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8315463

Gustafson, Richard Dale

A LAND USE PROJECTION MODEL APPLIED TO EMMET COUNTY,
MICHIGAN

Michigan State University

University
Microfilms
International

PH.D. 1983

300 N. Zeeb Road, Ann Arbor, MI 48106

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A LAND USE PROJECTION MODEL
APPLIED TO EMMET COUNTY, MICHIGAN
By
Richard Dale Gustafson

A DISSERTATION

Submitted to
Michigan State University
partial fulfillment of the requirements
for the degree of

DOCTOR

OF PHILOSOPHY

Department of Forestry

ABSTRACT
A LAND USE PROJECTION MODEL
APPLIED TO EMMET COUNTY, MICHIGAN
By
Richard Dale Gustafson

This study was part of a cooperative regional project
concerned with developing guidelines
forest

and

states.
on

recreation

resources

in

for management of
the

north

central

Anticipating problems due to increasing demands

scarce

and often

fragile

resources,

one

component

this project was aimed at developing land use models

of
for

predicting and planning to alleviate such problems.
This

study was

to build upon a base of previously

proposed models to develop and apply a land use projection
model to a small region with considerable spatial resolu­
tion.

Problems with a proposed mixed integer land use

model are considered,

and alternative formulations of land

use linear programming models are presented.

A model that

incorporates

input-output

a

linear program
acreage

small,

spatially

to derive

requirements

levels

aggregated
of output by

sector

and

and rents by use and then allocates

specific parcels to uses apart from the linear program is
described and applied to Emmet County.
Development
County,
reduction

using

of

both

techniques,

an

input-output

primary
is

survey

described.

model

and

for

secondary

Steps

Emmet
data

in acquiring

and compiling other data required for this model, much of
which is geospecific (e.g. soil type, slope, travel times,
current use), are also described.
Results

of

three

demonstration

runs

of the model

reflecting different rates of regional economic growth are
presented

in

the

form

of

maps

of

changing

land

use.

Problems with this model and this application and with
land use modeling in general that limit current usefulness
are discussed along with implications for future research.

ACKNOWLEDGMENTS
I wish to express my appreciation to the members of
my graduate committee,
ment

of

pelle,

Resource

Professor

Development,

Department of Forestry

Development,

Professor

Lee

Raleigh Barlowe,

Professor

Daniel

E.

and Department of

M.

James,

Depart­

Department

Chap-

Resource
of

For­

estry and Professor Lawrence Libby, Department of Agricul­
tural

Economics,

for guiding me through this program and

for helpful comments
Chappelle,
deserves

my

major

in finalizing this document.
professor

special thanks

and

committee

Dr.

chairman,

for his patience and persistence,

without which this program would not have been completed.
Mr.

Max

Putters

and Mr.

Jeff

Phillips of the Emmet

County Department of Planning and Zoning provided a great
deal

of

essential

data

on and information about Emmet

County.
My colleagues

at Crown

Zellerbach,

especially Dr.

William A. Atkinson and Dr. Michael D. Huddy, provided
encouragement

in this

effort when

it

always

seemed

that

more pressing problems were demanding attention.
Finally,
parents,
Laura,

Mr.
and

I wish
and Mrs.

children,

to acknowledge

the

support

Stanley R. Gustafson,
Geoff,

Jenny,

of my

and my wife,

Amy and Emily,

who

gracefully tolerated many evenings and weekends of neglect.
ii

TABLE OF CONTENTS
LIST OF TABLES

iv

LIST OF FIGURES

V

INTRODUCTION
The Problem
Study Objectives
The Study Region

1
1
3
5

CHAPTER I. RELATED
Urban Land Use
Evaluations of
Rural Regional

LITERATURE
Models
Urban Land Use Modeling
Land Use Models

11
11
14
19

CHAPTER II. THE MODEL
Input-Ouptput and Linear Programming
A Mixed Integer Programming Land Use Model
Problems With the Integer Programming Model
Alternative Large Scale Linear Programming Models
The Land Use Projection Model

25
25
33
36
41
50

CHAPTER III.
DATA AND METHODS
The Input-Output Model
Spatially Referenced Data
Spatial Resolution
Land Use
Soils
Travel Times
Zoning
Ownership

56
57
66
66
73
75
76
83
84

CHAPTER IV.
RESULTS AND CONCLUSIONS
Emmet County Analyses and Results
Problems with the Model and Apllication
Reflections on Land Use Modeling

86
86
121
131

APPENDIX.

138

LAND USE MODEL FORTRAN SOURCE LISTING

REFERENCES

157

iii

LIST OF TABLES

Emmet County Input-Output Analysis
Sectorization

60

Emmet County Input-Output Analysis
Transactions

64

Input-Output Analysis Direct Requirements

67

Input-Output Analysis I-A Matrix

68

Direct and Indirect Requirements

69

Direct and Indirect Requirements,
With Households

70

Emmet County Input-Output Analysis
Multipliers

71

Initial and Projected Final Demands and
Gross Outputs for the First Run

90

Projected Final Demands and Gross
Outputs for the Second Run

102

Final Demand Inputs and Implied Gross
Outputs for the Wood Products Sector
in the Third Run

119

Unconstrained and Constrained Final
Demands and Gross Outputs for Period
3, Run 3

120

iv

LIST OF FIGURES

Figure
Figure
Figure

1.
2.
3.

Location and Important Features
of Emmet County

6

An Alternative Land Use Linear
Program Formulation

43

Use Specific Objective Coefficients
Land Use Linear Program Formulation

47

Figure

4.

Land Use Projection Model Flow Chart

52

Figure

5.

Soil Productivity for Agricultural Use

77

Figure

6.

Current Proportion of Area in
Agricultural Use

78

Figure

7.

Woodland Soil Productivity

79

Figure

8.

Travel Times to Commercial Centers

81

Figure

9.

Assumed Impact of Travel Time on Rent

82

Current Proportion of Area in
Developed Uses

91

Projected Proportion of Area in
Developed Uses, Run 1, Period 1

92

Projected Proportion of Area in
Developed Uses, Run 1, Period 2

93

Projected Proportion of Area in
Developed Uses, Run 1, Period 3

94

Projected Changes in Commercial Use,
Run 1, Period 3

95

Projected Changes in Residential Use,
Run 1, Period 3

96

Projected Changes in Industrial Use,
Run 1, Period 3

97

Figure 10.
Figure 11.
Figure 12.
Figure 13.
Figure 14.
Figure 15.
Figure 16.

v

Figure 17.
Figure 18.
Figure 19.
Figure 20.
Figure 21.
Figure 22.
Figure 23.
Figure 24.
Figure 25.
Figure 26.
Figure 27.
Figure 28.
Figure 29.
Figure 30.

Projected Changes in Agricultural Use,
Run 1, Period 3

98

Projected Changes in Recreation
Residential Use, Run 1, Period 3

99

Projected Proportion of Area in
Developed Uses, Run 2, Period 1

103

Projected Proportion of Area in
Developed Uses, Run 2, Period 2

104

Projected Proportion of Area in
Developed Uses, Run 2, Period 3

105

Projected Changes in Commercial Use,
Run 2, Period 3

106

Projected Changes in Residential Use,
Run 2, Period 3

108

Projected Changes in Industrial Use,
Run 2, Period 3

109

Projected Changes in Agricultural Use,
Run 2, Period 3

110

Projected Changes in Recreation
Residential Use, Run 2, Period 3

111

Projected Proportion of Area in
Developed Uses, Run 3, Period 3

114

Assumed Current Proportion of Area in
Timber Production, Run 3

115

Projected Proportion of Area in Timber
Production, Run 3, Period 1

116

Projected Proportion of Area in Timber
Production, Run 3, Period 3

118

INTRODUCTION
The Problem
The

regional

Effective

research

Regional

project

Development

of

"Guidelines
Forest

and

For

More

Recreation

Resources in the North Central United States" was formed
to

investigate major

forces

affecting the use of forest

and recreation resources and to evaluate alternative means
for influencing these forces and managing these resources
to satisfy demands,
and

productivity

while maintaining the attractiveness

of

the

resources

(Countryman,

et

al.,

The motivation

for

such an investigation was the

1982).

recognition of and concern over problems arising from
increasing
resources.

demands by competing uses

for

various

scarce

Problems such as environmental degradation due

to intensive use of unsuitable lands,

close proximity of

incompatible uses to the detriment of one or both users,
and

declining

depletion

of

regional
some

economies

resource

were

due

to

degradation

identified

and were

or
of

primary concern in this regional project.
Several

conditions

existing

in the North

Central

Region, which contribute to these types of problems, were

1

2
identified.
tion

These include a high concentration of popula­

(approximately 30 percent of the national total) re­

lative to available recreactional land

(12 percent of the

national acreage primarily useful for outdoor recreation).
This relative imbalance coupled with increasing population
and

increasing rates

of participation

in outdoor

recrea­

tion add up to greatly intensifying demands on available
forest and recreation resources.
Fuel shortages and anticipation of fuel shortages may
also tend to increase the demands on forest lands within
the region.

It has been suggested that increasing cost and

decreasing or uncertain availability of fuel will encourage
shorter

trips rather than eliminate recreational trips

altogether.

For

the North Central Region this may mean

more intensive use of recreational resources, as residents
tend to travel more within the region,

instead of driving

to recreation sites in other parts of the country.
Aggravating

the problems posed by the current imbal­

ance and intensifying demands is the continuing pressure to
convert forest land to nonforest uses.
recreational

residential

development,

and conversion to crop or pasture
erode the forest land base.

Residential sprawl,
mineral

extraction,

land all continue to

This land becomes unavailable

not only for public outdoor recreation but for other forest
uses

as well,

thereby intensifying the

competition among

forest users for

theremaining forest resource.

Compounding

the problem is the fact that

those areas

3
within the region that may be most susceptible to dramatic,
negative effects of use conflicts and conversion are often
the areas which are least prepared to recognize the poten­
tial for such effects or to control or

influence further

development to reduce undesirable impacts

(Ragatz,

1970).

Study Objectives
Given

the

context

and

concerns

of

this

regional

project, the usefulness of, in fact the necessity for, some
capability
space

and

for predicting
time

and

future

land use patterns

for predicting the

alternative policies

consequences

in
of

intended to influence those land use

patterns is readily apparent.

Indeed, a major component of

the overall project was devoted to developing or at least
progressing toward just such a capability.
A

computerized

land use

projection

simulation model

was envisioned as the vehicle for providing this capabil­
ity.

if such a model could be perfected, it would be very

useful for decision making, policy analysis and planning to
alleviate the kinds of problems of major
regional project.

concern in this

Specific parcels within a region that

might be subject to pressure for development for which they
are

not

suited

could

be

identified.

Specific

resources

that may limit future economic growth of certain industrial
sectors within the region could be identified with impli­
cations for the industries in which local officials might
encourage or expect expansion.

What seem to be efficient

4
or

at. least

reasonable

land allocation

decisions

at

the

current time, might be seen to be serious restrictions to
desired future development through such a projection model.
The

effects

over

time

and

space

of public

facilities

development or public land ownership decisions in stimulat­
ing or limiting future private development could be examin­
ed, leading to better public decisions.
the potential

uses

for

a

"perfected"

These are a few of
land use model and

illustrate the underlying motivation for the model develop­
ment goals

of this

regional project.

study and of this

component of the

The extent to which the state-of-the-art

in land use modeling, both at the outset and at the comple­
tion of this study, falls short of such a "perfected" model
is acknowledged and is considered in some detail in sub­
sequent chapters of this thesis.

the

A previous dissertation (Miley,

1977) completed under

land

the regional

use modeling

component

of

project

provided the underlying concept for the land use model
that was

pursued in this

study.

A linear programming

formulation of an input-output model with land use and
resource constraints was used to reflect the interactions
among

different

sectors

in a regional

economy and the

dependence of those sectors on the land and resource base.
It was suggested that shadow prices

from the solution of

such a model could be used in evaluating the likelihood of
conversion from one use to another on specific parcels of
land in the region.

5
The primary purpose of this study was to build upon
these basic concepts

to formulate and program a land use

projection simulation model.

It was intended from the

outset that this study include a reasonably serious attempt
at applying the model to a region with a much finer spatial
resolution than was employed in Miley's work.
that

only

costs,

through

It was felt

such an attempt could the problems,

and benefits of employing such a model be realis­

tically assessed.
The Study Region
Several factors led to the selection of Emmet County,
Michigan as the
Emmet County,

study area to which to apply the model.

occupying the northwest tip of the lower

peninsula of Michigan,
study area,
viously
such,

see Figure 1, was part of a larger

18 counties of northern lower Michigan,

identified

for the

overall regional project.

pre­
As

Emmet County had been designated for study by other

components of the project, e.g. the legal component of the
regional project had profiled laws and institutions pert­
inent to the land use and development question, providing
potential

contributions

to this

study.

Emmet County was

also somewhat unique among the counties of the larger study
area because
years.

of

its

relatively rapid growth

The population of Emmet County increased by 45

percent between 1960 and 1980
1982).

in recent

(U.S.

Dept,

of Commerce,

Growth rate was considered important so that the

6

IFKT
COUNTV

MACKINAW
/ CITY

French
Lake
Wycamp
'Lake
LEVERING

Lake
Michigan

Paradise
Lake

CROSS
VILLAGE

EMMET
COUNTY

Little*

Traverse
Bay

PELLSTON-tCHEBOYGAN
COUNTY

SPRINGS ALANS0N
">
Round
Lake —
Crooked
Lake

PETOSKEY

Pickerel
Lake

CHARLEVOIX
COUNTY

Figure 1.

Location and Important Features of Emmet County

7
model would have some reasonable change in land use to
project

and also so that use conflicts or

land suitable for certain uses,

scarcity of

which the model was

sup­

posed to identify, would have some likelihood of occurring
in the near future.

Emmet County was also of interest

because questions about public
raised locally,
was

the

land ownership had been

and an intended refinement for this model

capability

to

explicitly

recognize

different

ownership classes and their effects on future land use
patterns.

Finally,

study area because
resources,

some

Emmet County seemed an appropriate
of

its endowment of varied natural

persistent

economic

disparities,

and

the

potential for those resources to contribute to alleviating
those disparities.
Through most of this century Emmet County,

like much

of the Upper Great Lakes region, has experienced a declin­
ing economy characterized by relatively high unemployment,
low per

capita

decline

followed the

resource

in

consequent

income,

the

and

decreasing

depletion

late

contraction

1800's
of

of

and

population.

the region's
early

1900's

the wood products

This
timber

and

the

industry.

During the last two decades these trends have been reversed
for

Emmet

County but,

although the county economy has

recently experienced rapid growth,

there remains a gap

between the general level of prosperity of this county and
that of the Michigan and the United States in general.

8
A simple location quotient analysis of employment data
suggests that construction, wood products,
turing,

cement manufac­

electrical equipment manufacturing,

equipment

manufacturing,

lodging

and

transportation

amusement

services,

and medical and health services are significant exporting
industries for the county.
After the depletion of the original forest, the asso­
ciated

decline

of

the wood products

industry,

and the

subsequent failure of agriculture on much of the cut-over
land

early

in this

century,

a new hardwood

established over much of the region.
Michigan

Department

180,000 acres

of

of

Natural

commercial

mostly in hardwood types.
soon will be

suitable

forest was

According to the

Resources

there

are

over

forest land in Emmet County,
Much of this forest is now or

for sawtimber and pulpwood produc­

tion, but it is estimated that presently only 20 percent of
the sustainable annual harvest is being utilized

(Pfeifer

and Spencer).
While
suggests

this

renewal

a potential

of

for

the

forest

expansion

of

in Emmet
the wood

County

products

industry, perhaps of even greater importance to the county
economy is the possibility for the continued growth of the
recreation

this

forest

resource and other physical assets of the county.

Recent

studies
gories

related

have
of

industries

indicated high potentials

recreational

second homes,

because

use

campgrounds,

and/or

of

for

several

development

cate­

including

picnic areas, hunting,

natural

9
and scenic areas,

and winter

sports areas.

Much of this

potential is due to the forest land base, over 68,000 acres
of which is publicly owned.

There is another 8,500 acres

of publicly owned recreation land in the county,

most of

which is forested.
Other
potential
graphy,

features
for

of

Emmet

recreational

County

development

important
include

to this

the

topo­

the abundance of surface water and shoreline,

and

the accessibility of the county to the large population of
southern Michigan.
elevation over

The relatively significant variation in

much of the county provides

scenic values

uncharacterisitic to much of the state as well as valuable
downhill

skiing

sites.

Two

ski areas have already been

developed in the central part of the county.

Emmet County

has over 60 miles of Lake Michigan shoreline (see Figure 2)
and over 10,500 acres of inland surface water.

Availabil­

ity of quality surface water is considered a prime attrac­
tion for second home developments as it is for other types
of

outdoor

recreation.

Three

major

highways

provide

year-around access to Emmet County from southern Michigan.
U.S.

31 runs

Traverse
county.

Bay

from the southwest corner south of Little
then north along the eastern edge of the

Michigan 131 enters the county at the south then

runs north and northwest along the western shoreline of the
county.
county

Interstate 75 parallels the eastern border of the
just a few miles to the east in Cheboygan County.

This

combination

of

year-around

attractions

and

10
year-around accessibility to the market and the potential
for

expanding

recreational

development

coupled

with

the

likelihood of continued increasing demand for all of these
types of recreation suggest an opportunity for the solution
of some the past problems of the county economy.
Petoskey is the largest city in the county with a
population of over 6,000 (U.S. Dept, of Commerce,1982) and
is

the

major

Petoskey

and

commercial
Harbor

center

Springs

are

for

the

located

county.
in

the

Both

southern

portion of the county on Little Traverse Bay (see Figure 2)
and are important resort communities.

It has been esti­

mated that with the influx of tourists and seasonal home
occupants

the population

during the summer months.
of the county,
peninsula

of the Petoskey

area triples

Mackinaw City at the north end

and at the very northern tip of the lower

of Michigan,

is

the

southern

terminus

Mackinaw Bridge that joins upper and lower Michigan.
towns

and prominent

features

for

the

Other

that will be referred to

throughout the following discussion are also identified on
the map of Figure 1.

CHAPTER I.
This

chapter

RELATED LITERATURE

is not

intended

to be

an exhaustive

review of the literature related to land use modeling but
attempts to describe briefly the breadth of that literature
and

to

distinguish

and

describe

in more

detail

those

elements that are particularly relevant to the Emmet County
study.
A general class of models,
use models,

referred to here as land

is distinguished from other kinds of planning

models simply by the primary purpose of projecting land use
over space and time.
models,

due,

land use,

Implicitly,

if nothing else,

spatial,

and

the complexity of these

to the degree of economic,

temporal

disaggregation,

necessi­

tates solution by digital computer.
Beyond this simple delineation of the general class of
models of interest, several attributes that can vary widely
from model to model and are useful for further classifica­
tion can be identified.

Such attributes include, but are

not limited to, the theroretical basis for the model,

its

empirical basis, the type of region to which it is applied,
land uses that are emphasized, degree of disaggregation of
a number

of

land use),

factors

(e.g.

space,

and mathematical

time,

economic sectors,

techniques used

in modeling.

Urban Land Use Modeling
For this discussion one of the most important attri­
butes mentioned above is the type of region to which the
11

12
model

applies.

Since

the late

1 9 5 0 's a great deal of

effort has been devoted to the development of land use
models, but the vast majority of these would be considered
urban models, i.e. focussed on developed uses in and around
major urban areas.
Although

these urban models

may not be particularly

useful for the purposes of this study, e.g. in developing
a land use model

for a rural area such as Emmet County,

there is a great deal to be learned from the overall urban
land

use modeling

experience

of the last two decades.

Fortunately,

in recent years there have been a number of

attempts

criticize,

to

quantify this

evaluate,

experience,

synthesize,

and

even

and these examinations are very

pertinent to this study.
Probably the two most well known of the urban land use
models are EMPIRIC
1971).

(Hill,

1965) and PLUM (Goldner, et al,

Both of these models have had wide application to

areas beyond those

for which they were originally devel­

oped.

for

EMPIRIC was

originally developed

the

area.

Boston

The

population and employment

model

in the

mid-sixties

allocates

forecasts among zones in the

region through a system of equations.

There are a number

of residential and employment categories

(activities) each

represented by an equation with transportation,
and current activities
which

vary between

exogenous

utilities

levels as independent variables

zones.

These

initial allocations are

13
adjusted to meet policy constraints on activity levels by
zones and then are translated into area by land use by zone
according to available land and allowable densities (Brand,
et

al,

1967).

EMPIRIC has

subsequently been applied

in

Atlanta, Philadelphia, and several other areas (Pack, 1978,
p. 33).
Originally developed for the San Francisco Bay Area in
the sixties,

PLUM

(Planning and Land Use Model) has also

subsequently been applied to a number of regions.
to

but

distinct

from

EMPRIC,

PLUM

allocates

Similar

exogenously

forecasted basic employment to residential zones based on
travel times from those zones to exogenously located places
of work.

This basic employment by zone is then used to

derive nonbasic employment and corresponding land use.
Both
than

EMIPRIC

and

PLUM

optimization models,

are

so past

finding efficient or optimal
appropriately criticized

simulation

models

statements

rather

about

their

land use patterns have been

(Pack, p.

31).

The Southeast

Wisconsin Region Planning Commission (SEWRPC) Land Use Plan
Design model (Schlager, 1965) was a well known urban region
land

use modeling

techniques,

i.e.

effort

that

did employ optimization

linear programs,

consideration here.

and so warrants

some

This model is described as a compre­

hensive urban plan design model, whose output is a land use
plan

that meets

development

constraints

for area by land

use (again totals are derived exogenously) while minimizing
development, operating,

and maintenance costs.

This model

14
development effort was viewed as research by the SEWRPC and
was

considered

to have

real world application.

achieved

very

limited

success

in

Yet, the general concept is still

considered valid and promising and continues to be re ­
searched.

For example Hopkins and Los (1979), Los (1978),

and Hopkins

(1977) have proposed even more complex and

realistic formulations of the land use plan design problem
and also present algorithms for solving it that avoid some
of

the major

problems

encountered

in the

SEWRPC

effort.

Evaluations of Urban Land use Modeling
Perhaps

more

important

to

County study than the history,

the

purpose

of

classification,

the

Emmet

or details

of the various land use models that have been developed is
a growing body of literature that attempts to evaluate the
land use modeling experience.

In response to the flurry of

activity in land use modeling in the 1960's, by the early
1970's
to

independent assessments of that activity had begun

emerge.

these

The apparent similarity

evaluations

is that

among

almost

all of

they are much more negative

(realistic?) about the capabilities and state-of-the-art of
land use modeling than were the proposals for and progress
reports on those modeling efforts.

There is, however,

a

range in degree of negativism and a variety of reasons for
those

negative

assessments

that

are

worth

examining.

One of the most well known and perhaps the most neg­
ative of the available evaluations of land use modeling is

15
Douglas B.

Lee's

"Requiem for Large-Scale Models"

Lee paints a picture of essentially total
urban modeling efforts.

(1973).

failure of the

According to Lee the modeling

movement had virtually died by the end of the 1960's, but
his requiem was necessary as a warning to those who, having
not learned the lesson of the sixties, were trying to raise
it from the dead.

Lee's stated purpose was to

"...evaluate in some detail the fundamental
flaws in attempts to construct and use large
models and to examine the planning context in
which the models, like dinosaurs, collapsed
rather than evolved.
The conclusions can be
summarized...
1.
In general, none of the goals
h e l d out for l a r g e - s c a l e m o d e l s h a v e b e e n
achieved, and there is little reason to expect
anything different in the future.
2.
For each
objective offered as a reason for building a
model, there is either a better way of achieving
the objective (more information at less cost) or
a better objective..."(Lee, p. 163)
Actually,
criticisms

of

Lee

makes

a number

of valid,

pertinent

land use modeling and modeling in general,

but his arguments would probably have been more effective
if his

tone had been less cynical.

For example he dis­

misses positive prospects due to increasing computational
efficiency with "There is no basis for this belief; bigger
computers
One has

simply
the

permit

bigger

mistakes"

(Lee,

p.

169).

feeling that no matter what may have been

accomplished in any of these efforts they would have been
pronounced rightfully dead simply because in Lee's view big
models are inherently bad.
A second important critique of urban modeling is Garry
Brewer's

Politicians,

Bureaucrats,

and the Consultant - A

16
Critique of Urban Problem Solving (1973).

Brewer uses the

San Francisco and Pittsburgh Community Renewal Program
modeling

experiences

as

case

studies

around which he

centers his discussion of the problems of and possibilities
for land use simulations.

He considers many of the prob­

lems that Lee mentions, but for Brewer, rather than cause
for despair,
necessity,

it is at least an open question,

that

these problems

"...promising

technique

complexity..."

can be

for

be

overcome

meeting

if not a

so that

the

this

challenge

effectively employed.

of

In Brewer's

view "Policy-makers must integrate their intuitive hunches
with

the practical

theories,

sights of specialists
theories

about

practitioner

the

and

models,

and descriptive

in such a way that the setting and

setting

specialist

are made
alike.

understandable to
Computer

models have that integrative capacity...."
Perhaps

the

most

comprehensive

simulation

(Brewer, p. 3).

evaluation

land use modeling to date is Urban Models;

of

to

"...investigate

model use by planning agencies;
models

are being

finally

used and the

(1)

urban

Diffusion and

Policy Application by Janet Rothenberg Pack (1978).
stated purpose was

in­

the

Pack's

extent

of

(2) the ways in which the
influences

they have?

and

(3) why some agencies adopted and used the models

and others

did not"

(Pack,

included two approaches:

p.

11).

The

investigation

extensive mail surveys of plann­

ing agencies and intensive case studies of several of the
regional

planning

agencies

that responded

to the mail

17
survey.

The mail

survey allowed wide

coverage,

while

the subsequent case studies permitted careful consideration
and clarification of specific questions,

aiding in the

interpretation of the mail survey results.
In presenting the results of these investigations Pack
also

includes

modeling,

a helpful historical

including

a

discussion

overview

of

of

federal

land use

legislation

and the associated political and institutional atmosphere
that

encouraged

interest

in and

development

of

land use

models.
Although problems

with

the

modeling

efforts

of

the

early 1960's and a reevaluation period in the late 1960's
are acknowledged,

Pack does not see the extreme cycle of

death and threatened rebirth that Lee described:
"The picture presented is one of widespread
failure in model development itself, or where
model development succeeded, of very limited
application....
As a result of these failures
model development has been alleged to have ’died'
in the mid-to-late 1960' s.. .
Even as these
assertions were being made in the early 1970's
there was a substantial amount of model develop­
ment in planning agencies, particularly regional
planning agencies." (Pack, p. 1,2)
Also

included

land use models.

is

a discussion

of potential

uses

of

A recurring theme in these evaluations of

modeling is the divergence between current capability and
expected or claimed uses and benefits of models.

Pack

reviews

pre­

this

ongoing

discussion

in preparation

for

senting the results of the surveys with respect to actual
versus

expected

uses

and

usefulness

and

implications

of

18
these for model adoption.

Pack is realistic about present

and past shortcomings:
"...it is not difficult to show that models have
often been oversold, little understood, and the
difficulties of their development underestimated,
with the result that many persons believe...that
they can be applied to the planning process in
ways which were and still remain well beyond the
state-of-the-art.
It is not surprising that the
reaction was harsh when unrealistic expectations
were measured against subsequent performance."
(Pack, p. 17)
The results of the mail survey were somewhat surpris­
ing given the bleak picture of failure and disillusionment
presented by some critics.
responded,

25

percent

Of the planning agencies that

were

either

currently

using

or

developing planning models and another 12 percent were at
the time considering the use of such models.

Planning

models in this context include several different types of
models, e.g. land use, transportation, population, and many
model using agencies used more than one type of model,
but

two-thirds

of these

using

agencies

land use models were among those in use.
currently
were

using

models,

53

percent

indicated that

Gf those agencies

indicated

the

models

"very useful," while only one percent said they were

"not useful."

To a related question 51 percent responded

that their models were "more useful than available alter­
natives"
useful

as

while

only two percent

alternatives.

said they were

Pack presents

not as

responses

to a

number of other questions and, of course, considers all of
these results

in much more detail than is appropriate to

19
include

here.

responses
in-house

to

Many

interesting

different

model

questions

development with

correlations
are

between

identified,

assessed

e.g.

usefulness,

and

some tentative explantations of what all these numbers
really mean are offered (Pack, pp. 55-89).
The subsequent case studies of several of the larger
regional
survey

planning

largely

agencies

confirmed,

results of that survey.

that

responded

clarified

and

Pack concludes

to the mail
extended

the

"our case studies

of model use are striking for their indication that land
use models are being successfully developed and incor­
porated

into the

agencies...,"
positive since
the models
types

anlaytical

but by

work

no means

of regional planning

considers

this

entirely

"still there are substantial problems with

themselves

of analyses

and with their suitability for the

in which they are employed."

(Pack,

p. 118)
Rural Regional Land Use Models
A class of rural land use models can be distinguished
from the

urban regional

models

considered

above.

These

models may still be largely concerned with developed uses,
e.g.

residential,

emphasis
uses

on how

commerical,

these

uses

and industrial,

interact

with

less

but with
intensive

in and around communities within predominantly rural

regions.

Characteristically,

these

models

explicitly

recognize inherent capabilities or resources of parcels of

20
land and major natural features of the region as indepen­
dent
to

variables

the

i.e.

usual

impacting

land

independent

transportation

use

variables

networks

and

decisions
of

the

in addition

urban models,

current

uses.

Some

models that can be included in this category focus exclu­
sively on these natural resources and capabilities and
their

associated

nonintensive

uses.

In

contrast

to

the

urban land use models, the rural regional models should be
more directly applicable to the model development goals of
the

Emmet

County

study.

Fortunately,

as with

the

urban

models, there is some recent literature that examines some
of these rural regional modeling efforts,

from which there

is much to be learned.
The regional project of which this
and

the

regional

study was a part

land use modeling work of Miley

(1977)

and its relationship to this study were mentioned in the
previous chapter and will be discussed in more detail in
the

following

chapter

and

so

will

not

be

considered

here.
The
and

Land

applied

(1975),

use

to

Model

for

Planning

(LUMP),

1100 square miles of Ontario by Nautiyal

is of interest because of its use of mathematical

programming.

According to the author

"Given the capabil­

ity of each section or parcel of land,
of population,
ducts,

formulated

the communication patterns, prices for pro­

transportation costs,

the model

the concentration

develops

economies of scale,

an optimal allocation of

etc.,

land parcels

21
to various uses by maximizing net benefit."
on

to

describe

the

LUMP mixed

integer

considers each parcel homogenous
explicitly considers capability,
use

for

each

implement
This

parcel.

linearized

formulation yielded

programming problem

an

in its attributes and

variables

cost

and

extremely

(190571 variables

635 parcel region.

formulation which

cost, and value for each

Integer

nonlinear

Nautiyal goes

are

value

large

used

to

functions.

mathematical

initially)

for this

A subsequent version of LUMP elim­

inated the integer variables and greatly reduced the size
of the problem and time and cost for solving it.
The

most

significant

effort

in regional

land use

modeling during the 1970's was the Regional Environmental
Systems Analysis
Science

Program

Foundation

at

(O R N L ) from 1971 to
documented

(RESA)

the
1975.

in numerous

comprehensive scope.

Oak

sponsored by the National
Ridge

National

Laboratory

This program has been well

ORNL publications

that reveal

its

The program dealt in depth not only

with land use modeling but with related areas of study
such

as

political

computerized

geographic

interactions

socioeconomic analysis,

in

information

regional

systems,

systems,
regional

and ecological impacts of land

use:
"The purpose of the program has been to develop
and communicate to the planning and management
community an improved basis for forecasting the
environmental impacts of public and private
decisions (such as land use)....
The research
strategy was to develop and validate a hierarchy

22
of computer models to assist in the analysis of
relevant economic, physical, ecological, and
social processes..." (Craven, 1977, p. v)
The
is

land use model developed under the RESA program

described

Voelker

in A

(1976).

Cell-Based

It was

Land-Use

Model

a simulation model

by A.

H.

for project­

ing future land use for a rural region of Eastern Tennes­
see.

The

model

allocated

land uses

to

40 acre

cells

stochastically on the basis of relative attractiveness of
a cell for a use.
was

based

attributes

on

Attractiveness of a parcel

a combination

of

indices

of that parcel that were

for a use

reflecting

considered

to the site selection decision for that use.

the

important
The sto­

chastic allocation mechanism allowed for the realistic
possibility of some sites with lower attractiveness being
selected prior to sites with higher attractiveness.
areas by

use

Total

to be distributed among the parcels within

the region were based

on exogenous

forecasts of economic

and population growth.
Voelker

acknowledged that a large part of the model

development effort centered around the construction of
indices

to describe

subsequent

individual

attractiveness

attributes

indices

based on

the indices for individual attributes.
cation by

Voelker,

Indices,

Spatial Data Bases

(1976),

building process.

The

often nominal data,

A

of parcels

Technique

composites

and
of

A separate publi­
for

Using

Large

considers in detail this index

challenge of converting raw data,

to ratio scale indices with a common

23
scale or one that can be used in composite equations or in
the

£inal

model

is

discussed.

Voelker

frankly admits

that
"In the best situation, an accepted theory exists
which describes the process well enough to allow
it to be quantified.
Short of this, it may be
necessary to hypothesize relationships in order
to complete an analysis.
Indices in this case
must arise from the mind of the index developer,
conforming to his intuition and tacit understand­
ing of the process being modeled." (Voelker,
Indices..., p. 3)
Numerous

examples

of

index

development

and associated

problems for a number of specific attributes are presented.
As with the urban land use models,

there is perhaps

more to be learned from the critical evaluations of rural
land use modeling efforts than from the models themselves.
Several

publications

from

the

RESA

program provide

evaluations of that particular effort.

such

Some of the points

made in these critiques echo those of the urban land use
modeling efforts.
In Some Pitfalls

of Land Use Model Building Voelker

(1975) claims that there had been a lack of documentation
of and openness about the real problems of land use mod­
eling within the modeling community.
and

comments

Through this paper

in other ORNL publications Voelker

attempts

to avoid this deficiency for the RESA experience.

Voelker

distinguishes
ceptual,
limited

two

types

encountered
the

utility

of

problems,

technical

in the

RESA modeling

of

models.

the

effort that

Major

problems included gaps in land use theory,

and per­

technical

failures in

24
quantifying important variables and relationships,
underestimating
Perceptual

time

problems

and
refer

costs

data

to barriers

planners and decision makers.
this problem,

of

and

acquisition.

to model

use by

There are many aspects to

including the modelers'

lack of understand­

ing of the real world of planning and subsequent unreal­
istic expectations for model adoption by planners, differ­
ing goals for models between planners and modelers, reluc­
tance of planners to adopt or try new tools,
istic

expectations

makers.

of

model

capabilities

and unreal­
by

decision

CHAPTER II.

THE MODEL

The primary purpose of this study was to build upon
the model suggested by Miley to develop a land use projec­
tion simulation model.

The basic concept proposed by Miley

was retained, that is, the model employs a linear program­
ming input-output model to estimate sectoral total outputs
in response to projected levels of final demand, subject to
resource constraints,

and to arrive at rents for various

uses of various parcels of land.

This chapter discusses

some alternative large scale linear programming land use
models based on this concept.

