Global population numbers may be wildly inaccurate, scientists warn

Hundreds of decisions—how to build roads, where to send medicine, how to plan for natural disasters—depend on knowing where people live. Over the past few decades, population maps have become powerful tools for making those choices. But a recent study suggests that these tools might be missing a big piece of the global puzzle: rural populations.

Across the globe, around 43 percent of the world's 8.2 billion people live in rural areas. Yet researchers from Aalto University in Finland have found that many of these individuals may not be fully counted in the most widely used global population datasets. Their findings, published in Nature Communications, show that rural populations are significantly underrepresented—by as much as 84 percent in some models.

The Aalto research team, led by postdoctoral researcher Josias Láng-Ritter, analyzed five major population datasets commonly used by researchers and governments. These maps divide the world into tiny grid cells, each with a population estimate based on census data, satellite imagery, and other sources.

The team didn’t use just any rural data for their comparison. They focused on human resettlement caused by the building of over 300 dams in 35 countries. When these large infrastructure projects are constructed, thousands of people are often relocated. Because dam builders are required to compensate displaced residents, these population counts are usually accurate and well-documented.

‘When dams are built, large areas are flooded and people need to be relocated,’ explains Láng-Ritter. ‘The relocated population is usually counted precisely because dam companies pay compensation to those affected. Unlike global population datasets, such local impact statements provide comprehensive, on-the-ground population counts that are not skewed by administrative boundaries.’

To support these numbers, the researchers used satellite data to map out the actual reservoir areas. That allowed them to link accurate resettlement counts with specific locations, bypassing the often blurry lines of administrative borders.

The study focused on datasets from 1975 to 2010 and found striking gaps in rural population counts. Even the most accurate of the datasets, WorldPop, missed over half the people actually living in these rural areas. Others did worse—GHS-POP, for example, underestimated rural population by 84 percent.

These errors aren’t limited to one country or region. The study found discrepancies across the globe, particularly in nations like China, Brazil, Australia, Poland, and Colombia. There, more reliable resettlement data were available, and the population datasets consistently undercounted rural residents.

Why is this happening? The problem starts with the data used to build the population maps. National censuses form the backbone of most global datasets, but in many countries, censuses are incomplete or outdated, especially in remote areas. Some regions are hard to reach, have scattered homes, or lack government resources for accurate surveys.

'Not all nations have the resources for precise data collection, and rural regions can be difficult to travel to,’ says Láng-Ritter. ‘Populations are spread thinly over vast areas.’

Even though newer versions of the datasets are available for 2015 and 2020, the team believes the same basic methods are still in use. That means rural populations could still be underestimated today. And because older maps are still widely used to track long-term trends, errors from the past continue to shape current decisions.

These findings have major implications. Governments, international organizations, and researchers rely on these population maps to decide how to allocate resources, respond to disasters, and plan future development.

‘In many countries, there may not be sufficient data available on a national level, so they rely on global population maps to support their decision-making,’ says Láng-Ritter. ‘Do we need an asphalted road or a hospital? How much medicine is required in a particular area? How many people could be affected by natural disasters such as earthquakes or floods?’

If rural populations are undercounted, they may be missing out on services and support. This could lead to fewer health clinics, poorly planned infrastructure, and slower emergency response times during disasters.

Láng-Ritter also warns that past decisions based on flawed data could be reinforcing long-standing inequalities. ‘Even if the most recent population maps reflected reality, earlier datasets have influenced decision-making for decades and are still used to monitor change, for instance providing a distorted picture of movement over time from the countryside to urban areas.’

Despite the gaps, Láng-Ritter stresses that global population maps are still valuable. They offer insights on a scale no single nation could manage alone. However, the methods behind them need to improve—especially for rural areas.

More accurate rural data could be achieved in several ways. The researchers suggest that nations strengthen their census methods, invest in alternative population counting tools, and build more balanced models that don’t rely too heavily on urban data. For countries that already have digital records, such as Finland, the situation is improving.

‘For example here in Finland, the population data is nowadays very reliable even in rural regions, as we were the second country in the world to start keeping digital population records already in 1990,’ says Láng-Ritter. ‘But especially in crisis-hit countries, the shift towards digital population registers could take years, even decades.’

Until then, he believes it's important to have honest conversations about the weaknesses in current data. Without that, rural communities may continue to be overlooked.

‘To provide rural communities with equal access to services and other resources, we need to have a critical discussion about the past and future applications of these population maps.’

Note: The article above provided above by The Brighter Side of News.

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