of children under one year of age who have received at least one dose of measles-containing vaccine.
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    • 50+ Downloads
    • This dataset updates: Every year
  • HDX
    Updated January 19, 2017 | Dataset date: Jan 1, 1990-Jan 1, 2014
    [Source: United Nations Development Programme] Ratio of the number of maternal deaths to the number of live births in a given year, expressed per 100,000 live births.
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    • 60+ Downloads
    • This dataset updates: Every year
  • HDX
    Updated January 19, 2017 | Dataset date: Jan 1, 2000-Jan 1, 2012
    [Source: United Nations Development Programme] A composite measure reflecting inequality in achievements between women and men in three dimensions: reproductive health, empowerment and the labour market.
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    • 100+ Downloads
    • This dataset updates: Every year
  • The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.
  • The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.
  • The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.
  • The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.