The discussion then turns

to a land use projection simulation model centered around
an allocation mechanism which was largely inspired by
these

large scale linear programming formulations but

which

relies

programming

directly

model.

on

a

Reasons

small,
for

aggregated

diverging

linear

from Miley's

original proposal are also discussed.
Input-Output and Linear Programming
Before discussing
appropriate

to

briefly

the land use model itself,
review

the

two

general

it is

economic

models, input-output analysis and linear programming, which
have already been mentioned as essential components of the
land use model.
Consider momentarily a simplified overview of the
Emmet

County

economy.

The

25

economy

is

comprised

of

26
individuals, firms, and institutions interacting within the
county and with similar entities outside of the county
through exchange of goods,

services,

and money.

Money

enters the economy primarily through sale of goods and
services produced within the county to sources outside the
county.

However,

all money

entering

the

county

economy

does not remain in the county, because goods and services
produced outside the county are purchased by sources in the
county.

For any period of time income to or net production

by the county economy depends on the amount and mix of
products produced
amount

and consumed within

the

county,

the

and mix of products produced in the county but

purchased by

outside

sources,

and the amount and mix

products imported to the county.

of

The level of income to

the county can change over time because of changes in any
of these

factors,

independent.

and obviously these categories are not

A change in exports will

likely lead to

changes in the amount and mix of products exchanged within
the county and to changes in imports.

A change in the

structure of interactions within the economy,

for example

the establishment of a new industry, can lead to changes in
the amount and mix of imports and exports.
Input-output accounts provide a means
the relationships between sectors
firms,

and

institutions)

for describing

(groups of individuals,

within the regional

economy and

the relationships between the regional economy and the eco­
nomies

of

other

regions

through

exports

and

imports.

27
Over the last twenty-five years input-output analysis
has

become

an important tool of regional economics,

and

there is a vast literature describing input-output theory
and its countless applications.

It is unnecessary here to

consider in detail the history or theory of input-output
analysis, but the reader is referred to Richardson (1972)
for a concise, comprehensive, objective overview of inputoutput

analysis

and

associated

issues

in

regional

eco­

nomics .
Having divided the economy into a number of sectors,
some of which are designated endogenous while the rest are
considered

exogenous,

an

input-output

model

depicts

the

economy as interactions among those sectors through linear
production

functions.

The

total

output of a sector

is

expressed as the sum of its sales to all endogenous and
exogenous sectors in the economy,

conversely total outlay

for a sector is the sum of its purchases from all sectors
in the economy.

Usually, by convention total output equals

total outlay for a sector, requiring balancing by capital
accounts included as exogenous demand and payment sectors.
These exchanges between sectors for a specified period of
time are typically expressed in common terms,
dollars,

in the transactions table.

chases by sector

Let tij be the pur­

j from sector i and xj be total outlay

which is equal to total output for sector j.
this

discussion

such as

nonsubscripted

represent vectors,

lower

case

(Throughout
letters

will

nonsubscripted upper case letters will

28
represent

matrices,

lower

case

letters with

scripts indicate elements of matrices.)
is

divided

into

an

endogenous

or

double

sub­

Assume the economy

processing

sectors,

m

exogenous or final demand sectors, and k exogenous or final
payments sectors, then:
n+m
xi =

2

t;i3

j=l
and
n+k
xj =
i=l
When used in forecasting or impact analysis a matrix
of

direct

effects

or

technical

designated the A matrix,

coefficients,

typically

is computed from these transac­

tions and total outlays for the endogenous sectors.

The

element a^j of A is the ratio of purchases by sector

j

from sector i to total outlay of sector j

aij = fcij/x j
The intermediate product, p^,
output

that

is used

for sector i, i.e. the

in production by endogenous sectors

rather than going to final demand, is defined by:
n

j=l

29
but may also be found by:
p = Ax
If f is the vector of total final demand, i.e.
n+m
fi =
j=n+l
then:
x = p+f = Ax + f
f = x - Ax
f =

(I-A)x

where I is, of course, an n x n identity matrix.
Since A reflects the portion of total output which is
required as

inputs to the endogenous sectors,

(I-A) can

conversely be thought of as indicating portions of total
output from the various sectors which are not required as
inputs by endogenous sectors and are therefore available
for final demand.
For

impact analysis

that multiplying both

using

sides

input-output
of

it is noted

the above equation by

(I—A)-* yields:
x = (I-A)-1f
With this equation a projected level of or change in final
demand can be translated into an expected level of or
change in total output.
There

are

several

fundamental

assumptions

on which

input-output analysis is based and which are necessary for
solution of the system of equations and for practical

30
implementation of the technique.

These include such tenets

as the

linearity

and additivity of the production func­

tions.

One important assumption which is made in conven­

tional static input-output analysis but which is unaccept­
able for the purpose of land use modeling is the assumption
of unlimited or perfectly elastic supply of resources
required as inputs by the various sectors.
Every sector in the regional economy is to some degree
directly dependent on the land and resources of the region,
if for nothing other than space for facilities.

Of course,

economic activities vary widely with respect to their
degree
great

of

dependence

attractions

of

on natural resources.
input-output

One of the

analysis

for

land use

modeling is that its flexibility with respect to sectorization allows distinction of activities according to their
dependence on various resources.

Conventional input-output

analysis with its assumption of nonconstraining resources
ignores this dependence of sectors on the resources, but by
expanding an input-output model into a linear programming
model by

adding an objective

function and resource con­

straints, both the relationships between the sectors in the
economy
regional

and

the

relationship between

resources

as well

as

the

the

limits

sectors

and

to the avail­

ability of these resources can be accounted for.

31
The

general

linear programming problem with n con­

straints and m activities can be depicted as follows:
maximize z = cx
subject to:
Bx

b

x > 0
where z is the scalar value resulting from multiplying the
lxm vector of objective coefficients c, by the mxl solution
vector, x.

B is the nxm matrix of constraint coefficients

with each row expressing the relationship between the
activities

and a limiting resource,

the availability of

which is indicated by the corresponding element of the nxl
right-hand-side vector b.

Stated verbally, the problem is

to find the vector x which maximizes the linear objective
function, cx, while satisfying the linear equations Bx>b.
By letting:
(I-A)
B =
-R
and
f
b =
-r
where (I-A) is the nxm Leontief matrix from an input-output
analysis,

and

R

is an nxm matrix of coefficients which

relate sectoral resources use to sectoral gross outputs for

32
m sectors and n resources.

Then the linear programming

problem,
maximize:

z

=cx

subject to:
Bx ^ b
x _> 0
where cx is some regional objective function, incorporates
both

intersectoral

quirements

of

production relationships

economic

sectors

for

and the

regional

re­

resources.

An important result of linear programming theory is
that corresponding to the above problem, called the primal,
there is a dual problem of the form:
minimize:

w

=b'p

B'p

<_ c'

subject to:

p _> 0
The elements of p, the solution vector for the dual pro­
blem, are shadow prices for the primal.

That is, the ith

element of p is the marginal contribution to the value of
the objective function of one additional unit of the ith
element of b.
function

these

Given the appropriate context and objective
shadow prices may be

viewed

as

economic

rents accruing to the corresponding resource or input in
the primal problem.

Only if a resource is completely

exhausted in the solution to the primal,
sponding

constraint

is

binding,

will

price or rent be associated with it.

i.e. the corre­

a positive

shadow

33
Linear programming theory and the algorithms to solve
such problems emerged during the 1940's.

Today all but the

such problems emerged during the 1940's.

Today all but the

most trivial problems are solved using digital computers.
Modern

linear programming software packages allow the

solution of problems with thousands of contraints and tens
of thousands of variables.

It is this great capacity which

makes possible the consideration of geospecific land use
linear programming models.

Rather than having merely one

constraint for each category of resource required by the
economy, as has been done with input-output linear program­
ming models for many years,

a separate constraint can be

used for each of hundreds of specific parcels of land in
the region to be modeled.

Recognition of this possibility

was the basis for the mixed integer programming land use
model considered by Miley.
A Mixed Integer Programming Land Use Model
The model suggested by Miley (1977) was based on the
contrained input-output model presented above, but with an
additional constraint for each parcel in the region so that
the model allocates different uses (use being the economic
sector

in

Additional

this

model)

to

spatially

referenced parcels.

constraints and solution with a mixed integer

programming algorithm assured the assignment of each parcel
to one and only one use.

34
For an economy with m endogenous sectors and n parcels
such a model would have m+nm variables and m+m+n c on­
straints.

As in the above constrained input-output model,

m constraints relate gross output through the (I-A) matrix
to final demands, and the next m contraints equate acreage
allocated to acreage required for each use for given levels
of

gross

output.

These

m

contraints

have

the

qiXf - biiPii rii*..- hi jPi jr^ j...-binpinrj_n

<_

form:
0

Where qi is a coefficient expressing the acreage require­
ments

in acres per

dollar.

The coefficient bij can be

thought of as the acreage in parcel j and is multiplied by
a coefficient, Pij, which reflects productivity of parcel
j for use by sector

i relative to some standard produc­

tivity on which qi is based.

An additional n constraints

of the form:

m
^

rij

£

1 for j=l,

....,n

i=l
assure that total acreage allocated from each parcel does
not exceed the acreage available from the parcel.
The solution vector is comprised of m gross output,
Xi,

elements and mn rij elements.

Given the above con­

straints this rij is the proportion of the area of parcel
j which

is allocated to use i.

In a standard linear

programming problem this rij could range from 0 to 1,
but the mixed integer algorithm allows

specifying all

35
r^j as binary,
this

i.e.

condition,

equal to either zero or one.

Under

to satisfy the above constraints no more

than one of a sequence of m elements with j constant may
be nonzero.

This means that any one parcel is allocated

entirely to one and only one use, although that "one" use
might actually reflect a fixed mix of uses.
The motivation for using integer programming was the
resulting availability of a shadow price or rent for the
sector

to which

the parcel was

allocated and the avail­

ability of opportunity costs associated with the parcel for
all other sectors.

These values are standard outputs from

modern linear programming packages.

With the shadow price

and opportunity costs the potential marginal contribution
to the objective function for any use of a given parcel is
known.

Without the integer stipulation a parcel could be

allocated to several uses so that the resulting shadow
price would not apply to any individual use but only to the
combination of uses associated with the parcel in a par­
ticular solution.
It

was

shadow prices

suggested

that

the rents

implied by

these

and opportunity costs enter an equation of

the form:

In this equation Vj^ is the periodic rent to use i, t is

36
the number of periods from the present, d is the discount
rate,

c^j is the cost of converting from use j to use i

on the given parcel.

The equation yields g, the discounted

net value over n periods obtainable by shifting from use j
to use i on the parcel under consideration.

It is hypo­

thesized that the probabilities that such use shifts will
occur

are

positively

accumulated

discounted

rent differentials
abilities,

correlated with
rent

these

differentials.

potential

Given

these

and their relationship to shift prob­

a matrix of probabilities of shifts from all

uses to all other uses for each parcel can be obtained.
Employing

Monte Carlo methods

shift probability matrices,

in conjunction with

various

possible

these

future

re­

gional land use patterns can be generated.
Problems With the Integer Programming Model
Several problems with the suggested mixed integer pro­
gramming

land

use

model have been recognized.

Some of

these problems derive from the requirement that each parcel
be allocated entirely to one use or a fixed mix of uses,
while others are related to certain details in formulation
and interpretation.

These types of problems can be alle­

viated to some extent by some alternative formulations of
the linear programming problem.

Still there are certain

inadequacies inherent in any linear programming model for
detailed

land use projections.

inadequacies

led to the

Recognition

of these

suggestion for using the linear

37
program only

indirectly to derive rent differentials

shift probabilities.

and

These inadequacies are acknowledged,

but the particular remedy that has been suggested also
presents a number of problems.
Unless

very

small parcels

are used,

the requirement

that one parcel be devoted entirely to one use can result
in

distorted,

unreasonable

land

use

patterns

with the solution to the linear programs.

associated

At first glance

it would seem that this would not be a serious problem as
long as the desired outputs

from the linear program were

only the rents and not the actual allocation of parcels to
uses. The problem is seen as more serious, however, when it
is recognized that such distorted allocations of land may
be accompanied by unreasonable gross outputs in the solu­
tion

and

a

distorted

total

objective

function

value,

resulting in inappropriate rents.
Another serious problem with the proposed formulation
stems

from the desire to obtain a rent for each use

each parcel,

which necessitates

final demand inequalities.

for

greater than or equal to

A positive shadow price is

obtained only for those constraints that are binding on the
solution.

Since a positive objective function coefficient

is generally associated with each gross output variable and
output available for final demand is positively correlated
with

gross

output,

the greater

than or equal

to final

demand constraints assure that every parcel will be totally
allocated

to a use

and will

therefore have associated

38
positive rents.
and allocation

Of course the gross outputs, final demands
of parcels to uses resulting

from such a

model may bear little resemblance to reality, since in most
regions where such a model would be applied the levels of
gross

outputs

and

final demands

for most sectors

at the

present time are constrained more by available markets than
by exhaustion

of

suitable land and resources.

One must

realize then that the rents resulting from such a model are
no more valid for the near future than are the levels of
gross outputs and final demands.
Further questions regarding the applicability of these
rents arise from the nature of the objective function and
with regard to suggestions for determining values for its
coefficients.

This problem applies to the standard linear

programming land use model as well as to the mixed integer
formulation.
tions has

The question regarding the objective

func­

two aspects which cannot be totally separated.

First., there is the question of what to maximize or mini­
mize.

It has been suggested that various regional objec­

tives,

for example maximizing regional output, employment,

or income,

would be appropriate for such a model.

there is a role for these types of objectives,
policy analysis,

While
e.g. in

they are probably not the appropriate

objectives for projection of likely future land use under a
capitalistic
a normative

economy.
mode,

then

If the model
these

is

regional

to be

used

objectives

in
are

entirely appropriate and the resulting rents will reflect

39
societal values rather than surplus value to the individual
land owner, but if the model is used in a predictive mode
for a decentralized economy,

then the objective

function

should be some reflection of surplus value to the land
owner,

e.g.

excess profits,

conforming to the concept of

land rent (Barlowe, 1972, pp. 157-159).
It is doubtful that individuals or firms in their
decisions to buy, sell, or convert use on specific parcels
are primarily motivated by the contribution of such deci­
sions to such regional objectives.

Rather,

it is assumed

that such decisions are largely motivated by the desire of
the

individual

or

firm to maximize

its own net returns.

This brings up the second aspect of the objective function
problem, for even if sectoral profit rate coefficients were
used in the objective function, the rents derived from such
a function would be averages

over the

sector,

and the

resulting rent differentials would not necessarily apply to
any one owner or parcel.

The optimal solution for the

linear program is optimal for the system as a whole but is
not

necessarily

optimal

from the

perspective

of

any

one

sector or any one entity within a sector.
A

final

problem with

the proposed model

deserves

attention before considering some alternative formulations
intended to alleviate some of these problems.

Again this

problem applies to the standard linear program as well as
to the mixed integer formulation.
incapacity

of the proposed

The problem concerns the

formulation to generate rents

40
which

adequately

reflect

certain

differences

between

parcels in profit potential.
The vehicle for distinguishing relative profitability
between parcels is a productivity coefficient which can be
employed directly as a coefficient in the linear program,
or,

as in the preceding description, may be multiplied by

acres

in the parcel to yield the coefficient.

The pro­

ductivity coefficient ranges from 0 to 1, and indicates a
parcel's productivity for each use relative to some stand­
ard or ideal parcel for that use.
This
relative
others

approach

is quite adequate for some types of

productivity
it

is

totally

or

profitability

inadequate.

effects,

The

probably best be explained by example.

but

for

difference

can

Consider the case

of the effect of soil fertility on the production of some
crop.

For a given input mix the output or profit from a

parcel that is less fertile than the ideal parcel could be
approximated as a proportion of the ideal input or profit.
An inherent property of the parcel,

irrespective of loca­

tion or demand, results in lower output and profit per acre
relative to the standard.
can be

The effect of lower fertility

offset by bringing more

Consider
establishment

acres

into production.

on the other hand the case of the retail
located on an isolated back road.

The per

acre output and profit for land allocated to this use on
this parcel would likely be substantially less than for the
same use in an ideal location,

say a city center.

The

41
reduced

output

and

profit,

however,

is

due

to

market

limitations associated with location rather than to supply
effects
case

from inherent properties of the parcel.

increasing

the

In this

acreage devoted to this use at this

location would not increase total output or profit.
A zero to one productivity coefficient employed as a
constraint coefficient in the land use linear programming
model would account
adequately

account

distinguishing
tions,

for the

for the

between

second

supply

the model would

first case.

It would not

situation,

effects

and

because

demand

not

limita­

attempt to offset reduced p r o ­

ductivity in the isolated parcel by simply allocating more
land to the use.
The

two

cases

can

be

considered

in

terms

of

the

differences in theories of rent as developed by Ricardo and
von

Thnen.

constraints

Zero

to

one

coefficients

productivity

coefficients

as

adequately reflect the Ricardian

rents but may result in distorted allocations and levels of
output if used in an attempt to account for Thunien rents.
A

solution

to

this

problem will

be

considered

in the

following sections.
Alternative Large Scale Linear Programming Models
Minor modifications to the proposed mixed integer pro­
gramming

land

limitations

use

model

mentioned

can

above.

alleviate

several

of

Such a revised model

the
is

42
presented in Figure 2, where a large scale linear program­
ming

problem

is

depicted

in

explicit

matrix

notation

as comprised of a number of matrix and vector components.
In

Figure

sectors,
the

2 m is the number

of endogenous

economic

k is the number of land use categories and n is

number

of parcels.

OBJ

is the objective

function

vector and the I-A matrix is from the input-output analy­
sis, as discussed above.

GO is the gross output component

of the solution vector and multiplying OBJ yields the value
of the objective function,

scalar z.

The ACPIUJ solution

vector represents the acres of each parcel i allocated to
each use

j.

The PARSUM matrix simply assures that the

acres allocated to various uses from a given parcel do not
exceed the total acres of that parcel as indicated in the
ACRES right-hand-side vector.
This

formulation

features

from above and below
feature,
ment,

final

demands

constrained

(FDN and FDO in Figure

2).

This

coupled with abandonment of the integer require­

results

resource

in

reasonable

requirements.

The

levels
upper

of

gross

outputs

constraint

on

and

final

demands is intended to reflect the constraints imposed on
all sectors by limited exogenous markets, while the lower
constraint on final demand reflects some expected degree of
stability

in

the

distribution

of

sectoral

outputs

to

historical markets.
Relaxing the

integer

stipulation that a parcel be

devoted entirely to one and only one use can result in

m

[

OBJ

0

I

l x m .
a

■

•

m

4

ft

m

mx(k*n)

mxl

■

a

i

-

•

P

4

•

4

FDO

I - A
0
mx(k*n)
*

•

m

«

•

Pn

ALURQ

P21
P12
0

kxm
4
4

*

•

p
1 1 1 .

0

0
*

P23

•

mxl

.

nxm
4

■

kx(k*n)

kxl
■

m

9

4

0
ACRES

1 1 1 .
0

PARSUN
£

•

nx(k*n)

nxl
«

•

(k*n)xl
*

Figure 2.

4

• • • pi j • • •

•

*

m

P

0

«
.

•

*

PROCO

p22
P13

---

9

ACPIUJ

>

mxm
»

ft

■

FDN

mxl

0

inxin

t

z

GO
«

I - A

•

=

lx(k*n)

•

m

An Alternative Land Use Linear Program Formulation

4

«

44
different portions of a single parcel being allocated to
different uses.

The result is more reasonable distribu­

tions of uses over space and avoidance of irregularities in
total allocations of land to uses.
Another feature of this formulation is the allocation
of land to use categories rather than to specific economic
sectors.

The requirements of each sector for land in each

use category are expressed by the ALURQ matrix in Figure
2.

A

single economic

different use

sector may

categories,

employ land in several

and conversely land in any one

use category may be required by several different sectors.
A major advantage of this approach is that land use
categories

can

be

defined

to

closely

conform

to

the

categories that are typically used by planners and in land
use

regulations,

while

retaining

a

sectorization

scheme

which conforms to convention and to available information
sources.
This

feature also recognizes

the

fact that a single

sector or entity within a sector may require two or more
substantially different types of locations,
facilities.

resources,

or

For example a large resource based manufactur­

ing operation may require vast acreage to supply its basic
raw material while requiring land of substantially differ­
ent attributes for its processing plant,
another

location

with

company headquarters.

still

other

and perhaps even

properties

for

the

45
Finally,

this

feature enables

the model to realist­

ically reflect the various land requirements of the various
sectors, while minimizing the total number of land use
categories that must be distinguished.

As will become

apparent, this is an important factor in keeping the model
to a size that is practical and feasible to solve.
The

coefficients

in the

PROCO matrix

of

Figure

2

indicate the relative productivity of respective parcels as
inputs in the production process of respective uses.
use

categories

factors
this

such

as

coefficient

such

as

soil

fertility relate directly to yield

can

agriculture

range

from zero

or

to

forestry where

one,

reflecting

productivity relative to some ideally productive acre.
other

use

For

categories

where

gross

output

does

For

not relate

directly through the production process to some character­
istic of the land the coefficient would assume a value of
either zero or one, simply indicating whether the parcel is
or is not suitable for the use.

This distinction avoids

the problem of the model trying to offset demand limita­
tions with additional resource allocation, as was discussed
in the preceding
not

section.

adequately reflect

demand

relative

to the

This treatment,

however,

the reduced rents due
ideal

location or

to

similar

does

reduced
influ­

ences .
Reference was made above to the role and selection of
the

objective

function and associated problems.

solution does not avoid those problems.

This

If the model is to

46
be

used

either

shadow prices

directly or

apart

indirectly,

from the

resource

i.e.

using the

allocation

in the

solution, to project future land use patterns due to market
activity in a decentralized economy, then the coefficients
of the objective function should be some reflection of
profits or investment return in order to result in meaning­
ful shadow prices for this purpose.
Ideally the objective function coefficient would be a
proportion,
yield

the

which

when

multiplied

contribution

respective sector.

of

an

observable

this particular
land use

land

gross

output would

to profit

for

the

There is a problem of course in arriv­

ing at such coefficients
not

the

by

since contribution to profit is

entity.

The problem is compounded

in

formulation by the association of several

categories

with

a single economic

sector

therefore a single objective function coefficient.

and

Such a

condition may dictate erroneous relationships between the
imputed contributions of the different land use categories.
A slight modification to the I-A and ALURQ matrices of
Figure

2

can

elminate

this

particular

aspect

of

the

objective function problem.

Revised rows and columns for

I-A and ALURQ,

in Figure

as

indicated

objective coefficients,
with

each

required
sector.

sector
simply

i.

c^j,

3,

for each use

allow

j associated

Another matrix component,

to equate

distinct

output across uses

SOEQ,

is

for each

This formulation makes more practical the use of

empirically based coefficients,

e.g. coefficients based on

47

Sector 1

Sector 3
Sector 2
■>
1
. "*
C21 c23 c32 c34 c35 C37 ”

t

OBJ]

[

0

]

Sector
1-a^ 0

0

”a12 0
-a2i: : 1_a22 !
*a31 * ’
”a32 *

"a 13 0
“a23 !
1-a33 *

0
!
-

0

I - A

FDN

•

GO
I - A

FDO

Sector
o

i -i

{
Use
1
2

3
4
5

i -i
i
-i
i
-i

SOEQ

o.

<21

o
<

q32

<12

<13

6

7

<23

<J34

ALURQ

PROCO

q35
q16
q37

PARSUM

Figure 3.

Use Specific Objective Coefficients Land Use
Linear Program Formulation

t/>
LU
oc
o
<

48
assessed values or market prices of land,

capitalized and

translated into annual rates.
Miley recognized that the large scale linear program­
ming land use model itself could not serve adequately as a
projection device and so suggested a stochastic model whose
probabilities derive from the shadow prices from the linear
program.

The linear program solution is inadequate because

it cannot take

into account the existing distribution of

uses and the costs of converting from those uses.
alternative

linear

certainly do not

program

formulations

eliminate this problem,

suggested
in fact

The
above

in the

preceding discussion additional inadequacies are revealed,
e.g.

the

model

cannot

adequately

account

for

Thunien

rents.
The need for a mechanism beyond the large scale linear
program

is acknowledged,

stochastic

shift model

in

but
that

the

appropriateness

role

of a

is questionable

for

several reasons.
Since Miley

(1977)

did not expand on the suggestion

for a probabilistic shift process the following comments on
possible limitations of such a process rely on speculation
as to its exact form.
One potentially serious problem with such a process is
that if the probabilities of certain shifts on certain
parcels

are

considered

to

be

independent

probabilities,

then the land use allocations from any one run of the model
would not necessarily be,

in fact would more than

likely

49
not

be,

consistent

with

the

acreage

requirement

results

from the economic model from which the rents and therefore
the

probabilities

were

derived.

Spatial

disaggregation

with a relatively large number of relatively small parcels
could reduce the seriousness of but not eliminate this
problem.
As with any stochastic model, the use of such a shift
process would entail a large number of repetitions of any
one problem in order to begin to establish patterns of
expected future conditions and events.
outputs
economic

of

variables

resources,
probably

the model,

be

this
a

and

for example

levels of aggregate

identification

averaging over

reasonable,

With many of the

of

likely

a number

straightforward

limiting

of runs would
process.

For

one very important output, however, namely patterns of land
use

over

space,

the task may not be

so straightforward.

The question that must be faced is how one averages, over
multiple runs,

the different uses that occur on a parcel,

to arrive at expected patterns of use.
The
process
tions

as

most

obvious

problem with

is the derivation of

the

stochastic

shift

shift probability distribu­

functions of rent differentials

for the various

uses.
Given these doubts
chastic

allocation

about

device,

the practicality of a sto­

it was

decided

to attempt

develop and employ a deterministic shift process.

to
The

resulting model is described in the following section.

50
The Land Use Projection Model
The
in this

land use projection model developed and employed
study derives

depicted in Figure
led

to the

from the

linear programming

model

2 and from the same contention that

contemplation

of

a

stochastic

shift process.

That contention is

that the probability of a shift from one

use to another is

directly related to a rent differential

between the two uses on a given parcel.
step
use

further
shift

greater

is

than

This model goes a

in using the consequent relationship that a
expected,
fifty

i.e.

probability of the

percent,

if

the

rent

shift

is

differential

exceeds a certain

threshold. This model treats the process

as deterministic,

in that ifa use shift is expected and if

a need for such a

shift is dictated by the requirements of

the economic model then the

shift will occur.

Futhermore,

within the model such shifts are designated in order of the
magnitude of the rent differential.

The model treats the

process as deterministic not because it is denied that
there are relevant influences other than rent differential,
but

primarily because

it

is

felt

that running

of and

interpreting the

output

considerably more

practical. If a planner is to use such a

model routinely,

e.g.

from the deterministic version is

to answer

"what if" questions,

then

the numerous solutions that might be necessary to establish
patterns with a stochastic model would not be practical.
The overall structure of the model is depicted in the

51
flow chart of Figure 4.

An unconstrained input-output

model is solved for gross outputs given projected maximum
final demands for a period.

These gross outputs, the land

use requirements coefficients,

and the objective

function

coefficients can be used to compute area required in each
land use and standard rents for each use in the case where
availability of suitable land is not constraining.
The model then enters a shift possibilities phase in
which a file is created which lists all shifts from exist­
ing uses

on parcels

positive

rent

to other

differentials.

uses which would result in
In

computing

rent

differ­

entials, for constructing this file the relative productiv­
ity and suitability for each use on each parcel is taken
into consideration.
factor

for

Each parcel is assigned a suitability

each use which reflects various attributes of

the parcel on which attractiveness for the particular use
is dependent.
on a parcel

The combination of these factors for a use
is used to adjust the standard rent for that

use to obtain the rent for that use on the specific parcel.
Only a limited number of values are allowed for the suit­
ability

factor

for any use.

The

fact that there is a

limited number of factors, a limited number of uses, and a
finite

number

of parcels means

number of possible use shifts.
possibilities phase

that there

is a finite

The output from this shift

is a file in which each record

indi­

cates a rent differential for a shift between two uses and

CHECK SLACK USES FOR
ACRES SUITABLE FOR
UNSATISFIED
REQUIREMENTS
• SHIFT AS NEEDED

SHIFT BETWEEN USES
TO MEET ACREAGE
REQUIREMENTS

ACREAGE ^
REQUIREMENTS
SATISFIEO .

SOLVE l-Q/LP FOR
ADJUSTED RENTS
NITH NEW CONSTRAINTS

H A D FINAL
DEMANDS FOR
CURRENT PERIOO

'

CREATE NEU FILE OF
‘AVGRABLE USE-SHlFTS
YES

SORT
ENDOGENOUS INDEX
ADJUSTMENTS
SOLVE UNCONSTRAINED
1-0 MOOEL FOR ACRES
REQUIRED AND RENTS
BY USE

SOLVE CONSTRMNEO
l-O/LP MOOEL FOR
ACRES ACQDlttO
AMO RENTS BY USE
EXOGENOUS INOEI
ADJUSTMENTS

YES
C K A T E FILE OF
USE-SHIFTS
RESULTING IN
POSITIVE RENT
DIFFERENTIALS

SORT USE-SHJFT FILE
BT MAGNITUDE OF
RENT DIFFERENTIAL

YES
LAST PERIOD

ABSOLUTE DEFICIENCY
• SOLVE I-O/LP FOR
CROSS OUTPUTS
FINAL OCHANDS NITH
ACREAGE CONSTRAINTS

INCREMENT TO NEXT
T 1 K PERIOD

STOP

Figure 4. Land Use Projection Model Flow Chart

53
also indicates all parcels which would yield that pa r ­
ticular differential for that shift.
The file produced by the shift possibilities phase is
then sorted according to rent differentials in descending
order.

This

ordered

file and the acreage requirements by

uses from the solution of the input-output model become the
primary inputs to a shift phase.

In this phase the ordered

file of possible shifts is searched to find use shifts to
eliminate any differences between acreage requirements and
current

acreage

through the
are

allocations

for

all uses.

The search

file is repeated until all such deficiencies

eliminated

or

until

iterations is reached.

a specified maximum number of

If all acreage requirements can be

satisfied during this phase then the model proceeds to the
reporting function for the current period, after which the
entire process
acreage

is repeated for subsequent periods.

deficiencies

for

some

If

uses remain then a rent

adjustment phase is entered.
Unsatisfied acreage requirements from the initial pass
through

the

shift phase

indicate

that

availability of

suitable land for a particular use is constraining and
suggests that the gross ouputs and standard rents from the
unconstrained input-output model are inappropriate.

It is

in this situation that a small spatially aggregated inputoutput
adjusted
This

linear
gross

aggregated

programming
outputs,
linear

model

acreage

is

employed

requirements,

program has

an

to
and

activity

yield
rents.

for

each

54
sector

and

current

constraints.

acreage

allocations

for

the

land use

The fact that these current acreages are

constraining for one or more uses will result in positive
shadow prices which may be greater than the standard direct
contribution to the objective for those uses.
These shadow prices for the constraining uses are then
used in computing rents for construction of another ordered
file of rent differentials

for possible shifts from non­

binding uses to binding uses.
until

The process is repeated

sufficient acreage is allocated to satisfy require­

ments for each use or until a specified maximum number of
iterations is reached.

If the maximum number of iterations

is reached without satisfying acreage requirements,

an

unresolvable deficiency for the use in the current period
is assumed and the outputs of economic sectors directly and
indirectly dependent on the use are adjusted correspond­
ingly by solving the linear program with the final acreage
allocations
current

as

the

right-hand

period

are

then

side.

written

Reports

before

for

the

repeating

the

process for subsequent periods.
It
optimal

is

not

claimed that this process

solution

for

the

arrives

nonlinear problem,

but

at the
it does

approach this optimal and in so doing yields rents which
surpass

those

from the

large

scale

linear

reflecting the true nature of the problem.

program

in

The shifts

search and rent adjustment procedure can be considered a
case of

"heuristic programming"

(see for example Dykstra,

55
1976

or

Khumawala,

1971).

The

allocation

process

is

reasonable and understandable and may even approximate the
appropriate

real

world

allocation

process.

While

an

optimal solution is not guaranteed, the allocation that is
obtained is expected to be considerably closer to that
optimum than would be obtained by inspection or intuition.

CHAPTER III.
The

previous

DATA AND METHODS

chapter

use projection model.

describes

a comprehensive

land

The regional economy is modeled with

some sectoral detail, while the regional resource base and
land use are addressed with
The

economy

ductivity,
ships.

is

considerable

spatial

detail.

linked to the resource base through pro­

suitability,

and

land

requirements

relation­

Obviously, such a model encompasses a wide range of

variables,

and a wide variety of data and techniques

employing them are required.

for

This chapter considers data

sources and steps involved in compiling those data for
submission to the land use model.
The model can be thought of as consisting of two major
components,
resources

the

economic

component.

component

and

the

land use

and

The economic component includes the

I-A matrix of an input-output model,

a total final demand

vector used as the right-hand-side for the linear program,
and the objective function,
of the regional economy.

all of which focus on sectors

The land use requirements matrix,

which is the link between the two major

components,

dis­

tinguishes land use categories as well as economic sectors.
The major

variables

for the

land use and resources

com­

ponent are land use and resources by spatially referenced
parcels
models

of

lands.

Within this

for any number

or parcel

of

component

there are

sub­

explicitly recognized resources

characteristics.

56

This

chapter

is organized

57
around

these major

components

and submodels.

This

is

appropriate since the different components required differ­
ent

types

and

sources

of data and different methods

for

manipulating them.
The Input-Output Model
As has been thoroughly discussed elsewhere (Isard and
Langford,

1971,

made before

Richardson 1972),

embarking

analysis phases of

on

the

many decisions must be

actual

data

collection

and

an input-output study.

Primary among

these decisions is that of regionalization.

Will more than

one

region be

considered

in detail

or will

the analysis

focus on one region with its linkages to all other regions
represented grossly by an import row and export column?

In

either

or

case

what

are

the

regions to be considered?
region of
original

focus

project

the Introduction.

boundaries

the

region

That Emmet County would be the

for this project was
proposals

of

for

the

specified in the

reasons

discussed

in

That it would be the sole region explic­

itly

considered was

seen

to be well

dictated by the

founded)

that time,

anticipations
costs,

and

(later

computer

capacity limitations would be strained even with just the
single region.
Another important decision regarding the input-output
analysis,

which could not,

however,

easily, was that of sectorization.

be dispensed with so

As described in Chapter

II an input-output model represents a regional economy as

58
a matrix of linear relationships between different groups
or

sectors

of households,

firms,

or institutions.

Again

the question of how many sectors as well as that of the
exact definition of each sector must be addressed.
has been written about both of these questions.

Much

One of the

attractive features of input-output analysis is, of course,
its capability for recognition of many different sectors,
but in this case the value of fine sectoral resolution is
questionable

since the

effects within the model are

fun-

neled into the land use categories the number of which by
necessity is limited.

Of course the number of sectors also

directly affects the size of the linear programming problem
and

therefore

should be no

larger

than necessary.

When

direct surveys are used to obtain data for the input-output
model,

two

sectors.

other

factors

One would

dictate

expect

that

a

limited

total

number

sample

size

of
to

achieve a desired level of precision in each sector would
increase as the number of sectors increase thereby increas­
ing data collection costs.

Secondly,

and particularly

important when dealing with a small region, high sectoral
disaggregation can result in very few firms
important

sectors

with

resulting

in certain

disclosure

problems.

