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AN ESTIMATION OF USER BENEFITS ASSOCIATED
WITH THE MICHIGAN PUBLIC ACCESS SITE
PROGRAM FOR INLAND LAKES
By
Thomas Donald Warner

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
Department of Resource Development
1976

ABSTRACT
AN ESTIMATION OF USER BENEFITS ASSOCIATED
WITH THE MICHIGAN PUBLIC ACCESS SITE
PROGRAM FOR INLAND LAKES
By
Thomas Donald Warner

The 1930's saw the first state effort in Michigan
to provide public access sites to the state's vast water
resources.

Since the initiation of the state public

access site program, many changes have come about in the
design of the sites and their administration.

Since 1968,

when the Michigan State Waterways Commission became the
primary public access site administrator, an on-going re­
search process has evaluated the criteria used for site
selection.
Conscious of both the economics of public expend­
itures and the need for a scientific basis for future ac­
quisitions, the Waterways Commission sought a study that
would estimate dollar benefits attributable to the use of
the public access sites.

By estimating dollar benefits,

existing sites can be measured for cost effectiveness and
proposed sites would have a basis for selection and
development:

sites with the highest estimated dollar

benefits receive priority for development.

Thomas Donald Warner
What was proposed for this study was the develop­
ment of a site visitation model that could be utilized in
generating Hotelling-Clawson demand curves and ultimately
site related dollar benefits.

The demand curve approach

to estimate site benefits was selected since the recrea­
tional use of public access sites has no fixed market
price to determine dollar benefits for site use.

By

creating a series of site specific visitation equations,
based on related studies conducted in Texas and Michigan
and data gathered from 16 lake public access sites during
the summer of 1975, the visitation estimation model for
this study was developed.

The equation used for the model

is given below.

Y + C = A

x®2

jj|3

X®4 x|5

Y = Number of annual visitors to the access site
(from origin "time zone").
C = Constant (Usually 1.0) used with double logrithmic transforms of the data.
X^ = Time zone population (origin).
X 2 = Travel costs (converted time increments).
X^ = Average family income.
X. = Gravity variable (alternative water-based
recreational opportunities— around site of
visitor origin.
X c = Surface lake acreation (destination).
o

Thomas Donald Warner
By developing the 16 "site specific" visitation
equations, and using multipliers to expand the data to
annual site visitations, a total of 622,737 annual visi­
tors was predicted.

By increasing the value of the travel

cost variable incrementally, 16 "site specific" demand
curves were created.

The area of consumer surplus under

each of the demand curves then represented the visitation
related site benefits.

The annual dollar benefits or con­

sumer surplus for the surveyed sites totalled $1,860,602.
After completing the estimations of site visita­
tions and dollar benefits for the study's surveyed sites,
the 16 "site specific" equations were combined into three
separate series of visitation predicting equations:

(1)

"state-wide"— a single equation combining data from all
survey respondents,

(2) "regional"— two equations, each

combining data gathered of survey sites in the upper and
lower regions of Michigan's lower peninsula, and (3) "sub­
regional"— four equations, combining data from survey
sites on a sub-regional basis.

After testing the three

series of combined equations for predictive accuracy
(using visitation data from sites with vehicle counters),
the "state-wide" single equation was selected for use in
predicting site visitations at non-surveyed existing public
access sites.
By applying the single combined visitation equa­
tion to the existing 339 lake public access sites (60

Thomas Donald Warner
percent of all Waterways Division public access sites) a
total of 5,741/774 estimated visitors was projected.

The

totalled figures for visitations represents 68 percent of
the counter count annual visitations set at 8,466,390.
The total site benefit figure for all 339 lake access
sites totalled $20,341,473.

The above dollar figure rep­

resents the site benefits generated by annual visitations
to the existing lake sites in Michigan's lower peninsula.
The model as conceived does not give consistently
accurate individual site visitation projections for site
development planning.

Additional refinement is desirable

before the model is used for this purpose.

A number of

variables should be sought out to improve the model's
predictive power.

One variable that should be explored

for inclusion in the model is "site attractivity."

This dissertation is dedicated to
my daughter,
Holly Michel Warner

ACKNOWLEDGMENTS

The list of individuals who were instrumental in
the success of the Public Access Site Benefit Estimation
Study and this resultant dissertation, represents literal­
ly thousands of people.

However, to narrow this list down

to the key contributors, I must start with Dr. Donald
Holecek, my research supervisor.

Dr. Holecek has proven

to be invaluable as a source of information on economic
theory and application of this theory to determine site
specific recreational dollar benefits.

It has truly been

a learning experience for me to work with this man.

In

the same light, I must thank Dr. Lewis W. Moncrief, my
Ph.D. Degree Committee Chairman, for his review and recom­
mendations of this research project.

The remaining Ph.D.

Committee members who have guided me in both this research
effort and in the classroom setting are:

Dr. Michael

Chubb from the Geography Department, Dr. Eckhart Dersch
from the Department of Resource Development and Dr. Robert
McIntosh from the School of Hotel, Restaurant and Institu­
tional Management.
Funding for this project was provided by a grant
from the Michigan State Waterways Division of the Michigan
iii

Department of Natural Resources.

Those individuals within

the Waterways Division that provided assistance for this
study by supplying data related to the State Public Access
Site System include:

Mr. Keith E. Wilson, Division Chief;

Mr. James E. Oakwood, Planning Analyst; and Edward E.
Eckart, Launching Ramp Administrator.
A special thanks goes out to the four graduate re­
search assistants who collected public access site study
data during the summer of 1975.

The four research assist­

ants were Michael Huddy, Lenny Govoni, Martha Grant and
Nancy Mullen.

The computer programming used in this study

was carried out by Daniel Stynes, a Senior Level Graduate
Research Assistant in the Recreation Research and Planning
Unit at Michigan State University.

Without Dan's help in

running the programs, this study would have been difficult
to complete within schedule limitations.
The two secretaries in the Recreation Research and
Planning Unit that were responsible for the data coding
and typing of this manuscript were Ms. Jean Geis and Ms.
Laura Welmers.

TABLE OF CONTENTS

Page
ACKNOWLEDGMENTS .................................... iii
LIST OF TABLES......................................viii
LIST OF F I G U R E S ........................... ..
LIST OF APPENDICES.

x

............................. xii

Chapter
I.

II.

STUDY ISSUES AND RESEARCH PROBLEM. ........

.

1

Introduction . ............................
Problem Statement..........................
Objective of the S t u d y ....................

1
4
5

RESEARCH DESIGN.............................

7

Review of the Literature ..................
7
8
Single Value Criteria....................
Willingness-to-Pay .
.....................11
Imputed Demand Curves from Travel
Cost Data................................ 13
The Research Model
. . . . .
19
Research Hypothesis.......................... 28
III.

RESEARCH ADMINISTRATION. . . . .

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

Determination of the Sample Population
of Public Access S i t e s ...........
Design of the Survey Instrument..............
Pre-Testing the SurveyInstrument.............
Data Collection..............................
Data Preparation Prior toAnalysis .........
IV.

