SURVEY BOUNDARIES & INDICATOR DATA

Since 1984, The DHS Program has provided technical assistance to more than 400 surveys in over 90 countries, advancing global understanding of health and population trends in developing countries. DHS has earned a worldwide reputation for collecting and disseminating accurate, nationally representative data on fertility, family planning, maternal and child health, gender, HIV/AIDS, malaria, and nutrition. The information is processed and presented in reports and data formats that describe the situation of the relevant country. Details on The DHS Program standard procedures, methodologies, and manuals can be found here.

  • Data Structure

    All data avialable is available in shapefile format for use in a GIS. Each DHS Program downloadable SDR file contains the following:

    • One file for use in a GIS. File choices are:
      • A shapefile for use in a GIS. Each shapefile is made up of 7 files containing the following extensions: .dbf, .prj, .sbn, .sbx, .shp, .shp.xml, .shx
        OR
      • A geodatabase file for use in ESRI ArcGIS products
    • An explanatory notes file containing citation and data use guidelines
    • A data availablility file containing a chart of data available for the indicators and surveys selected during the download process
    • A field names file that contains titles and indicator definitions
    • A XML metadata file containing general information about the shapefile as well as detailed information about each shapefile attribute
  • Subnational Boundaries

    GIS data are provided "as is" and boundary representations are not necessarily authoritative. The national and subnational geometry in the downloadable shapefiles comes from the following sources:

MODELED SURFACES

The DHS Program provides a standard set of spatially modeled map surfaces for recent population-based survey. Each modeled surfaces is produced using standardized geostatistical methods, publically available DHS data, and a standardized set of covariates across countries. Each map package contains a mean estimate surface, an uncertainty surface, and corresponding information on the model creation process and validation. Not all indicators are available for all countries, depending on availability of appropriate data inputs in any particular survey.

For more information on the modeling methods, refer to Spatial Analysis Report 11. For more information on the use of these surfaces in decision making, refer to Spatial Analysis Report 14.

  • Data Structure

    The download package is a ZIPPED folder which contains 6 components:

    • Mean estimate modeled surface (raster file in GeoTIFF format)
    • Uncertainty estimate modeled surface (raster file in GeoTIFF format)
    • Image of mean estimate modeled surface (PNG format)
    • Image of uncertainty estimate modeled surface (PNG format)
    • Indicator specific document on modeling procedures (PDF format)
    • READ ME text file which contains data use and attribution instructions.

    The modeled surface package naming conventions follow a standard derived from The DHS Program API (application program interface). The API provides standard names for country surveys and indicators, and allows users to find corresponding information about the country survey and associated information on the specific indicator. Each dataset has a standard naming convention that identifies the country survey year, the indicator, the type of data, and the version number. The fields are described below.

    These fields are combined for each component of the data packages. These are described in Table 3 with an example for the Ghana 2008 DHS Children under 5 stunted indicator.

    GenericExample
    Folder NameSurveyID _SDRID_MS_v#GH2008DHS_CNNUTSCHA2_MS_v01
    DatasetsSurveyID _SDRID_MS_TYPE_v#
    GH2008DHS_CNNUTSCHA2_MS_MEAN_v01
    GH2008DHS_CNNUTSCHA2_MS_CI_v01
    Image FilesSurveyID _SDRID_MS_TYPE_v#
    GH2008DHS_CNNUTSCHA2_MS_MEAN_v01
    GH2008DHS_CNNUTSCHA2_MS_CI_v01
    DocumentationSurveyID _SDRID_MS_v#GH2008DHS_CNNUTSCHA2_MS_v01

GEOSPATIAL COVARIATES

The geospatial covariate datasets link survey cluster locations to ancillary data - known as covariates – that contain data on topics including population, climate, and environmental factors. This allows individuals with limited Geographic Information Systems (GIS) experience to conduct geospatial statistical analysis without having to manually source and link these covariates to cluster locations. These covariates are extracted from freely available global datasets. The covariates are packaged as a zip and include a .csv data file and a data description PDF file that defines each of the included covariates. See the included documentation PDF for more information about how the data was extracted and the sources that were used. For information about earlier covariate dataset releases, see the First Edition or Second Edition of the documentation.

  • Data Structure

    The download is a zip file containing:

    • Covariates data related to the survey (CSV format)
    • DHS Covariates Extract Data Description file describing the covariate data and the extraction procedures used (PDF format)

    The covariate datasets are named using The DHS Program’s standard naming convention.