Based on these considerations and the relative import­
ance of certain activities to the Emmet County economy, as
indicated in published data, the sectors indicated in Table
1 were delineated.
codes

corresponding

Where applicable,
to

these

sectors

two digit S.I.C.

for

firms

in Emmet

59
County are also shown in Table 1.

The row and column

numbers in Table 1 refer to the various input-output tables
included below.
A final major design question

concerned the sources

of data and methods of obtaining the input-output coeffi­
cients.

The preferred approach to constructing such models

has been to use direct survey for all sectors,

but the

costs of this approach have long been recognized as a major
impediment to the development of input-output models.

In

recent years a great deal of effort has gone into devel­
oping and evaluating various techniques
input-output

coefficients

for

a

for estimating

particular

region

while

avoiding or at least reducing primary data collection.
These
existing
region,

techniques

survey based

generally

involve

modifying

an

input-output model

from some other

referred to as the base table,

to more closely

resemble the economy of the region in question than would
the unadjusted base table.

Often for regional studies in

the United States the national input-output model is used
as the base model.

Typically,

some effort is given to

delineating the sectors from the base table that correspond
most closely to the sectors of the region.

Published data

can often be used to estimate regional total outputs and
some final demand and/or payment vectors.
or technical coefficients

The transactions

for the appropriate sectors are

then adjusted to reflect known differences within sectors
or in the structure of the economy between the region and

60
Table 1.

Emmet County Input-Output Analysis Sectorization

Sector
Agriculture &
Forestry
Construction
Wood Products &
Furniture
Manufacturing
Mining & Cement &
Concrete Products
Manufacturing
Electrical & Trans­
portation Equip­
ment Manufacturing
Primary Metal &
Metal Fabrication
Manufacturing
Nondurables
Manufacturing
Transportation,
Communication &
Utilities
Wholesale & Retail
Trade
Finance, Insurance &
Real Estate
Lodging & Amusement
Services
Medical Services
Other Services
Government Enterprises
Households
Imports, Taxes &
Other Payments
Total Payments
Seasonal Residents
Tourists
Other Export
Investment
Exogenous Government
Total Gross Output

S.i.e.
Code

1-0 Table
Row No.

1-0 Table
Colunm No.

01,07,08,09
15,17

1
2

1
2

24,25

3

3

14,32

4

4

36,37

5

5

33,34

6

6

20,22,27,30

7

7

41,42,44,45
48,49
50,52,53,54
55,56,57,58, 59

8

8

9

9

60,61,64,65

10

10

70,79
80
72,73,75,81,
82,89

11
12
13

11
12
13

14
15

14
15

16
17
16
18
19
20
21
22

61
the base region,

and the model

is balanced to accomodate

the estimates of total or intermediate outputs.

For a

thorough review of the many variations on this theme see
Stipe

(1975),

Richardson

(1972),

McMenamin and Haring

(1974), and Morrison and Smith (1974).
A

third

approach

to

developing

input-output

models

employs both direct survey and the secondary data reduction
techniques.
most
and

Typically direct survey would be used for the

important or unique
for those

sectors of the regional economy

final demands or payments

for which it is

very difficult if not impossible to obtain reliable esti­
mates

from

published

sources,

e.g.

imports

and

exports,

while coefficients adjusted from a base table would be used
to complete the model.
census that

There seems to be a growing con­

such a hybrid model will often be an appro­

priate compromise between the higher accuracy of the pure
survey model and the low cost of the secondary data reduc­
tion approach.
A combined direct survey and data reduction approach
was

adopted

Emmet

for this

County

general

economy

suggested

study.

Some unique aspects of the

as well

the need

as

improved

accuracy

in

for some primary data collec­

tion, while the limited resources for the project prohib­
ited and the objectives of the project cast doubt on the
need for a full survey model.
The

construction,

manufacturing,

medical,

motel, and resort sectors were surveyed.

and hotel,

Sample sizes were

62
determined based on the variance in establishment size from
published

data

in order

to be able to estimate

sector

employment totals within plus or minus ten percent with 95
percent confidence.
sample was

For the manufacturing sectors the

stratified

over

the

individual

recognized in the input-output model.

sectors

being

For the medical

sector only the major hospital and clinic were contacted
with secondary techniques used to account for the smaller
establishments.
Preparation

of

questionnaires

and

initial

contacts

with the selected establishments occured in the winter and
spring

of

1977.

Interviews,

during

which

the

question­

naires were explained in detail, followed in the summer and
follow-up contacts continued into the fall.

Despite these

efforts response was poor and the usefulness of the results
was

limited,

so the

input-output model became

even more

dependent on secondary data than was originally intended.
Estimates of gross outputs were obtained by multiply­
ing 1976 employment for a sector by the ratio of output to
employment for the most recent year for which census data
on

output

were

available

for

that

particular

sector.

Employment data were obtained from several sources, includ­
ing County Business Patterns of the U.S.

Department of

Commerce,

Security

the

Michigan

State

Employment

Com­

mission, and the 1976 Michigan Directory of Manufacturers.
Some useable data on
obtained

from the

survey.

final demands and payments was
Where

such data were

lacking

63
for imports and exports,

location quotient techniques were

used to derive reasonable estimates.
example personal

In some cases,

consumption expenditures,

for

national aver­

ages from published sources were used to fill in missing
elements in the final demands and/or payments sectors.
An

iterative

input-output
final

balancing

transactions

technique

matrix

given

for

deriving

a base

table

demands and payments described by McMenamin

an
and

(1973)

was used.
The 1967 U.S.

input-output model was the most recent

available national model at the outset of this study and
was used as the base table.
delineating
national

those

table

sectors

that

Considerable effort went into
from

most

the

closely

highly

disaggregated

corresponded

to

the

various industries as they existed in Emmet County.
A FORTRAN program was written to apply the iterative
balancing of the base table transactions to the estimated
regional control totals.

This program allowed specifica­

tion of certain regional transactions for which direct data
were available and then balanced the rest of the model
around

these

fixed

regional

transactions

as well

as

the

total intermediate outputs by sectors.
The standard input-output tables and matrices result­
ing from this process are included below.

The transactions

shown in Table 2 are the estimated dollars paid by purchas­
ing or final demand sectors to producing or final payment

Table 2.

PR O D U C I N G
OR PA Y M E N T
SECT OR
1

Emmet County Input-Output Analysis Transactions (dollars)

purchasing

1

2

3

4

5

U R F I N A L D E H A N O SE CT OR
6
T

a

9

10

11

1089250.

56511.

40000.

0.

0.

0.

8189.

5870.

34101.

26 20 02.

120990.

41060.

6342.

6376.

162902.

38458.

11795.

21034.

388535.

120709.

9295S2.

242910.

3

3600.

46 05 70.

5 7 50 60.

25 00 00.

60 00 .

2500.

9000.

605.

45000.

8440.

2000.

4

465.

1021049.

3383.

24 69 566 .

96568.

248*

5324.

3942.

24447.

3451.

7647.

0.

0.

0.

0.

0.

0.

0.

0.

0.

0.

0.

2200.

50300.

100000.

73400.

2 2 S0 00.

110000.

14800.

7300.

11600.

3450.

0.

20600.

34550.

*
T

4000.

53220.

0.

100910.

119400.

10700.

74650.

21 21 70.

53060.

8

73409.

386432.

36840.

93 12 07.

21 73 83.

43163.

151759.

1154092.

732209.

23 72 92.

5 5 75 54.

9

184890.

1577980.

119895.

41 77 70.

3 1 88 20.

72840.

166490*

22 91 80.

68 55 00.

24 42 60.

228180.

10

69134.

93660.

6776.

199219.

49001.

31634.

47531.

134719.

85 27 69.

718497.

565057.

11

0.

0.

0.

0.

0.

0.

0.

35327.

12397.

2611 0.

65 12 81.

12

9141.

0.

0.

0.

0.

0.

0.

0.

0.

32384.

1500.

13

49864.

534120.

13352.

26 39 76.

2 1 85 16.

36734.

102612.

193500.

1156646.

460448.

49 75 27.

26 86 86.

176079.

51S32.
3420000.

1*

591.

2745.

1059.

21959.

IS

21 25 000 .

69 92 000 .

1416500.

22 29700.

23 54 600 .

1545850.

2 1 22 050 .

39S9 300 .

1T 70 T00 0.

2510 800 .

16

1044396.

11835071.

1707759.

9S 82 391 .

56 52 526 .

2401967.

27 08 613 .

6 0 85 249 .

12454766.

10444175.

31 59272.

17

46 97 000 .

2307 000 0.

40 27 000 .

16703000.

93 14 000 .

4271 000 .

54 76 000 .

12800000.

3431 800 0.

16 110000.

95 40 000 .

1772B.

3569.

4394 8.

5 8 17 81.

ON

Table 2.

(Continued)

PHODUCI n G
OH PAVHENT

SECTOR

12

13

U

15

P U R C H A S I N G O H FI NA L D E M A N D S E C T O R
16
17
18

19

20

21

1

27275*

15.

575.

1150000.

80500.

170000.

16*1722.

0.

10000.

4697000.

2

22681U.

137*32.

265312.

5 0 00 00.

35000.

0.

1070772.

13365000.

5500 000 .

2307 000 0.

3

0.

1000.

0.

56500.

5000.

0.

26 01 725 .

0.

0.

*0 27 000 .

A

*765.

2*338.

361.

25000.

2000.

0.

1 3010*46.

0.

0.

16703000.

s

0.

0*

0.

0.

0.

0.

93 01 000 .

0.

13000.

93 1*000.

6

U.

*2000.

0.

0.

0.

0.

36 30 950 .

0.

0*

*2 71 000 .

7

506*0.

1057300.

50 50 .

382200.

30000.

5730 0.

32 10 250 .

0.

0.

5* 76 000 .

e

67 57 38.

6 1 *5 66.

20 *8 56.

58 06 500 .

*32000.

165000.

0.

0.

300000.

12800000.

9

66 05 90.

* 6 75 60.

15520.

17888300.

1313000.

2700 000 .

767225.

1360000.

*900000.

3*310000.

0

6 7 7 AOA.

3*7353.

172**.

11 300000.

750000.

0.

0.

0.

25 00 00.

16110000.

1

0.

1071a.

12.

1555000.

11*000.

70 00 000 .

135158.

0.

0.

95*0000.

2

809875.

0.

7*3.

*5 20 000 .

*50000.

*50000.

1620 035 8.

0.

90 00 000 .

31 *7 *00 0.

3

A A 07 77.

*0 47 38.

31193.

67 12 000 .

*9 3 0 0 0 .

1010000.

0.

0.

1500000.

1*119000.

A

16A726.

151137.

2659.

* 2 58 00.

31000.

60000.

0.

0.

250000.

22 55 000 .

5

17*65000.

39 31 500 .

1*52000.

750000.

5250 0*

0.

4220 600 .

0.

32388000. 1066*2*00.

6

10270A00.

69 29 3*6 .

2 5 9* 75.

3*135700.

17*9500.

30 52700.

1.

0.

289000. 123762307.

7

31*7*000.

1*119000.

2255000•

85 28 700 0.

5537 500 .

1*665000.

5 5 79 020 7.

1*725000.

54*0 000 0. *1 85 787 07 .

66
sectors.
nical

Table 3 shows the direct requirements or tech­

coefficients,

purchasing
sector.

sectors

which

are

the

proportions

II,

is

included

with households,
Table 6.

as discussed in

in the constraint matrix of the

linear programming model.
called the direct

the

total payments paid to each producing

Table 4 is the I-A matrix which,

Chapter

of

The inverse I-A matrices,

and indirect requirements,
respectively,

are

shown

also

without

in Table

and

5 and

The two different inverses are needed, along with

the direct requirements of Table 3 to derive the output and
income multipliers shown in Table 7.
Spatially Referenced Data
Spatially indexed land use and resources data had to
be

collected

model.

and prepared

Again

for

input to the projection

certain design decisions had to be made

regarding spatial resolution, number and definition of land
use

categories,

resources

and

the number

characteristics

and nature of other

land

to be explicitly recognized.

Spatial Resolution
As was discussed in the Introduction a goal of this
study was to substantially improve the spatial resolution
over

Miley's

previous

work.

parcels in his application.
point

stated

that

Miley had used counties

as

An Emmet County planner at one

a one-eighth

acre

city

lot was

the

appropriate parcel for projections useful for his planning.

Table 3.
4U0UCING
SECTOR

Input-Output Analysis Direct Requirements

1

3

2

4

P U R C H A S I N G SECT OR
7
6
9

6

5

10

11

12

14

13

15

0. 00 150 0. 00 046 0 . 0 0 0 9 9 0 . 01 626 0 01 26 0 0 . 00 007 0 00000 0 00 02 5 0 . 01 340

1

0.23190 0.00245 0 . 00 993 0.0

2

0.00874 0.00027 0 . 00 158 0. 00 975 0. 00 413 0 . 00 276 0. 00 384 0 . 03 035 0. 0 0 3 5 2 0 . 05 770 0 02 54 6 0.00721 0 0 0 97 3 0 11765 0 . 0 0 5 0 6

3

0.0U077 0 . 01 996 0. 14 280 0. 01 497 0.00064 0. 0 0 0 5 9 0. 00 164 0. 00 005 0.00131 0 . 00 052 0 00021 0.0

0.0

0.0

0 00 00 7 0 0

0.00066

4

0.00010 0.04426 0 . 00 084 0 * 14 785 0.01037 0 . 00 006 0. 00 097 0.00031 0 . 00 071 0*00021 0 00000 0 . 0 0 0 1 5 0 0 0 17 2 0 00 01 6 0 . 0 0 0 2 9

5

0.0

6

0.00047 0. 00 218 0 . 02 483 0 . 00 439 0 . 02 416 0. 0 2 5 7 6 0 . 00 270 0.00057 0 . 00 034 0.00021 0 0

0.0

0.0

0.0

0.0

0.0
0.0

0 00297 0 0

0.0

7

0.00085 0.00231 0.0

a

0.015O3 0.01675 0 . 00 915 0* 05 575 0. 02 334 0.01011 0.02771 0 . 09 016 0. 0 2 1 3 4 0 . 01 473 0 05044 0. 0 2 1 4 7 0 04 35 3 0 09 00 5 0. 0 6 9 0 2

9

0. 03 936 0.06840 0 . 02 977 0.02501 0. 03 423 0. 0 1 7 0 5 0 . 03 040 0. 01 790 0 . 01 997 0 . 01 516 0 02 39 2 0 . 0 2 0 9 9 0 03 31 2 0 60 6 0 0 9. 20 974

10

0. 01 472 0. 00 406 0. 00 168 0. 01 193 0. 00 526 0.00741 0 . 00 868 0 . 01 052 0 . 0 2 4 8 5 0 . 04 460 0 05923 0 . 0 2 1 5 2 0 02460 0 00 7 6 5 0 . 1 3 2 4 9

11

0.0

0. 00 604 0 . 01 282 0.90251 0 . 01 363 0.00161 0. 0 0 6 1 8 0 . 0 0 3 2 9 0 00362 0.00161 0 0 7 4 0 0 0 00224 0 . 0 0 4 4 0

0.0

0.0

0.0

0.0

0.0

0.0

0 . 0 0 2 7 6 0 . 0 0 0 3 6 0. 0 0 1 6 2 0 06027 0. 0

12

0.00195 0.0

0.0

0.0

0.0

0.0

0.0

0.0

13

0 . 01 062 0. 0 2 3 1 5 0.00332 0 . 01 580 0 . 02 346 0 . 00 860 0. 01 874 0. 0 1 5 1 2 0 . 03 370 0. 02 050 0 0 5 2 1 5 0. 0 1 4 0 0 0 02067 0 01 30 3 0 . 07 070

14

0. 00 013 0. 00 012 0. 00 026 0.00131 0. 00 190 0. 0 0 0 8 4 0 . 00 803 0 . 04 545 0 . 0 0 7 8 3 0. 0 1 0 9 3 8 00 5 4 0 0 . 0 0 5 2 3 0 01 07 0 0 00 11 0 0 . 0 8 4 9 9

15

0. 45 242 0 . 30 308 0 . 35 175 0 . 13 349 0 . 25 280 0 . 36 194 0. 3 8 7 5 2 0 . 30 932 0 . 51 597 0. 15 505 0 3 5 0 4 9 0 . 5 5 4 9 0 0 2 7 0 4 5 0 64 3 9 0 0 . 0 0 0 7 9

0.0

0 00 07 6 0 08001 0 . 0 1 0 2 3

0.00201 0 00 0 1 6 0. 0 2 5 7 3 0 0

8 00 03 3 0. 0 5 3 0 0

Table 4.
PR O D U C I N G
SE CT OR
1

Input-Output Analysis I - A Matrix

1

2

3

0 . 7 6 b 1 -0 .0 02* - 0 . 0 0 9 9

2

-0 .0 087

3

-0 .0 008 -0 .0 200

«

-0.0001 -0 . 0 * * 3 - 0 . 0 0 0 8

5

0.0

0.9997

0.0

A
0.0

S
0.0

PURCHASING SECTOR
7
8
9

6
0.0

0.85 72 - 0 . 0 1 5 0 -0 . 0 0 0 6 - 0 . 0 0 0 6 - 0 . 0 0 1 6 - 0 . 0 0 0 0 - 0 . 0 0 1 3 - 0 . 0 0 0 5 - 0 . 0 0 0 2

0.0

0.8521 -0 . 0 1 0 * -0 .0 001
0. 0

l.UOOO

0.0

0. 0

0.0

-0.0009 -0.0023

8

-0 . 0 1 5 b - 0 . 0 1 6 8 -0 .0 091 - 0 . 0 5 5 8 - 0 . 0 2 3 3 -0 . 0 1 0 1

-0 . 0 2 7 7

- 0 .0 39* - 0 . 0 6 8 * - 0 . 0 2 9 8 -0 . 0 2 5 0 -0 . 0 3 * 2 -0 . 0 1 7 1

-0 . 0 3 0 * - 0 . 0 1 7 9

0.0

0. 0

0. 0

0 . 97 *2 - 0 . 0 0 2 7 - 0 * 0 0 0 6 - 0 . 0 0 0 3 - 0 . 0 0 0 2

- 0 .0 060 -0.CI128 - 0 . 0 0 2 5

lb

15

0. 0 -0 .0 001

0.0 -0 .0 007

0.0

0. 0

0 .0

0 .0

0. 0
- 0 .0 0 3 0

0. 0

0.0

0 .0

0 .0

0 . 9 8 6 * - 0 . 0 0 1 6 - 0 . 0 0 6 2 - 0 . 0 0 3 3 - 0 . 0 0 3 6 - 0 . 0 0 1 6 - 0 . 0 7 * 9 - 0 .0 0 2 2 - 0 . 0 0 * 5
0. 90 98 - 0 . 0 2 1 3 - 0 . 0 1 * 7 - O . O S O * - 0 . 0 2 1 5 - 0 . 0 * 3 5 - 0 .0 9 0 0 - 0 .0 6 9 0
0. 98 00 - 0 . 0 1 5 2 - 0 . 0 2 3 9 - 0 .0 2 1 0 -0 .0 3 3 1 - 0 .0 0 6 9 - 0 .2 0 9 7

-0 . 0 1 * 7 -0 .0 0*1 -0 . 0 0 1 7 - 0 . 0 1 1 9 - 0 . 0 0 5 3 - 0 . 0 0 7 * -0 . 0 0 8 7 - 0 . 0 1 0 5 - 0 . 0 2 * 8
0.0

13

- 0 . 0 0 1 0 - 0 . 0 0 0 3 - 0 . 0 0 0 7 - 0 . 0 0 0 2 - 0 . 0 0 0 8 - 0 . 0 0 0 2 - 0 .0 017 - 0 . 0 0 0 2 - 0 .0 0 0 3

7

11

12

- 0 . 0 0 1 6 - 0 . 0 0 9 8 -0.00*1 - 0 . 0 0 2 8 - 0 . 0 0 3 8 - 0 . 0 3 0 * - 0 . 0 0 3 5 - 0 . 0 5 7 7 - 0 . 0 2 5 5 - 0 . 0 0 7 2 - 0 . 0 0 9 7 - 0 . 1 1 7 7 - 0 . 0 0 5 9

-0 . 0 0 0 5 - 0 . 0 0 2 2 - 0 . 0 2 * 8 -0 . 0 0 * * - 0 . 0 2 * 2

9

II

- 0 . 0 0 1 5 -0 . 0 0 0 5 - 0 . 0 0 1 0 - 0 . 0 1 6 3 -0 . 0 1 2 T - 0 . 0 0 0 9 - 0 . 0 0 0 0 - 0 . 0 0 0 3 -0 . 0 1 3 5

6

10

10

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0. 0

0.0

0.95 5* - 0 . 0 5 9 2 - 0 . 0 2 1 5 - 0 . 0 2 * 6 - 0 . 0 0 7 6 - 0 . 1 3 2 5

-0.0028 -0.000* -0.0016
0. 0

0. 0

0.9317

-0.0020 -0.0002

0. 0
0.97*3

- 0 .0 0 0 8 - 8 .0 0 0 0 - 0 .0 1 8 2

12

-0 . 0 0 1 9

13

-0 . 0 1 0 6 -0 . 0 2 3 2 - 0 . 0 0 3 3 - 0 . 0 1 5 8 - 0 . 0 2 3 5 - 0 . 0 0 8 6 - 0 . 0 1 8 7

1*

-0.0001 - 0 .0 001 -0 . 0 0 0 3 -0 . 0 0 1 3 - 0 . 0 0 1 9 - 0 . 0 0 0 8 -O . O O B Q - 0 . 0 * 5 5 - 0 . 0 0 7 8 - 0 . 0 1 0 9 - 0 . 0 0 5 * - 0 . 0 0 5 2 -0 . 0 1 0 7

15

-0 .4 52* - 0 .3 031 - 0 . 3 5 1 8 -0 . 1 3 3 5 - 0 . 2 5 2 8 - 0 . 3 6 1 9 - 0 . 3 8 7 5 - 0 . 3 0 9 3 - 0 . 5 1 6 0 - 0 . 1 5 5 9 - 0 . 3 5 8 5 - 0 . 5 5 * 9 - 0 .2 7 8 5 - 0 . 6 * 3 9 0 .9 9 1 2

-0 .0 151 - 0 . 0 3 3 7 - 0 . 0 2 8 6 - 0 . 0 5 2 2 - 0 . 0 1 * 0

0.0

- 0 . 0 0 0 3 - 0 .0 5 3 0

0 . 97 13 - 0 .0 1 3 8 - 0 .0 7 8 7
0 . 99 88 - 0 . 0 0 5 0

Table 5.

P R OD UCI NG
S E C. OK

Direct and Indirect Requirements

1

2

3

4

5

6

P U R C H A S I N G S E CT OR
7
d
9

10

11

14

13

12
•

M

O

M

M

1

1.3026

0.0030

0.01 52

0.0000

0.00 03

0.00 03

0.00 23

0.00 12

0.00 20

0 .0 2 2 5

0 .0 1 9 5

0 .0 0 1 6

0 .0 0 0 9

0 0011

2

0.0141

1.0029

0.0031

0.01 59

0.00 67

0.00 42

0.00 72

0.0409

0.00 77

0 .0 6 3 6

0 .0 3 6 0

o .o io a

0 .0 1 5 7

0 1220

3

0.00 16

0.0244

1.1660

0.0210

0 . 00 12

0 . 00 00

0.0022

0.0011

0. 00 16

0 .0 0 2 3

0 .0 0 1 3

0 .0 0 0 3

0 .0 0 0 7

0 0030

4

0.0010

0.05 23

Q.0014

1.1745

0.01 26

0 . 00 03

0.00 16

0 . 00 26

0.00 14

0 .0 0 3 7

0 .0 0 3 1

0 .0 0 0 0

0 .0 0 3 1

0 0067

S

0.0

6

0.0000

0.0032

0.02 90

0.0060

0.0251

1.026S

0.0030

0 . 00 09

0.0006

0 .0 0 0 6

0 .0 0 0 4

0 .0 0 0 1

0 .0 0 3 S

0 0005

7

0.0030

0.00 54

0.0000

0.00 94

0* 01 55

0. 00 36

1.0159

0.0030

0.00 94

0 .0 0 6 5

0 .0 0 9 5

0 .0 0 3 3

0 .0 7 9 1

0 0045

0

0.0256

0.02 56

0.01 39

0.0756

0.0301

0. 01 29

0.03 43

1.1073

0. 0 2 7 9

0 .0 2 2 7

0 .0 7 6 6

0 .0 2 7 1

0 .0 5 5 4

0 1050

9

0.0550

0.0740

0.03 74

0.0346

0.03 03

0.0191

0.03 39

0.02 47

1.0237

0 .0 2 3 7

0 .0 3 4 6

0 .0 2 4 5

0 .0 4 0 3

0 0109

10

0.0224

0.0001

0. 00 39

0.01 T3

0.00O1

0.00 09

0.01 13

0 . 01 43

0 . 02 62

1 .0 4 9 4

0 .0 7 0 6

0 .0 2 4 7

0 .0 2 9 4

0 0109

u

0.00 02

0.0001

0.0001

0.0003

0.0001

0.0001

0.00 02

0 . 00 33

0. 00 06

0 .0 0 1 9

1 .0 7 3 7

0 .0 0 0 1

0 .0 0 1 1

0 0004

12

0.0026

0.0000

0.00 00

0.00 00

0.00 00

0.00 00

0.00 00

0.00 00

0.0001

0 .0 0 2 2

0 .0 0 0 4

1 .0 2 0 5

0 .0 0 0 1

0 0004

0.02 20

0.0271

0.01 04

0.0221

0. 02 05

0. 03 74

0 .0 3 4 3

0 .0 0 3 0

0 .0 1 7 3

1 .0 3 4 9

0 0201

0.0050

0.0041

0 . 00 10

0*0104

0.05 10

0.0101

0 .0 1 3 2

0 .0 1 1 1

0 .0 0 7 3

0 .0 1 4 9

1 0005

13

0.0177

0.02 03

0.00 62

14

0.0022

0.0024

0.0014

Table 6.

Direct and Indirect Requirements, With Households

3

2

1

4

5

6

H

•

(ODUClNb
iECTOR

PURCHASING SECTOR
7
8
9

10

11

12

13

IS
1

1

1.3218

0.01 52

0.028*

0.00 76

0.00 96

0.01 20

0.01 55

0.01 35

0 . 01 89

0 . 02 96

0.03 40

0.02 00

0 . 01 22

0 . 02 32

0.02 98

2

0.0337

1.0146

0.01 70

0.02 29

0.01 62

0.0161

0.02 06

0.0534

0 . 02 49

0.07 07

0. 05 09

0. 02 93

0 . 02 72

0.1451

0.03 04

3

0.01)32

0.0254

1.1679

0.02 15

0.0020

0.00 18

0.00 33

0.00 22

0.00 32

0 . 00 29

0.002S

0.0019

0 . 00 17

0 . 00 49

0.0025

4

0.0028

0.0533

0.00 26

1.1751

0.01 35

0.0014

0.00 28

0.0037

0 . 00 29

0 . 00 43

0.00 44

0.0025

0.0041

0.00 87

0 . 00 28

5

0.0

0.0

0.0

0.0

1.0000

0.0

0.0

0.0

0.0

6

0.0014

0.00 36

0.0302

0.00 62

0.0253

1.0269

0.0034

0.00 12

0.0011

0. 00 08

0 . 00 08

0.00 07

0 . 00 38

0 . 00 12

0. 00 09

7

0.0157

0.01 29

0.00 99

0 . 01 39

0.0217

0.0114

1.0246

0.01 20

0 . 02 06

0. 01 12

0.0191

0.01 S4

0.0866

0.0191

0.0198

8

0.1088

0.0749

0.07 29

0.10 53

0.07 05

0.06 38

0.09 14

1.1606

0.1011

0.05 32

0 . 13 95

0 . 10 59

0.10 44

0 . 20 06

0.1293

9

0.2557

0.1930

0.17 98

0.10 62

0. 13 59

0.14 20

0.17 17

0.15 35

1.2004

0.09 74

0 . 18 66

0 . 21 47

0 . 15 84

8.2496

0.31 20

10

0.15 68

0.08 77

0.0991

0.06 52

0.07 34

0. 09 12

0.10 35

0.10 04

0.1464

1.09 88

0.1723

0.15 20

0 . 10 84

0.1653

0 . 20 88

11

0.0182

0.01 08

0.01 28

0.00 67

0 . 00 89

0.0111

0 . 01 25

0.0149

0.01 64

0.0005

1.0873

0 . 01 72

0.0117

0 . 02 10

0.02 80

12

0.0513

0.02 88

0.03 45

0.0174

0.02 37

0 . 02 98

0.0334

0.03 12

0. 04 29

0.0201

0 . 03 72

1.07 25

0.0287

0 . 05 62

0. 07 56

13

0.1047

0.07 98

0.06 79

0.05 38

0.0694

0.0637

0.08 18

0 . 07 63

0 . 11 39

0.0663

0 . 12 94

0 . 09 97

1.0861

0 . 12 00

0.1351

14

0.0149

0.00 99

0.0104

0.01 03

0.01 03

0. 00 96

0.0191

0.0591

0 . 02 12

0. 01 78

0 . 02 07

0.0193

0.0224

1.0211

0 . 01 97

15

0.8878

0.5262

0.6297

0.31 64

0.43 16

0.54 36

0.60 95

0 . 56 95

0.78 14

0.32 60

0 . 67 20

0 . 84 12

0 . 52 23

1.02 04

1.37 99

71
Table

7.

Emmet County

Input-Output Analysis Multipliers

Sector

Output

Income
Type I
Type II

Agriculture & Forestry

1.45

1.42

1.96

Construction

1.23

1.26

1.74

Wood Products & Furniture
Manufacturing

1.28

1.30

1.79

Mining & Cement & Concrete
Products Manufacturing

1.38

1.72

2.37

Electrical & Transportation
Equipment Manufacturing

1.17

1.24

1.71

Primary Metal & Metal
Fabrication Manufacturing

1.09

1.09

1.50

Nondurables Manufacturing

1.14

1.14

1.57

Transportation, Communication
& Utilities

1.27

1.33

1.84

Wholesale & Retail Trade

1.15

1.10

1.51

Finance, Insurance &
Real Estate

1.25

1.52

2.09

Lodging & Amusement Services

1.40

1.36

1.87

Medical Services

1.14

1.10

1.52

Other Services

1.28

1.15

1.58

Government Enterprises

1.30

1.15

1.58

72
As a compromise between these extremes,

it was decided to

use a section as the basic unit of land for this applica­
tion of the model to Emmet County.
that

any

size parcel

Recall from Chapter II

could be used and in fact size can

vary from parcel to parcel in a given analysis, but as the
number of parcels increases the problem to be solved either
by linear programming or by sorting, searching, and shift­
ing increases exponentially.

The section as the basic

spatial unit resulted in approximately 500 parcels in Emmet
County which with a reasonable number of land use categor­
ies would yield a problem that could be handled by either
approach
The

with

the

computational

capacity

section as the basic parcel

resulted

then

available.

in a degree of

spatial resolution which seemed appropriate for the devel­
opment and demonstrative purpose of the project.
Use of a fixed grid of square mile cells was consid­
ered,

but

better

it was

facilitate

felt that

use of actual sections would

data collection

and compilation.

Land

characteristic and resources data were taken from many
different
nated.

maps

which

Section areas,

typically had

section

lines

desig­

both total and land surface,

were

determined from the photo based maps of the Emmet County
Soil Survey using the DATATIZER digitizer at the Michigan
State University Computer Center.
It was anticipated that many of the displays of inputs
to and results

from the model would be

simple printer cell maps.

in the form of

The use of sections as parcels

73
facilitated this type of display since they approximate a
grid of equal size cells.

Conceivably,

if more sophisti­

cated mapping hardware and software were available for
displaying

inputs

a grid

equal parcel

or

and results

one would

size.

not

need

Definition

either

of parcels

could be based on more appropriate considerations such
as homogeneity of resources,

zoning,

or ownership.

Much

of the spatially indexed land characteristic and resource
data considered below was collected and compiled by or in
cooperation with the information systems component of the
regional project.

See McRae and Shelton

description

information

of

the

systems

(1982)

component

for a
of

the

regional project.
Land Use
There were two main sources of current land use data
for Emmet County.

During the summer of 1978, an extensive

ground survey of all types of developments
was

conducted.

This

survey was

in the county

a cooperative

effort

between the Emmet County Department of Planning and Zoning
and this project.
was

Every mile of rural road in the county

traveled and every building,

mineral

development

and

farm was plotted on a map and identified according to land
use category,

e.g. residential,

commercial,

industrial, by

a local planner who was familiar with most of the county.
The second source of land use data for the county was
a series of aerial photographs flown in the summer of 1978.

74
These photos were

supplied by the Michigan Department of

Natural

and

Resources

interpreted

by

the

Michigan

State

University Remote Sensing Project in conjunction with the
information systems component of the regional project.

A

grid of ten acre cells was overlaid on each section of the
county and the dominant cover or use recorded for each
cell.
While each of these sources had its own deficiencies,
the two proved to be quite complementary.

For example, the

ground survey did not record vegetative cover or recognize
associated extensive uses such as agriculture and forestry,
but

vegetative

cover by

several different categories was

obtained from the aerial photos allowing estimates of area
in agricultural use.

Conversely the approach of recording

dominant

ten-acre

use

distinguish
throughout

in the
the

the

numerous

county,

cell

rural

could

not

residences

possibly
scattered

but every one of these was

tified by the ground survey.

iden­

Considerable time and effort

was spent in reconciling and combining data from these two
sources to yield final estimates of current area devoted to
each of eight land use categories for every section in the
county.

The effect of number of land use categories on the

size of the linear programming problem or on the number of
shift possibilities

in the heuristic programming approach

necessitates restraint in the number of such categories, so
although the

land use data was originally collected with

some additional distinctions

the following eight land use

75
categories were finally designated for explicit considera­
tion

in the model:

mineral

agriculture,

commercial,

recreation,

residential,

extraction,

industrial,
recreation

residential, and forest and open.
Soils
Soil type and slope were considered key parcel charac­
teristics for determining productivity and suitability for
the various uses.

Soil type and slope were recorded from

the photo based maps in the Soil Survey of Emmet County,
Michigan (USDA SCS,

1973) by overlaying a grid of ten-acre

cells on each section.

The dominant type and slope in each

ten-acre cell was recorded.

The data were then keypunched

and the computer was used to tally the number of cells by
each type and slope for each parcel.
Factors

indicating

productivity

of

each

soil

type/

slope combination for the mix of crops produced in Emmet
County

(as

indicated

in the

1974 Census

of Agriculture)

were derived from a table of predicted average yields for
crops in the soil survey.

The maximum predicted produc­

tion for each crop over all soils was used as the standard
for that crop (i.e. productivity equals 1.0) and for lower
levels of production proportional productivity was assumed.
For each soil type average productivity was then computed
from those proportions and weights reflecting crop mix.
average

productivity

factor

for

each

parcel

was

An
then

derived from soil type productivity factors weighted by the

76
number of cells of each soil type in each parcel.

Figure

5 displays the resulting agricultural productivity indexes
by parcels for Emmet County.

Compare Figure 5 to Figure 6

which indicates current agricultural use.
A similar procedure was used to derive woodland pro­
ductivity factors by soil type and parcel based on a table
of

"potential productivity ratings per acre per year

for

woodland types" in the Emmet County soil survey (USDA SCS,
1973, p. 50).
Emmet

County

expected,

Resulting woodland productivity classes for
are

displayed

in Figure

7.