30
30
32
37
38
42

ANALYSIS OF D A T A ............................. 43
Introduction ..............................
43
Cross-Tabulation of the Public Access
Site Survey D a t a .......................... 43
Day of the Week Interviews were
Conducted......................
44
v

Chapter

Page
Time of the Day That Interview was
Conducted............................
45
Did Site Visitor Bring a Boat to the
Public Access Site? ..................
45
Travel Time to DestinationSite . . . . .
47
Site Use Categories....................
48
Number in Visting Party ................
49
Income Levels ..........................
49
Multiple Regression Analysis of the 16
Surveyed Public Access Sites............
50
1.
AUSTIN LAKE/Kalamazoo County.
56
2.
ORCHARD LAKE/Oakland County ........
57
3.
WOLVERINE LAKE/Oakland County . . . .
58
4.
SHERMAN LAKE/Kalamazoo County . . . .
58
59
5.
LAKE FENTON/Genesee County....
6. -UNION LAKE/Branch County............
59
7.
SWAN LAKE/Montcalm C o u n t y ....
60
8.
MUSKRAT LAKE/Clinton County .......
61
9.
HIGGINS LAKE/Roscommon County . . . .
61
10.
LAKE ST. HELEN/Roscommon County . . .
62
11.
CHIPPEWA LAKE/Mecosta County.
62
12.
CLEAR LAKE/Mecosta County .........
63
13.
WIXOM LAKE/Gladwin County .........
64
14.
BIG STAR LAKE/Lake C o u n t y ....
64
15.
WIGGINS LAKE/Gladwin County .......
65
16.
BIG TWIN LAKE/Kalkaska County . . . .
65
Establishing Demand Curves for Surveyed
S i t e s ..................................
66
Estimation of Dollar Benefits for the
16 Surveyed Public Access Sites.. .......
71
Determination of Combined Site
Visitation Equations....................
92
The Aggregated Model.............
92
The Regional M o d e l s .............
93
The Subregional Models...........
96
^Application of Combined Site Visitation
Equations to Create Demand Curves for
Existing Non-Surveyed Public Access
S i t e s .................................... 103
Application of the Study Model toProposed
'
Public Access Sites in Michigan's Lower
Peninsula................................ 105

V.

TESTING THE STUDY HYPOTHESIS.

.............. 107

The Testing of Study Sub-Hypotheses. . . .
108
Sub-Hypothesis # 1 ........................ 108
vi

Chapter

Page
Sub-Hypothesis
Sub-Hypothesis
Sub-Hypothesis
Sub-Hypothesis
Sub-Hypothesis

#2
#3
#4
#5
#6

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

109
110
Ill
112
113

VI.

STUDY S U M M A R Y ................................ 114

VII.

STUDY RECOMMENDATIONS........................ 118

SELECTED BIBLIOGRAPHY................................ 121
APPENDICES.......................................... 127

vii

LIST OF TABLES

Table

Page

1.

Variation in Recreation Area Consumption
by Access Costs................................ 17

2.

Cross-Tabulation Number and Percentage of
Interviews at 16 Sites by Day of theWeek. . .

44

3.

Cross-Tabulation Number and Percentage of
Interviews by Time of Day...................... 46

4.

Cross-Tabulation of People Bringing Boats
.............. 47
to Public Access Sites . „ . .

5. Cross-Tabulation Travel Time to Destina­
tion Public Access Sites ....................

48

6. Cross-Tabulation Number and Percentage
of Primary Site Use Categories................ 49
7. Cross-Tabulation Total Numbers and
Percentages for Party Size ........

. . . . .

50

8. Cross-Tabulation Total Number and
Percentages for Income Classes ..............

51

9.

Visitation Projected by Time Zone of
Visitor Origin as Travel Cost Increases
(Wolverine Lake) Time Zones (15 Minute
Intervals).................................... 70

10.

Estimated Consumer Surplus Wolverine Lake
Site Benefit Estimation (Expanded to Annual
Visitations.................................... 72

11.

Estimated Annual Site Visitations and Con­
sumer Surplus Values Summary (16 Surveyed
S i t e s ) ........................................ 90

12.

Visitation Models Test Results ................

viii

102

Page

Table
13.

14.
15.

State-Wide Lake Public Access Site
Visitations and Site Benefits (Lower
Peninsula)...........

104

Estimated Site Visitations and Consumer
Surplus...............................

116

State-Wide Lake Public Access Site
Visitations and Site Benefits................116

ix

LIST OF FIGURES

Figure

Page

1.

Hypothetical Demand Curve and Area of Con­
sumer Surplusfor a Recreation S i t e ............14

2.

Michigan Department of Transportation
Interstate Zones.............................. 25

3.

Waterways Division Administered Public
Access Sites.................................. 31

4.

Michigan State Waterways Division Number
of Public Access Sites with Vehicle
Counters...................................... 33

5.

Michigan State Waterways Division Sites
Selected for the Visitor S u r v e y .............

34

6.

Austin Lake Public Access Site Demand
Curve and Consumer Surplus.................... 73

7.

Orchard Lake Public Access Site Demand
Curve and Consumer Surplus.................... 74

8.

Wolverine Lake Public Access Site Demand
Curve and Consumer Surplus.................... 75

9.

Sherman Lake Public Access Site Demand
Curve and Consumer Surplus.................... 76

10.

Fenton Lake Public Access Site Demand
Curve and Consumer Surplus.................... 77

11.

Union Lake Public Access Site Demand
Curve and Consumer Surplus.................... 78

12.

Swan Lake Public Access Site Demand
Curve and Consumer Surplus.................... 79

13.

Muskrat Lake Public Access Site Demand
Curve and Consumer Surplus.................... 80
x

Figure

Page

14.

Higgins Lake Public Access Site Demand
Curve and Consumer Surplus................... 81

15.

Lake St. Helen Public Access Site Demand
Curve and Consumer Surplus................... 8.2

16.

Chippewa Lake Public Access Site Demand
Curve and Consumer Surplus................... 83

17.

Clear Lake Public Access Site Demand
Curve and Consumer Surplus................... 84

18.

Wixom Lake Public Access Site Demand
Curve and
Consumer Surplus................... 85

19.

Big Star Lake Public Access Site Demand
Curve and
Consumer Surplus................... 86

20.

Wiggins Lake Public Access Site Demand
Curve and
Consumer Surplus................... 87

21.

Big Twin Lake Public Access Site Demand
Curve and
Consumer Surplus................... 88

22.

Michigan Lower Peninsula Regions
(Locationof Surveyed Sites)
...............

23.

94

Site Visitation Equations (Sub-Regional/
Lake A c r e s ) .................................. 97

xi

CHAPTER I
STUDY ISSUES AND RESEARCH PROBLEM
Introduction
Since 1939, when the State of Michigan ushered in
the "Public Fishing Site Program," various Divisions under
the Department of Conservation (now the Department of
Natural Resources) have worked to provide increasing ac­
cess to this state's water resources.

The "Fishing Site

Program" was sponsored through increases in fishing li­
cense fees and was designed to provide "walk-in" access
only, to inland water bodies.^

After World War II, Mich­

igan experienced a marked increase in the number of recre­
ational boats throughout the state.

As the number of

boaters increased, so did the pressure on the public ac­
cess sites.

The initial sites had not been designed to

handle trailered craft with their requirements for launch­
ing ramps and praking facilities.
Through the decades of the 1950's and the 60's,
the boat population continued to grow at an ever increas­
ing rate.

