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References for external data sources used in prediction tool

1. Armed Conflict Location and Event Database (ACLED).

(https://acleddata.com/#/dashboard)

  • Raleigh, Clionadh, Andrew Linke, Håvard Hegre, and Joakim Karlsen. (2010). “Introducing ACLED-Armed Conflict Location and Event Data.” Journal of Peace Research 47(5) 651- 660
  • Curated regional datasets downloaded on October 1st 2021.
  • For each water point, the total number of recorded events within a 10 km buffer of its GPS location for the past 10 years (2010 – 2020?) is calculated.

2. British Geological Survey Africa Groundwater Atlas.

(https://www2.bgs.ac.uk/groundwater/international/africangroundwater/mapsDownload.html)

  • Based upon mapping provided by British Geological Survey © NERC 2012. All rights reserved                                              
  • MacDonald, A M, Bonsor, H C, Ó Dochartaigh, B E, Taylor, R G.  2012.  Quantitative maps of groundwater resources in Africa.  Environmental Research Letters 7, 024009.
  • From each of the three continental-scale modeled groundwater data maps (depth to groundwater (mbgl), groundwater storage (mm), groundwater productivity (l/s)), a point value is extracted for each water point GPS location. 
  • The value extracted is the numerical lower end of the range. For example, for groundwater productivity, extracted values include 20 (very high), 5 (high), 1 (moderate), 0.5 (low-moderate), 0.1 (low), 0  (very low)

3. British Geological Survey, Groundwater recharge in Africa from ground based measurements.

(https://webapps.bgs.ac.uk/services/ngdc/accessions/index.html#item139265)

Contains data supplied by Natural Environment Research Council. British Geological Survey © UKRI 2020. MacDonald et. al., Environ. Res. Lett. 16 (2021) 034012

  • From the continental-scale modeled map, a point value is extracted for each water point GPS location. 
  • The value extracted from the Africa_Recharge.tif file is in mmpa (not m/dec)
  • Essential climate variables for assessment of climate variability from 1979 to present dataset (https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecv-for-climate-change?tab=overview) Generated using Copernicus Climate Change Service Information [2010-2020]
  • Extraction of point values at each water point GPS location for 5-year and 10-year average precipitation (2015 – 2020 and 2010 – 2020).
4. European Commission Global Human Settlement Layer


(https://ghsl.jrc.ec.europa.eu/ghs_stat_ucdb2015mt_r2019a.php)

lorczyk A., Corbane C,. Schiavina M., Pesaresi M., Maffenini L.,Melchiorri, M., Politis P., Sabo F., Freire S., Ehrlich D., Kemper T., Tommasi P., Airaghi D., Zanchetta L. (2019) GHS Urban Centre Database 2015, multitemporal and multidimensional attributes, R2019A. European Commission, Joint Research Centre (JRC)PID: https://data.jrc.ec.europa.eu/dataset/53473144-b88c-44bc-b4a3-4583ed1f547e

5. European Space Agency Climate Change Initiative Land Cover dataset (v2.1.1).

(https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview)

  • Dataset generated using Copernicus Climate Change Service Information [2020]
  • Extraction of point values for landcover at each water point GPS location from 2020 dataset. Extracted values are the landcover global Values defined below. Full description of dataset here.

6. Meta. High Resolution Population Density Maps + Demographic Estimates. 

(https://data.humdata.org/organization/facebook)

Facebook Connectivity Lab and Center for International Earth Science Information Network – CIESIN – Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. 

Population data used for estimates of people living within 1km of water point and for estimating likely users and number of people who could gain access if a non-functional water point was repaired.

  • Demographic data used to calculate ratios based on age and sex per 1km, 10km and 100km radius.
    • Children ratio: Number of children aged 0 – 5 and under divided by total population
    • Youth ratio: Number of people aged 15 – 24 and under divided by total population
    • Elderly ratio: Number of people aged 60 and over divided by total population
    • Women of reproductive age ratio: Number of women aged 15 49 divided by total population
    • Male ratio: Number of men divided by total population
    • Female ratio: Number of women divided by total population

7. Meta Relative Wealth Index.

(https://data.humdata.org/dataset/relative-wealth-index)

Methodology: Chi, G., Fang, H., Chatterjee, S, & Blmenstock, J. (2021). Micro-Estimates of Wealth for all Low- and Middle-Income Countries. Research pre-print available online at https://arxiv.org/ftp/arxiv/papers/2104/2104.07761.pdf

  • There are roughly 20 million 2.4 km² micro-regions on earth’s surface. This index is an estimate of relative wealth of the people living in each micro-region relative to others in the same country.
  • The data is provided for 93 low and middle-income countries at 2.4km resolution. 
  • Extracted point values for each water point GPS.
8. OpenStreetMap

 
  • Distance to nearest primary, secondary tertiary road, town and city are calculated for each water point.

9. USGS’s Africa Ecosystems Mapping project.

(https://rmgsc.cr.usgs.gov/outgoing/ecosystems/AfricaData/)

  • Extracted point values for each water point GPS location for African IsoBioClimates.
  • Extracted value is 

10. World Resources Institute Overall Water Risk Raw Value.

(https://www.wri.org/data/aqueduct-global-maps-30-data)

Hofste, R., S. Kuzma, S. Walker, E.H. Sutanudjaja, et. al. 2019. “Aqueduct 3.0: Updated Decision-Relevant Global Water Risk Indicators.” Technical Note.Washington, DC: World Resources Institute. Available online at:https://www.wri.org/publication/aqueduct-30.

  • Extracted point values for each water point GPS
  • Extracted values are the ‘w_awr_def_tot_raw’ value from the dataset