As would

be

there is a noticeable correlation between agri­

cultural and woodland productivity.
Travel Times
The

importance

of

distance

to

some

key

location

in

determining the value of a parcel of land in a given use
is one of the fundamentals of land economics.

Indeed, the

roots of the concept of land rent can be traced to von
Thunen's simple isolated state model where concentric zones
of land use around a market center were determined by the
nature of the product and distance to that market

(see

Barlowe, 1 9 1 2 , p. 35-37).
Just as
Northern

the relative remoteness of Emmet County and

Lower

Michigan

in

general

to

existing

major

regional markets and production centers affects the kinds
of establishments that can locate in the county, allocation
of land to uses within the county is affected by location

77

1

2

3

4

S

»

7

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Soil Productivity for Agricultural Use

14

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22
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31
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78

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CURRENT PROPORTION Of RRE*
IN AGRICULTURAL USE

Figure 6.

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Current Proportion of Area in Agricultural Use

79
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Figure 7.

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Woodland Soil Productivity

-

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80
with

respect

to

and resources.
times

existing

establishments,

infrastructure,

To reflect these kinds of influences travel

from every parcel

in the

county to the major com­

mercial center, Petoskey, and to lesser commercial centers,
Harbor

Springs,

Mackinaw City,

Cross Village were derived.

Pellston,

Alanson,

and

Maps indicating travel times

along major roads my segments were provided by the State of
Michigan

Department

and extrapolating

of

Transportation.

By

interpolating

from these maps travel times for every

section of the county were estimated.

Travel times to

commercial centers are displayed in Figure 8.
While
problem,

obtaining

travel

times by parcel was

not

a

knowing how to use them in deriving suitability

factors,

e.g.

assessing

expected

rents

of being

for a given use the impact on
two minutes

from the

commercial

center versus ten minutes, was a substantial problem.

It

must be admitted that the limitations in scope and resour­
ces for this project did not permit rigorous development of
this kind of relationship.
it was

felt

Rather, for each use for which

that travel time was

an important factor an

assumption was made as to the maximum impact this
would have

on rent

for that use and at what point,

travel time, this maximum impact would be reached.
polation between
impact point

at

factor

this

maximum

some minimal

i.e.

Inter­

impact point and a zero

distance

to the center was

used to derive factors for adjusting rents for intermediate
categories of travel times.

Figure 9 shows these assumed

81

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12
U
13
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14
10

II

KTO E E N 10 t 10 MINUTES
*F7«E(N 0 6 10 MINUTES
* MINUTES OB LESS

Travel Times to Commercial Centers

10

]9
J*

20

20
27
2T
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29

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30
31
31
32
32

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82

1.

.6

.4

Rent

Adjustment

Factor

.8

2

0.
0

5

10

15

20

25

Travel Time to Nearest Commercial Center - minutes
Commercial
Industrial • • • •
Residential — — —

Figure 9.

Assumed Impact of Travel Time on Rent

83
relationships between

impact on rent and travel times to

commercial centers for commercial,
tial uses.
things

industrial and residen­

For example Figure 9 indicates that, all other

being

equal,

the rent

for commercial use

for a

parcel with a ten minute travel time to the nearest exist­
ing commercial center could be obtained by multiplying by a
factor

of

.6 the

rent

for

a parcel

at the

commercial

center.
Zoning
Zoning is obviously an important variable for explicit
consideration
existing

in the model,

legal

limitations

not only because it reflects
on

productivity

and/or

suit­

ability of a parcel for a use, but also because it is the
most obvious tool available to planners and decision makers
for attempting to control future land use patterns.
Emmet County has a county wide zoning ordinance which
in some cases is superseded by township or city ordinances.
Maps indicating zones and the descriptions of those zones
for all of these ordinances were obtained from the Emmet
County Department of Planning and Zoning

(Emmet County

Zoning Ordinance, 1977).
Zones were recorded
grid

of

ten-acre

cells

from these maps by overlaying a
on each

section of the county.

Areas by zones for each section were then used in conjunc­
tion with minimum lot sizes and allowable types of dwelling
units by zone to yield productivity factors for residential

84
use for each section of the county.
Zoning

could also be used

through a feature

of the

model which allows specifying maximum areas that can shift
from or to a given use in a given parcel.

These con­

straints on maximum area shifting to a use could be based
on

limited appropriate

zoning

for that

use in a parcel.

Ownership
As with zoning,

ownership has

important implications

for the availability of a parcel for a given use.

Owner­

ship data were collected from the 1975 Emmet County plat
book by overlaying a grid of ten-acre cells on each section
of

the

county.

recognized:
state

park.

The

following

private,

ownership

categories

private-subdivided,

University

of

Michigan,

state

were

forest,

village-city,

other

public, and quasi-public.
Again the constraints on maximum area allowed to shift
from or to a given use
reflect

expected

in a given parcel were used to

limitations

imposed

by

ownership.

For

example in a parcel well suited to residential development
but

with

all

underdeveloped

ownership category,
forest

use

to

land

in

the

state

forest

no area would be allowed to shift from

residential

use

unless

the

constraint was

relaxed during the course of the run to reflect a sale or
land

exchange by
Many

other

the Department
land

of Natural

characteristics

were

Resources.

or

could be

considered for explicit recognition in the land use model,

85
indeed,

some data for other characteristics than these

mentioned
viewpoints,
type.

above

were

present

actually

and planned

collected,
sewer

e.g.

scenic

service and forest

That these other characteristics were not ultimately

used in the analysis reported here is more a reflection of
the

limitations

of this

study

(purpose,

funds,

and time)

than an assessment of the importance of these character­
istics in influencing land use shifts.
serious

limitation

Of course the most

in actually using many of these other

factors, and indeed for some of the factors mentioned above
that were used,

is the lack of documented empirical or

quantifiable theorectical

relationships

indicating the

effect of these factors on suitability of land for a given
use.

CHAPTER IV.

RESULTS AND CONCLUSIONS

This chapter has three distinguishable but interdepen­
dent purposes.

First,

model

in Chapter

described

derivations

from those

are presented.
at best,

II,

employing the data and

data as described in Chapter

III,

But these runs and results are considered,

demonstrations of the model rather than serious

predictions
disclaimer
which

the results of some runs of the

of

future

leads

to

land use

the

in Emmet County.

second purpose

is to acknowledge and consider

of

Such a

this

chapter,

in some detail many

shortcomings of the model and its application in this study
to Emmet County.

Finally,

recognition of the continuing

problems with this model, or more generally this approach,
relates closely to other recent attempts at and literature
on land use modeling, as discussed in Chapter I, and leads
to some reflections on land use modeling in general and on
how

experiences

those

reported

in the
from

Emmet

other

County
land

study

use

coincide with

modeling

efforts.

Emmet County Analyses and Results
Originally,
were

a number

contemplated.

initially

of different runs of the model

Once

constructed

the major

then

a number

model
of

components

variables

can

are
be

changed with relative ease to yield different projections.
Likely
grouped

candidates
for

for alteration

convenience

as

from run to run

policy

86

control

can be

variables

and

87
variables

for

which

input

information

is

relatively

un­

certain.
Policy control variables are those which reflect the
tools

available to regional

decision makers

influencing economic and land use development.

for actively
Included in

this class might be zoning regulations that are incorpor­
ated into the model through the geospecific indexes or con­
straints.

Also included in this class could be public land

ownership and public

facilities

location decisions,

again

implemented in the model through indexes and constraints,
as well as initial land uses.

Although not strictly a

policy tool, the objective function could be included here
as a likely candidate for analysis because of its implica­
tions for policy.
There is a great deal of uncertain information, econ­
omic and geographic,
model.

comprising the data base

for this

A common practice in modeling is sensitivity analy­

sis, which involves selecting variables for which there is
considerable uncertainty and varying those values to assess
the

impacts

on

important

output variables.

Given the

number and levels of uncertainties in this model countless
analyses of this type could be envisioned, but perhaps no
variable,

or more precisely vector of variables,

is more

uncertain and at the same time more important to the model
than final demands.
is

the

exogenous

As explained previously,

driver

final demand

of the economic model,

which

in

turn drives the land requirements and allocation component.

88
Obviously

then,

final demand

is a prime candidate

for

alteration from run to run.
It was initially intended to make a series of runs,
varying several of the variables mentioned above,
zoning,

ownership,

objective

function,

and

final

i.e.

demand.

The first few runs of the model with the full data base,
however,

cast doubt on the value of making many of the

other runs.

These first runs involved different levels of

final demand, and perhaps the most notable result of these
runs is that even with very optimistic projections of the
future rate of economic growth in Emmet County,

suitable

land and resources to support that growth is not revealed
to be constraining.
Following the reasoning presented in Chapter II, the
objective function for these analyses was a reflection of
after tax profit by
Service data

sector derived from Internal Revenue

(U.S. Treasury Dept.,

1979 and U.S. Treasury

Dept., 1981).
The
was

first

intended

usual"
County.

run,
to

which can be

reflect

scenario over
That

is,

the

the

a

considered a base run,

conservative

"business

next fifteen years

model

was

run with

for Emmet

all

of

major variables and the structure of the economy
constant over the time horizon,

as

the
held

simulating current zoning

regulations, current public ownership patterns, and current
and planned public

facilities

and

utilities.

The major

input change from period to period in this run was a modest

89
across the board increase in final demands of five percent
per five year period.

This rate of growth was based on the

most recent available Bureau of the Census projections for
population growth in Michigan, reasoning that much of these
final

demands,

e.g.

export

of

intermediate

products,

would be largely dependent on overall growth in the state.
The
sector

final

over

demands

time

and

for this

resulting

gross

outputs by

run are displayed in Table 8.

A general impression of changing land use over the projec­
tion period can be seen in the printer maps of Figure 10,
Figure

11,

Figure

12,

and

Figure

13.

In

these

proportion of parcel area in developed uses
mercial,

industrial,

and residential)

maps

(i.e. com­

is used as an index

to provide an overall impression of the trends in land use
over

time.

The

divisions

between

intensity

levels

dis­

played on the maps are somewhat arbitrary and are simply
intended
veloped,

to provide

some

less developed,

contrast between

totally d e ­

and virtually undeveloped areas.

At this rate of economic growth not much change is detected
in this
expected,

index over

this

series

of maps.

As would be

those that do show movement from one category to

the next are in the southern portion of the county,
current commercial

and

near

industrial centers and along major

transport routes.
The maps in Figure
17,

and

Figure

14,

18 reveal

Figure 15,
changes

Figure

16,

Figure

in land allocated to

specific uses not revealed in the preceeding series of

Tabl* 8.

Initial and Projected Final Demand and Grose Outputs for the First Bun

Sector

Current
Final
Gross
Demand
Output

Period 1
Gross
Demand
Output
Final

(Thousands of Dollars)

Period 2
Final
Gross
Demand
Output

Period 3
Final
Gross
Demand
Output

1929

5495

2028

5784

2134

6087

2232

6366

20251

24215

21292

25490

22412

26831

23434

20055

Hood Products
Furniture Manufacture

2643

4105

2779

4321

2925

4548

3059

4756

Cement a Concrete
Products Manufacture

10551

13893

11093

14624

11676

15393

12209

16095

Electrical a Transportation
Equipment Manufacture

9441

9434

9930

9930

10452

10452

10929

10929

Primary Metal a Metal
Fabrication

3682

4331

3871

4559

4075

4799

4261

5019

nondurable Manufacture

3344

5979

3516

6294

3701

6625

3870

6928

910

15670

956

16495

1007

17363

1053

1815S

11195

41610

11771

43800

12390

46104

12955

48207

Finance, Insurance a
Real Estate

4491

24487

4722

25776

4970

27132

5197

28369

Lodging a Amusement
Services

7350

10279

7728

10820

8135

11389

8506

11909

26465

33538

27826

35303

29289

37159

30625

38854

3045

17320

3202

18232

3370

19190

3524

20066

346

2743

364

2887

383

3039

400

3177

37174

116570

39085

122706

41141

129159

43017

135051

Agriculture
Construction

Transportation. Utilities,
Co— lunication
Wholesale a Retail Trade

Medical Services
Other Services
Endogenous Government
Households

91
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1

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CURRENT PROPORTION OF »B£4
IN DEVELOPED USES

Figure 10.

8

9 10 U

I
1
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3

3
4
4

5
5
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6
7

t
4
4

o
9
10
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12
13
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15
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36
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12 13 1* 15 16 17 IB
8

4
4
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0.50 0 .1 0 0 .0 0 0 .0
-

1 .1 0

0.50
0 .1 0
0 .0 0

Current Proportion of Area in Developed Uses

92
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PN0JCC7E0 280P08710M OF
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Figure 11.

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4

10

11

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5
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1

1

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1 2 13

8
♦
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14

1 5 16
0.50 .
4.10 •
0 .0 0 0 .0
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17

—

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—

T
T
8
S
4
9

10

10

u

11
12

12

13
13
14
14

15
IS
14
14
17
17
14
14
14
14

20
20
21
71

22
22
23
23
24
74
25
25
26
24
27
27
28

28
24
24

30
30
31
31
32
32
33
33
34
34
35
35
36
36
37
3T

18

1 .1 0

0.50
0 .1 0
0 .0 0

Projected Proportion of Area in Developed Uses,
Run 1, Period 1

93

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8
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— — 444— — 444— — — — 4444
4 44 — 444—
— —
444—
—
4
-444— 444— — — — 444— — — — 444
t0
4—
-— — — 444— 444— — — — —
|0
444—
—
444— 444—
— — —
11
444444— — — — 444444444444— — 444-----11
#44444444— — — — 444444444444— — 444— —
12
4 4 — 444444— —
444444— 444— — 444— —
12
4 4 4 — 444444— —
444444— 444— — 444— —
13------------ — 444444444444444— 444— 444—
— — — -----]3
4— 444444444444444— 4 4— 444— — —
-----14-----------44—
-444— — --- 44 4444—
—
444----|4

|3
15
16

16
IT
IT
18
18
19
19
0
0
1
1
2
2
3

444—

...444—

—

—

444

4 4 4 4 4 . . . . . . . . . . . . . . . 4 4 4 . . ----

4—

— 4 4 4 4 4 4 — ......4 4 4 4 4 4 — — 4 4 4 — — — 4 4 4 4 4 4 —
444----- 444444-------- 444444----- 444—
444444—
444—
4 4 4 4 4 4 ......
4444-.— — —
— — —
— — -- —
444444-----444-------------------- 444---444444-----444---------------------444----444444----4 4 4 4 4 4 — .—
444—
444-— — — —
4 4 4 — 444— --444444—
-- — 444----- 444-----—
444— 444— —
44444
444444--44444-- —
— — — — — — — — —
—
444444-44444444—
444—
444—
— — —
444444444—
4444444— — 444— — 444—
—
—
444444444—
4444444---- --444— -- —
444444
— 444.-444444—
444444— — 444—
-— 444444— — 444— ---444444—
444444— — 444444— — — .— —
444444— 444-----444444— — 444444—
—
— — 444444— 444— —
4 4 4 4 — 4 4 4 4 4 4 ......444444444— — — — — 444— 444
444—

3

444+44.—

444444444.

..—

—

444—

444

44444444— 444444444444— — 444— 444444444— —
4444444— 444444444444—
444— 444444444—
444444444444444444444444444444444— 444444444444

5

9444444444444444444444444444444— 444444444444

6
6
T

4444948444444444444444444444444444444444444
•
4444444444444444444444 444444444
84444444
4
— 44444+

7

44

44444

4

44—

44444+

44
4444444
4— 4 44444+
444444444444444— 4
4+
444444444444444— — ♦♦++ +4+
8944**444444444444— — 4 4 4 4 + 4 4 4 4
■4II49444444444444444444444444444—
— 4*t+4*8i4t9844*+4+++++4+++++4++++++4+44—
444444444444444444944444444444444444—

1

1
2
2
3
3
*
4
5
5
6
6
7
7

44444444+

—

—

444+4444+444+4+44+444+44+44+444+4444--- 444444444----++++44+4++44+4+4+44+4+44+++4+++4+44+444444— — 4+4—
444+4444+++444+4+4444444++++4++44+4+444+44— — 444—
44++44++++4+++++4+4+++4++++—
++++++— —
4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 — 4 4 4 4 4 4 — ......
♦4444+++4+44444444+— — — 444—
—
-4 4 4 +4 +4 4 +4 4 4 4 + 4 4 4 4 — — — +4 4 — — —
—
4*4444 44+4+444— 444+44— — — —
— —
44444+ 4+44+44— 44+4+4----------------4+44+44
444444

1

2

3

4

5

4

T

8

4
5
5
6
t
7
7

8
8
9

9
1C
to
I)
11
12
12

13
13
14
t4
15

15
16

16
17
IT
18
if)
19
19
20
20
21
21
22
22
23
23

2*
2*
25
25
26
26
2T
27

2«
28
29
29
30
30
31

31
3?
32
33
33
3*
3*
35
35
36
36
37
37

9 10 11 12 13 14 15 16 IT 18 19 20

0.0

Figure 12.

—

*
*
5

8
8
9
9
0
0

I

444

444--" 4 4 4 ..... -

4
5
5
4
6
T

i

i
2

. 0.00

Projected Proportion of Area in Developed Uses,
Run 1, Period 2

94

1

2

3

4

S

6

T

•

9 10 II 12 13 14 15 16 17 16 19 20

I

-.—

-44

♦ 4 --- 4 4
444- -44
.........4 ♦ - ----- 4 4 4 —

♦44--

4 4 4 ------ 4 4 4
444—
—
444
444—
— —
444— —
—

m

4

44
44—
444—
44
4 --- 4 4
«4.....
444—
4—
44
444444♦4444444 4 4 ---- - --444444—
4 4 4 4 4 4 -- - - 44444
44444
♦4444444—
4444444—
4444444—
♦44444—
444444—
♦♦♦♦+♦—
4444—
♦44—
4444
♦44
44

.........4 4 4
-------- 4 44

444
♦44444
♦44444—
444444—
444444—
44444444
44444444
444—
—
444— —
444—
444—

------ 4 4 4 4 4 4
-444444
— — 444444
...444—
444
—
4 4 --- 4 4 4
4444—
444 44444—
444444—
444444—

...444—
444-

—
—
—
—
—

444—
444—
444—
444—
44444

444— —
—
—
444444—
—
444444—

6
4
444

♦44-♦44—
♦44— —
♦44—
444—
■

—

♦ 4 4 --4 4 4 ---

44

11

44— —
4 4 -- - - 4 4 --- —

11

4 4 -- - - 44444—
44444—
4 4 ---

------ 4

— 444— 444-

-444—
-444—
-444444-444444-

♦ 4 -- - - 4 4 ---4 4 -- - —
44444
44444—

4 4 4 4 4 --4 4 4 4 4 --44—
4 4 — ---44—
444
4 4 --- 4 4 4
4 4 -----44—
—
♦4444444

444

•♦♦♦♦♦♦

♦4 4

MB444444444
••••••444444444
— ♦♦•♦♦•••••••♦♦♦444444
444444444444444444444444444444444
♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦
444444444444444444444444444444444
♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦4444
444444444444444444444444
44444444444444444444444
4444444444444444444— '
♦♦♦+♦♦♦♦♦♦♦♦4 ♦♦♦♦♦— 1
♦ 4 4 4 4 0 4 4 4 4 4 4 4 4 --- 4 4 4
444444
4444444—
444
4444444
♦44444

1

2

3

6

5

6

7

•

M0JEC7CD 2009007I O N O F • « [ ♦
I N DCVELOPED USES
•UN • ! - 9 E 0 1 0 D 3

Figure 13.

9

1 0 11

10
10
12
12

13

444444—
4 4 --- 4 4 4 4
444444—
4 4—
4444
4444444444444

■

7
7
•
«
9
9

44.....

44.....

........

♦44444—
444444—
444—
44
444—
44
44444444

3
*

6

44.....
4 4 4 - -----4 4 4 ---------4 4 4 ---------4 4 4 ----------

3
«
5
5

— — 444—
....4 4 4 —

♦4

1
f
f

4
♦4

444—
4444444
♦444444
—
4444
—
4444
444444444444— 4444
— 4444
- 4 4 4 -444-

..44444
-44444
♦4444
4
444
44
4444444
444444444444—
4 4 ----— 444—
— 444—

13
14

14
15
15
16
16
17
17

IB
lt<
19
19
20
20
21
21
22
22
23
23

24
24
25
25
26
26
27
27
24
24
29
29
30

30
31

31
32
32

33
33
34

34
35
35
36
36
37
37

12 13 1 4 1 5 16 1 7 10
•
4
4
-

0.50 0 .1 0 -

0.00 >
0.0 -

1 .1 0

0.50
o.io
0.00

Projected Proportion of Area in Developed Uses.
Run 1, Period 3

95
II 12 13 I* IS It IT IB 19 29
I
1
— — 999
?
999
2------------------------------------------------------ ---- 999
3
— 999
3
-999
4
.......... .......
... .... ....
4
.........................
....
5
5
...............................
4

........—................-.
........—....... ..........
— ---- —— ----------------- . . . . . .

b

T
999
T
............................-- 999
A
...........---------99
B------------------------- ---------------------------- 9

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . --- . . .

9
1D

----------------- --------------------.............................------ —
............
............
999— ---— -------------- 999----999--------------------------------999----------094---------*-'-'----------------------4 4 4 ---- ......--4 4 4 .---— — — —
— —
—
—
— —
-------- —
— — -

10

11
11
]2
12

13

......—
......
.............
..............-- ---------- ------ ....-------944— -— ----------------------.... 4 4 4 -----

14

15
15
16

if
in
11
11
jg
ij
|3
{4

IS
15
16
444

-

16

-

IT
4 4 4 -------------------- 4 4 4 ----17
17
......--4 4 4 ------4 4 4 -----17
IB------ -------------- 4 4 4 ----- 4 4 4 ----------------------------lb
IB
— — --- ------- 444----- 944----------......16
19
..............---- ......
..........................
]9
19
........--- .................................-19
20
...
..................
.....................
2(
,
20
.............-- ...................................--2 (i
21
..............................------- -444----21
21
.....---------...4 4 *.
21
22
..................................
2?
22
..............................
.............
2?
23
23
..............-- .*944444444----------------------23
....-4 4 4 4 4 4 9 4 4 -------....
pj
24
..........--- .........------ ................--2*
2*
— --------------------------------------------------24
— 444----25
25
944----- 444------------25
4 ----- 4 4 4 ------------------------- . 4 4 4 ----25
26
.4 4 4 — 444------ 444-- 444494494444*------------26
---- 4 4 4 ...44444444444 —
---26
27
-444-..4
....— —
2T
27
44
-444»
— — --27
2»
44 44-----..... ......
28
26
444444—
— — — —
—
28
29
444444---------- — ----29
JO
Rf§464466
..........
29
30
2(91)4444— —
— — —
—
----30
30
--444444BBB949444
....................
30
31
444444— 444----- BB9444--- — -------------31
31
444444— 444----- 9B9444--------- ........-- .........
31
32
4 4 4 .-......................
3p
32
..........------ . 4 4 4 ----------------------32
33
33
33
— -----— ----------- ........------------33
34
..................................34
34
..........................................
34
35
44-----.........................
3b
35
...... 4 --------------........----35
36
.......
36
36
36
37
37
37
37
1

2

3

4

S

4

projected changes in

COHMCHCIAL LAND USE
RUN 41 - PERIOD 3

Figure 14.

7

B

26

9 10 II 12 13 14 15 16 17 IB 19 20

§

increased commercial use

4 PREVIOUS COMMERCIAL USE
- LITTLE/NO COMMERCIAL USE

Projected Changes in Commercial Use, Run 1,
Period 3

16

96
I

2

3

*

5

6

T

•

9 10 I I

12 13 |4 IS 16 17 IB 19 20
1
1
2

44444444
.
— ooo
...

...

**

..

2

* * ---------***. ....

3
3

... .... ....

*

4
----------- ...........---.
..S
5
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
*
............................... 6T
- - - - - - - - - - - - - - - - - - - ******--

-

----------- * * * ------------------------------

................

......« * * * * « ...

T

**

(

***...***-----.*«*

*

«

***---------------------------------- 9

«... .................................. 10lo

*« « . — .
...........................
******
* * * --------------------- * * * —
*********
* * * --------------------- * « *
12
* « . . . . . . ------- . . . . . . . ---- . . . . . ----. « « *

11
11

..«*«......

---------------- « * « . . . * * * --------- ---------------------------------------------------------------------------------------------«---------------- * * * — * * * - —-

***-------- --- .....«**

*«.--------------- ***-----

I*

—

IS
15

« ------- * ♦ * .................... * ♦ ♦ - « * * ---------------- * * « ----------- . . - * * *

« « *---------------------------------------------------------- . . . . . . . . . . . . . . . . . . . . . . .
****

..............

« **...........---.........--------------------------------

««*-.................................-......«**--

Of****..-.-...-.—
—

ia
ie

....-...-.-.............-***.—

..

* * .......
*
.* * *
***
***
« * * * -----------------* * * -----------------------------------------* * * --------- * * *
* « « ....
..* * * ..
— * * * ---------- ♦ ♦ * - * * * -----------M l ................................................ * ♦ ♦ — ♦ ♦ * .............

***

***

—

**«-*««

* * * * ---------- * * * ---------------- . * * * * * * --------------------------------------- * * *
* * * ---------- * * * ------------------* * * * * * ---------------4 **
999««------- - - ♦ * ♦ ♦ ♦ ♦ * ♦ ♦ ♦ ♦ *
♦♦♦♦♦♦— —
****
*« **« *« ***« *
« « **« «
—
«♦ ♦♦♦ ♦♦♦ ♦♦♦ ♦♦♦ ♦♦♦ ♦♦♦ ♦♦♦ ♦*♦♦♦ ♦♦
...ft* * * * * * * * * *
4444444444444444444444444444—
444444444444
9 9 4 « « » *a a a a a a a a a ****4 *M a m a a a a a a m *9 « —
a
H a H « 9 * * « * n a 4 9 4 M a a a 44* 444—
a a a a ia a i
— •♦*♦ ♦ ♦
•a
aaaa* « ...* * * * * *
M
•****« ■
9— 4 —
••••••**4 m » *4 *— 4
—
•••••••••***4 4 4 —
4 4 *4 444
• • • • • • • • • • • 4 * * * * * 4 — — ♦ *♦ •♦ ♦ ••♦
• • • • • • • • • • • • * 4 4 — — 4 4 ***4 4 **4 4 4 —
— M 4 a ii4 a iitta « « a a t4 4 4 —
- 444444444444—
* 4 4 — 944— • • • * * « • • • • ■ • — * 4 4 * 4 4 — — 444— * * * — - —
44 «— 444— a t l 4 4 4 * t a i a » — 444444----------- 4 * 4 — 4 * * ---------444444— 444444444444444— — 444— — 4 4 4 — —
—
444444— 444444444444444—
.-4 4 4 —
444— ----------------444444444444444444444444444— — — — — —
44444444444444444444444444—
— — —
4444444444444444444— — — — — — — ----444444444444444444— — — — — — — —
§41444 M * 4 4 - . - — 444— — — — — — —
• •4 4 4 4 • ♦ ♦ ♦ ----------- 444---------------------------------------4444444
444444

2

3

*

5

6

PROJECTED CHANGES IN
RESIDENTIAL LAND USE
RUN «1 - PER100 3

Figure 15.

]s
16
17
17

.—

....

* * . . . . . . ----- . . . . . . . . . . . . . . . . . . . . . . . ------- . . . . . . . . . . . . . . -------

1

|?

13
13
1*

T

B

19
19
20
20
2)
21
22
22
2J
23
2*
2*
25
25
26
26
27
27
«
2a
29
29
30
30
31
3)
32
32
33
33
3*
3*
35
35
36
36
37
37

9 10 11 12 13 14 15 16 IT 18 19 29
• INCREASED RE5TDCNTL USE

4 PREVIOUS RESIDENTIAL USE
- LITTLE/ND RESIOENTl USE

Projected Changes in Residential Use, Run 1,
Period 3

97
1

2

3

S*

*

T

a

•

10 11 12 13

1* 15 I *

IT 16 19 20

1

1

»

1
2
2

j

...

— ------— — ...
----- 6 6 6
..
.... -644
.......... .......
... .... ....
.........................
....

...

3

4
4

5

2

z
3
3
t
4

5

........................

5
*

. . . . . . . . . . . . . ----------------------

.. ..

S
t

..................--------7
---- ---------- .........-- ......
T----------------------------- .........................--------

t
1

a

a

6

7

. . . . . . . . . ---------------- . . . . . . . . . . .

•
«

-- ..................— ........ — ...... .

e
«

9
10
10 —
11
11
12
12

9
—
—

.........

10

10

-

.....................-------- ............— .
11
........—
......................--------------------11
.....---|2
............................
12
13------------ ............................---------- ............
n
........................
13
13
..................-- 1*
------- Off---------............
.........
i*
1*
......................-i«
15
15
...............................-------- ..................
15
16
....
.6661*0
16
16
— —
.6660(0
—
16
17
................-----------------666666
17-----6 6 6 6 6 6 ----17
16
........-- .....-- .......
...........................
]B
|0
16
19
19
16
........------- .............-- .........-------------19

........

..........................

20
20
21
21
22
22

15

17

.....

.......
............
—
.......---- ..........
....
.........
...
...............
.........

20
20
21
21
22
22

....
............
23
23
23
.........--------.......................---23
2*
.............
.......
......
26
26
.................
.........................
26
25
.............-............
......
25
25
....................----- ....................
25
26
6 6 6 6 6 6 -------------26
26
666666
.........
26
27
..........27
27
--------------27
26
—
— — — ..
......
20
28
...................
..
26
29
......................... —
29
29
6 6 6 -— ------------------------29
30
M S ------ . . . . . . . . . . . . . - * 6 6
-----30
..6 6 6 — 0 0 0 ---------- —
---- 6 6 6 -------30
31--------- -------- 406----- 6 6 6
..........................
3)
31
....---- 9f6— — 6 6 6
......
31
32
6 6 6 ------------------- —
-------------------3?
32
6 6 6 ------- ...............................----32
33
-------- ..............
33
33
......----........----33
34
............
34
34
..........................................
34
35
......---------35
35
...... ....
......................
35
30
.......
36
30
......
36
37
37
37
37
1

2

3

6

5

6

PROJECTED CHANCES IN
INDUSTRIAL LAND USE
RUN 01 - RCRIOD 3

Figure 16.

7

•

9 10 II 12 13 14 15 15 17 10 19 20
■ INCREASED INDUSTRIAL USE
9 PREVIOUS INDUSTRIAL USE
- U7TLE/N0 INDUSTRIAL USE

Projected Changes in Industrial Use, Run 1.
Period 3

98
1

2

3

*

9

6

T

■

9 ]0 11 12 13 I* 15 16 IT 16 19 20

1

1
|

|
2

—....

2

2

}

1
3

--

—

........
—
.......... .......

«

4

3

3

..............—

—

....

.at*

4
4

-at*

— --------. . . . . . . ----- . . . . . . . . . . . ----------

5

5

3

......-------- .................

b

...............................

6

. . . . . . . . . . . --- . . . . . . . . . .

6

6

...

6

.................................

7
7

1
7

a
a

******---------******

«

.........(4«t4(

444444-.— --44

a
a

-

9
9

-

9
..........9*44*4...— 994444-- — 94*-10 ----------------------- --....4 4 4 4 4 *4 4 4 .— 4 4 4 4 4 4 4 4 4 ...— ....

|n

--- ---- — 4 4 4 4 4 4 4 4 4 — 4 **4 4 4 4 4 4 —
— ---—
4*4
—
44444*444444—
—
—
-- . —
— 444
.— .— 4 4 4 4 4 4 4 4 4 4 *4 ..— —
aa---- —
...4 4 4 .— ...............4 4 4 ..
.......
m b . . . . . . . . . . . . * * * . . — . . . . . . . . . . . . . . 4 4 4 -- — —
-13
444444---- 444444-444-----------13
—
.—
4 4 4 4 4 4 — * 4 4 4 4 — .4 *4 —
------- 4 4 4 ----- 4 4 *-.................— aaa—
I*
14
4 4 4 ...— .9 4 4 ...
15-------4444*4------ 444---- 444*444*4—
—
—
—
15-----444444------ 444---- *44*44444--------------]«
...44*444----- ------------ 444-- 444-------- ....— —
14
— .4 4 4 4 *4 —
— —
—
**4 — 4 4 4 — —
— —
— — —
|7
. 4 4 4 --- --- ---- ----...----------|7
444
—
IB
4 4 4 -------444444----------- 444-- 4 4 *4 9 4 ..
.......
ie
— 4 4 4 ---------4 4 4 4 4 4 — — .—
— .4 4 4 — .4 *4 4 4 4 .— .........
19
.............. 4 4 *— — — — — — — — — — — 444— 491*44
19
. . . . . . . . . —
. 4 4 4 -................--- —
4 4 4 — .8*1*44
20
—
444—
— 4 4 *.— — —
4 4 4 — .....— — 4*44*4444—
20
4 9 4 --444
-***4*4444— 21
—
-------------- 4444*4
— .— — ***4**444444
21
— -- —
------------- 44444*----------- a*t4***4***4
22
— 4 4 4 ..—
—
4*4**4***— — .
22
........................4 *4 .....--44*444**4
—
23
......................4 4 4 — —
— —
.— . 4 4 *......
23
.....................4 4 4 . . . . —
—
. — . . . 4 4 4 ----2*
—
.....444— 444*4*
*44
2*
-------------- 4 4 4 ----4 * 4 * * * -------------------------------------- ------------------ 444
10

10

11

11

|1
12
12

1|
12
12

13
13
i*
u
15

15
16
16
17
17
]e
ie
19

19
20
20
21

21
22

2?
23
23

2*

25
—
— — —
— ——
———— — —
— — B 4»
25
26 -------------------- ------------------ 444*1*11*-------- 444--26
------ ------- 444******----- — 4*4-27
— 4**.
.
4*4 .- 4 4 4
27
—
4— —
- — 494
444

2*
25
?s
26
26
27
27

2B
29
29
30
30

26
29
29
So
30

—

—

—

---- . . . . . . . . . . . . . . .
***4 4 4 — 4 *4 —

*9*44t—

9 **—

—

—

—

..
—

— —

-= = 4 4 9 9 4 4 4 4 4 4 4 4 4 4 4 — 4 4 4 --------— —

—
—
— *4**44***44**44— 444----------31
...444*44-- 4*4----- . — 4 4 4 4 4 4 ---- —
-- 4444*4----31
— 4 4 4 *4 4 — 4*4—
.— **4444— — — — — — 4*4444— —
32 --------944494444-------- — 444------- —
---------32
— — ..4 *4 4 4 4 4 4 *.— ........4 4 4 ...—
33
4 4 4 4 4 4 *4 4 4 4 *..—
..— — —
444— —
—
33
4 4 4 4 4 4 4 4 4 4 4 --------- . —
. 4 4 4 ----------34
— . . . . . . . — . . . . . . . . . . . . . —4 44. . . . . — . — .
3*
. — — — . — . . . . — —. —. 444 . . ——
—
35
35
36

—
......
. . . . ---

36---------------------- —

— — — — —

—

—

31
31
3?
32

—

S3
34
34
35
35

36

----

36

37
37

37
37
11 12 13 1 * 15 16 17 IB
PROJECTED CHANGES IN
AGRICULTURAL LAND USE
RUN * 1 - PERIOD 3

Figure 17.