By 1968, to more adequately meet the needs of

^"Outboard Boating Club of America. Proceedings:
Sixth National Conference on Access to Recreational Waters.
(September 1969, p. 12.)
1

2
the boaters, the state's Public Access Program was shifted
to the Michigan State Waterways Commission.

The commission

was able to increase its operating budget to handle the new
program through allocations from the state's marine fuel
tax.

With the transfer of administrative responsibility,

the newly acquired sites are now being developed to accom­
modate the large number of trailered and car-top craft.
However, with over a half a million registered boats in
the State of Michigan (59.7 percent transported at least
2
once annually) and an additional 100,000+ craft (not re­
quiring registration) attempting to gain access to the
state's water bodies, the Waterways Commission and its
operational division have a sizeable task in providing
adequate public access.
Like all public agencies, the Waterways Division
operates on a limited budget.

The problem then is how can

the division in the face of spiraling demand allocate its
limited funds on the Public Access Site Program in order
to obtain maximum benefits for Michigan boaters?

The site

acquisition problem was brought to the forefront in June
of 1970, when Governor William Milliken imposed a ban on
further acquisition of Public Access Sites until criteria
for the selection of such sites could be reviewed and
approved.

The research staff and public access site

2
.
Recreation Resource Consultants, 1974 Michigan
Recreational Boating Study (East Lansing, 1975), p. 36.

3
administrators put together a "Statement of Public Access
Site Land Acquisition Program Criteria" to lift the ban on
site acquisition.

The site acquisition criteria statement

was completed in 1971, and, upon accepting it, the Governor
lifted his ban on acquiring new sites.
Since the first "criteria" statement which con­
sidered in broad terms:

(1) magnitude of anticipated use,

(2) feasibility of acquisition,

(3) ecological considera­

tions, (4) safety and regulation,

(5) increased satisfac­

tion or quality of experience, (6) interprogram effects,
(7) resource preservation,

(8) cost effectiveness,

(9)

secondary benefits, and (10) equitable distribution of
facilities, revisions were made to clarify the importance
3
of each of the above factors.
The question that arose within the Waterways
Division was "how useful were the initial site selection
criteria when none of the factors were quantified?"

In

order to increase efficiency in the public access site
selection process, a second "selection criteria" was de­
veloped.

The second criteria emphasized that "acquisition

and development efforts will be guided by our -desire to
provide for the greatest number of recreational
3
Michigan State Waterways Division. "Statement
of Public Access Site Land Acquisition Program Criteria."
(Lansing: Michigan Department of Natural Resources,
1971).

opportunities for the fewest dollars expended."

4

In order

to carry out this planning directive, the second site
acquisition criteria was developed aroung the following
factors:

(1) the existing availability of public access

to the lake, (2) proximity to population centers, (3) po­
tential for recreation opportunities, (4) lake size, shape
and island influence, (5) geographic distribution of oppor­
tunities, and (6) proximity to public road system.
The components of the second "site selection cri­
teria" seem to be based upon widely accepted factors which
explain levels of site usage.

Because of the immediate

need and lack of alternatives, the existing lake ranking
system is based upon a subjective numerical scaling, and
does not rest on data derived from sound research efforts.
Problem Statement
The Michigan State Waterways Division, in its on­
going research program, has recognized the need for a de­
tailed study to determine the dollar benefits which accrue
to the public who use lake access sites.
would:

Such a study

(a) document visitations at selected existing

sites; (b) establish demand functions for lakes with
existing sites; (c) provide for extrapolation of the de­
mand curves to lakes where public access sites are
^Michigan State Waterways Division. "Inland Lake
Acquisition Priority."
(Lansing: Michigan Department of
Natural Resources, December, 1972).

5
proposed; and (d) allow for the measurement of dollar
benefits and cost effectiveness for existing and/or pro­
posed public access sites.
Through the development of the site visitation
and demand estimation model, the Waterways Division would
have a tool to use in selecting future sites more effect­
ively than is now provided through the use of the existing
"weighted site selection criteria."

The division, as in­

dicated earlier, has assumed a large task in providing
access for the boaters in the State of Michigan.

The

question of where public dollars should be spent for ac­
cess site development should be addressed more rigorously
than is possible with the existing subjective system.

It

is toward development of this more rigorous decision-making
tool that this study is focused.
Objective of the Study
The primary objective of this study is to determine
the dollar benefits which can be attributed to annual
visitations to Michigan State Waterways Division adminis­
tered public access sites.
The region of the state to be studied includes all
of Michigan:s lower peninsula taking in Department of
Natural Resources' Regions II and III (see Figure 3).

The

reasoning behind the selection for study of only two of
the state's three regions will be stated under the Research
Administration section of this dissertation.

6
The data gathered and the models developed will
be utilized by the Michigan State Waterways Division to:
(1) help determine the overall cost-effectiveness of
their existing public access site program, and (2) provide
a method for selecting new sites to add to the existing
system.
Variables used in the estimation of a site's
annual dollar benefits will incorporate quantified ele­
ments currently used for site selection and listed in the
Waterways Division's Second "Criteria on Site Selection"
(see page 3).

Upon estimating annual visitation and de­

veloping demand curves for surveyed sites, a multiple re­
gression equation will be created which will be used to
estimate annual visitation at non-surveyed sites.

Once

total annual benefits are determined, a comparison with
annual costs can provide a benefit/cost ratio for the
existing site program.
The direct costs incurred by the Waterways Divi­
sion are to be computed by the Waterways Division engineer­
ing staff.

Beyond providing the Waterways Commission with

information related to existing program efficiency, the
resultant study model, once tested, should strengthen the
existing criteria for future site selection since it can
be used to estimate potential benefits from new sites.

CHAPTER II
RESEARCH DESIGN
Review of the Literature
(Estimating Recreation Dollar Benefits)
The main task of this research project, as pre­
viously outlined, was the determination of recreational
benefits which accrue to inland lake public access sites.
The benefits or "dollar value" for site visitations has
been historically difficult to determine because, as is
often the case with publicly provided recreational oppor­
tunities, fees are not charged at most sites in the system.
Without related market price data (i.e. entrance fees)
for the recreational experience, estimations utilizing
substitutes for market prices or politically set dollar
valuations are then used as surrogates in determining project benefits.

5

The literature review for this dissertation pro­
vides basic background on selected approaches utilized in
estimating site related recreational benefits.

Three

distinct valuation approaches are presented, and some of
5

A. A. Schmid, "Analysis of Non-Market and Dis­
tribution Effects." Course Notes: RD 811/Public Program
Analysis, Michigan State University, 1975.
7

8
their strengths and weaknesses are discussed.

The three

approaches will be compared in an effort to show the
selection process used to determine the most appropriate
valuation estimation method for this study.
The three site valuation approaches discussed in
this literature review are:

(1) Single Value Criteria,

(2) Willingness-to-Pay, and (3) Imputed Demand Curves.
It should be remembered that the above site valuation
approaches as well as others, are utilized in the public
sector to aid in determining which public projects should
be undertaken.

By setting standards by which dollar bene­

fits can be estimated, projects can be ranked according
to benefit/cost ratios.

