DOI: 10.7910/dvn/5ogwym
title: High-resolution poverty maps in Sub-Saharan Africa

description: The purpose of this dataset is to provide village-level wealth estimates for places where up-to-date information about geographic wealth distribution is needed. This dataset contains information on buildings, roads, points of interest (POIs), night-time luminosity, population density, and estimated wealth index for 1-mi² inhabited places identified by the underlying datasets. The wealth level is an estimated value of the International Wealth Index which is a comparable asset based wealth index covering the complete developing world.
description: There are 5 types of sub-datasets for each country. Feature catalog CSV file contains the description and definition for each field.<br>
<b>1. inhabited places</b><br>
<b>2. buildings, roads, POIs</b><br>
<b>3. night-time luminosity</b><br>
<b>4. population density</b><br>
<b>5. estimated wealth index</b><br>
description: <b>1. Identifying inhabited places:</b> <br>
This step combines OCHA, OSM, HRSL and WorldPop to create a harmonized 1-mi²-level settlement dataset.<br>
It first gets information on inhabited places from the OCHA dataset if it's available. Then it adds OSM places where the place tag is 'city', 'town', 'village', 'hamlet', or 'isolated_dwelling'. If the distance from an OSM place to an OCHA place is less than 300 m (around 1000 feet), it assumes that they are the same place and adds supplemental info to the OCHA place. If the minimum distance from an OSM place to any OCHA places is more than 300 m (around 1000 feet), it adds the OSM place as a separate inhabited place.<br>
In order not to overlook numerous unnamed places and many smaller communities within big cities, this step has an additional process. It divides each of the smallest admin regions into a grid of 10 km² (around 4 mi²) ss and finds a 1-mi² area that has the largest population within a grid. If the population is larger than 100 and there is no listed place in UN OCHA and OSM datasets within 2 km around the HRSL place, it is added as a separate inhabited place.<br>
Sources:<br>
OCHA Settlements<br>
OSM GEOFABRIK dump<br>
High Resolution Population Density Maps<br>
WorldPop gridded population estimate<br><br>

<b>2. Extracting buildings, roads, and points of interest (POIs) information for each inhabited place:</b> <br>
This step extracts information on man-made structures and services from OSM.<br>
Building info: Total building area, and the number of buildings within 1 km radius surrounding the GPS coordinates of each inhabited place.<br>
Road info: Total length of roads and number of junctions within 1 km radius surrounding the GPS coordinates of each inhabited place. Distance to the closest road and junctions from the GPS coordinates of each inhabited place.<br>
POI info: The number of and distance to 110 types of service locations. POIs: bar, bbq, biergarten, cafe, drinking water, fast food, food court, ice cream, pub, restaurant, college, driving school, kindergarten, language school, library, toy library, music school, school, university, bicycle parking, bicycle repair station, bicycle rental, boat rental, boat sharing, bus station, car rental, car sharing, car wash, vehicle inspection, charging station, ferry terminal, fuel, grit bin, motorcycle parking, parking, parking entrance, parking space, taxi, atm, bank, bureau de change, baby hatch, clinic, dentist, doctors, hospital, nursing home, pharmacy, social facility, veterinary, arts centre, brothel, casino, cinema, community centre, fountain, gambling, nightclub, planetarium, public bookcase, social centre, stripclub, studio, swingerclub, theatre, animal boarding, animal shelter, baking oven, bench, childcare, clock, conference centre, courthouse, crematorium, dive centre, embassy, fire station, give box, grave yard, gym, hunting stand, internet cafe, kitchen, kneipp water cure, marketplace, monastery, photo booth, place of worship, police, post box, post depot, post office, prison, public bath, ranger station, recycling, refugee site, sanitary dump station, sauna, shelter, shower, telephone, toilets, townhall, vending machine, waste basket, waste disposal, waste transfer station, watering place, water point.<br>
Sources:<br>
OSM GEOFABRIK dump<br><br>

<b>3. Extracting population density information for each inhabited place:</b> <br>
This step extracts estimated population within 1mi², 25km², 100km² areas around each inhabited place.<br>
Sources:<br>
High Resolution Population Density Maps<br>
WorldPop gridded population estimate<br><br>

<b>4. Extracting nighttime luminosity information for each inhabited place:</b> <br>
This step extracts nighttime luminosity within 1mi², 25km², 100km² areas around each inhabited place.<br>
Sources:<br>
Annual VIIRS Cloud Mask - Outlier Removed - Nighttime Lights<br><br>

<b>5. Training XGBoost model and CNN model:</b> <br>
This step trains/estimates/validates wealth index for each inhabited place.<br>
First, an XGBoost model is trained on the buildings, roads, POIs, night-time luminosity, and population density features. It estimates wealth levels for all inhabited places.<br>
Second, a customized CNN model is trained on the estimated wealth index from the XGBoost model and day-time satellite images (zoom-16, 640px x 640px) downloaded from Google Static Map. It estimates the 4 probabilities that each populated place is a rich, upper-middle, lower-middle, or poor area.<br>
Third, the featurized information (4 probabilities) from the CNN model is added to the XGBoost model as an additional inputs resulting in better estimates.<br>
Fourth, the CNN model is re-trained on the better estimates from the XGBoost model.<br>
The whole process creates a cycle where the two models improve each other by providing better training data with every iteration. The wealth index dataset is the result of the 7th iteration.<br>
Sources:<br>
Demographic and Health Surveys<br>
Daytime satellite images through Google Static Maps API<br>