33

19 20

*

INCREASED AGR1CULTRL USE
4 PREVIOUS AGRlCULTUHL USE
4 DECREASED AGRICULTNL USE
•
L IT T L E /N O AGR1CULTRL USE

Projected Changes in Agricultural Use, Run 1,
Period 3

99
I

2

3

4

S

6

7

«

a 10 11 I? 13 1* 15 16 17 la )» 20

I

a ****—

1
2
2

3
j
4
4

—

—

5

i

— ---—
4*44 .......
... .... ....
******---- 444-----— -

3
3
4
4

..............

i

—
.
.
.
.
.
..............

6

—

5

------------- -♦♦♦---

6
6

I
2
2

444

T
...........................444444
7---------------------------- — -- — —
444444
I
........................— 44
a
4444
.
9
-------------------- 4 4 4
444
4
«----------------------- --------------------- 444- . . 4 4 4 ------ 4 4 4
10
---- -444---------------------------10
... 44-- ......................---..................
-444.-.......
, 1 ..................
444

-

11

12

5
6

T
7
a
!
9
9

10
10
11
11

12

—

12

12

444---- .............4 4 4 ......
13
.......--13
4-4 ---444----14---------- ..................--4 -------------------------14
........................ 4 4 ....-- ....................
15
4---------------------...........--------------|5------- 444--------------------------------------------------16-------444-------------------------------16----- -444---------- ............--- ...........................
17-------444--------------------------------17------- 444--- —
— — — —
— —
— —
—
—
—
—
Ia------ ---------------------------------- 4 4 4 -----------IB------ ------------------------------- — 444— -- ---- — — —
19
4 4 4 ---- 4 4 4 -------------------------------19
4 4 4 ---- 444-------------- — — — — — — — -20
—
— .4 4 4 -.---- — -- .........---- ....................
20
4 4 4 ----21
444
444
4 4 4 --444
21 --------22
444---------------------------------- 444--22
4 4 4 ------------------------- --------- 4 4 4 -23
— — — —
—
— —
23-----------.........-- .............................-2*------------- 44--- --------------------- —
— — — — — —
24-------------- 4---25--------------- 444— ------------------------------------ — —
25
f—
—
— .....—
—
—
—
—

13
13
14
14
IS
15
16
16
17
17
is

26

«—

26
27
27
26

—

♦♦♦ ♦♦*— — —

------

—

— 444(61(1 —
44441HI
6
66
M 66*
6
6 *—
M
—
—
6
§aa— —

28

— ♦ ♦ fa a a ta a —

4 4 ---------

——

—

—6

—

— — —

2

3

4

5

6

7

B

|9
20
20

2]
21

22
22

23
23

24
24
25
25

26

— -6*4*44
♦♦

26
29
29
30

30
31
3)
32
32
33
33
3*
3*
35

35
36

36
37
37

9 10 11 12 13 1* 15 16 17 18 19 20

RROJECTED CH6 NGES IN
RECREATION RESIDENTIAL LAND USE
RUN 61 . RERIOD 3

Figure 18.

]9

26
27
27
2*

29-------------------------------- 4*4-------------------- 4 444
29
*44444
— 444444
30
— 444-— --30------------------- --------------------------------- . 4 4 9 ----31
— ............ -........................-......
31.........
-.........................
— 32
— -- -444------------------32
4*4---------------------- -- — — — — ----33
33
— — — — — — — — — — —
—
----3*
*44-------------------------------3*
— 44*-------------------------35
644---35
6 6 *—
— — ---— ---................— -.......
36
36
—
—
37
37
1

\^

t
6

-

INCREASED DEC RESID USE
RREVlOUS REC RES10 USE
LITTLE/NO REC RESID USE

Projected Changes in Recreation Residential Use,
Run 1, Period 3

100
maps.

Again intensification of commercial use in or near

those parcels already containing significant commercial use
and increasing residential use in several parcels, predom­
inantly east and northeast of Petoskey and in the Harbor
Springs

area

are

indicated

and would

be

expected.

A

somewhat striking absence of further development in other
parts of the county is suggested by this series of maps.
More will be said about this result in the next section.
Independent

of the

future development,
is that
as

to

the total

not

question

an important

of

distribution

result

from this run

level of future development

strain the

supply of

suitable

of

land

is such
for

any

of the various uses, at least to an extent that is detect­
able by this model in conjunction with this data base.
This leads to some serious questions about the effective­
ness of the model for its intended purpose, and these also
will be considered in the following section.
to the question of whether

It also leads

such a result holds true for

substantially higher rates of economic growth.
It

is

not

difficult

to

justify

consideration

higher rates of economic growth for Emmet County.

of

First of

all, in the last two decades Emmet County has had a higher
population growth rate than Michigan in general. Secondly,
but more importantly, historic real economic growth in the
United States has been much higher than population growth
rates.

Following this reasoning, a second run was executed

with final demands established in order to result in gross

101
output

growth

rates

that

approximate

the

costant

dollar

growth in contribution to gross domestic product by sector
during the 1970's.
about

The real

3.4 percent per year

economic growth rate had been
or about

18 percent per five

year period (based on data from the U.S. Dept, of Commerce,
Bureau of Economic Analysis reported in the Economic Report
of the President,
percent per

1981,

p.

245) as opposed to the five

five year period used for the first run.

So

economic growth and corresponding land use requirements are
substantially higher for this second run.
The
sector

final

demands

and

resulting

gross

outputs by

from this second run are shown in Table 9.

Again

proportion of parcel area in developed uses is used as an
index to indicate overall

land use trends

in the printer

maps of Figure 19, Figure 20, and Figure 21 for this run.
Again,

increased

Petoskey

is

developed

indicated,

but

use
is

east

and

even more

northeast

pronounced,

contrary to the previous run, by the third period
21)

noticeable

Petoskey,

and

(Figure

development also occurs south and west of

in Harbor Springs, and north along Highway 31 at

Pellston,

Levering,

Paradise

Projected changes
in Figure 22.
development
ures,

of

and Mackinaw City.

in commercial use are displayed

The pattern observed reflects

trends

with most

Lake,

seen

of the

the overall

in the preceeding series of fig­
increase

occurring

in and around

Petoskey but with some also in Harbor Springs, north along
Highway 31, and even some, perhaps questionably,

in Cross

Table 9.

Projected Final Demands and Gross Outputs for the Second Run

Sector

Final
Demand

Period 1
Gross
Output

(Thousands of Dollars)

Period 2
Final
Gross
Demand
Output

Period 3
Final
Gross
Demand
Output

1929

5953

1929

6515

1929

7213

20251

25060

20251

26088

20251

27376

2935

4530

3238

4980

3567

5480

Cement a Concrete
Producte Manufacture

11677

15330

12919

16911

14279

18622

Electrical a Transportation
Equipment Manufacture

11783

11783

14717

14717

18382

18382

Primary Metal a Metal
Fabrication

4041

4804

4433

5326

4857

5909

Nondurable Manufacture

3920

7071

4590

8375

5366

9938

Transportation, utilities.
Communication

2549

19639

4742

24699

7634

31135

15257

49681

20210

59532

26258

71617

Finance, Insurance a
Real Estate

7597

30499

11666

38133

16946

47816

Lodging a Amusement
Services

9120

12504

11301

15233

13974

18582

32696

40778

40361

49643

49736

60495

4804

21212

7058

26075

9099

32153

346

3234

346

3846

346

4613

37174

131280

37174

149295

37174

171494

Agriculture
Construction
Wood products
Furniture Manufacture

Wholesale a Retail Trade

Medical Services
Other Services
Endogenous Government
Households

103

1 2 3 4 5 6 T 8 « 10 I) 12 13 1* 15 16 17 IB 19 20
i
1
2
2
3
3
4
44 —
.—
.444—
4
— ---------------------------------- 5

1
1
2
2 ---------------------------------------------------------------

J

—

44999MB
944-— 949
4 4 --- 4 4 4
44—
444
—
444- *444
—
-444
4 4 4 ----- —
-444

—

3

-

*
4

5
5

— --

6
6

—
— —

T
T
e
a
9
9
,e
to

—

4 4 4 -------------------------------------------—
—
— — —
444444444

444—

— —

—

—

— —

.4 4 4 4 4 4 4 4 4

— 4 4 4 ------ 4 4 4 —
—
---- 4 4
— —
444—
—
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—
4444
444— .444—
—
—
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— .... 4
-444—
4 4 4 — — ----—
444
444
4—
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444. ...
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— 444—
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444444— — — — — — 444444444444—
—
444—
—
444444444444—
4 4 4 — ----1 1 - - - - - - - - - - - - - - - - - - 4 4 4 4 4 4 4 4 4 ------ —
12
44—
444444—
—
444444—
444— —
444—
—
12--------------- 4 4 4 —
4 4 4 4 4 4 ------ —
444444—
444—
444
—
13
—
444444444444444—
444—
444— — —
-------------13
4.-444444444444444—
4 4 --- 4 4 4 - -- —
— ----- —
14
4 4 ------ 4 4 4 — — — —
4 4 --- 4 4 4 4 —
— — — — - -- - 4 4 4 - ----)4
444— —
444—
—
4 4 4 4 4 4 4 4 ----- — —
--- —
4 4 4 --- —
15
4—
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— 4 4 4 4 4 4 --15
444—
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—
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—
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16
444—
- - - - - - - - - — --- —
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---- . . 4 4 4 4 4 4 --- —
16
4 444.— — .— — —
.—
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- - - - - - - - - - - - - - - 4 4 4 4 4 4 — - --17
444—
- - - - - — — --- —
—
444—
—
—
— — —
444444— — —
17
444—
—
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— — 444— — —
- — — ----- - 4 4 4 4 4 4
—
18
444444—
—
— 444—
--- 4 4 4 —
—
— —
—
444—
444—
IB
444444— —
—
444—
—
444—
—
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444—
444—
—
19
44444—
— —
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— — — — — 444444—
----- —
—
— — — — — — - - - - - — . 4 4 4 4 4 4 --19
4 4 4 4 4 ----- — —
20
44444444— —
444—
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—
—
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20
4444444—
—
444—
—
444— — — —
—
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21
4444444—
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444—
444444—
21
444444—
444—
—
.444444—
444—
—
444444—
22
444444—
—
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—
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— — --- 4 4 4 4 4 4 —
444— —
22
444444—
—
444444— —
—
— —
—
444444—
444— —
23
4444—
444444—
444444444—
---------------4 4 4 —
444
2 3 -------------- 4 4 4 —
4 4 4 4 4 4 ------ 4 4 4 4 4 4 4 4 4 —
-444—
444
24
44444444—
444444444444—
4 4 4 --- 4 4 4 4 4 4 4 4 4 - - - - - 2*
4444444—
444444444444—
444—
444444444—
25
444444444444444444444444444444444—
444444444444
25
4444444444444444444444444444444—
444444444444
26
4444080444444444444444444444444444444444444
26
■
4444444444444444444444 444444444
27
#4444444
4
—
444444
*7
44
44444
4
44—
444444
26
44
4444444
4—
4 444444
44
26
4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 --- 4
29
444444449444444444—
4444
444
29
#99444494444444444444— -444444444
30
##f •99444444444444444444444444444— 30
— 444444999999444444444444444444444444444—
31
4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 --- 4 4 4 4 4 4 4 4 4 —
31
444444444944444444444444444444444444—
444444444—
---32
444444444444444444444444444444444444444444—
—
444—
32
444444444444444444444444444444444444444444—
444—
33
444444444444444444444444444—
444444
—
33
44444444444444444444444444—
444444—
—
34
9444444444444444444—
— 444— — — — — —
3*
444444444444444444—
—
444—
—
—
35
944444 44444444—
444444—
—
— —
—
—
35
444444
4444444—
4 4 4 4 4 4 ------------------------36
4444444
36
444444
37
37
1

Figure 19.

2

3

4

5

6

T

8

9

1 0 11

12

13 14 15

16

17 18

0.0

-

0.00

6
6
4

7
7
(
ft
4

9
in
10
1)
11
12
12
13
13
1*
14
16
15
16
16
17
17
)ft
16
19
|4

2n
20
21
21

22
22

23
23
2*
24
25
25
26
26
27
2»
2ft
28
29
29
30
30
31
31
32
32
33
33
3*
3»
35
36
36
36
37
37

19 20

Projected Proportion of Area in Developed Uses,
Run 2, Period 1

104

I 2 3 * 5 6 T t 9 10 11 12 13 I* IS 16 IT 18 19 20
♦♦♦♦♦•oo

t
t

♦♦4— 999
44— 494
««— 444
.......----..
444- -444
-444
. . . . . . . . . 4 4----......4 4 4 ..—
4 4 4 ..— ...
.4 4 4

2
2
3
3
*
«

...............................
444— ---444----------------6
........-- ....--- -----444444444
— ------ ....-— .....--- 444444994
.,444— — 444------44
4 4 4 ----- 4 4 4 .......
44444
444— -444----------- 444
.444— -444— — — — 444------- —
444
4-- --------- ++4— 444-------------444-- - - - - - - 444- - 444
..........
♦44444— — — — 444444444444— — 444— —
444444444— — — — 444444444444— — 444--- —
44— 444444--- — .— .-444444— 444— — 444— --444— 444444— — — — 444444— 444— — 444— —
— 444444444444444— 444— 444—
—
4— 444444444444444— 4 4— 444— -- — -- —
--44----- 444-------- 44
4 4 4 4 -- .....---444--444—
— 444— — — 444 44444--------------- 444----4— —
444444-------- 444444------ 444----- — 444444-444— —
444444— —
444444—
444—
— 444444—
444----------------------------------- — — 4 4 4 8 M --4444—
—
444---. . —
. 4 4 4 -- --- .... ------444444----444—
----- ------- ---444*----- ---------- 444444----444444— —
444— — .444— — — — — —
444— 444— --444444— ---- -444----- 444---- ...— —
444— 444—
44444*.— —
— — — — — — — — — — — — —
— — 444444—
44444-------------------------- — ....--- . . . . 4 4 4 4 4 4 -44444444—
444— — — 444— — — — —
—
444444444—
4444444—
444---- -444--...4 4 4 4 4 4 4 4 4 —
4444444—
— 444— —
444444— —
444— — 444444—
444444— —
444-- — — 444444— -- 444— — 444444—
444444—
444444*—
—
—
444444— 444—
444444— — 444444— — — — — — — —
444444-- 444-----*
4444— 444444— — 444444444—
—
— — 444— 444
4 4 4 — 444444—
— 444444444—
—
444— 444
♦4444444.— 444444444444— — 444— 444444444—
—
4444444— 444444444444—
444— 444444444—
♦44444444444444444444444444444444-- 444444444444
4444444444444444444444444444444— 444444444444
4494aaa444444444444444444444444444444444444
•
4444444444444444444444 444444444
#4444444
4
+44+44
44
44444
4 # 4 — .4 4 4 4 4 4
♦4 4444444
4— 4 444444
♦44444444444444— 4
44
♦44444444444444444— 4444 444
••t444444444444444444— 444444444
#§•888444444444444444444444444444-— 4444448#8##8444444444444444444444444444— 444444444444444444444444444444444444— 444444444—
444444444444444444444444444444444444— 444444444— -444444444444444444444444444444444444444444—
444—
444444444444444444444444444444444444444444— *— 444-444444444444444444444444444— -444444—
—
♦4444444444444444444444444— 444444—
— —
♦+44444444444444444— — —
444— — — *— —
444444444444444444— — — — 444— — — — — —
♦44444 44444444— 444444—
—
—
♦44444 4444444— 444444— — — — —
—
♦♦♦♦444
444444

5

...----

.................-- ......----—

1

2

3

4

5

4

7

8

PR0JEC7E0 PROP0 9 7 ]0 N 07 ARE4
IN DEVELOPED USES
R U N P2 * PERIOD 2

Figure 20.

9

5
6

7
7
6
8

9
9
»0

|o
11
JI
12
I2
>3
13
14
14
13
15
16
16
17
17
18
18
19
10

20
20
21
21
22
22
23
23
2*
2*
25
25
26
26
27
27
28
28
29
29
30
30
31
31
32
3?
33
33
3*
34
35
35
36
36
37
37

in 11 12 13 1* 15 |6 17 |8
•
4
4
*

0.50 •

1 .1 0

0 .1 0 *
0 .0 0 0 .0
*

0 .1 0
0 .0 0

0.50

Projected Proportion of Area in Developed Uses,
Run 2 Period 2

105
1 2 3 * 5 6 7 t 9 10 II 12 13 I* 15 16 IT 16 19 20

1

1

1

♦64-- 9
♦♦— 4

2

2

♦♦ ♦

3
3

444- -4

4

....

-4 6---

.+4 4 ...

*
5

...4 4 4

.4

----- —

5
-- 444-— 444-

6
6

7
7
a
a
9

— —

9
10

4 ---

44 4 - --

10
11

20
20
21
21
22
22
23
23
2*
2*

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6
4

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444— — — 444— — — 444 44444 ------------- 444 --4—
—
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—
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— 444— — — —
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444—
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4 4 4 4 4 4 4 ---- - . + 4 4 —
—
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—
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—
—
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444—
4
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—
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4
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4 4 4 4 4 4 4 - - - 4 4 4 4 4 4 4 4 4 4 4 4 ------ 4 4 4 --- 4 4 4 4 4 4 4 4 4 —

25
25
26
26
27
27

•4444444
4
— 4444
94
44944
4 44— 4444
44 4444444
4— 4 4444
444444444444444— 4
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444444444444444444666404444444444444— 444444444—
444444444444444444444444444444444444444444— — 444444444444444444444444444444444444444444444— — 444♦44444444444444444444444444— 444444—
44444444444444444444444444— 444444--—

26
26

29
29
30
30
31
31
32
32
33
33
36
3*
35
35
36
36
37
37

444444444444444444-------- 444------♦44444 4*444444— 444444— —
— —
♦44444 4444444— 444444------------♦444444
444444

1

2

3

4

5

6

7

6

P60JCC7ED 4609067ION OF 6 6 E6
IN DEVELOPED U5FS
6 UN 92 - PERIOO 3

Figure 21.

—
—

444
444—
—
—
—
—
444—

7
7
6

4 4 4 4 4 4 4 4 4 ------------- 4 4 4 4 4 4 4 4 4 4 4 4 —
4 4 4 ---—
444444—
—
.—
— 444444—
444—
— 444—
4 4 4 . - 4 4 4 4 4 4 ------------- 4 4 4 4 4 4 —
444—
444—
—
444444444444444—
444—
444—
—
—
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4 . - . 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 . • 4 4 --- 4 4 4 — —
—
---- - ---44—
444—
44
4444----------4 4 4 -- -

1*
1*
15
16

16
19
19

6

44

13
13

16

—

444444—

11
12
12

16
16
17
17

— 444—
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.—
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1
2
2
3
3
*
*
5
5
6

9
9
10
10
11

11
12
12
13
13

1*
14
15
16
16
16
17
17
16
16
19
19
20

20
21
21
22
22
23
23
24
24
25
25
26
26
27
*7
26
24
29
29
30
30
31
31
32
32
33
33
34
34
35
35
36
36
37
37

• 10 11 12 13 14 15 16 17 16
•
♦
4
.

0.50 0 .1 0 0 .0 0 0 .0
-

1 .1 0

0.50
0 .1 0
0 .0 0

Projected Proportion of Area in Developed Uses,
Run 2, Period 3

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. . . . . . . . . . . --35
36
36
37
37

1

2

3

*

5

f

7

RROJECTEO CNANGES IN
COMMERCIAL LAND USE
RUN 12 * RERIOD 3

Figure 22

•

9

16 11 12 13 1 * 15 16 17 IB 19 20

• INCREASED CO*«<f»CI»L U«E
f RREVIOUS COMMERCIAL USE
- L1T1LE/NO COMMERCIAL USF

Projected Changes in Commercial Use, Run 2,
Period 3

17

107
Village.
dential

Figure
use,

23

displays

indicating

projected

increases

changes

west

and

in resi­
south

of

Petoskey, as well within the town itself, east and north to
Harbor Springs, and northeast along the highway.
industrial

park

industrial use,

at

Pellston

and

current

Given the

locations

of

the projected increases in industrial use

shown in Figure 24 seem reasonable, except perhaps for that
at Levering.

Projected increases in agricultural use are

are shown in Figure 25 and should be compared to the map of
soil productivity
Chapter

III.

agricultural
needed

for

for agricultural

use of Figure 6 in

Notice in Figure 25 that no shifts out of
use
other

occur,
uses

indicating
over

coming out of the forest

that

additional

time through

this

and open category.

area

run

is

Figure

26

shows projected changes in seasonal home land use for this
run.
Again,
about
be

even

economic

with

growth,

constraining

for

these

very

suitable
any

use.

optimistic

land

is not

However,

assumptions
revealed

potential

intensification of what might be conflicting

to
for

uses within

close proximity is suggested by the individual use maps of
Figure 22,
central

Figure 23,

Petoskey

and Figure 24.

(column

indicated as an area that

9, rows 30,

Central and south
31,

and

32)

is

islikely to experience inten­

sified commercial, residential, and industrial use.
Since neither of these first two runs encountered con­
straints due to insufficient suitable area, a final run was

108
1 2 3 4 3 6 7 6 6 10 11 12 13 1* 15 16 17 10 1« 20
I

1

1
2

-- Ml
— 44444444
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3

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5

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5
5

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6
7

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9

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13 ------------ — ---- — 444-- 444------------------------ ------—
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24
30
30
31
31
32
32
33

444444444444444444444—

446494464

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— 499644644646464444444— 444444444444444—
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444— — — —
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33
3*
3*
35
35
34
36
37

37
1

2

3

4

5

6

PROJECTED CHANGES IN
RESIDENTIAL LAND USE
RUN 42 - PERIOD 3

Figure 23.

2

4

— -------------------------

6

1
2

T

6

« 10 11 12 13 1 * 15 16 17 IB
6

4
-

7
7

ft

8

«

o
In
10

11
11
12
J2
13
13
14
14
15
15
16
16

IT
17
Ik
18
19
20
20

21
21
2?

22
23
23

2*
?4
25
25
26
26

27
27

28
24
24
29
30
30
31
31
32
32
33

33
}«
34
35
35
36
36
37
37

19 20

INCREASED RESIOCNTL USE
PREVIOUS RESIDENTIAL USE
LITTLE/NO RESIDFNTl USE

Projected Changes in Residential Use, Run 2,
Period 3

M ---------------- 3

109

1

2

3

9

S

*

T

•

( It II 12 13 1« 15 16 IT It 19 20

)

1
2
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12

13
13

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15

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20

21

22

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25

. . . . . . . . . . . . . . . . . . . . . . . . . .

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26

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27

20
29

S Sfi. . . . . . . . . . -------------------------------------

29

aaa- - - - - - - m -----..999—
a t * ----------------------------- 9 9 6 ----------— aa a
— ------------------h i - - - - aaa- - - - - - - - - - - - - - - - - - - - - - - 999
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so
si
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-- . . f f f . . . .....-|(l...............
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30

32
33

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........................................... 33.
------------------------------------------------------ 3.
- - - - - - . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . --35

...............

J5

.......
36
----------------------------------------------------- 36
37
37

1

2

3

9

5

6

PROJECTED CHANGES IN
INDUSTRIAL LAND USE
run *2 - period 3

Figure 24

7

a

*

10 U

12 13

19 15 16 IT

ia

19 20

• INCREASED INDUSTRIAL USE
* PREVIOUS INDUSTRIAL USE
- l i ttle /n o industrial use

Projected Changes in Industrial Use, Run 2,
Period 3

110

1 2 3 4 5 6 T * * 10 11 12 13 14 15 16 IT 16 19 20

1
1
2
2
3
3
4
4
5

5

6

t

1
•
•
1
9
I
1
«
»
•
9
1
•
9
1
•
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•
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1
9
•
9
9
9
9
9
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9
1
9
1

6

•
9
1
1
•
t
9
f
I
•
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•
1
•
1
1
9
•
•
1
1
•
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1
1
1
9
9
•
•
9
9
1

T
T
•
«
9
9
1U
10
1)
11
12
12
13
13

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-----------------------* 0 4 4 4 4 -------------------- ----------------------------- 4 4 4 * * 4 ------------ 4 4 4 4 4 4 ------------4 4
———
444444— —
444444— — 4 44 —
-------- . . . . * 4 4 4 * 4 4 * 4 . — 4 4 4 4 * * 4 4 4 —
—
.... .. .
. . 0 4 4 4 9 4 4 9 4 - . . 9 9 4 4 4 4 4 4 4 ----------------------------------------------> 4 4 4 — ------. — 4 4 4 4 4 4 4 4 4 4 4 4 --------------------------- ------------------- 4 4 4 ---------- --------4 4 4 4 9 4 4 4 4 4 4 4 — -----■ 0 ------------------------ 4 4 4 -------------- -----------------------4 4 4 ............ .................
M l ------------------------ 4 4 4 — — ----------------------------- 4 4 4 ------------------ ------------------------ — 4 4 4 4 4 4 . . . 4 4 4 4 4 4 — . 4 4 4 ---------------------------------------------------------------4 4 4 4 4 4 . . 4 4 4 4 4 . . * 4 4 4 ---------------------------------------------------- 4 4 4 ------------ 4 4 4 —
--------------------------------------' I I I ----------------------------- 4 4 4 ------------ 4 4 4 - — — — —
—
—
MB— —
— ---------------- 4 4 4 4 4 4 ------------ 4 4 4 ------------ 4 4 4 4 4 4 4 4 4 ---------- ---------------- —

14
1*
15

10
10
11
11
12
12
13
13
1*

14
IS
15
16
16

IS
— .4 4 4 4 4 4 ----- ---------- * 4 4 4 ---4 4 4 ---------------------- 4 4 4 4 4 4 ----------------- 4 4 4 — 4 4 4 --- ------- ------------ 4 4 4 -------------------------------- BOO-----------

16
16

IT
IT
IB
IB

-- 4 4 4 -------- 4 4 4 4 4 4 ----------- 4 4 4 - — 4 4 4 4 4 4 0 0 0 --------- 4 4 4 -------- 4 4 4 4 4 4 -- ---- --- 4 4 4 -- 4 4 4 4 4 4 1 0 0 ------------------------- ------. - 4 4 4 -------------------------- BOO-- § 0 0 4 4 4
------------- 4 4 4 -------------------------- (||-- BB0444
---- 4 4 4 ----- 4 4 4 ----------- 4 4 4 ----------- B B 0 0 00 4 4 4 —

19
19

20
20

IB
IB
19
14

20

--------4 4 4 ------------ 4 4 4 -------------------------4 4 4 -------------------------M 0 0 0 0 4 4 4 —
---------------------------------------------------- 4 4 4 4 4 4 -----------------------* 0 0 0 0 0 0 4 4 4 4 4 4
----------- --------------------------------------4 4 4 4 4 4 ------------------------- M B 0 0 B 4 4 4 4 4 4
--------------------------------------------------4 4 4 ------------------------- 4 4 4 9 4 4 0 0 0 --------------------------------- ---------------------------4 4 4 ------------------------- 4 4 4 4 4 4 | ( ( — ------------------------ . • 0 0 * 0 0 ------------ 4 4 4 --------------------------------------M O - - ' -------------------- .* 0 0 0 0 0 ------------ 4 4 4 - — ------------ ------------------- ( d -------------------------- 4 4 4 ------4 4 4 1 * 1 ------------------------------------------- M O — * 0 0
----------— 4 4 4 — 4 4 4 * * * ----------------------------------- --------• * ( — . * ( *

21

21
22

22
23
23
24

•
•

9

m
m
m

•

T

25
25
26
26
2T
2T
20
20
2v
29
30
30
31
31
32
32
33
33
34
34
35
35
36
36
3T
3T

20

21
21
22
22
23
23
24
24
25
25

•
•
•

24

- M l * * * ---------------- -----------------------------------------------------------B IB

26
2T

rr

9
9
9

1

!

I
•

9
9

4 4 4 ------4 44
— 4 4 4 — 444

t

-

•
9
1

1

-

9
9
9

1
1

...-* * * .
—
* --------

9

1

26
26
29
29
30
30
31
31
3?
32
33
33
3*
3*
35
35
36
36
3T
37

— * 4 4 4 4 4 — 4 9 4 --------—
. . . ------- 4 4 4 4 4 4 -- 4 4 4 - * — . ---------- 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 ---4 4 4 -------------------------------- 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 ------4 4 4 ---- ------- 4 4 4 4 4 4 -- 4 4 4 -- *--- * 4 4 4 4 4 4 — ----- — .4 4 4 4 4 4 ----- — 4 4 4 4 4 4 - — 4 4 4 ------------------ 4 4 4 4 4 4 -----------------. — 4 4 4 4 4 4 - — --------- .4 4 4 4 4 4 4 4 4 ----------- 4 4 4 ------------------------------------------------------ 4 4 9 4 4 4 4 4 9 -------- — 4 4 4 -------- --------- -- —
4 4 4 4 4 4 4 4 4 4 4 4 * 1 * -------------- 4 4 4 ----------* 4 4 4 4 4 4 4 4 4 4 * 1 * --------— — ------------ . 4 4 4 --------------------------------------------------------------------------------- 4 4 4 ------------ ------------------------------------------------------------------- 4 4 4 ---------------- -------

1

2

3

4

S

6

PROJECTED CHAN6ES IN
AGRICULTURAL LAND U5E
RUN * 2 - PERIOD 3

Figure 25.

IT

T

•

9 10 U

12 13 14 IS

§
*
♦
-

IB

IT

IB

19 20

INCREASED AORICULTRL
PREVIOUS ASRICULTORL
DECREASED AGRICULTRL
L IT T L E /N O A6RICULTRL

USE
USE
USE
USE

Projected Changes in Agricultural Use, Run 2 ,
Period 3

Ill
1 I 3 * S 6 T 8 9 10 11 12 13 1* 15 I* IT 18 19 20

1
1

1

4 9 9 9 9 ------

2

1

2

III----

2

l|---

3
3
4
4

—

.. ... ... .

5- - - - - - - - - - - - - - - - - - - - - - - - -

—
—..
— 4 4 4 # 9 -----------—
•—
. . . 9 9 9 9 4 1 — -------- 9 9 9 --------------

........- - - - -

2
3
3
*
9

.. ..
——

. . . . . . . . . --------------------------—
444— —
— — — — —
6----------------------------------------------------------------------------- 9 9 9 ---------------------------------------7------------------------------------------------------------------. . . . . . . . . . . . . . . . ----- —
444444
7
.. .. . . . .
................9 9 9 9 9 9
B
---------------- . . . . . . . . ----------------99
6------------------------------------------------------- . . . . . . . . . . . . . . . . . . . . . --------------- 9 9 9 9
9---------------------------------------------------9 9 9 ----- 9 9 9 ----------9
9-------------------------------------------------. . . . . . . . 9 9 9 ----- 9 9 9 — ---------- 9 9 9
10
.... . 9 9 9 -------................-----10
—— —
9 9 ----------------------------------------------- ---------------11
. . . . . . . . . . . . . . . . . . . . . ------------ 9 9 4 ---------------- -------------11
— -----------------------------. . . . . . . . . . . . . . 9 4 4 — — —
—

S

5 --------------------------------------------------------------------

5

6

12
12

...................................................

9

6
7
7
6
9
9
9
|o
in
11
11

12
12

-

13
. . . ---------------------7 * 4 -------------- ------ - 9 9 9
—
13
13
. . . . . . . . . . . . . . . . . . . . . 9 9 ---------------------. . . . - . . - 4 9 0 — —
13
1*
. . . . . . . . . . . . . . . . . -----------4 ---------------------------------------------19
1*
. . . . . . . . . . . . . . . . . . . . -4 4 ------------------------—
I*
15------------------ 9 ---------. . . . . . . . . . . . . . . . . -------------- . . . . . . . . ------------15
15
9 9 9 ------------------.. .. . . . . . . . . . . . . . . . . . . . . . . . . .
is
16
9 9 4 - - . - - — — - - - - -------- — . — . . . . . . . . . . .
. . ... ... ..
16
16
- 9 4 4 ------. . . . -----------------------------. . . . . . . . . . . --------- ------------------ ------------------ — - 16
17
9 4 4 -------------------. . . . . . . ------. . . . . . . . . . . . ------------17
17
944
.. . . . . . . . . . . . . . . . . . .
17
18
. . . . . . . . . . . . - - - - - - - - - - - - - - - - - - - - - ------ 4 4 4 — --- - - - - - - - - - - - - - lt18
. . . . . . . ---------— ------------------------------------------------4 4 4
.. ... . . . . . . . . .
18
IV
---------------- 4 4 4 ------------------ 4 4 4
.. ... ... . . . . . . . . . . . . . . . .
19
14
---------------- 4 4 4 — --------4 4 4 -----------------------------------------------------10

-------

20

20
21

21
22

— -- ....................

po

- . . . . - . 4 4 4 . -------------. . . . . . . . . . . ... ... ... ... ... .. ... .
— -------4 4 4 --------------------444
. . . . . . . -------- — 4 4 4 ---------444
. . . . --------- 4 4 4 ----------------------------------------------------------------------------9 4 4 ------

4 4 4 ......---------

21
22

4 4 4 ----------------

22

2 3 -------------------------— -----—

23
24

24

—

....

.—

9 4 4 ...

20
2

)

2?

---------------------------------------. . . . . . . . . . . . . . . . . ----------

73

— -- ..............................................
4 4 ----------------------------------------------4 ----------------. . . . ---- . . . . . . . . . . . . . . . . . . . . . . . . . . .

23
24

24

25
4 4 4 ---------------------------------------------------------------------------------2 5 ------------------------------------- 4 ------------------------------------------------------------.. ... . . . . . . . .
26
* - ---------4 4 4 4 4 4 -------------------------4 4 4 8 B 9 M 8 ------------------26
44— — —
—
4 4 4 M 8 II — — — —
27
4 4 4 4 8 II9
9 ------- — — ------27
99
9 9 1 *4
9 # 9 — — -----18
M
— — --------— - 4 9 9 9 9 *9
28
to o
4
44
29
9 4 4 -------------------------------------------4
444
20
494444
444444
30
. . . . . . . . . . . — . . . — . . . -------- — 4 4 4 --------- —
30
31
. . . . . . . . . . . --------------------------- . . . . . . . . . . . . . . . . . ------ . . . . . . -------31
32-------------------- ------------ 4 4 4 ---------------------------------------------- -------. . . . . . . . . . . . . . . . . . . .
32-------------------- ------------ 4 4 4 - ------------------------- . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
33
— —
— —
—
— —
—

25
25
26
26
*7
27
«
28
29
29
30

33

....................

33

34-------------------------------------------4 4 4 ---------------------------------------------------------------------------34
. . . 444—
. . . . . . . . . . . . . . —
—
—
35
994— — — —
— —
— —
—
—
35
4 4 4 ----. . . . . . . . . . . . . . . . . . . . . ------ . . . . . . . -----36
—
36
—
37
37

3*
34
35
35
36
36
37
37

..................

1

2

3

4

5

6

7

8

9 |0

PROJECTED CHANGES IN
RECREATION RESID ENTIAL LANO USE
RUN 9 2 > PERIOD 3

Figure 26.

11 12 13

-

444

30

31
31
32
32
33

14 15 1 6 17 18 19 2 0

9 INCREASED REC RESID USE
4 PREVIOUS REC RCSIO USF
- L lT T L E /N O REC RES10 USE

Projected Changes in Recreation Residential Use,
Run 2, Period 3

112
set up largely as a demonstration of how the model reacts
when suitable land does become constraining.
When

this

study began,

it was

suggested that Emmet

County's rich resource base had potential for alleviating
some

persistent

economic

disparities.