The establishment of project ben­

efit estimations is essential for the resource manager in
the decision making process:

which project has the highest

ration of benefits over costs.
Single Value Criteria:
The single value criteria approach is often util­
ized when in-depth site valuation studies have not been
made (i.e. project fund restrictions) prior to actual re­
source management decisions.

This approach utilizes a

politically established value per visitation to a specific
site.

An example of this would be the action taken by

the U.S. Congress in 1964 in establishing a value range
of $ .50 to $1.50 for most recreational activities on a

9

unit day basis.®

A range in values was selected to re­

flect the amount of development across the sites in the
system.

The range of values set by Congress in 1964 for

specialized recreational activities was from $2.00 to
$6.00 per unit day.
In 1973, the U.S. Water Resources Council increased
the values of both general recreational experiences
($ .75 to $2.25/day) and specialized recreational activi­
ties ($3.00 to $9.00/day).
Below are the steps taken by the Bureau of Outdoor
Recreation (utilizing the "single value" approach) to
7
estimate project benefits:
(1)

Estimate the zone of influence of the project.

(2) Determine the present and future populations
that would probably be served by the recreation area.
(3) Estimate visitor-days or activity occasions
for each activity within the study area during the life
of the project.
(4) Standard values are then attached to partici­
pation by activity and the resulting number represents an
unweighted benefit of those activities. Values per day
range from $ .75 to $9.00 depending on whether the activity
is strictly routine or of a highly specialized type.
(5) The values so obtained are then weighted up
or down depending on such factors as water quality, scenic
beauty, etc... which vary from site to site.
g

Jack L. Knetsch. Outdoor Recreation and Water
Resources Planning, (Washington, D.C., American Geophysical
Union, 1974), p. 65.
7
Orris C. Herfindahl and Allen V. Kneese, Economic
Theory of Natural Resources, (Columbus: Merrill Publish­
ing, 1974), p. 262.

10
(6)
The weighted value represents the benefits
which will accrue to the facility and which are used in
the benefit-cost analysis.
The utilization of the "single value" criteria has
received considerable criticism for a number of reasons.
The first being the lack of consistency in applying values
of similar weight from one project to another.

The ulti­

mate goal of determining project benefits is to provide a
measuring stick by which one project can be compared to
another.

The tendency has been that where politically

priced recreational values are utilized, projects with
equal degrees of development are not given equal benefit
values.

Just as the value scale itself was established

on a subjective basis so is the application of the value
scale often made on a subjective basis.
The second criticism of utilizing the single value
criteria is the inability of this approach to take into
account differences in demand curves from one recreational
site to another. 8

If two recreational sites attract 1,000

persons per day with no entrance fee and the congressionally set value for the experience is $1.00 per person, the
daily value for both sites is $1,000.

This $1,000 figure

is set and does not take into account variations in willingness-to-pay, which would produce value figures lower
or higher than the fixed $1,000 value.
g

Knetsch, p. 66.

11
In an instance where little or no information is
available for valuing a recreational experience or proj­
ect/ the "single value" criteria with its politically es­
tablished values can be used, but only when its short­
comings in predictive accuracy are recognized.

After re­

viewing this valuation estimation approach, utilizing set
standards for benefits, it was decided that this approach
would not be used for looking at Michigan public access
sites.
Willingness-to-Pay;
A second approach used to determine the value of
a recreational experience is called the willingness-topay approach.

As the name implies, the user of a recrea­

tion area or facility is asked how much he would be will­
ing to pay to continue using the site or to prevent the
loss of the site.

Knetsch compares willingness-to-pay

between market and non-market goods in this manner:
In a market economy, resources are allocated to uses
for which consumers are willing to pay a price that
bids them away from alternatives; those uses for
which the willingness to pay is insufficient will not
be undertaken. Comparable to the role of price as an
objective rationing device that ensures that goods
and services end up in uses for which willingness to
pay is the greatest, the criterion of an implied
willingness to pay is equally applicable for commod­
ities that are not allocated by means of competitive
pricing.9
9

Knetsch, p. 60.

12
Given willingness to pay information from a non­
biased sample of site visitors, a site specific demand
curve can be developed by extrapolating the sample infor­
mation to the entire site user population.

A considerable

number of water resource related benefit/cost analysis
studies in the past have utilized the willingness to pay
approach to estimate project benefits.

This approach is

considered a guide for social choice since benefit esti­
mations are developed through the site users own estima­
tion of worth of the experience.
Although the willingness to pay approach to pre­
dict project benefits is widely used, this method does
possess some internal weaknesses.

A problem in determin­

ing willingness to pay for the use of an area, is extract­
ing accurate data from the respondent.

In the case of a

public provided recreation area where no fees are charged,
when asked "how much would you be willing to pay for a
day's use of this site?" a respondent could answer "I
don't pay anything now so I would not pay any amount to
use the site."
If the respondent felt that the information being
sought would be utilized to establish entrance fees to a
site where no fees existed before, the individual's re­
sponse would be intentionally low.

On the other hand, if

the respondent felt that more sites would be developed if
he provided a high response, he would be inclined to

13
inflate his true willingness to pay.

There is also a

problem of the user being able to give a value for some­
thing for which he has never paid.
In utilizing this approach in a study, the diffi­
culty is one of soliciting accurate data from the respond­
ent.

It is a problem that can only partially be solved

through a well designed survey asking the same question,
reworded, several times during the interview.

In order

to provide backup benefit estimations for this study, a
substudy was carried out which collected and analysed
willingness-to-pay data from Michigan public access site
users.
Imputed Demand Curves from
Travel Cost Data:
The third and final method discussed here for es­
timating non-market priced recreational benefits is the
"imputed demand curve" approach.

This method utilizes

expenditure behavior as a surrogate for pices.

By creat­

ing a visitation prediction model for a site, demand curves
can then be produced.

The initial point determined on

the demand curve is the total attendance with the price
set at zero.

By placing a series of increasing fees (cor­

responding to travel distance zones) into the model, suffi­
cient points can be established to plot the entire demand
curve.

14
The site benefits (referred to as "consumer sur­
plus") related to visitations then fall under the area of
the demand curve (see Figure 1, below).
FIGURE 1
Hypothetical Demand Curve and Area of
Consumer Surplus for a Recreation
Site

$8

$7
(0
•p
(0
o
u
•H

$6

Demand Curve

(!)

>

<d
■P

$5

C

•H
a) ro
o a)
aw
td (d
SH 0)
■p u

ao

■H
a)

$4

3

■p

-p
•H

Area of
Consumer Surplus
($ Benefits)

+)
(0

A
0
rn

0
1

2

3

4

5

6

Number of Visitations
(X 1000)

7

8

15
Consumer surplus is described as:

"the surplus

received by consumers from the purchase of some quantity
of a good is the difference between the value of the util­
ity they recieve from that quantity of the good and the
actual cost of that quantity.'*'®

In this instance, the per

unit value of a good (i.e. recreational experience) would
reflect the highest price the consumer is willing to pay.
This is referred to as perfect price discrimination.

Since

empirical measurement of price discrimination is usually
not possible, a determined per unit price is set, with the
consumer surplus value lying above the set price.
There has been considerable debate over the use of
consumer surplus as the measure of total benefits attri­
butable to a recreation site.

