Identifying the size, structure and distribution of the world’s population is essential for planning development-related works. The United Nations’ 2030 Agenda for Sustainable Development has recognized the need for high-quality, timely and reliable population data, including demographic information, to support the achievement of the Sustainable Development Goals (SDGs) in all countries of the world. To achieve the SDGs, we need to have reliable population counts to know where people are, to improve access to basic services, and to understand what infrastructure is available.
“We’ve found that 73 SDG indicators are connected with population data in one way or the other. Reliable and timely population data is critical for monitoring and measuring these indicators to ensure that no one is left behind,” says SDSN TReNDS Manager, Maryam Rabiee.
While, policymakers frequently rely on traditional data sources, such as censuses and surveys for information on population, these methods have their set of limitations. For example, census data is often only collected once every ten years at minimum and a lot can change within a decade. For some countries, it’s even less frequent. According to a UNDP report, at least nine countries rely on census data collected before 2000.
Another limitation of these methods is the inability of enumerators to access certain locations, especially conflict or disaster-hit regions, as well as areas where local language presents a communications barrier. In such cases, the population residing in these areas is likely to go uncounted.
Advancements in Population Estimation: Gridded Population Data
The advancements in geospatial technology and remote sensing have paved the way for the production of more frequent population estimates.
Geospatial data derived from satellite imagery can be used to create a base map of geographic cells, with each cell or a pixel representing an area on the surface of the Earth. The collection of these cells and rows and columns define a grid area.
An increasing number of data providers are combining information from censuses with satellite-derived geospatial features to redistribute population data across grids or within different geographic boundaries to produce gridded population datasets that can be used for various applications, including identifying and characterizing settlements and built infrastructure, managing resources, urban and rural planning, risk management and disaster response.
Satellite imagery is significant to this method of producing population estimates because it does not face geographical and temporal limitations of traditional data sources and allows for more frequent population estimates.
“Despite the progress around gridded population data, there is still considerable ambiguity with regard to the datasets. Each of the datasets have been developed for different purposes and each have their advantages and disadvantages. In our research with stakeholders, it was apparent that most policymakers and other data users lack the time and technical expertise to understand their different characteristics and applications, limitations, and potential,” says Rabiee.
To address many of these challenges around gridded population data, POPGRID was established. Led by the Center for International Earth Science Information Network (CIESIN) at Columbia University, SDSN TReNDS, and the Global Partnership for Sustainable Development Data (GPSDD), POPGRID is a ‘data collaborative’ which aims to accelerate the development and use of high quality, georeferenced data on human settlements, infrastructure and populations by convening and drawing on the expertise of an international, interdisciplinary community of data developers and users from both the public and private sectors.
“Our main focus of POPGRID is to do a better job sharing, accessing, and documenting these kinds of data, and in particular, to work with the stakeholder and user community to have a better sense of their priorities and needs and see if we can match the provision of data with the demand and use for gridded population data,” states Dr. Robert Chen, SDSN TReNDS Co-Chair, Director of CIESIN, and Manager of NASA’s Socioeconomic Data and Applications Center (SEDAC).
Reducing the Knowledge Gap
On behalf of the POPGRID Collaborative, SDSN TReNDS will be releasing an upcoming report this Spring with the objective of narrowing the knowledge gap around gridded population data and helping to improve the accessibility and understanding of gridded population datasets for policymakers and other users. The report will draw from an extensive literature review and interviews with key data providers and users in POPGRID, and present an overview, analysis, and recommendations for the use of gridded population datasets in a wide range of application areas, such as in disaster response, health interventions, and survey planning. The report will also present an intercomparison assessment of the use of different datasets and their varying outputs, and will address many of the misconceptions around gridded population data.
“With only ten years left to achieve the SDGs and fulfill the promise of leaving no one left behind, gridded population data offer a promising resource to help count everyone. However, these datasets are only as good as users’ understanding of their limitations, applications, and fitness for use. We must continue this important research on gridded population data to ensure that no one is left off the map,” contends Rabiee.