Timber

is

one

resource in the county that is substantially underutilized
according
study

to

a Michigan

Department

(Pfeifer and Spencer).

of

Natural

Resources

The scenario for this third

run involved increasing the wood products industry to the
point of full utilization of the timber producing potential
of the current 182,700 acres of commercial forest land in
the county.

The DNR study also provided an estimate of the

sustainable annual harvest from that commercial

forest

land.
Final demands for this run were the same as for the
second run, except for the wood products sector whose final
demands were increased so that by the third period gross
output for that sector would be such that requirements for
timber producing forest land would exceed availability of
suitable land.

A crude assumption about the current use by

the wood products
timber

sector

of timber

from within versus

from outside of Emmet County was made based on

ratios of forest based employment and timber harvests for
the county and for the United States (USDA Forest Service,
1980).

An assumption was made that future increases in the

113
wood products

sector would

be

entirely

dependent

creased timber production within the county.

on

in­

This assump­

tion implies a changing ratio of wood products sector gross
output dollars to acres required for timber production
within the county,

and so was simulated by increasing the

appropriate land use requirements coefficient each period
through the run.

That coefficient was

calculated on the

basis of sufficient acreage to provide on a sustained yield
basis the annual harvests

implied by the level of wood

products sector gross output.
Figure

27 shows a map of the index of developed use

for the third period of this run.

When compared to the

corresponding map for the second run

(Figure 21) the only

noticeable difference is lower levels of developed use in
some of the parcels south of Petoskey.
28

, Figure

29,

expanding and

Figure

30,

The maps of Figure

and Figure

intensifying use

31 reflect

the

of forest land for timber

production through time in this run in terms of proportion
of parcel area devoted to timber production.
Table
outputs
inputs
and

10

the

final

demands

and

implied

gross

for the wood products sector by period that were
for this run.

implied

constrained
third

shows

(unconstrained)
final

period

products

Table 11 shows final demand inputs

of

sector

demands
this

and

run.

gross

outputs

outputs by
Notice

that

sector

for

the
the

only the wood

is constrained by resources

the projected maximum final demand,

versus

from meeting

but gross output for

114

I 2 3 4 S 6 T « V |0 11 It 13 14 15 14 IT IB 19 SO
1

1
1

1
2
2

---

3
3
4
4
5
5

---

4 4 4 --- 4
4 4 --- 4
44—
4
- - - - - - - - - - ---—
444- -4
- - - - - - - - - - 4 4 - -------- - -4
------ 4 4 4 ------ 4 4 * -------4

—
444—
— —
—
—
—
---- - - - 4 4 4 - - - - - - - - - - - ---- ------- -

6
6

7
7
O
a
9
9

— 4 4 4 ---- — 4 4 4 — — — —
—
— 444— —
444— — —
044- — 444—
- ------ 4 4 4 —
- 4 4 4 --- 4 4 4 —
—
444—
4...
—
..444—
444—
4 4 4 - --— ---- . . 4 4 4 — . 4 4 4 —
944444—
— — —
— 444444444444—

10

)>'
11
11
12
12

13
13
14
14
15
15
1
ft
1B
16
■1 5»

4 4 4 4 4 4 — ----- —
.444444—
444—
444—
444444—
— —
444444—
444—
4 4 4 ---4 4 4 4 4 4 4 4 4 4 4 4 --- 4 4 4 --- 4 4 4 --- — --- - - - - - - - - - - - 444444444444—
4 4—
444—
---- — --- - -----444—
---- 4 4
4444—
- - - - - - - - - - - 4 4 4 ---4 4 4 --- — — — 4 4 4 4 4 4 4 4 —
— —
—
444—
444.—
...444444..—
.+++—
— —
.444444444—
— 444444— —
4 4 4 ---- . —
449444.

■4
44
444

4—
444—
TtT*"
AAAA___
ft9*“*
+++—
444444
444444
44444
44444
44444

\y

is
1 ■»
19
19
?o
?0
21
21
2?
22

—
........444— —
— —
...444—
444—
— —
—
4 4 4 -- - —
4 4 4 ---- —
—
- ------- . . . — - - - - - - - - - - --- - - - - - - - - - - - - - - - - - - - - - - - - ---

—
— —
444444— —
—
4 4 4 --- 4 4 4 —
—
— 444—
444—
— ---------- 4 4 4 4 4 4---------— . 4 4 4 4 4 4 .

...444— —
444—
— — — —
—
444444444—
4 4 4 — ------- 4 4 4 4 4 4 —
--- 4 4 4 - — --- 4 4 4 4 4 4 - - - 4 4 4 - - - - - - - - - - 4 4 4 4 4 4 ------ 4 4 4 ------ 4 4 4 4 4 4 -

4444
444
444
444
4

23

444444—
4 4 4 4 4 4 4 4 4 ------- —
- --- - 4 4 4 —
4
4 4 4 --- 4 4 4 4 4 4 4 4 4 4 4 4 --- - — 4 4 4 —
4 4 4 4 4 4 4 4 4 ---4 4 4 - - - 4 4 4 4 4 4 4 4 4 4 4 4 ------ 4 4 4 —
4 4 4 4 4 4 4 4 4 ----

24
24
25
25
26
26
27
27
26
2e
29
29
30
30
31
31
32
3?
33
33
34
34
36
35
36
36
37
7

2
2
3
3
4

4
S
S

6
0
7
7
0

0
9
9

10
10
11

11
12

12
13
13

1*
14
IS
IS

16

16
17
17

IP
IP
19
19

2b
20
21
21
22
22

22

23
23

24
24
25

25
04440000004444444444444444444444444444444
•
4444444444444444444444 4444444
#4444444
4
—
4444
-44
44444
4
44—
4444
44
4444444
4—
4
4444
4 4 4 4 4 4 4 4 4 4 4 4 + 4 4 -- - 4
•00444444+4+444— —
44+4
4
••000044444444+444—
44+4444
aa0000444444++444+44++4+44+++++++— •••••••••••0444444+4444444444+4+44+4444444444444444444444000464444444444444—
4 4 4 4 4 4 4 4 4 - --444444444444444444000444044444444444—
444444444—
444444444444444444444444444444444444444444—
- 4 4 4 444444444444444444444444444444444444444444—
4444+4+444444444+4444444444++4—
444444—
—
44444444444444444444444444—
444444.—
4 + 4 4 + 4 + 4 4 + 4 4 4 4 4 4 4 4 + ----------4 4 + ----------4 + 4 4 ♦ 4 + 4 + 4 4 4 4 * 4 4 4 4 ----------4 4 4 ----------444444 44444444—
+44+44—
— —
—
— —
444444
4+4+44+—
4 + 4 + 4 + ------ ----------4+4+444
444+44

1

2

3

4

S

• Ta

« 10 11 17 1)

PROJECTED PROPORTION OF «RE4
IN DEVELOPED USE
RUN «3 - PERIOD 3

14 15 ]t
•
0
4

26
26

27
27

20
20
29

29
3b
30
31

31
32

32
33
33
34
34
35
35
36
36
37
37

11 It 19 27

0.00 - 1.00
0.10 • O.SO
0.00 - 0.10
0 .0

Figure 27.

— — 44
—
4444
--- —
--—
4
— ---- - ---— .........
—
444—

4
4
4
4
4
4
"

-

0 .0 0

Projected Proportion of Area in Developed Uses.
Run 3, Period 3

115

I 2 3 * 5 6 T

2

j

9 10 II 12 1J I* IS 16 IT IB 1* 20

a

...

r

44444444

3

—

4+4* 4444
4
.444 at**
,-----------------------------------— -- 4
aaaa
s------------------- --------- 444*1*444taa444444
3
-------------444*»*444*(I444444
4
-*44******4*4**4444******444—
«
-444t**aaa44444444****at*444—
T
4 4 4 -------- 4 4 4 ----- 4 4 4
444
7 ----------------------------- 4 ------ .*444---— --- 444— —
e
—
.4 4 4 — — ***44444444
a
3 a-------- 4 4 4 ■at *4 4 4 4 4 4 4 4 4 •
3

3

......--- ....... ...

•
*

5
6

4
*
T
7
a
a

9--------------- 444***------------ 444444-- -

9----------------------- -444***------------------- 444444-----10
49**-4-- 444
444—
10
444* *44 — 444
444—

— ..*44**4444

11
11

——

.4444*4

9
10
10

11

444444
1)
444ia*
12
444*1*
12
.— 444***444-- .-■•*•■•13
—
—
—
—
--------- 444BII444----- Bt**a»
13
—
—
— — —
4 4 4 4 — 4 4 4 4 4 4 — — ..— .4 4 *4 * 4
14
44444-- 444444
— 444*44
]*
-444444
444444444— 4 4 4 4 4 4 ™ -- 444
444444444
15
444444— — 444444444— 444444— — 444— — 444444444
15
....4 4 4 BBB4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 — 444a*aa*a— 444444***
16
— .4 4 4 ***4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 — 444******— 444444***
16
4444*6*a*464*46aaa*44444***444444***4*4444«M 444**4
IT
— 444*******4*******44***4***444**4***44*44*— 44*4*4
IT
444444
14
*******44444444----- ■*••— 444***----- 444
*******44444444—
•*•*— 444***—
-444— 444444
14
444444444
..4 4 4 .......—
...........—
4 4 4 -------------------- -—

12

12
13
13

]4
14
15
15
I*
16

—
—
—

17

17
1*

—

18
1*

—
—

— *(*444444444444444**1444444*1*444— —

—

—

.... -**(«44444444*«4444ta*444444**t444— — — — — —
20
— — — 444—
— — 444444**44**4**444***—
—
— 444
20
----- — 4 4 +—
—
4444*4**44444**444***—
—
444
2)
---- —
— .— 444***44*444444— 444444— — —
—
21
.— ..—
— 444444444444444— 444444— —
—
22
........—
..*4 4 4 4 4 ... 4 4 4 4 4 4 . . . . — — — —
444
22
. 4 4 4 4 4 4 -- 444*44----------------- 44+
23
— --444----- 444444----- *---------- 444
23-----------4 4 4 ------4 4 4 4 4 4 — ..-------444
2 *------------- ---- — ....—
—
.4 4 4 4 4 4 — «*4—
—
--24
—
—
----444444---444-- —
-----25
— ...— --— — .— 4 4 4 4 4 4 — 4 4 4 4 4 4 —
—
..
25
— —
— — —
444444— 444444—
--26
—
— — — — — — — — — —
—
—
26
— —
—
—
— — — .......
27
........
.
27
—
--—
-----28
— — —
.444* — —
29
— —
—
— — 4444
..
29
.—
—
— — 444444—
...
|4

30
3

4

...... . . ^

|-----------

r--------------------- r r ---- r i T r T r i r r r f l l i ! ■ ■ ■ —

t t

4 4

--------

32------— — — — — — -- — —
—
444444**4— 944
—
--------------- —
4444444*4— 44*
33
— — ——
—— —
(■■444444*44444444

)9

16
20
20

21
21
22

22
23
23
2*
2*
25
25
26
26
27

27
?8
2*

29
JO
3

]

3?

32

32

33------------------....---- — *** 4 4 4 *4 4 * 4 4 4 4 4 4 4 4
34
.............4 4 4 4 4 4 —
. ^ * * 4 4 4 4 4 4 .— (a****
34
—
—
.— 4 4 4 4 4 4 — .— ***4 4 4 4 4 4 . — * * ( 4 4 4
35
----- -------- ***444
******44**l****444
35
— — ------ ~***4**«— B****4*44*******44
36
—
.
36
—
37
37

33
34
34

I

2

3

«5

6

T ** 18 |1 12 13

currfnt rrorortion or rrc*
IN TIMBER 4R0DUCING FOREST L*ND
RUN 43

Figure 28.

1* 15 16

•
4
4

IT 1* 19

33

35
35
34
3*

37
37

*0

*.*o - 1 . 0 0
*.50 - #.80
*.20 - *.50

*.« - *.20

Assumed Current Proportion of Area in Timber
Prodaution, Run 3

116

ii
?

........
.........
—

— — —
a *******

—

?
3

-----------..
4444 4444
..........------... - 4 4 4
|(t4
--4
(•««
------------- 444(M444ti|444444
— ----------- 444(M444II(444444
>*44***0*044***4*******0*44*—
-♦♦4****0*****4****66#0**4**—
.........4 4 4 .........4 4 4 — .— 444
4 -------- 4 4 4 -------- 444----- 444
4 4 4 4 4 4 ----- (((44444444
**...— 4 4 4 4 4 4 — •••**(44*4444444
* 4 * 6 M — *444*4— ****44— 444***—
-*4*«M— 4444*4— *4*444— *44**4— —
4*(( 4 .— *4*444444—
— 444444— 444-*44* *44
— ***444444— — 444444— 444—
— ♦**(*•
4 4 4 4 4 4 4 4 4 .----------- — 444
.........*4 4 4 4 4 4 4 4 — —
—
444— — **•(•*
.«*****..--- 4 4 4 -- 4 * 4 ------ *44444444— 444(*(
—
«**«*«—
4 4 4 — ***— — ***444***— ******
—
4**4*4444444--------- — 444***444444— •*(*(*
------ 444(M******---M i d i
— ...— ******444444—
— **4 4 4 *
4««-------4444— *444444**4---— 444444
— 4 4 4 4 4 *— *44-------- 44444— *444*4***— — ♦♦♦♦♦♦
>444444---- .**4444*44— ♦♦♦4 4 4 —
*44—
**4*44444
— 444444— —
444444444— >4**444—
*44— — 4*4444444
>— **********i9*********444444***— *4♦••••••>— ♦♦♦♦*♦•••
—
♦♦♦♦♦♦••••••♦♦♦♦♦♦♦♦*444444***— ♦♦♦••••••— ♦♦♦♦♦♦••»
...*******4******4****(I4*«*4«***444******4**444—
—
«***4*«***(*'********4***4****444****((***444—
...444******44*444444—
■*•***444600—
444—

3

4
4
6

*
6
6

7
7
»
S
9
4
10

10
11
11

12
12

13
13
14
1*
IS
IS
I*
1*

♦♦♦♦♦♦
*•»***
444444

17
17
14

...444****»**«*444444--- — •••♦♦♦444***— —
444— 444444
— ..*a*444444444**«***»**444444M*444— —
— — —
---- •■•♦♦*444444******(**44444**((4*4---- -- --- -- -— —
444— --- ..4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 *1 *......... . . . 4 4 4
...--- 444—
—
•444444444444444444a**— —
—
444
—
..........4444*44**444««*— 444444— —
— — --...............44444444*444***— 444444—
— —
......--- —
—
444444— *44444*— —
— —
—
— 444
.— ..............***444— .4 4 4 ***— —
— — — — 444
..............__444....._444444—
........ .— 444
-----4 4 4 -44444*
—
—
444
--------------------- .4 4 4 4 4 4 -- *44------------------------------- *4444*-- *44--------------------------------- 444444-- 444444----------...--------------- 4 4 4 4 4 4 ...*4 4 4 *4 .— — ......

14
19
14
?o
20

‘I—

—

—

.

.

---

21

21
22
22

23
23
2*
?»
2S
?5

27

-♦♦♦♦ — —

-------------- 4444

20

—

20

—
444

29
30

-------- 4 4 4 ...4 4 4 * 4 4 4 4 4 ---- . 4 4 4 --- 4 4 4 ----------- —
-------- *44— 444444444
*44---*44
—
******4 4 4 — .444******
............— .***44****— ***
******444.— 444******----- -------- ...***4 4 ****.— ♦*♦
♦♦♦>— 44*— —
— —
— — •••44**********444
4 4 — 444— — —
— — — ■■•444«********444
♦>— ♦♦♦♦♦♦— 444*44—
— ■••♦♦♦♦♦*— >••*♦♦♦
...4**444— 444444—
•*•*•*«*«— •*•***
...... ......— *aa4 4 4 ...aa*4 4 4 4 *4 *a* * * * 4 4 4
......***444-- •((*•******•(■(444

3]
31
32

..................444999—
....

—

32

33
33
3*
3*
35

3S
36
37
37

1

2

3

4

S

*

7

*

* 10 11 12 13 14 IS 16 17 IB 19 20

6.0 > 6.20

Figure 29.

Projected Proportion of Area in Timber
Production, Run 3, Period 1

117
1 2 3 4 5 6 1 • 9 10 II 12 13 1* IS 16 IT 16 19 2b
1

—

— ♦+4—
4 4 4 -----..4 4 4 —
4 40R 0+44

—

.............

..

444* (444

. . ... ... .. .. ... ..
.. .
.4 4 4
((((
------------------------------------------- 4
---------------- ------------ 4 4 4 4 4 * » 4 4 t t M 4 * 4 4 4
. . . . . -------------- - * 4 4 t ( ( 4 4 4 ( ( ( « ( ( 4 4 4
—4 4 4 0 0 0 0 0 0 0 6 6 4 4 4 4 4 4 0 4 4 6 4 6 4 4 4 —
• 4 4 4 0 0 0 0 0 ((0 0 4 4 4 4 4 4 0 0 6 0 0 0 4 4 4 —
4 4 4 4 4 4 4 ( I4 4 4 4 4 4 4 4 4 ( 4 ( - — " 444
« . . . 4 4 4 * 4 * ( ( * 4 4 4 4 4 4 4 * 4 ( ( 0 — — 444
.....4 4 4 4 4 4 4 4 4 4 4 4 4 ( 4 4 4 4 4 4 4 4 4
# 0 — — 4 4 4 4 4 4 4 4 4 4 4 4 (0 0 4 4 4 4 4 4 4 4 4 4
4
4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 *4 4 4 4 4 4 4 + 4 4 4 —
* 4 4 4 4 ( ( 444444444444444444444444444“ • • • • •
(4 (4
* . . “ 4 44 444444444— 444444444444444
« * * • *4 4
-- 4 4 4 4 4 4 4 4 4 4 4 4 — 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
444— 444444444— 444444444— 444— 4 44 M 4 4 44
. . . 4 44 — 444 4 4 44 4 4 — 444 4 4 44 4 4 — 444— 4 4 4 4 (4 (4 4
4 4 - — 44444444444444444444444444 444444444444 4 M 4 4 44
9 4 4 ------4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 — — 4 4 4 4 4 4 4 4 4 t4 (4 4 4 4 4 4 4 4 4 B B B (4 (
•4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 — - -4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 (4 4 4
— 44444444444444444444
* * 4 * * * * * 4 * * 4 4 4 4 * 4 4 4 — 4 *4 4 4 4
— 4 4 4 4 4 4 4 4 4 *4 4 4 4 4 4 4 4 4 4 * 4 4 4 4 4 * 4 4 * 4 * 4 4 4 * 4 4 4 4 4 — 4 * 4 4 4 *
4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 * 4 4 4 4 4 § 4 4 tif4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
444 4 4 4 4 4 4 4 4 4 4 4 4 4 *4 4 4 4 4 4 4 444B 4444444444 4444444444444444444
4 4 4 4 4 4 4 4 4 ( ( ( ( ( ( ( ( | ( ( ( ( ( * ( ( ( 4 4 4 ( ( ( — 4 4 4 ( ( ( ( ( ( — 44444444B
-4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 I4 4 4 4 4 4 4 4 4 — 4 4 4 4 4 4 4 4 4 “ “ “ 4 4 4 4 4 4 4 4 4
4 4 4 4 4 4 4 4 4 ( ( |( ( * ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( 4 * * ( ( ( ( ( ( ( ( ( 4 4 4 — 4 **4 4 *
* 4 * * 4 4 * 4 * * ( ( * ( 4 * ( * * * ( ( ( ( l( ( ( * * l( * 4 * * ( 4 ( ( 4 ( ( 4 ( 4 4 4 — *4 *4 4 *
4 4 4 4 4 4 4 ((4 4 4 4 (» *« 4 *4 *4 4 4 4 *« (**4 *4 4 ***4 *4 4 4 4 4 *4 4 4 — 444444
4 4 4 4 4 4 B ((* (((B (4 4 4 + 4 4 * * * * 4 4 B B (4 4 4 4 4 * (* B 4 4 4 f* * 4 4 4 — 444444
4 4 4 4 4 B § 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 B B B 4 4 4 4 4 4 (((+ 4 4 — — — — — — — —
4 4 4 4 4 4 4 4 44444 4444444444B B B 444444B B B 4 4 4 -----•4 * * 4 4 ----- 4 4 4 -------------- — 4 4 4 4 4 4 4 4 4 4 4 4 4 *4 4 4 4 4 ( 4 “ — - ------— — 4 4 4
4 4 4 4 — 4 4 4 — — — — 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 i( 4 — — — — —
444
4444. . . . —
. 4 4 4 * * * * 4 * 4 4 4 4 4 4 — 4 4 4 4 4 4 --------— ----------------4 4 4 . — ----------------4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 — - 4 4 4 4 4 4 — — — — — —
4 4 4 ...............4*4444-— 4 4 4 ***—

—

—

—

—

—

1
2
2
3
3
4
4
5
S
6
6
7
T
6
»
9
9
10
10
11
11
12
12
13
13
1*
1*
IS
IS
14
14
17
17
IB
1*
19
19
2(.
20
21
21

4+4

22

444. . . . — . . . . . . . . 4 4 4 4 4 4 — 4 4 4 4 *4
444
4 ----------------—
— .4 4 4 4 4 4 4 4 4 4 4 4 B 4 I4 4 9 4 4 4 4 4 4 4 4 4 4 4 4 — 444
. . . . . . .. .......4 4 4 4 * 4 4 4 4 * * 4 ( 4 ( 4 4 * 4 4 4 4 4 * 4 4 * 4 4 4 — 444
.. 4 4 * * * 4 4 4 4 4 4 4 ------------------ * 4 4 4 * 4 — 4 4 4
— 444—
.* 4 4 4 *4 4 4 4 4 4 4 — — —
4 4 4 **4 — 444— — 4 *4 — —
. . . . . . . . . . . . . . . . . . . . . 4 4 + 4 4 4 — 4 4 4 4 4 4 — 4 + 4 *4 4 + 4 4
. . . . . . . • • • • • • • 4 4 4 4 4 4 . . . 4 4 4 4 4 4 . — 4 4 4 9 4 *4 4 4
. . . . . . . . . . . . . . . . 444444. . . . . . . . . — * 4 4 — 4 4 *
444444—
------------ 4 4 4 — 4 * 4
-4 4 4 4 4 4 --4 4 4 —
44
4— 4
—
444—
—
— 4 ***4
4444*
4 4 4 4 4 4 4 4 4 4 4 (4
..
444— — 4 4 *4 4 4 **4 4 4 ***4 4
4 *4
— 444— — 4 4 4 4 4 4 4 *4 4 ****4 *4 4 4 4 *
—
---------444444444444444444444

22
23
23
2*
2*
2S
25
26
2*
27
27
26
26
29
24
4r

— 444444
- — — 4 4 4 4 4 4 4 4 *4 4 4 4 4 4 4 4 4 4 4 *
30
4 444 4 44 4 *44 4 — 4 4 4 * 4 4 * * 4 — — 4 4 4 4 4 4M 4 4 4 44 4 4 44 4 4 44 4 4*
31
•4 4 4 4 4 4 4 4 4 4 4 4 — 4 4 4 4 4 4 * 4 4 — —
4 4 4 4 4 4R 0 0 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
31
4 4 * * * 4 4 4 * — 4 4 4 4 ***4 4 4 4 4 — *4 4 *4 4 —
444444444— 4 4 *
32
'4 4 4 4 4 4 4 4 4 — 4 4 4 4 4 4 4 4 4 4 4 4 -— 4 4 4 4 4 4 - — — 4 4 4 4 4 4 4 4 4 — 4 4 4
32
4 4 4 — 444444— —
— . ----- — M 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 *
33
4 4 — 444444— . . . . — . .. ..( 4 ( 4 4 4 4 4 4 4 4 * * * 4 4 4 4
33
4 4 ***4 *4 4 ****4 4 4 4 4 4 — — (((4 * 4 4 * 4 — ( ( ( 4 4 *
3*
4 4 4 4 * 4 * 4 4 * 4 * 4 4 4 * 4 4 ------------( ( 4 4 * 4 4 4 4 — - ( ( 4 4 4 4
3*
— 4 *4 — *4 4 — ( ( ( 4 4 4 — ( * * * * * * * 4 ( ( * ( ( ( 4 4 4
35
. . . 4* *
-4 *4 — (((4 4 4 — (((4 * * 4 * 4 ((((((4 4 4
35
.. ... ..
36
----------------------------------------------------- 36
37
37
1

2

3

4

5

6

T

6 9 10 11 12 13

PROJECTED PROPORTION OF « R £ *
IN
TIMBEP PRODUCING FOREST
RUN ( 3 “ PER100 2

Figure 30.

L4N 0

1 * IS
•
*
4

16 IT

16 19 2>)

0 .6 0
0 .5 0
0 .2 0

-

1 .0 0
0 .6 0
0 .5 0

0.0

-

0.20

Projected Proportion of Area in Timber
Production, Run 3, Period 2

118

1

> 3 4 4 * T • 4 10 II I? 13 1* 15 16 IT IB 14 20
I
—

----------

♦4444—

J

♦♦♦♦♦♦♦♦♦

2

« M M «
2
♦♦440444
3
—
M M M M
3
------------------------M « 4 4 4 4
4444
*
. . . . . . . . -------------- M M M M M
••••
*
—
. —
— 444444444444440444
3
----------------------------------------------------------------------- 5
m

M
M M
IM M
M M M

m

m

m

h

m

m

m

M H IM H IM
M M M M M M
M M M M M M
M «M 4M «M

i

m

i

m

i

*m

i

m

m

M IIM iM M IIM M
M M M tM M M M M
M M M IM M M M ft
H M H H M )

MmMMMMMmMMMIIHHm

*

-

M H M M M 4 i« M 7 t M M M M « M M IM 4 *
♦
- # a 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 B 4 a + + *4 4 4
M H
4 44 4 4 44 4 4 44 4 4 44 4 — 4 4 *4 4 4 4 4 4 4 4 4 4 4 4
♦ 4 4 4 *4 4 44444444444444
4 44 4 4 4 4 4 4 4 4 *4 4 4
M M t f H H H 4 4 4 4 4 4 4 4 4 4 4 M 4 4 ------4 4 4 — 4 4 4 M M 4 *
444444444444444444444444444444— 444— 444444440
4H M 44M 44444444444M 44M 44M 444M 444M 44M M M M
4 4 * 4 4 4 ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ * ♦ ♦ ♦ ♦ # 4 44444444444444444444444440400
♦ 4 *4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 0 4 4 4 4 4 4 4 4 4 4 4 4
444444444444444444444 4 4444444444444444444444444044
M 444444444444M 444444
M 4 4 4 4 4 4 4 4 4 4 M 4 4 4 4 4 4 M M M II
■ 4 ** 4 * * ** 4 * ** 4 4 4 * * 4 4 * 4 4 4 4 *4 4 4 *4 4 *4 4 4 4 4 *4 4 4 4 4 4 4 4 4 # » » » 4 *
4444444044444444444444444H 44M 4444444444444444444H 444
4 M M M M 4 4 4 4 4 4 4 4 4 M 4 4 4 4 4 4 M M M (4 4 4 4 4 M M 4 4 M 4 M 4 4 M M 4 4
M 4 4 4 4 4 4 4 IIH H M H M IIIIM 4 4 4 n M H 4 4 M tM M 4 4 4 4 M IH H >
4 4 M 4 4 4 4 4 4 IIM IH IM M H IM M M M M 4 t4 M M M M 4 4 4 4 4 4 M IM I
4 4 9 ****4 4 a 4 4 4 4 4 4 4 4 4 4 4 *4 4 4 4 4 4 4 4 4 4 4 4 4 *4 4 4 4 a 4 4 4 4 4 4 4 4 4 4 4 a 4 4 *4
♦♦4 4 *4 *4 *0 4 4 4 4 4 4 4 4 0 4 4 4 4 4 4 4 4 4 4 *4 4 4 4 **4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
4 4 4 4 4 4 4 4 4 0 4 *4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 *4 4 4 4 4 *4 4 **4 4 4 *4 4 4 4 *4 4 4 4 4 4 4 *
4 4 4 4 4 4 a » » » » » a » » 4 4 4 4 4 4 4 4 * * 4 * B a » 4 4 * 4 4 4 i« l4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
4 4 4 4 4 « » » a » t 4 4 4 4 4 4 a a tli2 4 » a 4 * 4 4 4 4 a a tt t» * 4 4 4 4 4 4 4 4 4 4 4 — 4 4 4
• * 4 4 4 4 a » 4 a a t * 4 4 4 4 4 a a tiia a ta 4 4 4 4 * 4 a a a a a # 4 4 4 4 * 4 4 4 4 4 4 4 — 444
4 44 4 4 44 4 a a» 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 a a a 4 4 4 a a a *4 4 a a a 4 4 4 4 4 4 4 4 4 4 4 4 4 4 *
♦ 4 4 *4 **4 4 4 4 **4 4 **4 4 ****4 4 4 4 4 4 ***4 4 *4 4 4 4 4 4 4 *4 4 4 4 4 4 4 4 4 4 4 4
4444444444444444444444444444444444444444444444444444444
4 *4 *4 *4 4 4 *4 *4 4 **4 4 **4 4 *4 **4 *9 *4 4 ***4 4 *4 4 4 *4 4 4 4 4 4 4 4 ***4
♦44444444444444444924444444444242444444444444444444444
♦44444444444444444aaa444444444aaa444444444444444444444
♦444444444444444444444444444222444444444444444444444
444444444444444444444444444444444444444444444444444
♦ *4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 a a a a a a 4 4 4 a a a 4 4 4 4 4 4 4 4 4 4 4 4 —
♦444444444444444444444444044444444444444444444—
♦44444444444444444444444444444444444444444444444
♦444444444444444444444444444444444444444444444
4 9 9 9 — 9499999994444999999* 9999* 9 4 9 9 9 — 9 9 9
♦ 4 4 4 4 4 4 4 4 4444444444444 ♦ ♦ ♦ — ♦ 4 4
-4 4 4 4 4 4 0
♦
— 444—
♦4
44444
4 ♦♦— ♦♦♦—
—
4444444
44844
444444
— 444444444444B444
44
4 44 *4 4 44444444444444 4 4 4
. . . 4 4 4 — — 4 4 4 4 * *4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
444444499444499499944
* 4 4 -------------------------------» * * 4 4 * 4 4 4 * * 4 4 4 4 4 4 4 * 4 4 *
•4 4 4 4 4 4 4 4 4 4 4 4 — * 4 4 4 4 4 4 4 4 —
—■ ♦ ♦♦4 4 4B B B 44 4 44 4 4 44 4 4 44 4 4
•4 4 4 4 4 4 4 4 4 4 4 4 — 4 4 4 4 4 4 4 4 4 — — 4 4 4 4 4 4 a a B 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
4 44 4 4 44 4 4 — 444444444444444444444444444444444444444444
4 4 4 4 4 4 4 4 4 — 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 *4 4 4 4 4
444 — 444444444444444444444444444444B 44444444
4 4 — 444444444444444444444444444444444444444
4444444444444444444444444444444444444444444
444444444444444444444444444444444444444444
4 4 4 4 4 4 44444444444444444444444444444444444
♦44444 4444444444444444444444444444444444
4444444
444444

1

2

3

4

5

4

7

8

9 10 11

PROJECTED PROPORTION 0T 4RC*
IN TIMBER PRODUCING FOREST L6ND
RUN 4 3 - PERIOD 3

Figure 31

12 13 I *

a
♦
4
-

15 16 17 14 19 20
0 .5 0 0 .5 0 -

0.20
0.0

1.00
0 .5 0
0 .5 0

0.20

Projected Proportion of Area in Timber
Production, Run 3, Period 3

*

6
7
7
0

4

9
0
10
10
11
11
12
12
13
13
1*
1*
15
15
15
I6
17
17
10
12
19
19
20
20
21
21
22
22
23
23
24
24
25
25
25
26
27
27
25
25
29
29
SO
30
31
31
3?
32
33
33
34
34
35
35
36
36
37
37

119

Table 10.

Final Demand Inputs and Implied Gross Outputs
for the Wood Products Sector in the Third Run
(Thousands of Dollars)

Period

Final
Demand

Gross
Output

1

3383

5053

2

4330

6255

3

5542

7786

120
Table 11.

Unconstrained and Constrained Final Demands and
Gross Outputs for Period 3, Run 3 (Thousands of
Dollars)

Sector

Unconstrained
Final
Gross
Demand
Output

Constrained
Final
Gross
Demand
Output

1929

7270

1929

7249

20251

27409

20251

27397

5542

7786

4805

6926

Cement & Concrete
Products
Manufacture

14279

18667

14279

18665

Electrical &
Transportation
Equipment
Manufacture

18382

18382

18382

18382

Primary Metal &
Metal Fabrication

4857

5969

4857

5947

Nondurable
Manufacture

5366

9958

5366

9950

Transportation,
Utilities,
Communication

7634

31279

7634

31225

Wholesale & Retail
Trade

26258

71973

26258

71840

Finance, Insurance
& Real Estate

16946

48012

16946

47939

Lodging & Amusement 13974
Services

18608

13974

18598

Medical Services

49736

60563

49736

60538

9909

32287

9909

32237

346

4633

364

4626

37174

172738

37174

172274

Agriculture
Construction
Wood Products
Furniture
Manufacture

Other Services
Endogenous
Government
Households

121
several sectors is reduced due to the interaction of those
sectors with the wood products sector.
Problems With the Model and Application
Examining
in the

series

the

overall

of maps

of

land

use

trends

levels of

as

reflected

total developed use,

e.g. compare Figure 10 to Figure 21, one might be satisfied
that projected land use patterns from the model are some­
what reasonable.
however,
become

before

One does not have to look too closely,
certain

apparent.

problems

Compare

with

these

the projected

projections

changes

in com­

mercial use from the second run in Figure 22 to the pro­
jected changes in residential use in Figure 23.
residential
Petoskey

use

and

is

largely

concentrated

Harbor

Springs

with

some

Expanded

in ..nd around

at Mackinaw City.

Increased commercial use also occurs predominantly in the
Petoskey

and

Harbor

Springs

areas,

but

with

notice­

able changes in several towns along Highway 31 and even in
Cross Village on Highway 131 in the northwest portion of
the

county.

projected

It is reasonable

intensifying

to be

commercial

suspicious

use

where

of the

there

is

little or no projected increase in residential use.
This
the

is

results

known

just

one

example

from the model,

deficiencies

in

the

of

but

model

an

inconsistency

it relates
in

its

in

to several

current

form,

and many other inconsistencies could no doubt be found
under

close

examination

of these runs

or

in other types

122
of

runs.

It

ciencies,

is

appropriate

to

consider

not only to acknowledge

the

these

current

de f i ­

limita­

tions of the model and these results but also to identify
those areas in which further study is needed.
The

dilemma

uncertainty

of

of
final

been mentioned.
this model,

the

simultaneous

demand

This,

importance

projections

of course,

has

and

already

is not exclusive to

in fact it pervades not only land use modeling

in general but much of economic planning and modeling.
Of importance is not just total final demand but how demand
from a number

of different exogenous categories

is allo­

cated among various endogenous categories over time, which
compounds the uncertainties. When this study began it was
intended that a serious attempt be made to lessen this
problem, but this was one of several goals that was pared
as study resources became limiting and as the scope of the
task became appreciated.

A more analytical basis,

more

have

credibility,

could

been

added

by

if no

employing

shift-share analysis to arrive at final demand pro j e c ­
tions.

