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Standards of service provide important assessments of existing public facilities and programs in light of desired objectives and the population/client groups to be served.

Estimates of future population, including demographic and geographic distribution, are required to translate these standards into future capital improvement needs.

Demographic Techniques

Until very recently demographic projections (frequently known as "conditional forecasts") have had no predictive pretensions.

Demographers often apply a range of projections in lieu of more definitive estimates of future population characteristics. Such parameters usually are extrapolated from current data with insufficient detail to be of much utility to the capital facilities planner.

The basic demographic equation is P2 = P1 + B - D + I - O, which indicates that the population at any given point in time (P2) is a function of the population at a previous point in time (P1) plus the amount of natural increase (births minus deaths) and the net migration (in-migration minus out-migration) during the interim.

Population estimates are used to update population data gathered from the last census to approximate the current situation.

Population projections refer to future population levels and indicate what changes might occur, given assumptions inherent in the projection method and data. Analysts typically develop more than one set of projections, each set embodying different assumptions.

A population forecast is the set of projections deemed most likely to occur. Projections do not necessarily lead to forecasts.

Population change involves three separate components: births, deaths, and migration.

Noncomponent models may be based on past patterns of net population growth or may relate net growth to some indicator, such as changes in housing or the economic base of the community.

Symptomatic data are used to determine a correlation between population size and various other events, such as tax returns, voter registration, school enrollments, utility connections, telephone installations, occupancy permits issued, and motor vehicle licenses.

Noncomponent models lack detailed age-sex breakdowns which are useful in planning for schools, community services, and different housing types.

Most models that project population below the state scale are usually of the noncomponent variety because of data limitations (and demographic skills).

Births and deaths are referred to as vital statistics, usually available on an annual basis.

Migration is subject to relatively rapid fluctuations and is influenced by the location, size, shape, and economic base of the locality. A large county or city will have a lower proportion of migrants than will a small one, since many moves cover a relatively short distance.

The intercensal component method of estimating migration makes use of the population balancing equation which rewrites the basic population equation as follows: I - O = P2 - P1 - B + D

The reverse survival rate method may be used to produce net migration estimates by age, race, and sex groups.

Types of Population Models

In choosing a population model, it is important to consider its relative accuracy, the type of data available, the quality of available data, the scale of the analysis, the length of the projection period, the purpose of the projections, and the budget and time frame implications of the study.

Trend extrapolation is involved, to some extent, in nearly all projection methods.

Comparative forecasting examines the locality's past growth pattern in conjunction with growth patterns of older, larger, civil divisions, the assumption being that the locality's pattern will match that of communities more advanced in their stage of growth.

Ratio trend or step-down techniques assume that the relationship of a locality to some larger geographic entity--county or state--will prevail in the future and that population projections at the larger scale represent degrees of reliability and component detail that are not possible to achieve at the smaller scale of analysis.

Density ceiling models use capacity constraints by assuming that when a given density is reached, population will either stabilize or decline.

The ratio correlation method is similar to the ratio trend method except that population is treated as a function of some other independent variables--employment, housing units, motor vehicles registered, or other symptomatic data. Multiple regression may be used to determine the population's historic relationship to the independent variables.

The housing unit method establishes a relationship between the number of dwelling units and population via a family-size multiplier.

Market force methods include: deterministic regression models, holding capacity, multiplier studies, and mathematical programming.

The Greenberg-Kruckeberg-Mautner (GKM) model combines historical extrapolation, ratio trend, and density ceiling alternatives at the local scale, constrained by federal-state-county projections developed by component and market force techniques, and provides the option of five separate submodels to project local population.

The residual method--through an examination of the records of births and deaths, the known population (based on the last census) is adjusted accordingly to produce an estimate of current population--the difference between this anticipated population and the actual population is assumed to be the result of net migration.

The vital rates method is a ratio technique that relates total population to births and deaths.

Cohort-survival models project future population based on growth due to natural increase.

Various cohort-component methods have been developed by the Bureau of the Census.

The composite model applies various techniques to different segments of the total population.

The choice of the methods employed to develop population estimates or projections usually involves a trade-off of some sort with the level of accuracy required, the availability of data, and the composition of the final product as the chief determining factors.

Exhibit 1. Comparison of Population Estimation & Projection Methods

Type of Model Estimation or Projection Historic Counts Vital Statistics Other Indices Period Scale Complexity
Trend Extrapolation Both X X Short Local Moderate
Comparative Forecast Projection X Short Local Simple
Ratio Trend Both X Short-Middle


Local-State Simple
Density Ceiling Projection X Middle-Long Local Complex
Ratio Correlation Estimation X X Short Local Complex
Housing Unit Both X X Short-Middle Local Complex
Market Force Projection X X X Short-Middle




GKM Projection X X X Short-Middle


Local-State Complex
Residual Estimation X Short Local-State


Vital Rates Both X Short Local-State Moderate
Cohort-Survival Both X Short-Middle




Cohort-Component Both X Short-Middle




Composite Both X X X Short-Middle


Local-State Complex