However, a number of authors

(some described below) have utilized the concept of con­
sumer surplus in estimating recreation site dollar bene­
fits.
The approach described above subsitutes travel
costs for price (i.e. entrance fee) in estimating recrea­
tion area demand curves.

This approach was first suggest­

ed by Hotelling in 1949 and reported by Roy A. Prewitt in
"The Economics of Public Recreation— An economic Survey
of the Monetary Evaluation of Recreation in the National
^®Walter Nickolson, Microeconomic Theory (Hins­
dale, Illinois: Dryden Press, 1972), pp. 300-301.

16
Parks" in 1949.^

Since the Prewitt article, a number of

refinements in the travel cost imputed demand curve model
have been made.

In 1959 Marion Clawson looked at travel

cost data related to visitation of National Parks in the
western portion of the United States to determine site
. .
12
specific demand curves.
His resulting article, "Methods
of Measuring the Demand for and Value of Outdoor Recrea­
tion," outlined his approach which is a refined Hotelling
model.
Since 1959 numerous travel expenditure studies pre­
dicting recreation demand and valuation have been carried
out by researchers in the recreation field.

Of greatest

relevance to this project is the "Texas Water Plan"
13
study.
The Texas study involved the estimation of the
recreational value attributable to existing and yet to be
created reservoirs.

The underlying idea for estimating

demand and dollar values is similar to the Clawson model:
increased travel costs associated with distance reflect
demand patterns similar to increases in entrance or park
usage fees.
■^Marion Clawson and Jack Knetsch, Economics of
Outdoor Recreation.
(Baltimore: John Hopkins Press,
1966), p. 64.
12Ibid., p. 72.
13Texas Water Development Board, "Economic Eval­
uation of Water-Oriented Recreation in the Preliminary
Texas Water Plan," (Austin, 1968), Report No. 84.

17
Table 1 below shows reaction of a three zone mar­
ket area reflecting increases in travel distance.

Table 1.— Variation in Recreation Area Consumption by
Access Costs.14

Zone

1
2
3

Population

1,000
4,000
10,000

Access Cost
Per Visit
$1
$3
$5

Number of
Visitors
500
1,200
1,000

Visits Per
1,000
Population
500
300
100

Referring to Table 1, if there is no entrance fee,
then 2,700 visitors (the sum of column 4) can be expected
at the park.

However, if a $1.00 entrance fee is simula­

ted by increasing the access cost by zone an increment
of $1.00, the expected result would be lower area attend­
ance.

The lower figure would represent one point on the

imputed demand curve where the entrance fee equals $1.00.
Simulating increases in access costs until visitations
reach zero, the additional points on a recreation site's
demand curve can be produced.
The Texas study adopted the view put forth by
Hotelling and Clawson that the area under the demand curve
is the dollar benefit yielded by the recreation facility
15
or the total willingness to pay for it.
■^Knetsch, p. 264.

18
In order to create accurate estimates of site
visitations, a survey of existing reservoir sites in Texas
was conducted.

The study determined where the visitors

were coming from and their socio-economic characteristics.
With .the survey data on visitor characteristics as well
as information on reservoir construction characteristics
and alternate water-based recreational opportunities avail­
able to visitors, a model was created to predict reser­
voir visitations (the actual visitation model will be
discussed at length in the following section of this
paper).
The members of the study team concluded that by
utilizing the basic approach set forth in the Texas Water
Plan study and by injecting proper revisions to adapt the
model to Michigan public access sites, estimation of site
worth could be delineated.

Both benefits attributable to

existing sites and those sites yet to be developed could
be calculated through one or a series of models.

The pro­

cedures for gathering data and applying this information
to the Michigan model/s will be covered under the "Re­
search Administration" segment of this report.
This literature review has not attempted to cover
all of the methods that have been utilized in estimating
the benefits of non-market priced recreational opportuni­
ties.

It has instead briefly covered three major valua­

tion estimation approaches:

(1) single value criteria

19
(politically set value), (2) direct willingness to pay, and
(3)

imputed demand curves from travel cost data.

This

review was designed to weigh the applicability of each
approach in estimating the value of Michigan public access
sites.

It was felt that imputed demand curves utilizing

travel cost data would provide the best valuation esti­
mates.

However, as indicated earlier, additional willing­

ness-to-pay data was gathered as a measure of comparison.
The Research Model
The research model utilized in this study draws
upon the work of two previously conducted studies.

The

first study, discussed in the literature review section
of this dissertation, is the Texas Water Plan study on
"Economic Evaluation of Water Oriented Recreation."

The

Texas study was utilized as a guide in the development of
the site visitation estimation model.

The second study,

conducted by Michael Freed deals with the criteria for the
selection of explanatory variables used in the Michigan
model.^
The "research model" section of this disseration
will briefly review the work conducted in the two studies
mentioned above.

This review will outline the importance

■^Michael Dale Freed, "Criteria for the Selection
of Public Access Sites on Inland Lakes in Michigan,"
(Michigan State University: Ph.D. Dissertation), 1973.

20
each of the two studies had in developing the Michigan
visitation estimation model for public access sites.
The Texas Water Plan Study outlined the procedure
used to develop.a site visitation estimation model.

This

model was ultimately used to predict reservoir site recre­
ational benefits.
cluded:

The steps taken in the Texas study in­

(1) the gathering of data from visitors at exist­

ing reservoir sites, (2) utilizing the collected data to
create a series of site specific visitation estimation
equations,

(3) varying the travel cost variable in the

equation, incrementally, to determine visitation levels
at various prices (travel costs) for each of the existing
reservoirs,

(4) calculating the area under the demand curve

to use as a value estimate (consumer surplus) for surveyed
existing sites.

(5) Combining the visitation equations

developed for existing sites to create a single equation
visitation estimation model,

(6) applying the single equa­

tion model to yet-to-be developed reservoirs to predict
site visitations and demand curves, (7) calculating the
consumer surplus under the demand curves generated for the
projected reservoirs.
As indicated earlier, the Texas study was designed
to determine the recreational dollar benefits that could
be attributable to yet to be constructed reservoirs.

In

the model building process, the following data was gathered
at eight existing reservoirs:

21
(1) Number in the party
(2) Visitor origin county
(3) Traveling time between visitor origin county
and reservoir
(4) Age group of head of party
(5) Income group of head of party
(6) Primary purpose of the trip
(7) Occupation of head of party
(8) Educational level achieved by head of party
The study utilized "counties" as the origin obser­
vation unit.

A 100 mile radius was then established

around each reservoir site, with those counties located
within the 100 mile radius included for analysis. 17 For
each destination site (reservoir) a series of multiple re­
gression equations was computed.
on origin counties.

Each equation was based

The predicted visitations (Y's) for

time zones represented the sum of the Y's for the counties
in each zone.

The equation with its variables is listed

below.
b. b„ b„ b . b_
(y±j + 0.8) = a x11 x22 x33 x44 x55
Y . . = Number of visitor days from the origin
^
county i to reservoir j .
j = 1...8; i = 1... all counties within 100
miles of lake j.
X^ = Population of the origin county.
X2 = Round trip cost of travel.
X^ = Per capita income in origin county.
17

The 100 mile radius in the Texas Study covered
95 percent of all site visitations to existing reser­
voirs.