Shift-share

analysis

national production by sector,

relies on time trends

in

as was used in the second

run reported here, but also considers the recent trend in
share of those sectoral totals for the region in question.
This has

for some time been a commonly applied technique

for exogenous demand forecasts, but its validity has long
been questioned.

It is argued that the observed changes in

123
regional share are somewhat volatile and therefore unsuit­
able for this purpose and perhaps less reliable than simply
using the national trends alone

(Kuehn,

1974).

Also the

regional share is essentially a residual which includes all
of the error.
with minimal

So for this analysis, sets of final demands
rationale behind them were used,

being con­

sidered suitable for demonstration purposes though not
serious

forecasts,

and all that can be claimed is that a

wide range of economic growth was considered.
As

mentioned

previously,

the

first two runs,

and

given

ject,

the

i.e.

given this

original

concern

use

in

either

the

surprising

result

of

range of final demands

impetus

over

land use conflicts,
any

a

for

the

regional

possibility

of

pro­

critical

was that lack of suitable area for
run

was

not

detected.

Either

the

original premise of scarcity of suitable land to satisfy
all competing uses or the ability of this model with this
data base to detect relevant scarcity and conflict must be
questioned.
basis

In fact,

for Emmet County, there is probably

for both of the doubts expressed above,

i.e.

for

Emmet County there may not be the major impending conflicts
that

loom

for other areas

in the region or nation where

initial use intensity and prospects for future growth are
higher,

but also there are definitely deficiencies in the

current model that may prevent the detection of some of the
problems that are in the future for Emmet County.
A

major problem with

this

model,

or more

precisely

124
this application,
problem.
model,

A resolution problem is not inherent

but

Chapter

may be referred to as the resolution

for

III,

any given application,

levels

of

spatial,

in the

as discussed

sectoral,

temporal,

in
and

land use resolution or aggregation must be chosen, usually
to a large degree before data

collection

is begun.

The

degree of resolution in all of these areas can affect the
ability of the resulting model to identify use conflicts
and

constraints

arising

from

lack

of

suitable

land.

Problems related to resolution often stem from the effects
of averaging differing

traits or levels of some variable

over a defined class or unit to come up with a single value
to represent that unit.
unit
an

(e.g.

That single average value for the

one coefficient to relate to broad sectors

input-output model,

large

parcel,

requirements

or

of

a

in

an average soil suitability for a

one

coefficient

sector

to reflect

land

for a broadly defined

use

land use

category) often does not adequately reflect the impact of
the variablility of that factor within the unit.
The

rationale

this study,
Chapter

III,

and

choice was

the

chosen
on

practical

the

spatial

resolution

i.e. one section parcels,

the

based

for

though

the rationale

resolution

homogeneity
standpoint

of
the

allows

important
number

is still valid,
Ideally,

defining parcels

traits,
of

in

was presented in

not without adverse effects.

spatial

used

but

different

from

a

traits

considered and the limitations on total number of parcels

125
may result in parcels that are not homogeneous for even one
of those traits.
Soil,
examples

terrain,

of

and

water

frontage

are

but

a

factors which may not be homogeneous

few

over

a

parcel but whose implications for suitability for certain
land

uses

can

not be adequately reflected by an average

value for the parcel.
suitable

for

some

because

of

without

additional

For example a parcel could be rated

recreational

the presence

parcel would

be

of

use

or

seasonal

undeveloped water

constraints

the entire

treated as though

it were

homes

front,

area

but

of the

suitable even

though only a portion of the area is actually adjacent to
the water.
could be

A parcel homogenous with respect to this trait
defined by a narrow corridor along the water

front, and as mentioned previously such an irregular parcel
could be handled by the model.
Water
the

front

recreation

also

resolution problem with

fication

in

the

in Chapter III,
were used,
gory.

Emmet

provides

respect

County

to

an

land

application.

example
use

As

of

classi­

explained

for this study eight land use categories

one of which was a "recreational

lands"

cate­

This one category includes everything from the water

front oriented parks near Petoskey to the ski areas to the
wild
model

lands

of Wilderness

State Park.

At

this point

the

does not distinguish between these substantially

different recreation resources,

and so does not address a

likely future,

land use problem in Emmet

if not current,

126
County,

i.e. available,

suitable waterfront for public

recreation.
The spatial resolution problem is closely related
to another

serious problem with the current model, which

may be referred

to as the intraregional allocation or

distribution problem.
in

the

tendency

of

One aspect of this problem is seen
the

model

to allocate

all

of

the

increase in area for a use in a period to a single parcel,
subject of course to the availability of suitable land in
that parcel.
which

This

is a natural result of the algorithm

deterministically

allocates

increased

use

require­

ments to the parcel with the highest rent differential for
a shift to that use.
Again because of large parcel size and the impli­
cit assumption of homogeneity within any one use category
within that parcel,

a relatively large portion of a given

parcel would be treated as though all of
same

rent

differential

that

portion

of

the

from a
parcel

certain
a range

it yielded the

shift,
of

while

over

suitabilities,

productivities, and conversion costs actually exist result­
ing in a wide range of rent differentials.

More reasonable

projected patterns of land use would result if part of any
increase in a use requirement were spread over a number of
parcels,

talcing advantage of the high end of that range of

differentials,

rather than all being concentrated in a

single parcel.

To reduce the effects of this problem,

but

certainly not

solving

it,

constraints on the maximum

127
area in any parcel that can shift to any use in any one
period were employed.
tions

to

this

More theoretically appealing solu­

problem can be

envisioned,

for example an

"interregional" approach to the economic component could be
used to yield land use requirements by subregions
county,

thus

increases

could be

least to some degree projected

in different uses without increasing the number

of parcels.
however,

spreading at

in the

The practicality of such an approach is,

certainly

questionable.

alleviated with

Of

course

the

problem

smaller parcels but with the

resulting costs of many more parcels.
Another problem with

the current application that

relates directly to the inability of the model to detect
deficiencies of suitable land is the exclusion of conver­
sion costs in these runs.

In Chapter II cost of convert­

ing land from one use to another

is acknowledged as an

important component of the rent differential equation for
identifying and ranking possible land use shifts,
model

can account

for conversion costs,

number of variables,

as resources

and the

but as with a

for the study became

limiting and as the difficulty of determining such costs on
a parcel by parcel basis was realized,

it was decided to

exclude

initial

analyses

explicitly

included,

conversion

costs

from

these

(except as noted for the third run).
Even had
with

the

conversion

current

spatial

costs been
resolution

it

is doubtful

that

their effects could have been adequately modeled.

The

128
average conversion cost for a shift from one use to another
would apply to all of the area in the current use in a
given parcel,
other

but

factors

again because

{e.g.

terrain,

of

the heterogeneity

access,

vegetation)

of

that

average cost would understate true costs for part of the
area while overstating costs for other parts.
would appear

to be

The shift

either profitable or unprofitable

the entire area.

for

The real effects of conversion costs

could only be reflected if the spatial resolution allowed
delineation of these kinds of differences.
While

it was

suggested that several of the problems

mentioned above could account

for the model's

failure to

detect suitable land deficiencies, other problems with the
current

model would

tend to have

overstating land use requirements.
IV,

the

land use

the opposite

effect by

As mentioned in Chapter

requirements coefficients were based on

current area by land use, current gross outputs by sector,
and some specific land use information from the inputoutput survey.

In other words existing average ratios of

acres by use to dollars of gross output by sector were
used.
in

These ratios were used with awareness of the dangers

their

use,

i.e.

that

these

average

ratios

may

not

closely approximate current or future marginal ratios and
their

use

capacity.

implicitly assumes

current utilization at

full

That this is a serious problem can probably be

appreciated

by

considering

the

historic

increases

in

output relative to land input as observed in agriculture.

129
The

land use

requirements

coefficients

could

be

made

to

vary from period to period through a run, but a better
basis for determining initial marginal ratios and how they
would be likely to change over time is needed.
A similar, but perhaps even more serious problem,

is

the static nature of the input-output technical coeffici­
ents.

Instability in technical coefficients and especially

in interregional trade coefficients has long been consid­
ered in the input-output modeling literature, but little in
the way of practical remedies have been offered.

Again,

there would be no particular mechanical problem in varying
these coefficients from period to period if it was possible
to project how they should change.

The importance of this

problem to the analyses discussed above can be understood
by

considering the record of

over

the years.

increasing

labor

efficiency

For the second run the average rate of

real economic growth during the 1970's was used as the
basis for future levels of final demands, and it was noted
that real economic growth had been much higher than popula­
tion growth.
the

fallacy

This disparity in growth rates is evidence of
of

stable

coefficients

for

the households

sector and suggests that the residential land use require­
ments projections
cerns

are overstated.

about unstable

trade

The relevance of con­

coefficients

for this kind of

analysis was seen in the third run, where one of the major
assumptions
products

was

sector

changing relative
on timber

dependence of the wood

from within versus timber from

130
outside

of

Emmet County.

The model

does not currently

explicitly recognize or constrain interregional trade,

so

this changing relationship had to be approximated by some
ad hoc
through

changes

in a land use requirements

the run.

coefficient

More explicit recognition of inter­

regional trade could be added and would represent a sub­
stantial

improvement,

and

again,

the

coefficients

vary between periods where there was
projections,

a basis

could

for

such

but interregional trade data are very diffi­

cult to obtain.
The

current

coefficients
iency.
to

may

Late

employ

nondynamic
seem

of

another

to be an even more

in the

static

nature

set

of

serious defic­

study a conscious decision was made

rather

productivity indexes.

than

dynamic

suitability

and

Although this may seem to seriously

violate the intent of the simulation, there was a rationale
for the decision.

It was realized that the real limitation

in the indexing process was not the mechanics or software
for updating the indexes from period to period through the
run, but in the index submodels and composites themselves,
i.e.

in defining

the

relationship between

the various

parcel attributes and parcel suitability and productivity
for a use.
to program

While it would have taken considerable effort
for

dynamic

indexing,

little would have

been

gained given the admittedly crude state of the suitability
and productivity

submodels.

In most cases,

given the

simple submodels currently being employed, dynamic indexing

131
would

simply have

reinforced

the

effects

of

the

current

approach.

Dynamic indexing should definitely be added to

the

if

model

serious

projections

are

to be made,

but

improving the indexing submodels is an even more fundamen­
tal need at this point.

This indexing process is really a

key to the model and the current deficiencies contribute to
the intraregional allocation problem mentioned above, since
through
basis

their

contribution to rents

for allocation over space.

the

indexes

in the model,

there

the

Although it would be a

step backward with respect to incorporating
basis

are

a behavioral

could conceivably be a geo­

specific land use model without the economic component of
this model, simply relying on exogenous statements of areas
required by use over time, but without the indexing pro­
cess,

or

something similar,

there could not be a geo­

specific land use model.
Reflections on Land Use Modeling
The preceding section dealt with a number of specific
problems with the current model and its application to
Emmet

County,

impressions
ered.

but

from

there

this

are

a number

experience

that

of more
should

be

general
consid­

These impressions are worth considering as cautions

or guidance for subsequent research,

but they are also of

interest because they corroborate conclusions from previous
land use modeling efforts.
The preceding section gave considerable attention to

132
the resolution problem,

especially the problem associated

with relatively gross spatial resolution, but there is an
opposing perspective on the issue of resolution that must
not be neglected.

This study involved a constant struggle

between an urge to increase detail in order to adequately
handle the micro-level effects of importance and the need
to limit scope and resolution

so that any progress could

be made toward the macro-level goals of the study.
times

the data gathering,

processing,

and error

At

checking

requirements seemed overwhelming, and finer spatial resolu­
tion would have compounded the problem.

Of course the

Emmet County study was not the first land use modeling
effort to encounter this problem.
cost

of

data

collection

Underestimating time and

and manipulation was

one

of the

serious technical problems identified by Voelker (1975) in
the Oak Ridge National Laboratory's Regional Environmental
Systems Analysis

(RESA) program,

as mentioned in Chapter I

of this thesis.

This experience suggests the need for and

should help provide understanding of the enormity of the
data compilation task
has

implications

for this kind of research but also

for the practicality of routine,

tional use of this kind of

opera­

system by a planning agency.

Development, modification, and use of such a system may not
be infeasible, but it is costly, and these costs should be
appreciated before the fact.
Despite the above remarks,
was not a negative experience.

the data compilation task
The exposure to such a

133
variety of data variables and sources was extremely valu­
able.

Several

routines

data

handling methods

for aggregating,

mapping,

and programs

(e.g.

and debugging) were

developed and should be of at least limited usefulness
beyond this study.
A

pervasive theme in the

literature

evaluating

land

use modeling is that model developers more often than not
have unrealistic expectations for their models.

There are

often unrealistic expectations and corresponding claims for
the capabilities of the models,
expectations

and there are unrealistic

for the acceptance of models by planners.

Certainly this observation applied to the Emmet County
effort,

especially in the initial stages.

unrealistic

These types of

expectations are addressed by both Voelker

(1975) and Pack (1979).
Associated with

the unrealistic

expectations with

respect to model capability is the often cited problem
of lack of land use theory or at least lack of explanatory
power in the theory that does exist.

Again this problem

was experienced first hand in this study and relates to the
discussion in the preceding section of the crude state of
the indexing submodels.

This study did at least attempt to

incorporate some theory into the model with its concern for
rents and its inclusion of the input-output linear program­
ming model.

This would seem to be a step forward from what

Pack identifies as the mechanical models of the past that
lacked a behavioral basis for location decisions.

134
Even

if

the

first

type

of

unrealistic

expectation,

i.e. resulting from limited predictive capability, was not
as common as it is, the second type of unrealistic expecta­
tion would still occur frequently, i.e. planners in general
or a "client" planning agency in particular would still be
much more

reluctant

would expect.

to embrace a model

Pack's

survey results

than the modeler

indicate that model

adoption does not seem to depend on model quality but on
personal factors such as the presence or absence of model
or quantitatively oriented people in the planning agency.
As it is, given the very real limits of model capabilites
and the notoriety that past overly optimistic claims have
achieved, the reluctance on the part of planners to accept
models is understandable.

Again this study provided first

hand experience with these kinds of attitudes.
A
power

corollary
in

to

current

identifying

land

use

the

theory

lack

of

explanatory

as perhaps

the main

factor limiting the capability of these models for reliable
and reasonable land use projections,

is the conclusion

that model software is not the most pressing need.

This is

another common conclusion in the land use modeling evalua­
tions
County

and

again was

study.

This

development in this

independently

realized

is not to suggest

in the

Emmet

that the software

study was not necessary for the pur­

poses of this study, but it must be acknowledged, as it was
in the preceeding section,
model

development

and

the

that theoretical and empirical
data

on which

to base

that

135
development are more pressing needs than computer code to
implement existing conceptual models.
A lengthy,

but certainly not exhaustive,

compilation

of problems with the current model and application has been
provided.

The intent is not, however, to present a predom­

inantly negative picture of this experience.

Some of the

very things that made the experience somewhat frustrating
and less than totally successful,
ness of data requirements,
valuable

educationally.

e.g. the comprehensive­

have also made it extremely
Also,

suggestions

for

future

research in this area can be distilled from this experi­
ence, a few of which are summarized below.
Probably

the greatest weakness

in the

and application is in the area of the
for

adjusting

parcel.

rents

based

on

variate models
attributes

that

relate

of parcels

indexing submodels

attributes

Empirically estimated,

current model

of

the

specific

theoretically based multi­

value

are needed.

in use to observable
The

requirement of a

theoretical basis is meant to imply that the submodels can
to some extent
variables

(at least in identification of relevant

and perhaps

equation

forms and rough orders of

magnitude for coefficients) be transferred with calibration
to other regions.
Despite a fairly careful rationale for the resolution
decisions
pervasive

made

in this

in explaining

levels of land use,

study,

resolution

limitations

economic,

problems

of this effort.

are
The

and spatial aggregation all

136
presented

certain

difficulties.

The

restrictions

on

resolution were felt necessary because of what turned out
to be somewhat artificial restrictions on computer capac­
ity.

If a similar analysis

is to be undertaken in the

future greater disaggregation of land use categories and of
land parcels

(either through irregularly shaped,

sized parcels or many more smaller parcels)

variable

should be em­

ployed to alleviate some of the problems mentioned above.
Related to the discussion of the preceding paragraph,
rather

artifical

computing

limitations were

also

largely

responsible for the early abandonment of the large scale,
spatially disaggregated linear programming approach to land
use models.
promising
program

This approach is now perceived to be more of a
avenue

than

it was

previously.

The

linear

formulations of Chapter II or variations on them

could be applied to a region,

and because proven solution

techniques and software could be used, proportionately more
time could be spent on data collection,

submodel develop­

ment and analysis than was possible in this study.
It
this

is

kind

strongly
be

done

recommended
in

close

that

future

research

conjunction with

of

a client

planning agency in the study region that is truly inter­
ested in the entire concept,

i.e. application of the land

use model, rather than merely in isolated parts or products
of the study.
The importance of final demand projections in driving
the land use model has been mentioned several times,

and

137
current

limitations

in arriving at reliable

predictions have been acknowledged.

final demand

While the importance

of and current weakness in this area should not be mini­
mized,
goes

the need for and scope of such research certainly
far beyond the context of land use modeling.

If

progress in land use modeling had to wait for a definitive,
concensus answer to the exogenous demand problem it would
be waiting a long time.
being resolved to the

The implication is a need for

fact that the product of land use

modeling is and will continue to be projections rather than
predictions or forecasts.

The consolation being that land

use models can reflect whatever projections or forecasts of
exogenous variables are available and provide the only
means

for

a comprehensive,

detailed analysis

of their

impacts.
This attempt at understanding and modeling this whole
has

identified or

at least emphasized many holes

in the

process, perhaps more vividly than any alternative approach
could have.

The filling of these holes with better infor­

mation and models

through additional research would take

time but could eventually lead to a practical, useful, and
needed tool.

APPENDIX

Fun [HAN Iv til

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THIS COOE IMPLEMENTS THE LAND USE PROJECTION MODEL. THE PROGHAN
15 STILL VERY MUCH IN A RESEARCH MOOEt RATHER THAN A THOROUGHLY
TESTED HUSEH FRIENDLY** TOOL.

c

LISTING

C
C
C
C
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MAJOR VARIABLE DEFINITION!
NPHD a NUMBER OF PERIODS IN THE RUN
NSEC b NUMBER OF ECONOMIC SECTORS
NLUC ■ NUMBER OF LAND USE CATEGORIES
NPAR a NUMBER OF PARCELS
CUSEd.JI b CURRENT LAND USE - ACRES OF PARCEL I ALLOCATED TO USE J
ACALIJ) b TOTAL ACRES ALLOCATEO TO USE J
ACRU(J) B TOTAL **ID£AL** ACRES REOUIHE0 IN USE J (BY CURRENT
SOLUTION OF ECONOMIC MODEL)
ACU(J) s TOTAL ACTUAL ACRES ALLOCATED TO EACH USE J
DMASFKJ) a DEFAULT MAXIMUM ACHES THAT CAN SHIFT INTO USE J IN
A SINGLE PERIOD
AMXSFT(l.J) B CONSTRAINT On MAXIMUM ACRES IN PARCEL I THAT CAN
SHIFT In t o U s e J IN a s i n g l e p e r i o d
XGU(K) a TOTAL GROSS OUTPUT FOR EACH SECTOR A fhOM SOLVING THE
INPUT-OUTPUT MODEL
FDN(K) a FINAL DEMAND FOR EACH SECTOR K FORCURRENT
PERIOD
FUU(A) B FINAL DEMAND FUR PREVIOUS PERIOD
AIO(K.R) a INPUT-OUTPUT TECHNICAL COEFFICIENTS MATRIX
AlMA(K.A) S INPUT-OUTPUT 1 - A MATRIX
ALURU(J.X) a MATRIX OF LANO USE REOUIREMENTS COEFFICIENTS ACRES/DOLLAR UF GROSS OUTPUT FOR A SECTORS 4 J USES
OBJ(A) a OBJECTIVE COEFFICIENT
obj(A) a objective function for linear program
IPHlX(l.J) a PRODUCTIVITY INDEX FOR USE J ON PARCEL I
ISUIX(I.J) a SUITABILITY INDEX FOR USE J ON PARCEL I
ICVIX(I.J) a c o n v e r s i o n c o s t i n d e x f o r u s e J o n pa r c e l I
CVNCSTIJ.JJ) a STANDARD CONVERSION COSTS FOR CONVERTING FROM
USE J
TO USE JJ

SOURCE

c

This Pr o g r a m b AS ORIGINALLY BRITTEN FOR A SYSTEM ON aHICH MEMORY
HAS Ea THEMELY LIMITED SO OVERLAYS AND SUBSTANTIAL INPUT/OUTPUT
HERE USED THAT ARE NOT NECESSARY BUT ARE STILL REFLECTED IN
THE STRUCTURE OF THIS VERSION.

FORTRAN

138

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MODEL

THESE ROUTINES RELY ON SEVERAL SYSTEM DEPENDENT ROUTINES. IN THIS
CASES IBM SORT/MERGE. UNIVERSITY OF VICTORIA. B.C. FORCE FORTRAN
SOr T/h ERGE INTERFACE. THE INTERNATIONAL MATHEMATICS AND STATISTICS
LIBRARY (IHSL) LEOTIF LINEAR EOUATION SYSTEM SOLUTION ROUTINE. ANO
IHSL 2X3LP LINEAR PROGRAMMING SOLUTION ROUTINE.

PAGE QOOl

USE

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DATE * B30A1

LAND

0002

MAIN

PHUbHAN LUPRO
CUMMON/UNVHSL/ CUSE (525. 101.ACAL(151.ACHU(lbl.OFCl(151.HNTS(20)i
1 ACU(151.TACH5IS25I.OMXSF I(151.NLUC.NPAh .NLINE•ITMXS.XGO(ST)•
2 FUN(201.F00(20).NStC.OSCHl.ItHl•ltK2.IcCHOl.IECH02.NPHD.
3 1PHD.1PDLGT»1NVFLG*IFLG.IACU.IOPTU.NLDCX.ACHOMN(15).
A NUSE(15).LFLG.NC0UNT.IC0Un T.1SFLG.NLUCX»MUSL(1S>»0FCTMX(151•
5 HNIA(15)
DIMENSION 1013133)

F OH THAN

1* 111

MAIN

Ht EASk 2.0

PFCTR(N)
SFCTH(N)
CFCTHINI

DATE * 03UA1

19/51/07

PAGE 0002

PRODUCTIVITY FACTORS THAT CORRESPOND TO PRODUCTIVITY INDEXES
SUITABILITY FAC10HS Th a t CORRESPOND t o s u i t a b i l i t y INNOEXES
CONWENS ION COST AOJUSTMfcNT FACTORS THAT CORRESPOND TO
CONVERSION COST INDEXES

FILE OEF1N1TIONSt
I/O UNIT
HUN CONTKOL AND FINAL DEMANDS INPUT
2
STANOAHO OUTPUT DEVICE FOR P«INTEL) REPOHTS 6 TRACKING
6
FILE OF MAXIMUM AREA SHIFT CONSTRAINTS(AMXSFT<1.J>1
6
INPUT FILE FOR NUMBER OF 1NUEX CLASSES AND FOR REAL
10
f a c t o r s c o r r e s p o n d i n g to
-

12

-

13
1A

-

16
17

-

18
20

-

-

indexes

UNSOHTEO FILE OF POSITIVE RENT OIFFEHENTIAL LAND USE
SHIFT POSSIBILITIES
INPUT FILE UF CURRENT LANO USE BY PARCEL AT BEGINNING
OF HUN
SORTED f i l e o f u s e s h i f t p o s s i b i l i t i e s
INPUT FILE FOR PHOUCUTIVITY* SUITABILITY, 6 CONVERSION
COST INDEXES
FILE OF MINIMUM AREA CONSTRAINTS BY PARCEL AND LANO USE
FILE OF ACCUMULATED SHIFTS TO A USE IN A GIVEN PARCEL
M11H1N THE CURRENT PERIOO - OUTPUT 6 INPUT FROM SHIFT
ROUTINE
ECONOMIC MODEL COEFFICIENTS, I.E. 1-0,ALURO.OBJ.ETC.
OUTPUT FILE - AREA BY USE 8V PARCEL FOR EACH PERIOO

REwlNO 2
REMIND 21
NLUCXao
IPHD*0
CALL INITL
10 IPh O«IPRD*1
IF L G a O

ICOUNTaO
MRITEI6,10001 IPRO
DO 20 lal.NGEC
FDOdl aFON111
20 CONTINUE
READ(2,200V) cFDNdl»Ial,NSEC)
CALL IUSLV
IF(NLUCX.EU.O) GO TO AO
30 CALL LPSLV
AO CONTINUE
DO SO Ial.NLUC
NUSEdlal
NUSEdlal
SO
CONTINUE
NLUCXaNLUC
NLUC/aftLuC
60 1COUNT a1COUNT * 1
C all In h n
IF(NLlNb.kU.01 GO TO 65
CALL HNTSNT

139

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IF IIC O U N T •E O .N C O U N T ) 0 0 TO TO
C ALL LPHNT
IF ( N L U C Z .N E .O ) 6 0 TO 6 0
0 0 6 8 J a |> N L U C
I F t O F C T I O I . G T . O ) N R 1 T E ( 6 ,3 0 0 0 ) J
6 8 CONTINUE
7 0 C A LL LP S L V
8 0 CONTINUE
C
C A LL lN O iC A LL PHPHTS
I F ( IP H U . L T .N P H O ) GO TO 10
C a l l F h p h TS
H E w IN O 1 3
8 1 H £ A U (1 3 .* O O O tE N D > 8 2 > 1 0 1 3
W H IT E ( 6 . + 5 0 0 1 1 0 1 3
GO TO 81
8 2 CONTINUE
+ 0 0 0 F U H M A T I3 3 A + )
+ S 0 0 F O H M A I( 1 ) ( 3 3 A + )
1 0 0 0 F O H M A T U n l. lO X . 'P E R IO D ' . 1 3 1
2 0 0 0 F0H m a T ( 1 0 F 8 .0 )
3 0 0 0 FOMMAT ( / / ' IN S U F F IC IE N T S U lT A U L t AHEA FUH U S t ' . l + >
STOP
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FOHTRAN IV

HELEASt 2.0

S U 8H 0 U I1 N E I N I T L
COMMON/UHVMSL/ C U S E ( S Z S . ) O ) t A C A L ( I S ) . A C H U ( I S ) . O F C T ( I S ) t H N T S ( 2 0 > «
1 AC U ( IS ) iT A C H S ( S 2 5 > * D M X 5 F T ( IS ) .N L U C tN P A H .N L IN E • IT H X S tX G O ( S 7 ) •
2 FU n 1 2 0 ) . F 0 0 1 2 0 ) . N S E C . U S C H T , I t R I . IE H 2 , I E C H 0 1 . IE C H 0 2 , n P R D .
3 IP H U .IP D L tiT .IN V F L G .IF L G .T A C U .IO P tU iN L U C X tA C H U M N ( I s ) •
+ N U S E I IS ) ( L F L titN C O U N T • IC O U N T iIS F L G tN L U C Z (M U S E ( I S ) t D F C T M X ( I S ) •
S H N IX d S I

ouol

0002

:
:

T H IS H O U TIN E IN I T I A T E S HUN CONTHOL P A H A M E T tK S . F IN A L
VEC TO H t AND CUHRENT LAND USE AHHAV•

DEMAND

C
0003

000 +
0005
0006
0007
0008
0009
o u io

0011

0012

H E w lN D 2
HE m I n D 1 2
H E A D ( 2 t1 0 0 0 ) N P H D tIP D L tiT » N S E C « N L U C fN P A H t1 E C H 0 1 • IE C H 0 2 * IO P T U *
1
IT M X S tN C O U N T aOSCHT• ( F D N ( I ) • 1 * 1 tN s E C )
UU 10 IM . N P A R
h E A D ( 1 2 .2 0 Q O >
( C U S E ) I.J ) tJ * l,N L U C )
10
CONTINUE
1 0 0 0 FUMMAT ( l u l 5 t F 5 . 0 / ( 1 0 F 8 . 0 ) >
2 0 0 0 FOHMAT ( S ) .t 8 F 6 « 0 )
HETUHN
ENU

FURIRAN IV lal

RELEASE 2.0

QUUl
0uo2
0003
000*
0005
0006

01

RELEASE 2 . 0

SH T1N

IN P U T FUR SORT/MERGE

UATE * 030*1

1 9 /5 1 /0 7

PAGE 0 0 0 1

IN TER FAC E

NN«132
Ca l l g e t m i . a . n n i
I F I n n I 2 0 .1 0 .1 0
REAO 11 1 . 1 0 U 0 .E N O * 2 0 )
10 C A LL A O O H (A .IA D O R I
IH E T -1 2
RETURN
2 0 IR E T * B
1 0 0 0 F O R M A T (33 A 4 1
RETURN
ENO

0003

000*

0005
0006
0007
0006
0009

00 10

0011
G1

R ELEASE 2 . 0

0UO1
0002
0003

A

SRTOUT

SU BRO UTINE S R T O U T IA .I h E T )
D IM E N S IO N AC 331
OATA N N / 1 3 2 /
C
C
C

0005
0006
0007
000B

•/
«/

SU BRO UTINE S H T 1 N IIA U D R .1 R E 1 >
D IM E N S IO N A I 3 3 I

0002

000*

PAGE 0001

RE b IN U
11
R E R lN O
13
C A LL S O R T D IF L D S .L S .H E C D .L H .L C U R .S R T IN .S m TO UT1
E N U F IL E 13
H E * 1NO I t
M E v iN U
13
K tr u flN
En d

0001

FORTRAN I v

19/51/07

T H IS K O U IIN K C A LL S THE It fH S U H T /H E H U i'IN T E R F A C E R O U TIN E S FROM THE
U N I V . UF V IC T O R IA . B . C .
F O H C t PA C K A G E .

0007
0008
0009
0010
0011
0012
0013
o o i*

TRAN IV

UATE * B30*l

SUBRO UTIN E HNTSRT
O ln E N S lO M F L u S t T I .H E C U I7 )
EATEHNAL F S O R T .S R T IN .S h TOUT
0 *1 A L b /2 e /.L R /2 e /.L C 0 R /1 0 0 0 0 0 /
O A IA FlOS/« S O H ' . ' T H » . * t L O S * . » * l l / ' . * . 1 0 . > . < C H t 0 * » M
OATA R E C IV * H E C '. 'O R O • • • 1 Y P E < • < * F . L • . ’ E N G T *• * H > 1 3 > • * 2
C
C
C
C

fur

HNTS r T

OUTPUT FOR SORT/MERGE

IF 1 IH E T - * )
2 0 .1 0 .1 0
C
10 C A LL P U I 1 1 3 . A .N N )
10 R R IT E 1 1 3 • 1 0 0 0 ) A
1 0 0 0 F O R M A T I3 3 A * )
2 0 RETURN
ENO

IN TER FAC E

JA TE

*

8 3 0 *1

1 9 /5 1 /0 7

PAGE 0 0 0 1

FUH7HAN IV <il

HELEASt 2.0

UU01
0002

0005

0000

0009
0010

0011
0012

19/51/07

PAGE 0001

T H IS H O U IlN E W H ITES HEPOHTS AT tN U
BY USE It y PAHCEL A M ) TOTALS

UF EACH P E R IO O -

L IS T

OF ACHES

W H IT E ( 2 0 . 0 0 0 0 ) I ( C U S E I I • J ) » J * 1 . N L U C ) . T A C K S ( I > » l» l* N P A R )
4 0 0 0 FO N H A I (B F ( i . 1 . F H . 1 >
WHlTEIIOPru.lOOO) I P H U . ( J » J « 1 .N LU C >
W H 1 T E (1 0 P T U .2 0 0 0 ) < 1 • IC U S E <I . J ) . J * 1 . N L U C ) . T A C R S I I ) • I» 1 tN P A H )
WHITE(IO P T U .3 0 0 0 ) (A C U ( J ) » J « 1 . N L U C ) •TA C U . IA C A L ( J ) , J » 1 . N L U C ) »
1
( A C H U ( J ) ,J » l,N L u C )
1 0 0 0 F O K H A T « 1 H J / / / 1 0 X . * P E N I O D * , 1 3 / / / 4 2 X , ’ ACTUAL ACHES A L L O C A T E D *.
1
• FHOM P AR CELS TO U S E S * / / 6 3 X . * U S t S '/ / 9 X . * P A H C E L S * . 4 A .
2
0 1 9 , B X ,.T O T A L * /)
2 0 0 0 F O H H A T ( 9 X .I 5 . 8 X . 8 F 9 * 1 . F 1 2 . 1 )
3 0 0 0 F O H H A T !/* TO TAL ACTUAL A C H E S * /* ALLOCATED TO U S E S * . 4 X .
1
B F 9 . 1 . F 1 2 . 1 / / / * ID E A L ACHES A L L O C A T E D * > b F 9 .1 / /
2
• ID E A L ACRES H E O U IH LU * . 8 F 9 . 1 >
RETURN
END

142

0006
0007

UATE * 83041

S U B H O U TI n E PRPHTS
C O M N O N /U NVH SL/ C U S E( 5 2 5 . 1 0 ) .A C A L 1 1 5 ) tA C K U ( 1 5 ) .U F C T 1 1 5 ) . H N T S I 2 0 ) <
1 A C u ( l b ) . 1 A C H S ( 5 2 5 ) .U M X S F T ( 1 5 ) .N L U C .N P A H .N L 1 N E .IT H X S .X G 0 ( 5 7 > *
2 F 0 M 2 0 ) .F U O (2 0 > .N S E C .U 5 C H I.I E H 1 . I t N 2 . I t C H O l • lt C n 0 2 . N P H 0 .
3 I P H D . 1P 0LU T . IN V F L b . 1 F L 6 . 1 ACU . 1UPTU .N L U C X . ACHUNN ( 1 5 1 .
A MUSE 1 1 5 ) .L F L G .N C O U N T .IC O U N T .1 S F L G .N L U C Z .M U S E 11 5 ) .D F C T N X 1 1 5 ) *
S Hn T x I I S )
C
C
C
C

0003
0004

PHPHlS

FORTRAN IV 01

RELEASE 2.0

OOOl

0003

0004

0011
0012
0013
0014
0016
0016
0017
0018

0019

0020

0021
00 22
0023
0024
0026
0026
0U 2 7
0028
0029
0030
0031
0032
0033
0034
003b

0036
0037
0030

19/61/07

PAGE 0001

C
C
C
C
C

T h i s h o u t i n e s o l v e s t h e u n c o n s t r a i n e d i n p u i - u u t p u t MODEL f o r
GHOSS UUTPUTS t o s a t i s f y t h e f i n a l d e m a n d h r u j e c t i o n s f o r t h e
P E R IO D . THEN SOLVES FOR ACRES H E O U lH E D AND RENTS BY U S E .

current

OH 1 I t ( 6 • 6 6 5 6 ) IP R 1 ).N L U C • NSEC »NPAR
6 6 6 6 F O H H A T (» 0 lN I 0 S L V . 4 I S )
REM IND 1 8
H E A U ( I B . 1 0 0 0 1 ( ( A t O ( l . J ) . J * 1 . N S E C ) . 1 * 1 . N S E C ). < ( A L U H O l I . J ) •
1 J * l . N S t C ) . 1 * 1 . N L U C ) . ( O B J ( J ) . J b I . N S E O . ( F F C T H ( J ) .J * 1 .N S E C >
1 0 0 1 *7
1A *2 0
MM * 2
DO 2 0 1 * 1 .N 5 E C
DO 1 0 0 * 1 . NSEC
A IM A (1 .J )* -A IU (I.J )
lF ( l.E U .J ) A I H A ( l. J ) * l. - A I O ll, J )
10
CONTINUE
8 ( 1 . 1 ) *F O N (1 )
8 1 1 . 2 ) *F l> 0 ( I ) * F F C I H ( 11
C

C A LL

TO IN S L ^S IM U L T A N E O U S E d U A T IO N S O L V IN G H O U TIN E

C

C A LL L E U T lF 1A IM A .M M .N s E C .1 A . 8 . I D G T .B K A h E A . IE R 1 )
M H lT E ( 6 . 2 0 0 0 ) 1ER1
0 0 3 0 1 * 1 . NSEC
A G O (I I * d 1 1 .1 1
30
CONTINUE
DU 6 0 1 * 1 .N L U C
a Ch u ( 1 ) * 0 .
ACRUMN(1 > * 0 .
s u x *o .
DO SO J * 1 .N S E C
ACHU(1 > * X G 0 ( J ) * A L U H U ( 1 . 3 ) » ACHO( I )
ACRUMN (I)*B(U.2)• A LU R U (1.J)♦ACRUMN (1)
IF ( A L U H U ( I .J I. G T . O .) S U X -S O X *O b J (J )*X G O (J )
SO
CONTINUE
IF (A C H U (I)-0 .) 5 6 .5 5 .6 2
62
Hn T x ( 1 ) * S O K / a CHu ( 1 )

HNTS(1)*HNlXIl)
bU TO 6 0

66

HNlS(i)*0.
HNtX(l)*l).

