A population projection is a mathematical equation that calculates the estimated growth rate or change of future populations based on current populations. Governments use population projections for planning for public health, preparedness, housing, assistance, and school and hospital costruction. Such information also aids business and marketing.

#### TL;DR (Too Long; Didn't Read)

You can use a formula to calculate current populations and growth rates to predict the future population. Such information is used for government planning, services and businesses. More specific calculations for population projection may be needed at local levels and to address adverse events.

## Simple Equation for Population Equation

A simple equation for population projection can be expressed as:

Nt=Pe^{rt}

In this equation, (Nt) is the number of people at a future date, and (P) is equal to the present population. Next to (P) is (e), which is the natural logarithm base of 2.71828; (r) represents the rate of increase divided by 100, and (t) represents the time period.

## Uses for Population Projections

Population projections can be used for planning for food and water use, and public services such as health and education. Zoning and other demographic boundaries rely on population projections as well. Businesses use population projections for store location planning and marketing. Such projections also affect federal and state funding.

## Variables and Challenges

While such an equation seems straightforward, many variables come into play for population projections. When census demographers make population projections, they must use the components of fertility, mortality and net migration, all of which contribute to population growth estimates and projections. Demographers base fertility and mortality rates on birth and death statistics. Projections use the assumption that recent demographic trends will continue. They do not predict future trends in population.

This creates issues, such as recent-trend projections that do not tend to account for other events that could change the shape of population growth. For example, such scenarios as conflict, an epidemiological disaster, natural disasters and extreme weather events, and food scarcity are more pressing in the context of climate change. These potential variables make population projections more difficult, particularly on a local level (such as county level) rather than global or nationwide.

Challenging factors include country size and periods of time. Less-developed countries tend to have less reliable birth and death rate data, and analysts tend to work more with larger countries. Longer-term projections rely on assumptions about the future and fertility, mortality and migration trends. Again, with climate change, political unrest and any other unforeseen events, migration patterns could change unexpectedly. Epidemics could affect birth and death rates. Essentially, it is more difficult to project future population size with high accuracy.

## Novel Approaches for Local Projections

For more local population projections, demographers can use a different approach that accounts for various effects on local population distribution. One example is intelligent dasymetric modeling. This spatially explicit projection modeling incorporates socioeconomic and cultural influences on spatial population growth on the smaller scale.

As the human population approaches nearly 10 billion by 2050, climate change and socioeconomic factors will continue to pose a challenge for demographers. A need for more accurate population projection models becomes more crucial and more valuable for everyone.

#### References

- Population Reference Bureau: Understanding and Using Population Projections
- Proceedings of the National Academy of Sciences of the United States of America: Locally Adaptive, Spatially Explicit Projection of US Population for 2030 and 2050
- University of Nebraska Omaha: Population Projections
- Centers for Disease Control and Prevention: Population Projections 2004 - 2030 by State, Age and Sex Methodology Summary