22
X. = Gravity variable (existing reservoir acreage
within 100 miles of origin county.
Xc
5 = Surface acres of destination reservoir.
Since theoretical relationships exist between the
selected independent variables, the multiple regression
equation is multiplicative rather than additive.

The

study equation then is linear in the parameters (coeffi­
cients and exponents) but is curvilinear in the variables.
A double logarithmic transform was carried out on the
data to allow analysis of the data in a linear form.

The

constant 0.8 was added to the dependent variable Y to aid
in determining demand curves at the origin (zero visitors)
since the logarithm of zero is undefined.
To determine site visitation when the user fee is
zero, the solution for each equation (based on origin
county data) was determined and the sum of county equa­
tions out to 100 miles provided the visitation figure.
In order to obtain additional points on the site demand
curve, the travel cost variable was increased incremental­
ly.

Once the demand curve for the site was computed, the

dollar benefit estimate for that site wasderived by

cal­

culating the area under the curve.
In order to predict site visitations at non-exist­
ing reservoirs, the sample data for all eight surveyed
reservoirs was combined into a single equation.

Byapply­

ing data related to proposed sites to the aggregate

23
equation, visitation estimates and dollar value estimated
could be computed using the iterative approach outlined
for existing reservoirs.
Since the model developed in the Texas study ap­
peared theoretically sound and since it had been proven
to be operational, it was selected as a guide for the
study discussed herein.

What had to be determined prior

to the collection of data from the field was the applica­
bility of the independent variables included in the Texas
model to a Michigan setting.
The need for information on visitations to Michigan
public access sites lead to the study conducted by Freed.
In 1973, Michael Freed completed a Ph.D. dissertation at
Michigan State University entitled, "Criteria for the Se­
lection of Public Access Sites on Inland Lakes in Michi­
gan."

Freed in this study, identified a number of vari­

ables which explain in part public access site visitations
in Michigan.
The following variables were found to be signifi­
cant in predicting Michigan's public lake site visitations.
Mooring facilities

2.

Number of registered boats

3.

Number of angler days

4.

Number of seasonal homes

5.

Acres of lake surface

6.

R

2

1.

= .5190

R

2

R

2

= .0005

= .3143
R

R

2

2

= .0158

= .0067
2
Number of public campsites
R = .0028

24
R

2

7.

Disposable income

= .0059

8.

Parking spaces at public access sites 18
R2 = .0031

Freed's study, as in the Texas study, used county units
in the analysis of site of origin data.

In order to im­

prove on predictive accuracy of the Texas model, smaller
sections of individual Michigan counties (Michigan Depart19
ment of Transportation "Time Zones")
were selected for
the "Benefit Estimation" study covered by this disserta­
tion.
The Michigan Highway study breaks the 83 counties
in Michigan down into 508 individual "Time Zones" (see
Figure 2).

The Michigan "Time Zones" should not be con­

fused with the concentric rings of counties used in the
Texas study for time zones.

The use of Michigan "time

zones" improves accuracy in determining the geographic
distribution of site users, income distributions and other
additional variables used in the model.

Aggregation of

data on a county basis is in this way eliminated.
Since the study described by this dissertation
utilized Michigan Highway Department "Time Zones," not all
variables listed by Freed as significant in predicting
access sites visitations could be utilized in the model.
"^Freed, p. 51.
19Michigan Department of State Highways, Statewide
Transportation Analysis Research (Lansing, Michigan, 1973).

FIGURE 2
MICHIGAN DEPARTMENT OF
TRANSPORTATION INTRASTATE ZONES

5 4 ? ZONE STMEWOE MCCEL
I n state zo nes)

ccccusEftitn

25

26
The data had to be available on a Michigan "time zone"
basis.

After reviewing the information available on a

"time zone" basis, the following variables were selected
for preliminary inclusion in the Michigan site visitation
equation:

(Y + 1.0) = X ^ 1 X2b2 X3b3 X4b4 X5b5 Xgb6
Y

= Number of visitors (at selected lake site)

X^ = Time Zone population
X_ = Travel Cost (2X distance from the center of
the origin the zone X 20£ per mile)
X^ = Average Family Income for Origin time zone
X^ = Gravity Variable (lake acreage, stream and
Great Lakes shoreline miles within two hours
of origin time zone)
Xg = Lake Acreage (destination site)
Xg = Number of parking spaces at destination site

Of the above variables, income and lake acreage
data were used in both the Texas and Freed studies.

Travel

costs, "time zone" population and the "gravity" variables,
were taken from the significant variables found in the
Texas study.

The parking space variable was selected from

the Freed study, but was questionable in its impact in the
visitation model. 20

Part of the problem m

correlation

between parking spaces and site visitations in Michigan
relates to the historical development of "walk-in" access
sites.
Freed.

27
In reviewing the strengths and weaknesses of the
proposed Michigan model, it should be remembered that the
water bodies in the Texas study were manmade reservoirs.
All of these reservoirs would have similar design charac­
teristics so that the access site visitation patterns would
vary primarily with the location of population centers.
Because of the similarity of the Texas reservoirs, a single
site visitation equation, applicable to yet-to-be con­
structed reservoirs, could be developed.
The model used to predict visitations to Michigan
public access sites, unlike the Texas study, must take
into consideration the vast array of differences among
Michigan lakes.

The lakes selected for the survey of

site visitors in Michigan vary from a 39 acre mud bottom
lake that is ringed by dead trees, to a 9,900 acre lake
that has a sand bottom and is almost surrounded by managed
state forest and park land.

Unlike the Texas reservoir

situation, each Michigan public access site is likely to
be in a different recreational environment.
should be asked:

A question

"How much will the variation in site

characteristics affect the predicting accuracy of this
study's visitation model?"

The affect of variations of

lake characteristics for the Michigan model is not known
at this time.

In order to deal with the variation in

lake characteristics, it is possible that a series of site

prediction equations, instead of a single equation for the
entire state, may be required to improve model predictive
power.
Research Hypothesis
The primary hypothesis of this study is stated
below:
Study Hypothesis: The monetary value of Michigan's public
access sites can be determined through
the use of imputed demand curves.
By adopting the Hotelling-Clawson method, which
utilizes travel cost data as a substitute for site en­
trance fees, first site specific and then state-wide bene­
fits attributable to the recreational use of public access
sites will be estimated.
A series of related sub-hypotheses also provided
direction in this study.

The sub-hypotheses center on

the independent variables reviewed for use in the site
visitation equation.
Sub-Hypothesis #1:

The population of the origin
"Time Zone" (defined for this study
as a segment of area within a
single Michigan county) will regis­
ter a statistically significant
positive effect on site visitations.20

Sub-Hypothesis #2: Visitations to Michigan public
access sites are negatively corre­
lated with the travel cost vari­
able.
21

The "time zones" utilized for this study are
adopted from the Michigan Department of State Highways,
Statewide Transportation Analysis and Research publication,
1973.

29
Sub-Hypothesis #3:

Visitations to Michigan public
access sites are positively corre­
lated with family income.

Sub-Hypothesis #4:

As alternative water based oppor­
tunities (gravity) increase around
the origin "time zone" fewer vis­
itors are expected at the destina­
tion site. Gravity then has a
negative impact on site visita­
tions .