TO SOLVE

1 -0

143

uooa
0009
0910

UATE * 63041

SUBRO UTIN E 10S LV
C O M H O N /U NVH SL/ C U S E C 5 2 5 . 101 .A C A L ( 1 6 ) ,A C H U ( 1 6 ) .U F C T ( 1 6 ) .R N T S < 2 0 > .
1 AC l) ( 1 6 ) , T a C R S (5 2 5 ).U M X 5 F T ( 1 6 ) .N L U C . n P a k . N L I N E . 1 1 8 * 6 . AGO ( 6 7 ) .
2 C U N ( 2 0 ) .F U 0 ( 2 Q ) . C I 6 E C . D S C H I . I E H l. lE h 2 . lc C H 0 1 . I E C H U 2 . N P R 0 .
3 1 P H D .1 P D L G T .IN V F L G .IF L G .T A C U .IU P T U .N L U C A .A C h U H N I1 5 ) •
4 N U b E ( 1 6 ) .L F L G .N C O U N T • IC O U N T .1 6 F L G .N L U C 2 .M U 6 t ( 1 6 ) .U F C T M X 1 1 6 ) .
6 H N T X ( IS )
R E A L *B
A IO 1 2 0 .2 Q ).A 1 M A C 2 0 .2 0 ).U K A R tA (2 0 > .
1 A L U H O l1 6 . 2 0 ) . 0 8 0 ( 2 0 ) .F F C T K 1 2 0 ) . 8 ( 2 0 . 2 )

0UO2

0005
0006
0007

lUSLV

MODEL

FOrtJHAN Iv 61
0U39
0 0 *0
O u *l
0 0 *2
0 0 *3
00* *
0 0 *5
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HELEAiE £.0
6U

I05LV

UAlt * d30*l

C U N llN U E
h k IT E ( 6 « % 0 0 0 )
( a G O II)» I* 1 » M S E U
OH I r £ ( b , 3 o a 0 ) C A C K Q U I i H M b l l ) . I M t N L U C l
1 0 0 0 FUK m A T < 1 7 F 5 .I ) I
£ 0 0 0 EO kM A l ( / 1 0 X « M E t t l * • • 1 3 / 1
3 0 0 0 FO k h a T < / / ( 1 0 1 . F 1 £ . 2 . F 1 £ . 6 ) 1
* 0 0 0 F U H H A M /Z b A t 1 7 F 7 . 0 / 1
HETUMN

19/51/07

F O n lF lx N

iv

01

KtLEASt 2.0

OOOl
0004

C
C
C
C

0007

OOOB

T H IS H O U TIN E S U L V tS
8 1N 01N G

C
C
C

SET UP 1 - 0

0009
0010
0011
0012
0013
001*
0015
0016
0017

10

15
C

0018
0019
C
C

0020
0021

0022

20

0023
0024
0025
0028
0027

22
24
30

c

IH t

1 -0

HUOEL K IT H LANU USE AHEA C ON STR AIN TS

ANU AHEA C O n S T H A lN T S

UO 1 5 1 * 1 . NSEC
UU 1 0 J * l . N S t C
A 11 • J ) * —A1U (1 • J )
I F ( l . C U . U ) A ( 1 . J ) * 1 . —A 1 0 ( I • J )
A (1 * N S E C .J )* -A (1 .J )
c u n t in u f :
8 ( 1 ) * F 0 N 11 1
8 (N S E C * 1 > — F 0 0 1 1 ) *F F C T R ( I )
CONTINUE
0 0 3 0 K * 1 .N L U C X
UO 3 0 1 * 1 .N L U C
1 1 *N S E C *N 5 E C *1
I s N U S E IK )
I1 *K *N S E C > N S E C
UO 2 0 J » 1 .N 5 E C .
A ( 1 1 . J ) » A L U H O ( I.J )
CONTINUE
IF ( IF L G - O ) 2 2 .2 2 .2 4
H (1 1 )* A C A L (I)« A F C T H (I>
GO TO 3 0
8 ( 1 1 1 *ACA1. (1 >
CONTINUE
1 E H 1 *0
n i* n s e c * n s e i: * n lu c x
n i «n s e c * n s e c * n lu c

M2*0

c
C
C

0032
0033

19/51/07

U lH E N S tO N IM ( 1 3 0 )
ME h IN D 18
H E A O (1 8 . 1 0 0 0 1 ( l A I 0 l l , J ) , J » l . N S E C ) , I * l . N 5 E C ) . ( ( A L U h U l I , J ) , J * l , N 5 E C
1 ) . 1 * 1 . N L U C ).(O B J (J ).J * 1 .N 5 E C > • (F F C T H (J )« J * 1 .N S E C ).
2 ( A F C T R ( I ) . 1 * 1 . N LU C )
■ H IT E ( 6 . 5 5 5 5 > IP R O . N L U C .N S E C . N p AH
5 5 5 5 F O H H A T (< O IN L P S L V > 4 1 5 )

000*
0005
0006

0029
0030
0031

UATE * 83041

5UBHUUT1NE L H 5 L V
COHHON/UNVHSL/ C U S E ( 5 2 5 . 1 0 1 .A C A L 1 1 5 ) • A L X L I l b l . D F C T ( I S ) . K N T S 1 2 0 ) •
1 A C u ( 1 5 ) . T a Ck S 1 5 2 5 ) .U H X 5 F T 1 1 5 ) .N L U C .N P A H .N L iN t. IT N X S .X G O ( S T ) .
2 F U N ( 2 o ) .F U I J ( 2 0 I . N S t C . O S C k l . l t k l . I E H Z . l E t H O l . IE C H O 2 .N P M 0 .
3 I k h O . I k D L b f » I n V F L G .1 F L O .T A C U .1 0 P T U .N L U C X .A C M Q k N (1 5 ).
4 N U S t( 1 5 ) . L IL G .N C O U N T ,IC O U N T .l5 F L G .N L U C 2 .H U S t( 1 5 1 . U FC TH X 11 5 ) .
5 H N T X I1 5 )
H E A L»b
A 1 U 1 2 0 . 2 0 ) . A ( 5 7 . 2 0 ) . 8 ( 5 7 ) . U 8 J 1 2 0 ) . 8 8 ( 5 7 ) . A L U R U 1 1 S .2 0 ) .
1
D bO L( 5 7 1 » H 8 ( 2 0 7 0 ) .U ( 1 5 ) .A F C I k ( 1 5 ) .F F C T H I2 0 ) .X I5 7 )

0003

0028

LPSLV

lA * 5 7
SOLVE

I-O /L P

W IT H

IM S L L P H O U IIN t

C A LL Z X 3 L P ( A * 1 A . B . 0 8 J . N S E C . H 1 . N 2 . 0 8 J V .X . O S O L . H h . 1 8 . IE H 1 )
UO 4 0 1 * 1 . NSEC

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01

KELEAbE

1)003
C
C
C
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0009

0010
0011
00 12
0013
0016
0015
0016
0017
o o ia
0014

0020
0021
0022
0023
0026
0025
0026
0027
0020
0029
0030
0031

LR HNT

UATt

*

03061

1 9 /5 1 /0 7

SU BRO UTINE LE*NNT
C O M H U N /U N V H SL/ C U S C 1 5 2 5 . 1 0 1 .A C A L 1 1 5 * . ACMU( 1 5 1 .D E C T ( 1 5 * .H N T S 1 2 0 1 .
1 A C U ( 1 5 ) . 1 A C H 5 ( 5 2 S ) .U M A S t1 1 1 5 * .N L U C .N P A H ,N L 1 N E ,IT M X S .X 6 0 ( 5 7 1 »
2 F U N (2U > . e u u ( 2 0 ) . N 5 E C . 0 b C H l . l E H l . I t H 2 . l t C H O l . I t C H O 2 . N P N D .
J 1 H H 0 .1 P O L G T ,IN V F L G .IF L G .T A C U .IO P T U .N L U C X .A C H O M N O S ) .
6 N U b fc1 1 5 1 .L F L G .N C O U N T .IC O U N T • I b E L O . N L U C 2 . H O b t ( 1 5 ) . D F C IM X I1 5 1 .
5 Hn T X ( I S )
R tA L » 8
A I b 7 t . A I 5 7 . 2 0 ) . 0 ( 5 7 ) .U U J (2 0 I,d tt< 5 7 > .H O ( 2 0 7 0 ) .
1 O b O L ( 5 7 ) .A L U H O ) 1 5 . 2 0 ) . 6 1 0 ( 2 0 . 2 0 ) . U l 1 5 ) .F F C T H ( 2 0 )

0001

00U2

oou«
0005
0006
0007
oooa

2 .0

T H IS H O U TIN E SOLVES
C O N S T H A lN lN G .

I-O /L P

FUH H tN T S WHEN

a HEAS

fun

some

uses

D IM E N S IO N I* < 1 5 0 >
W N 1 I t < 6 .5 5 5 5 1 lP R D .N L U C .N 5 tC .N P A R
5 5 5 5 FU h M A I I ' Ol N L P R N T » . 6 l5 )
H EwINO 18
H t A D ( la . 1 0 0 0 ) ( ( A I O ( l. J ) , J * 1 . N S C C > » I * l. N b E C > . ( ( A L U H O ( I .J ) •
1 J * 1 . N S E C ).I» 1 .N L U C )• ( O H J ( J ) .J « l,N S E C > • (F F C T R IJ I.J * 1 ,N S E C >
C
C
SET UP 1 - 0 ANO AREA C ON STR A IN TS
C
DO 1 5 1 * 1 . NSEC
UO 1 0 J * ) , N S E C
A I1 .J 1 -A IO II.J )
IF (I.E U .J ) A (I,J )* 1 .-A 1 0 (1 .J >
A ( l* N S tC ,J ) * - A ( I,J )
10
CONTINUE
0 ( 1 ) *F D N ( 1 )
B ( I* N b E C )— FDO( II• F F C T N ( 1 I
15
CONTINUE
M1 bNSEC »NSEC *NLU C
N2 * 0
I a *5 7
UO 2 a 1 * 1 . NLUC
l l* N S t C * N S £ C * I
0 ( 1 1 )*A C A L ( I )
0 0 2 0 J * 1 .N S E C
A ( 1 1 . J ) *ALU H (J ( I • J )
20
CONTINUE
25
CONTINUE
IE H 1 * 0
■ H U E ( 2 2 , 6 0 0 0 ) ( (A 11 • J ) . J * 1 .N S E C > . 0 ( 1 ) . 1 * 1 . 1 1 )
0 H IT E I2 2 .6 O O O ) 1 * 6 0 ( 1 ) , 1 * 1 . N bE C )

c
c

6000 FOHMAT(ITFT.O)
SOLVE

I-O /L P

W IT H 1 MSL H O U T IN t

C
0032
0033
0036
0035

C A LL Z X 3 L P IA . |A « B .0 B J . N S E C , H 1 . H 2 , 0 U J V . X , D 5 0 L . R W . 1 W . I E R 1 1
W H IT E ( 6 . 5 0 0 0 ) X
5 0 0 0 F O R M A T ( IX .lO F lO .O )
6 0 0 0 F O R M A T I1 X .1 7 F 7 .4 .F 7 .0 )
C
c
COMPUTE ACHES ALLOCATED TO USES IM P L IE D IN N L P S O LU TIO N
C

ape

FUNIHAN IV G1

LFHNT

KELEASE 2.0

0036
0037

UATE - «3u*l

19/51/07

UO 35 1-si,NLUC
U ( 1 >»0.

0U3B
0030

O il 3 0 J * l« N S E C
1 1 = N S E C » N S E C *I

00*0
00*1

30

0 0 *2

35

U(1I*U<I»•A<II.J)**CJ1
CONTINUE
CONTINUE

C
C

ID E N T IF Y

SLACK AND DEFEC 1T USES -

SET FLA G S AND A S S IG N RENTS ACC0HD1NGLY

C
C
C
C
0 0 *3
0 0 **
0 0 *S
0 0 *6
0 0 *7
0 0 *8
0 0 *9
0050
0051
0052
0053
005*

0055
0056
0057
0058
0054
U 06 0
0061
0062
0063
006*
0065
0066
0067
0068
0069
0070
0071
0072
0073
007*

SET OH C O N S TH A lN TS ON H A A IH U H AHEA D E F IC IE N C IE S
FO h NEXT HOUND OF S H IF T S

ALLOMEO BY USE

N Lu C X *u
N L U C Z *0
DO 5 0 l f l t N L U C
I F IA C A L 1 1 ) • GT • ACHO11 1 1 GO TO * 0
N LU C X*N E U CX »1
N U S E IN L U C X I* !
I F ( A C A L ( I ) . 6 1 . U < I H GO TU * b
I F I U ( I ) .E O .A C H U H N d t i GO TO * 3
NLUCZ *N LU C Z * 1
M U S E T N LU C Z I* 1
*3
1 I* N S E C * N S E C * I
Hn T S I H - D S U L I I I I
IF IH n T S III.L T .R N T X II))
H n T S ( I) * M N T X ( 1 1
U F C T M X II Ia A C H U C I ) - A C M iH N ( I )
GO TU SO
*S
N L U C Z « N L U C i* l
N U 5 E IN L U C 2 I* 1
DFCTM a ( I ) « « C N 0 ( 1 I - U I I )
H N T S U » *R N T X < II
GO TO SO
*0
N L U C Z *N L U C 2 *l
H u S E (N L U C 2 I» 1
N N T S U l * . 01)0 0 0 1
O F C T N X l i l- O .
I F < 0 1 1 1 a G T n A C H Q ( I) 1 D F C T H X <I >* A C « Q <I ) - U U )
so
C o n tin u e
H H lIE I* t3 0 0 0 ) i
HNTS
3 0 0 0 F O H H A T ( 8 F 9 .5 )
1 0 0 0 F 0 H M A 7 I1 7 F 5 .0 7
2 0 0 0 FOH m A T l / / l 9 X - t I S « 3 X » F 1 0 . 1 « 3 X t F 1 2 . * t 3 X t F 1 2 . 2 » S A # l S )
HETUMN
END

O N IK A N

0001
uun2
U003
000*
000b
OOOO
0007
0008

IV

G1

H E L tA b E

2 .0

bUHHUUrlNE
rtF.lNU 10
1A

CALL lnllb
CALL KNTCMU
ENUEILE 11
HE1UHN
En u

I* * "

lflHN

UATE » «30*1

18/51/07

CUNTHAN IV 01

RELEASE 2.0

OOOl
1)01)2

0003
C
C
C
C
C

UU04
0006

0000

0007
oooa
0009

00 10

0011
0012
0013

IMTi

DATE a 83041

19/51/07

SUM W UUTINI 1 N 1 T 5
C O R M O N /u N kH S L / CUSE ( 5 2 S . lt > > .A O A L <151 , ACHvI (1 5 > ,O F C T (1 5 1 ,H N T S < 2 0 ) «
1 A C U 11 8 1 , T A C H S 1 5 2 5 ) .D M X S F T llS ) .N L U C N P A H . N L IN C , 1 T M A S .X G O lS T I <
2 F U l,( 2 u > ,F U U ( 2 U ) . N S E C . U 5 C H 1 ,ltH l, IE H 2 . 1 E C H 0 1 . 1 E C M 0 2 « N P R 0 ,
3 IP K U . lP D L G I . lN V F L G . lF L G . T A C U . I U P T U . N L U C A . A C H U H N I lb ) ,
A N U s E llS ) . L F L G . N C O U N T , I C O U N T , lb F L G . N L U C Z . M U S E ( I S ) . 0 F C T H * ( 1 5 I •
5 U N IX (1 5 )
COMMON/ 1 N T H N T / 1 H H 1 * ( 5 2 6 . 1 0 ) . 1 S U 1 * ( 5 2 5 . 10). 1 C V I * ( 8 2 5 . 1 0 1 ,
1 f>FCTH (5 0 1 , 8 ) C 1 H ( ) 6 ) .C F C T R 1 5 0 ) «NCLS< 1 5 . 0 ) . CVNCbT ( 1 5 , 1 5 )
T n IS H O U TIN E I N l T l A L I Z t b H H O D U C T IV lT r , S U I T A B I L I T Y . AND CONVERSION
C U 5 I IN D IC E S ANO FACTORS FOR C R E A T IO N OF S H IF T S P O S S I B I L I T I E S
F IL E .

■ H IT E ( 6 , 5 6 5 5 ) IP H O ,N L U C ,N S E C ,N P A R
5 5 5 5 F U R H A T M O IN I N I T S ' , 4 1 8 )
H E A D !1 0 , 1 0 2 0 ) ( ( N C L S ( I . J ) , 1 * 1 , N L U C ) , J a | , 3 )
R E A 0 4 1 0 , 1 0 3 0 ) COHXSFT( 1 1 , I« 1 ,N L U C >
H E A D ! U , 1 0 5 0 ) ( ( I P H l X d . J ) , J « 1 ,N L U C ) , 11 S O U < 1 , J l « J a l .N L U C ) •
1
< ) C v 1 * ( I . J ) . J * 1 . N L u C ) « I » 1 . n HA h )
H E A D ( 1 0 , 1 0 6 0 ) I( C V N C b T ( 1 . 0 ) . J « l . N L U C ) , I ■ l . N L U C )
C
■ H I T E ( 6 , 5 5 5 5 > IP R O .N L U C .N b tC .N P A H
C
0 0 5 l^ l.N L U C
C
0 0 5 U > 1 ,N L U C
C
C V N C S T ( 1 ,J ) > 0 .
C
6
C UN TIN U E
N CL5( 1 ,4 ) ,!l
N C L S d . b l 'l
N C L S ( 1 .6 ) * 1
N N a N LU C -1
C
M H 1T E ( 6 , 5 5 5 5 ) IH H U .N L U C .N S tC .N H AH
C

c

C
C

c
0014

0015
0016
0017

0010
C
0019

0020
0021

0022

0023
0024
C
0025
0026
0027
0028
0029
0030

S ET UP F a CTOH a r r a y KEYS ACCORDING TO T h e n u m b e r o f p r o
S U I T A B I L I T Y , UR CONVERSION C 0 5 T C A TEG O R IE S FUR EACH USE
0 0 1 0 1 * 1 , NN
N C L S 1 1 * 1 , 4 ) a N C L S (1,4)A N C L S ( 1 « 1 )
N C L S I1 * ],5 > a N C L S (1 , 5 ) aN C LS ( 1 , 2 )
N C L S (I* 1 , 6 ) - N C L S ( I,6 ) * N C L S ( I,3)
10
CON TIN U E
B R IT E ( 6 , 5 5 5 5 ) IP H O ,N L U C ,N S E C .N P A H
N N Ia N C L S ( N LU C , 4 > * N C L S ( N LU C , 1 ) - 1
R E A D !1 0 , 1 0 / 0 ) ( H F C T R I 1 ) , I a l , N N l )
N N 2 a N C L 5 (N L U C , 6 ) A N C L S ( N LU C , 2 ) - 1
H E A U llO . ld T O ) ( S F C T R ( l ) , I b 1 , N n 2 )
N N ja N C L 5 ( N LU C , 6 ) a N C L S ( N LU C , 3 ) - 1
H E A D d u . 1(17 0 ) ( C F C I H d ) , l a l , N N 3 )
U N IT E ( 6 , 5 5 5 5 ) IP R O .N L U C .N S E C « N P A H
0 0 3 0 1 " 1 , n PAN
T A C H b d ) BO.
UO 2 0 J a 1 , NLUC
TA C H S <I ) a T A C H S ( I ) a C U S E ( I , J )
20
CON TIN U E
30
C O N TIN U E

d u c t iv it y

,

M J H lH A N

IV

til

HtLEASt 2.0
C
C
C

0031
0032
0033
0034
0035
0036
0037

UATE ■ 03041

19/51/07

SUM 1UEAL ACRES A L L O C A T tO TO OSES b Y M U L T Ir L Y IN ti ACTUAL ACMES BY THE
P M O U U C T IV IT Y FACTOM FO k THE USE OVER A L L PARCELS

40
SO
C

003B
0039
0040
0041
0042
0043
0044

1NITS

1020
1030
1050
1060
1070

UO SO J ' l . N L U C
A C A L (U > *0 .
UO 4 0 1 »1 *N P A M
I P N « N C L S ( U * 4 > » IP R IX < 1 * J > - 1
ACAL I J ) OACAL I J ) *C U SE H t U ) •P F C 1 M 1 1 P N )
CONT IN U E
CONTINUE
W H lT E lb fS S S S I ]P R O *N L U C *N S E C *N P A R
FORMAT 1 2 4 1 2 1
F O R M A T C lO F d .01
FO H H A T( 1 0 * * 2 * 1 2 1
F 0 M M A 1 ia F 1 0 .C H
FOMMAT(1 0 F 0 .C H
HETUMN
En u

FOHTHAN IV 01

HELEASt 2.0

U 001
0002

0003
000*
C
C
C
C
C
C
C
C
C

c

0005
0006
0007
0008
0009
0010
0011
0012
0013
0014
0015
0016
0017
0 0 IB
0019
0020
0021
0U22
0023
0024
0025
0026
0027
0028
0029
0030
0031
0032
0033
0034
0035
0036
0037
003B
0039

HNTCHB

OATfc * 830*1

19/51/07

SU BRO UTINE HNTCHB
COHHON/UNV r S L / CUSE ( 5 2 5 . 1 0 ) .A C A L O S ) • A C H U C IS ).U F C T 1 1 5 ) « R N T S < 2 0 > .
1 A C U ( I S ) » T * C H S ( 5 2 5 ) .DNASEI ( IS ) .N L U C .N P A H .N L 1 N E .1 T m X S .X G O ( 5 7 ) •
2 F U N ( 2 0 ) . F 0 U I 2 0 ) . N S E C . U S L K I . I E H l. I E R 2 . I b C H 0 1 . lE C H 0 2 . N P R D .
J 1 P H O .IP D L b T .I N V F L G .IF L G .T a C U .IO P T U .N L U C X ,A C R U M N ( IS ) .
4 N U S t( 1 5 ) . L F L G .N C O U N T .IC O U N T .1 S F L G ,N L U C /.M U S E ( 1 5 1 . U F C T N X d S I .
5 M N TXdS)
C O M M O N /IN T R N T / I P R I X ( S 2 S * 1 0 ) . I S U l X 1 S 2 5 . 1 0 ) • IC V 1 X ( S 2 S . 1 0 ) .
1 P F C T R (S O ).S F C T H 1 7 5 ) ,C F C T h ( S O ) . N C L S I lS . t ) . C W N C S T ( 1 5 . I S )
D IM E N S IO N 1 P H C L IS 2 S ) .I C M b F 1 3 4 )
T n i s H O U TIN E CHEATES A F I L E OF P O S S IB L E USE S H IF T S FROM USE J TO
USE J J WHICH R ESULT IN P U S S IU V E HENT D IF F E R E N T IA L S .
S H IF T S ARE
CONSIDERED FOR A L L USES JJ FOR W HICH THEHE I S CURHENTLV IN S U F F IC IE N T
AHEA ALLOCATED AND FROM USES J FROM WHICH S H IF T S ARE ALLO W ED .
EACH
RECORD OF T n I S F I L E IN D IC A T E S THOSE PARCELS WHICH FOR A 6 IV E N S H IF T
( J TU J J ) HAVE ID E N T IC A L P R O D U C T IV IT Y . S U I T A B I L I T Y . AND CONVERSION
COST IN D E X E S FOR THE USES IN V O LVE D AND SO R ESULT IN THE SAME
RENT D I F F E h E N U a L .

W R IT E Ib .S S S S I IP R O .N L U C .N S E C .N P A R
SSSS FORMAT I ' D I M HNTCHB* * 4 1 5 )
REWIND 11
N N C *34
N L IN E -0
DU 1 0 0 J O a l.N L U C Z
J*M U S E ( JO )
N S *N C L S (J .2 )
N p w N C L S C U .l)
DO 9 b l S w l . N S
1 S 1 « N C L S (J ,5 )* 1 S -)
DO 9 0 I P * 1 . N P
IP 1 * h C LS ( J . 4 ) ♦ I P - l
H N T S ]* H N T 5 ( J l *S F C T H d S l ) *P F C T R ( I P 1 )
DO 2 0 1 * 1 . 5 0 0
IP H C L ( I ) * 0
20
CON TIN U E
NPHC«0
00 3 0 1 * 1 .N P A H
I F ( C U S E ( I . J ) . E U . O . ) GO TO 3 0
I F d ' . U l X d , J ) . N E . I S ) GO TU 3 0
I F I I P R I X d . J ) . N E . I P ) GO TO 3 0
N P i(C *N P R C » l
IP H C L (N P H C > *1
30
CONTINUE
1 F IN P R C .E O .O ) GD TU 9 0
DO 8 0 K J c l.N L U C X
J J * N U S E IK J )
I F ( J J . E O . J ) GO TD 0 0
N N s * N C L S ( J J .2 )
N N P w N C L S IJ J . il
IJO 7 5 1 1 S * 1 .N N S
1 S 2 * N C L S (J J .5 )+ 1 IS -1
DO 7 0 I I P w l . N N P
IP 2 * N C L S ( J J .4 ) ♦ ! IP - 1

FOHTHAN IV bl
0 0 *0
0 0 *1
0 0 *2
0 0 *3
00«*
0 0 *S
0 0 *0
0 0 *7
0 0 *8
0 0 *9
0050
0061
0052
0053
00b *
0055

HELtASE 2.0

65

5*

55
1

59
60

1
0065
0066
0067
0068
0069
0070
0071
0072
0073
00 7*
0075
0076

70
75
80
90
95
100

DATE * »3Q*1

19/51/07

c o n t in u e
c o n t in u e

C O N TIN U E
U N IT E 1 6 ( 2 0 0 0 1 N L IN E
1 0 0 0 FOKH a T ( 2 1 3 ( 2 F 5 « 3 ( F 1 0 ( 3 ( 1 3 ( 3 * 1 3 1
2 0 0 0 F O h m a T 1 1 0 A ( * M .IN E a • d 6 l
RETURN
ENO

PAGE 0002

153

0056
0057
0058
0059
0060
0061
0062
0063
006*

NNTCMU

6 6 fS 2 * W N T 5 1 J u l * 5 F C T h 1 IS 2 > » P F C T « 11Hd>
N M I> F 3 |H n TS2 - K N T 5 1 ) /0 S C H I “ C V N C b T I J 'J J I
IF (NNTUF . L t . u . l GO TU 7 0
OU 6 5 I l * l > 3 *
ICM bF < I I ) * U
C O N !IN U E
N C *li
DC 6 0 I I * 1 ( N F H C
1P N C *1 H H C L C I1 >
IF 1 I b l i l A ( lP K C t J J 1 .n£. I I S > GO TO 6 0
IF < I H H lA ( lP M C t J J ) . N E . 1 I P ) 6 0 TO 6 0
N C *N C *1
I F < N C . b T . 3 * l GO TO 5 5
IC M b F IN C > *1 P N C
GO TO 6 0
U N IT E ( 1 1 1 lu O O l J J t J ( H F C 7 h 1 I N 2 I .K F C T H U N 1 I (R N T D F (N N C (
IIC H 8 F ( L K l,L K * 1 ,N N C )
N C *0
N L lN £ u N L lN E * l
00 59 1 L *1 (3 *
IC M b F (IL )* U
CONTINUE
GO TO 5 *
CONTINUE
lF t U C . E Q . O l GO TU 7 0
UMIVE 1 1 1 ( 1 0 0 0 1 U J ( J ( k F C T H ( l k 2 ) ( H F C I H I I P 1 ) ( H N T O F ( N C (
I I C M U F I t K I ( L K * l( N C I
N L IM E a N L lN E ’ l
C O N TIN U E
CONT IN U E
C O N TIN U E

F UN fN A N

i

V 01

KELLASt 2.0

SHIM

DATE = B3U41

19/51/07

SUBROUTINE SHIFT
COh HUN/UNVRSL/ CUSE (526. 101 .ACAL 115) .ACh O 1151 tllFCT 1151 .RNTS(20) •
1 ACU(13).TACKS(525).UHXSF1(13).NLUC.NPAR.NLINt•I1HAS.XG0157)•
2 FUN(20).FDU(20).NSECtUSCNT.ItMl•IEH2.ittHOl•IECH02.NPHD.
3 IPHU.lPULbT.INVFLG.1FLG.TACU.IUPTU.NLUCX.ACRUMN(13)•
4 NUSL < 1 5 ) .LFLG.NCOUNT• ICOUNT.lbFLG.NLUC2.HUbt 1 1 3 ) .UFCTMX11 5 1 •

0 00 1
0(102

3
0003
0004
QG05

m i.

1 2 (1 3 )

C0HHUN/5HFIT/ ACnlN(323.10)•AMXSFT(523.10).ASHFT(525.10)
OlMENblUN 1CHUF134).1U13(33>
UAIA IbGF/ii/
C

C
C
C
C
C
0006
0007

oooa
U 009

0010

0011
00 12
0013
0014
0015
0016
0017

ooia
0019

0020
0021
0 0 22
0023
0024
0025

THIS HOUTINE StARCHES THE SOK1EO SHIFTS POSSIBILITIES FILE ANO SHIFTS
ACh ES TO USES THAT HAVE UNHET HEUUIk EHENTS. CONSTRAINTS ON MINIMUM
AHEA bv USE or PARCEL ANO CONSTRAINTS ON THE MAXIMUM AREA TO SHIFT
TO A USE IN A GIVEN PARCEL IN A SINGLE PERIOD.ARE RECOGNIZED.

WRITE(6.5533) 1PHD.NLUC.NSEC.NPAR
S5SS FOr MAII'OIFi SHIFT'.413)
REWIND 16
HEb In O a
REwlND 13
C
IF(IaGF.EU.O) GO TU 530
C 500 REAO(13.6000.ENO«S5U) 1013
C
WKlIE(6.6001) 1013
C
GO TO 500
C 550 IbuGal
C
IF(IRP0.E0.3) WRITE(6.5000) IbUG
IF(NLlNE.EO.O) GO TO IS
DO 3 J w I.n LUC
UO 2 I»1.NPAH
ASHFT(l.J)aO.
2
CONTINUE
3
CONTINUE
IF (IFLG.EQ.iO) 60 TO 12
HtaINO 17
C
1HUGW2
C
IF(1RP0.E0.3) WHITE(6.5000) IbUG
11 HEAO(lT.aOO.ENDal2) I.J.ASHFT(I.J)
GO TU 11
12 CONTINUE
UO 4 Jal.NLUC
DFCT(d)w a CRO(J)*ACAL(J)
1 F (U F C T (J ).G T .0 .)

IF L G a l

4
C
C

0026
0 U 27
0020
0029
0030
0031
0032

C U N T INUE
IbUG*3
1FIIHPU.EU.3) WRITE(G.5000)
IF (1FLG.NE 1.1) GO TU 76
00 6 1*1,.NPAR
DO S Jr-l.NLUC
ACM1N(I.J)*0.
AHASFT C1.J)aDMXSFI(J)
3
CUNrINUE
6
CONTINUE

C

Ib u G * 4

C

If(IRPU.Ea.3) WRITE(6.3000)

IbUG

IbUG

PAGE 0001

F0HTHAN IV Gl
UU3 3
0036
UOJS
0036
0037

0038
0039
0 0 *0

0001
0063
0063
0066
006b
0066
0067
0060
0069
OObO
00S 1
00b2

00b3
0056
OObb
0056
0057

0058
OObO

OObO
0061
0063
0063
0066
0065
0066
0067

0068
0069
0070
0071

HELEASt 2.0

SHIFT

UATE * 83061

19/51/07

7 H E A U 4 6 .V I1 U .E N 0 -8 ) I t J .A H X S F T ( I . J l
9 0 TO 7
i t H E » D tl6 .9 0 O .E N O * 9 ) 1 . J .A C M IN 41 . J )
0 0 TO a
9 iT * 0
C
Ib U G - b
C
I F I I H P U . E U . 3 I U R I I E I b . b O O O l IB U G
10 1 T » IT * 1
H E n IN U 13
1 *0
C
1 0 0 6 *0
C
1 F I I H P 0 . E U . 3 I H H I T E 4 6 .5 0 0 0 1 Ib U G
lb L *L *1
C
lB U G - 3 0
C
I F I l b O F . E O . l l W H lT E < 6 tb 0 0 0 l H iU b
HE a O 4 1 3 .1 0 0 0 1 I U N . IU O .P F n . p FO .R N T U F . n E i ( IC h U F ( l i t I * l t N E I
I F I D F C T llU N l- 0 . 1 1 6 . 1 0 . 3 0
1 6 I F 4 L . L T . 6 L I N E I 6 0 TO l b
6 0 TO bO
2 0 IF ID F C T C I U O I . 6 E .D F C T M X IIU 0 1 I CO TO 1 6
14*0
2 b K * K «1
I F H C a lC H tlF IK I
A V A C *C U S E 4 I P H C . I U O I - A C H I N ( IP R C .1 U O )
IF IA V A C I 6 b . 6 S . 2 6
2 6 A S F T O *A N *S F T 4 IP H C .IU N I- A S H F T I I P R C . I O N )
C
Ib U G * 11
C
I F 4 1 H G F . E U . il U H IT E Ib .b O O O ) Ib U G
IF IA V A C .b T .A b F T D I A V A C *A S FTO
OO A*O FC T h x I1 U O > - D F C T I1 u O I
A V A C P *A V A C *P F 0
IF IA V A C P .G T .0 0 A 1 A V A C *O O X /P FO
A O F *O F C T (IU N I-A V A C « P F N
C
IF II P H O .E 0 .2 I H K IT E 4 6 .9 0 0 0 I IT .1 U N .IU O .O F C T IIU N 1 .D F C T I1 U 0 I.
C
6 l P N C t C U 6 E llP H C . lU N I . C U S E 4 1 P N C . I U b l. A C N lN lI P A C . lU O I . A V A C .
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