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Bangladesh 1km Resolution Poverty Estimates - Mapping poverty using mobile phone and satellite data

Here we provide poverty data created using Bayesian model-based geostatistics in combination with high resolution gridded spatial covariates and aggregated mobile phone data applied to geolocated household survey data on poverty from the DHS wealth index (2011), the Progress out of Poverty Index (2014), and household income (2013). Citation: Steele, J. E. et al. Mapping poverty using mobile phone and satellite data. J. R. Soc. Interface 14, 20160690 (2017). Online here: http://rsif.royalsocietypublishing.org/content/14/127/20160690

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  • bgd2011wipov.tifgeotiff (514.3K)

    DATASET: 2011 estimates of mean DHS wealth index score per grid square, and associated uncertainty metrics. REGION: Asia SPATIAL RESOLUTION: 0.00833333 decimal degrees (approx. 1km at the equator) PROJECTION: Geographic, WGS84 UNITS: DHS wealth index score (poverty dataset); standard deviation (uncertainty dataset) MAPPING APPROACH: Bayesian model-based geostatistics in combination with high resolution gridded spatial covariates and aggregated mobile phone data applied to GPS-located household survey data on poverty from the DHS Program. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: bgd2011wipov.tif = Bangladesh (bgd) asset-based poverty map for 2011 showing estimates of mean DHS wealth index score per grid square. bgd2011wipovsd.tif = uncertainty dataset showing standard deviation per grid square. DATE OF PRODUCTION: January 2017 CITATION: Steele, J. E. et al. Mapping poverty using mobile phone and satellite data. J. R. Soc. Interface 14, 20160690 (2017). Online here: http://rsif.royalsocietypublishing.org/content/14/127/20160690

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  • bgd2011wipovsd.tifgeotiff (507.2K)

    DHS wealth index score Standard Deviation

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  • bgd2013incpov.tifgeotiff (507.8K)

    DATASET: 2013 estimates of income in USD per grid square, and associated uncertainty metrics. REGION: Asia SPATIAL RESOLUTION: 0.00833333 decimal degrees (approx. 1km at the equator) PROJECTION: Geographic, WGS84 UNITS: USD (poverty dataset); standard deviation (uncertainty dataset) MAPPING APPROACH: Bayesian model-based geostatistics in combination with high resolution gridded spatial covariates and aggregated mobile phone data applied to mobile tower-located household survey data on income from Grameenphone Ltd. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: bgd2013incpov.tif = Bangladesh (bgd) income-based poverty map for 2013 showing estimates of mean household income in USD per grid square. bgd2013incpovsd.tif = uncertainty dataset showing standard deviation per grid square. DATE OF PRODUCTION: January 2017 CITATION: Steele, J. E. et al. Mapping poverty using mobile phone and satellite data. J. R. Soc. Interface 14, 20160690 (2017). Online here: http://rsif.royalsocietypublishing.org/content/14/127/20160690

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  • bgd2013incpovsd.tifgeotiff (509.1K)

    Estimates of Income in USD Standard Deviation

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  • bgd2013ppipov.tifgeotiff (501.8K)

    DATASET: 2013 estimates of mean likelihood of living in poverty per grid square, as defined by $2.50 a day poverty line, and associated uncertainty metrics. REGION: Asia SPATIAL RESOLUTION: 0.00833333 decimal degrees (approx. 1km at the equator) PROJECTION: Geographic, WGS84 UNITS: Mean likelihood of living on less than $2.50 a day (poverty dataset); standard deviation (uncertainty dataset) MAPPING APPROACH: Bayesian model-based geostatistics in combination with high resolution gridded spatial covariates applied to GPS-located household survey data on poverty from the Progress out of Poverty Index. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: bgd2013ppipov.tif = Bangladesh (bgd) Poverty Index-based poverty map for 2013 showing estimates of mean likelihood of living in poverty per grid square. bgd2013ppipovsd.tif = uncertainty dataset showing standard deviation per grid square. DATE OF PRODUCTION: January 2017 CITATION: Steele, J. E. et al. Mapping poverty using mobile phone and satellite data. J. R. Soc. Interface 14, 20160690 (2017). Online here: http://rsif.royalsocietypublishing.org/content/14/127/20160690

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  • bgd2013ppipovsd.tifgeotiff (506.5K)

    Mean Likelihood of Living in Poverty Standard Deviation

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  • licence.txtgeotiff (18.8K)
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Metadata

Source Flowminder / WorldPop
Contributor Flowminder
Date of Dataset Feb 10, 2017
Expected Update Frequency Never
Location
Visibility
Public
License Creative Commons Attribution
Methodology

Bayesian model-based geostatistics in combination with high resolution gridded spatial covariates and aggregated mobile phone data applied to GPS-located household survey data.

Full methodology:

Steele, J. E. et al. Mapping poverty using mobile phone and satellite data. J. R. Soc. Interface 14, 20160690 (2017).

Online here: http://rsif.royalsocietypublishing.org/content/14/127/20160690

Caveats / Comments
Tags
bangladesh
dhs
flowminder
geotiff
gis
mobile phone
poverty
remote sensing
wealth
worldpop