Sub-Hypothesis #5:

As the lake size increases (des­
tination site) the number of vis­
itors will increase.

Sub-Hypothesis #6:

The greater the number of parking
spaces per access site the greater
the number of expected site visi­
tations.

CHAPTER III
RESEARCH ADMINISTRATION

This section of the dissertation will cover the
following research method components:

(1) determination

of the sample population of public access sites, (2) de­
sign of the survey instrument,

(3) pretesting the survey

instrument, (4) data collection from the field, and (5)
data preparation prior to analysis.

Steps 1 through 4

were initiated in May, 1975 and completed as of August
25th, 1975 when field data collection terminated.
Determination of the Sample Population
of Public Access Sites
In order to estimate site visitations and ulti­
mately site benefits, it was necessary for this study to
select a number of existing public access sites in the
state for survey purposes.

In all, the Michigan State

Waterways Division administers 573 public access sites,
which include inland lakes, streams, rivers and the Great
Lakes.

(See Figure 3, on the following page.)
Since this study deals specifically with the lower

peninsula of the state (Department of Natural Resources
Region II and III) and inland lake sites only, the total
30

31

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32
population of sites for visitation and benefit estimation
is limited to the 339 inland lake sites that exist in this
study area.

The Waterways Division maintains vehicle

counters at 35 of the 441 lower peninsula public access
sites (see Figure 4).
reflect:

These 35 sites were selected to

a range of lake acreage, amount of site develop­

ment, proximity to population centers and alternative
water-based recreational opportunities.
In order to select lake sites with the electronic
vehicle counters (used as 24 hour data compilers on visits)
and stay within the proposed budget, it was determined
that a total of 16 sites could be selected.

The selection

of the 16 sites (see Figure 5) was made in a manner to
reflect the broadest possible range of the following:
lake acreage,

(1)

(2) proximity to population centers, and (3)

the availability of alternate water bodies.

By collecting

visitation data from these 16 selected sites, the sum of
visitation equations then represents a composite for the
entire lower peninsula access site system.
Design of the Survey Instrument
In order to assure a high response rate while ob­
taining visitation information, two methods for eliciting
data were considered.

First, a questionnaire could be

handed to the site user with directions to return it
"filled in" before leaving the site.

Second, a survey in­

strument could be used in a person to person interview.

33
»

j

FIGURE 5

j

I
I
I

MICHIGAN STATE WATERWAYS DIVISION
SITES SELECTED
FOR THE VISITOR SURVEY

s

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MICHIGAN STATE WATERWAYS DIVISION
NUMBER OF PUBLIC
ACCESS SITES
WITH VEHICLE COUNTERS

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35
Since the data needed was not extensive and since "hand­
out" questionnaires generally illicit lower rates of re­
sponse than personal interviews, the person to person in­
terview method was selected.
The survey instrument (see Appendix B) was de­
signed to fit on two pages of 8%" x 11" paper.

The back

page which was filled out by the research assistant in the
field provided the following information:
1.

Observation by Research Assistant
A.

Site and Survey Information:
(filled out prior to interview)
(1)
(2)
(3)
(4)
(5)

Site number
Respondent number
Date of interview
Day of week
Weather conditions

(6) Time of interview

B.

Site Visitor Information:
(filled out prior to interview)
(1) Did the party bring a boat on the site
(2) Number in party

2.

Person to Person Interview
A.

Site Visitor Information
(1)
(2)
(3)
(4)

City of residence
County of residence
State of residence
Distance traveled to the public access
site
(5) Travel time
(6) Do you currently reside on a lake
(non-study related information—
requested by Waterways Division Staff)
Personal information (i.e., family income) to be
collected from the respondents was filled out by the

36
respondent after a clipboard, with the instrlament (ques­
tions located on the front page of the instrument) and a
marking pencil, was handed to the site visitor.

This

approach was taken to reduce expected hesitancy to provid­
ing personal socio-economic status data.
of the instrument read:

The first line

"Please check a single response

to each of the five questions below.

Your response remains

completely anonymous according to strict University re­
search codes."

Following these instructions and the assur­

ance of anonymity, five questions were asked on the survey
instrument:
3.

Respondent Writes in Answers
A.

Socio-Economic Status Information:
(1) Primary use of the site (nine cate­
gories)
(2) Number of people in immediate family
(3) Educational level achieved by head of
household
(4) Annual family income/before taxes
(5) If this site was not available for use,
how many miles would you be willing to
travel to utilize a site of similar
quality?

The

fifthquestionwas designed to provide data

related to

the

visitors"willingness to pay" to use the site.

The

willingness to pay information provides a back-up estima­
tion for site benefits collected in this study.
To assure a high response rate, the survey instru­
ment was designed to be administered with minimum time
being spent with each respondent.

Since the site visitors

would be anxious to participate in some water-based

37
recreational activity, administration time was deemed im­
portant.

Also, by letting the respondent fill in the

socio-economic status information, it was felt that a
higher response rate would be achieved, since individual
confidentiality was assured.
Pre-Testing the Survey Instrument
Once the initial survey instrument had been de­
signed and all questions had been reviewed by research
staff members for accuracy of meaning and predicted re­
sponse, the instrument was taken into the field for pre­
testing.

The pre-test was conducted to see whether or not

the questions could be understood by the respondent and if
understood would the answers given provide usable data.
The pre-test also provided an indication of the amount of
time needed to administer the instrument and the method to
be used when approaching the respondent (at entrance gate,
while launching the boat, after launching the boat, etc.).
Muskrat Lake in Clinton County was selected for
survey instrument pre-testing.
of 16 selected survey sites:

The lake is the smallest
only 39 acres.

Even though

the lake is small, it attracts a sizable number of access
site visitors (primarily fisherman) annually.

The lake's

popularity is likely due to its location in a portion of
the state containing relatively few lakes.

The 10th and

11th of May (Saturday and Sunday), 1975, were selected as
test days.

38
The results of the pre-test indicated that of the
31 individuals surveyed over the two day period there were
no recognizeable problems in respondents understanding the
questions or providing responses.

The time required to

complete an individual interview was less than two minutes
on the average.

One visitation trend that showed up over

the test weekend was the number of people just using the
site as a turn around area.

During the two days, nine

motorized vehicles used the site to turn around and were
not surveyed.
Data Collection
In order to collect data from the 16 selected sur­
vey sites, four graduate assistants in the Department of
Park and Recreation Resources at Michigan State University
were hired.

Each of the graduate assistants was assigned

to conduct surveys at four public access sites.

During

the months of June, July and August, each site was attended
on a once a month basis.

A schedule was made out for each

interviewer specifying which of his assigned sites should
be manned during each specific week of the summer sampling
period.

The days and hours of each week during which he

would conduct interviews was outlined.

A scheduling

example for one interviewer for the month of June is
listed below:
Time Shifts:

#1
#2

( 6:00 AM - 2:00 PM)
(12:00 PM - 8:00 PM)

PUBLIC ACCESS
SITE STUDY

39
Schedule for Interview
(Sample)
TIME
SHIFTS

JUNE
HIGGINS LAKE
(Roscommon County)

June

6 - Fri.
7
Sat.
8 - Sun.
9
Mon.
-

—

LAKE ST. HELEN
(Roscommon County)

June

13 _
14
15
16 —

Fri.
Sat.
Sun.
Mon.

