An increasing number of data providers are today combining information from censuses with satellite-derived geospatial features to redistribute populations and produce gridded population datasets.
The COVID-19 pandemic, which originated from Wuhan, China has created a war-like situation in the world. More than 170 countries are affected; people are mostly confined to their homes, if not battling for their lives or recovering in hospitals; and nations are struggling to limit the number of those infected. The situation is even more challenging in underdeveloped, or remote parts, where the administrations don’t even know the population count, let alone providing people with food, medicines and other essentials in this time of crisis.
Identifying the size, structure, and distribution of a population is essential for planning development related works. Without knowing where people are located, governments and policymakers cannot improve/ expand access to health, transportation, energy, and other services.
SDSN TReNDS Manager Maryam Rabiee, who has compiled with a report titled Leaving No One off the Map: A Guide for Gridded Population Data for Sustainable Development, says, “Population and the environment are constantly changing and to ensure that we leave no one behind, we need reliable population data. A number of SDG indicators are related to population, and they measure access to basic services and facilities.”
As population scientists have expanded the range of topics they study, increasingly focusing on the relationship between population and social, economic and health conditions, there have been enhancements in data collection and the emergence of new data collection techniques and procedures. This has also led to the evolution of the concept of gridded population data.
After creating a base map of geographic cells based on satellite imagery, each cell can be viewed as the representation of an area on the surface of the Earth, typically defined by its latitude-longitude coordinates. The collection of these cells, rows, and columns defines a grid area. The population data derived by this process is called gridded population data.
There are various methods of data collection, the most common and reliable among them being the census, or the complete enumeration survey. Governments all over the world bank on this method to collect data concerning population, housing, agriculture, etc. A population census is considered highly accurate because data from each household is collected and studied before drawing any conclusions.
However, the method has its limitations. For example, census data is only collected once in ten years and thus may not be fully accurate after a certain period, as a lot can change in just a few years. In some countries, the gap between the two censuses is even longer. Another limitation of these methods is the inability of the enumerators to access certain locations, especially conflict or disaster-hit regions, as well as areas where local language presents a communication barrier. In such cases, the population residing in an area is either miscounted or is completely left uncounted.
Integrating geospatial for accuracy
The advancements in geospatial technology and remote sensing have paved the way for the production of more frequent population data which is more accurate and helps governments to design development plans “leaving no one behind”.
An increasing number of data providers are combining information from censuses with satellite-derived geospatial features to redistribute populations and produce gridded population datasets. “Gridded population datasets can be used in a wide range of application areas, such as disaster response, health interventions and survey planning, and they can offer us more spatially refined estimates,” explains Rabiee.
The integration also allows redistributing population data within different geographic boundaries to identify and characterize settlements and built infrastructure, manage 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.