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EXTRAPOLATION
TECHNIQUE
Extrapolation
Techniques are "curve fitting" techniques in
which the analyst plots total population data from past
years, chooses a best-fitting curve for that data , and
then extends that curve to project future values. It is a
very simple procedure that is useful for small areas that
do not have access to detailed data and for general
projection figures for all areas.
Extrapolation
Techniques have very low data requirements, in the form
of an area's total population figures for a number of
past years, and therefore are preferred because of this.
It is important to note that the selection of the years
for past population figures is an important, but very
difficult pert of the process. Ideally, you want to
select those past population values that help provide a
curve that best projects future conditions. For slow,
steady growth areas, like older established cities, this
determination of useful data is likely not as difficult
as past figures tend to yield a slow, steady growth
curve. However, for fast growing suburban areas or
in-decline urban areas, population figures from forty
years ago likely provide little help when fitting an
appropriate curve, so more recent data is likely to be
relied heavily upon when using this method.
The limitations from
the Extrapolation Techniques should be readily apparent
from this brief description. The very low data
requirements that are so attractive also must be
recognized as an inherent limitation to the projections.
Total population figures for past years are being used to
project future conditions; there is no inclusion of
housing trends, economic changes, growth management, or
any other external pressures on population in this
technique. Any factors other than past population totals
are external to the method.
A further limitation
to the model, that is also implied by the above, is that
Extrapolation Techniques use past conditions to project
future conditions. There is absolutely no guarantee that
the past will have a strong bearing on the future. The
analyst assumes that past conditions will help to predict
the future. There is simply no assurance that past trends
will continue into the future. Therefore, extrapolation
techniques should be used carefully and with a full
understanding of their limitations.
Example of the Extrapolation Technique
UNDER CONSTRUCTION
The basic
procedure for Extrapolation Techniques:
1. Acquire population data for past years
2. Plot data to determine the best fitting curve
3. Extend the curve into the future
The
limitations of these techniques; the assumptions
of the extrapolation technique, the use of
aggregated data, and the lack of consideration of
any other factors that affect future population
levels.
The common
curves used in the extrapolation technique; linear,
geometric, parabolic, exponential, and gompertz (see Klosterman, Chapter 2).
When to use
this technique; when time, data, information, and
expertise in more advanced techniques are
limited.
The use of
past conditions to project future conditions.
Planners regularly must determine future
conditions and have little to guide them other
than past conditions and the experiences of other
similar areas. Usually, the first step in any
analysis is to determine past and current
conditions to inform their analysis of future
conditions.
The
difficulty of projecting future conditions. The
"crystal ball" that planners gaze into
is a cloudy one, with unforeseen changes to the
local economy, shifting residential preferences,
and other factors all acting to confound even the
most scientific and data intensive of projection
methods. With that said, an analyst must act to
clear away some of the cloudiness by researching
the area under study intensively, recognizing
local trends, and choosing only those past data
points that are most likely to impact future
conditions.
What is the
most significant assumption of this technique?
Why is this
assumption a dangerous one to employ?
What types of
areas are best suited to this projection
technique? What types of areas are not
well-suited to this technique?
How should
you determine which of these extrapolation
techniques to use? What process does Klosterman
recommend?
Why is it
necessary to generate multiple projections for a
given area? What are the advantages of doing so?
What are the disadvantages of doing so?
Armstrong, J. Scott.
1978. Long-range Forecasting: From crystal Ball to
Computer. New York: Jon Wiley and Sons.
Isserman, Andrew.
1977. "The Accuracy of Population Projections for
Sub-county Areas." Journal of the American
Institute of Planners 43: 247-259.
Isserman, Andrew.
1984. "Projection, Forecast, and Plan: On the Future
of Population Forecasting." Journal of the
American Planning Association. 50: 208-221.
Isserman, Andrew and
Peter Fisher. 1985. "Population Forecasting and
Local Economic Planning: the Limits of Community Control
over Uncertainty." Population Research and
Policy Review 3: 27-50.
Klosterman, Richard
E. 1990. Community and Analysis Planning Techniques.
Rowmand and Littlefield Publishers, Inc. Savage,
Maryland. See Chapters 1-3.
Klosterman, Richard
E., Richard K. Brail, and Earl G. Bossard. 1993. Spreadsheet
Models for Urban and Regional Analysis.
Pittenger, Donald.
1976. Projecting State and Local Populations.
Cambridge, MA: Ballinger.
Pittenger, Donald.
1977. "Population Forecasting Standards: Some
Considerations Concerning Their Necessity and Content."
Demography 14: 363-368.
Pittenger, Donald.
1980. "Some Problems in Forecasting Populations for
Government Planning Purposes." The American
Statistician 34: 135-139.
King County's Employment and Payroll Data (1994) by all SIC Codes
from the US Census Bureau.
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