    Topic Specific data Timeframe Units Source
    Agriculture Drought episodes 1980 - 2000 Individual classes between 1 (low drought) and 10 (high drought) SEDAC
    Agriculture Growing season length Bases on data collected between 1961-1991 16 categories representing a range of the number of days within the period of temperatures above 5 °C when moisture conditions are considered adequate IIASA/FAO
    Agriculture Irrigation 2005 Proportion of area equipped for irrigation FAO
    Agriculture Livestock (cattle, chickens, goats, pigs, sheep) 2006 Heads of livestock per square kilometer Geo-Wiki
    Environment Aridity 2000, 2005, 2010, 2015, 2020 Unitless index. Higher Aridity Index (AI) reflect more humid conditions, low AI reflect greater aridity. CRU
    Environment Enhanced vegetation index 2000, 2005, 2010, 2015, 2020 Unitless index. Higher scores indicate higher vegetation vigor/photosynthetic activity MODIS
    Environment Potential evapotranspiration 2000, 2005, 2010, 2015, 2020 Estimate of PET (mm/day) CRU
    Environment Elevation 2000 Meters SRTM
    Health ITN coverage 2000, 2005, 2010, 2015, 2019 Proportion of the population protected by insecticide-treated bednets (ITNs) MAP
    Health Malaria incidence 2000, 2005, 2010, 2015, 2019 Plasmodium falciparum malaria cases per person per year observed MAP
    Health Malaria prevalence 2000, 2005, 2010, 2015, 2019 Plasmodium falciparum parasite rate MAP
    Infrastructure Global human footprint 1995-2004 Unitless index. Higher values indicate higher human influence. SEDAC
    Infrastructure Nightlights 2015 annual composite Average cloud-free radiance values VIIRS (DNB)
    Infrastructure Travel times 2015 Estimated travel time (minutes) to the nearest high-density urban center in 2015 MAP
    Population Population count 2000, 2005, 2010, 2015 2020 Number of people/pixel WorldPop
    Population UN adjusted population count 2000, 2005, 2010, 2015 , 2020 (adjusted) Number of people/pixel GPW v4
    Population UN adjusted population density 2000, 2005, 2010, 2015, 2020 (adjusted) Number of people/km2 GPW v4
    Population Under 5 population count 2000, 2005, 2010, 2015, 2020 Number of people/pixel WorldPop
    Weather/climate Day land surface temperature 2000, 2005, 2010, 2015, 2020 Degrees Celsius MODIS
    Weather/climate Diurnal temperature range 2000, 2005, 2010, 2015, 2020 Degrees Celsius CRU
    Weather/climate Frost days 2000, 2005, 2010, 2015, 2020 Days CRU
    Weather/climate Land surface temperature 2000, 2005, 2010, 2015, 2020 Degrees Celsius MODIS
    Weather/climate Maximum temperature 2000, 2005, 2010, 2015, 2020 Degrees Celsius CRU
    Weather/climate Mean temperature 2000, 2005, 2010, 2015, 2020 Degrees Celsius CRU
    Weather/climate Minimum temperature 2000, 2005, 2010, 2015, 2020 Degrees Celsius CRU
    Weather/climate Night land surface temperature 2000, 2005, 2010, 2015, 2020 Degrees Celsius MODIS
    Weather/climate Precipitation 2000, 2005, 2010, 2015, 2020 Millimeters CRU
    Weather/climate Rainfall 2000, 2005, 2010, 2015, 2020 Millimeters CHIRPS
    Weather/climate Temperature (average monthly) January, February, March, April, May, June, July, August, September, October, November, December. Average for 1970-2000. Degrees Celsius WorldClim
    Weather/climate Wet days 2000, 2005, 2010, 2015, 2020 Days CRU

POPULATION ESTIMATES

Over the past six decades, the U.S. Census Bureau has worked in over 100 countries to provide both short- and long-term technical assistance on a wide variety of topics related to censuses, surveys, and information systems. The U.S. Census Bureau has an innovative program to develop global, subnational population data and maps for use in international humanitarian relief and disaster planning. The data from the U.S. Census Bureau provides subnational 5-year age/sex group population estimates and projections for 2000-2015 for designated countries in the President's Emergency Plan for AIDS Relief (PEPFAR) program.