Customize Consent Preferences

We use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below.

The cookies that are categorized as "Necessary" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... 

Always Active

Necessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.

No cookies to display.

Functional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.

No cookies to display.

Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.

No cookies to display.

Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.

No cookies to display.

Advertisement cookies are used to provide visitors with customized advertisements based on the pages you visited previously and to analyze the effectiveness of the ad campaigns.

No cookies to display.

WorldPop Projects

High resolution poverty mapping from cell phone and satellite data

Project leads: Andy Tatem, Jessica Steele

Collaborators/funders: Bill and Melinda Gates Foundation, Telenor, University of Washington

Eradicating poverty in all its forms remains a major challenge and the first target of the Sustainable Development Goals (SDGs). To eradicate poverty, it is crucial that information is available on where affected people live. These data promote informed decision making in resource allocation, poverty alleviation programs, and policies. This is especially pertinent for efforts aimed at reaching the SDGs, which need to be monitored at national and subnational levels over the coming 15 years. Spatial patterns of poverty between and within countries have been constructed using census data, but the irregular collection and unavailability of such data in many low income settings has limited the ability to produce regular updates. To complement census-based approaches, we combine data routinely collected by mobile phone operators with widely available remote sensing variables to produce accurate, high- resolution maps of multiple poverty indicators that can be readily updated. Together, indicators derived from satellite imagery and large-scale mobile operators capture distinct and complimentary correlates of human living conditions and behaviour. In Bangladesh, geostatistical models built with cell phone data, satellite-derived data, and geolocated household survey data line up well with existing small area poverty estimates, and with mapped slum areas at high resolution in Dhaka. The models explicitly incorporate the spatial relationships in the data between geographical areas, which is critically important. This creates maps with proper uncertainty metrics, and provides a means to utilize the temporal and spatial scales of non-census based datasets to regularly update poverty maps.

Fig.1. National level prediction map in Bangladesh for the mean wealth index generated using mobile phone features, remote sensing data, and Bayesian geostatistical models. Red indicates poorer areas, zoom area is the capital city, Dhaka.