2
1
2
1

Fri.
Sat.
Sun.
Mon.

2
1
2
1

27 _ Fri.
28 - Sat.
29
Sun.
30 - Mon.

2
1
2
1

-

-

WIGGINS LAKE
(Gladwin County)

WIXOM LAKE
(Gladwin County)

June

June

2
1
2
1

20
21
22
23

_
—

-

The selection of access sites for specific weeks
of the month was decided on a random basis.

There was

no attempt made to continue a set pattern, though inter­
viewers were not permitted to work the same site two weeks
in a row.
The days of the week that were selected for data
collection were influenced by two factors.

The first fac­

tor was the determination that the research budget was
sufficient to fund only four eight hour days of interview­
ing per month per site.

The second influencing factor was

that data from the previous year revealed that 60 to 80
percent of site use occurred on Fridays, Saturdays and
Sundays.

To minimize travel cost and stay within the

40
research budget, it was necessary to conduct interviews
during four consecutive days on each site once per month.
Given the above considerations, it was decided to conduct
interviews on Friday, Saturday, Sunday and Monday in June
and July.

In August, the interview period was Thursday

through Sunday.

No data was collected on either Tuesday

or Wednesday during the interviewing period, however,
counter information for each of the sites provided a source
of data on visitations over these days.

For analysis,

visitation patterns for Tuesdays and Wednesdays were
assumed to be the same as that determined for Monday and
Thursday.
In designing the methods used to collect the site
visitation data, it was necessary to decide what eight
hours per day should be devoted to interviewing.

Instead

of running a single eight or ten hour shift in the middle
of the day or three over-lapping shifts over the three
month period, it was decided that two different eighthour shifts would be utilized.

As was seen in the earlier

"Schedule of Interviews" example, the time shifts ran from
6:00 AM to 2:00 PM and from 12:00 PM to 8:00 PM.

In both

instances a site use fringe would be picked up (early morn­
ing and evening use).

The over-lapping of the two shifts

took place between 12:00 PM and 2:00 PM on the start of
the afternoon.

41
In order to determine the number of site visitors
to be sampled, counter data for the 16 survey sites was
reviewed.

For 1974, between May 17th and November 1st,

a total of 130,000 vehicles entered the 16 survey sites.
By running the survey on a four day a week schedule, eight
hours each day, it was estimated that some 6,000 visitors
would utilize the sites while interviewers were present.
For analysis purposes, it was decided that surveying half
of the 6,000 visitors would provide statistically signif­
icant data.

The interviewers were instructed for the

month of June to survey every other site visitation (ve­
hicle entering the site).

Upon meeting with the four in­

terviewers prior to the start of July data gathering,
two of the 16 sites were producing extremely low visitation
figures.

To insure that enough observations were collected

to permit statistical analysis, the sample frame for July
and August for these two sites was changed so that every
party entering the site was interviewed.
In addition to interviewing site visitors, counter
data was gathered by the four graduate assistants to test
the accuracy of the counter mechanisms.

Since the four

day per month data was to be expanded to cover all 30 or
31 days of the month using counter data, the counter read­
ings had to be verified.

In order to test the counter

accuracy, the counter was read by the interviewer at the
start and end of each eight hour shift.

Between the daily

42
\

check periods, the number of parties surveyed should equal
half the counter count.

(The daily log utilized for re­

cording counter counts, and additional data on visitors
bringing boats to the site is provided in Appendix C).
Data Preparation Prior to Analysis
As the survey data was being collected from study
site visitors, completed survey instruments were sent back
to the Recreation Research and Planning Unit at Michigan
State University to be processed.

Two work/study stu­

dents were hired to transfer all information from the sur­
vey instrument onto "mark sense" computer forms.

These

forms were read and data cards punched mechanically, thus
avoiding the time-consuming manual keypunching process.
After all the survey instrument data had been
transferred onto computer cards, this data was then trans­
ferred to magnetic tape for processing convenience.

With

all of the survey data on tape, analysis could then begin
in an efficient manner.

CHAPTER IV
ANALYSIS OF DATA
Introduction
The "analysis of data" chapter of this disserta­
tion covers:

(1) a brief review of the cross tabulation

of collected survey data, (2) the multiple regression
analysis (visitation estimation) of the surveyed sites,
(3) the creation of demand curves for the surveyed sites,
(4) the estimation of the dollar benefits (consumer sur­
plus) for the surveyed sites, (5) the determination of
combined site visitation equations,

(6) the application

of the combined visitation equation to non-surveyed exist­
ing public access sites, and (7) application of the study
model to proposed public access sites.
The first section of this chapter, the cross tab­
ulation of survey data, outlines information applicable
to the study model as well as providing some information
relevant to visitation patterns of Michigan public access
sites.
Cross-Tabulation of the Public Access
Site Survey Data
As was stated earlier in the Research Administra­
tion chapter of this paper, by using previous year counter

44
data, it was expected a total of 3,000 site visitors
would be interviewed, given the selected survey periods.
After twelve weeks of in-the-field survey work, a total
of 2,601 site users were interviewed.

This figure closely

approached the estimated number of site visitors that were
to be interviewed.
The following cross-tabulated data represents the
summed data for all 16 surveyed sites.

The site by site

breakdown for survey data cross tabulation is provided in
Appendix D.
Day of the Week Interviews
were Conducted
During the summer 1975 survey period, each of the
16 selected public access sites was manned a total of 12
days (one four day period for each of the three survey
months).

Table 2 below indicates the total percentages

of interviews broken down by day of the week.

Table 2.— Cross-Tabulation Number and Percentage of Inter­
views at 16 Sites by Day of the Week.
16 Sites

“°nday

T5ursday

?riday

Number of
Interviewed
Parties

241

230

675

Percent of
Interviewed
Parties

9.2%

8.8%

26.0%

S*t"
urday

688

26.5%

®un‘
Day

Total

767

2601

29.5%

100%

45
It should be noted that the number of days inter­
views were conducted on Thursdays (three days) totaled
only one-half those conducted on Mondays (six days).

In

order for the expanded sample visitation data to represent
total annual visitations/ the Monday through Thursday ex­
pected visitations would be averaged and then compared
with site counter data.

As is explained later in this

chapter, the survey data was not.expanded to provide an
estimate of annual visitations, but rather the counter
counts were utilized.
Time of the Day that Inter­
view was Conducted
Since the interview schedule consisted of two dif­
ferent eight hour shifts (6-2 and 12-8), there is over­
lapping of visitor interviews during the 12:00 to 2:00 PM
period.

Specific weights were not assigned to the 12:00

to 2:00 time period.

By not weighting this two hour per­

iod it is hypothesized that the sample is possibly biased
toward persons traveling greater distances.

This bias

might effect the final estimation of site benefits by over­
estimating dollar benefits.

Table 3 indicates the aver­

aged results of the 16 surveyed sites.
Did Site Visitor Bring a Boat
to The Public Access Site?
To estimate specific site benefits related to the
use of Michigan State Waterways Division administered

46
Table 3.— Cross-Tabulation Number and Percentage of Inter
views by Time of Day.
Number
of
Sampled
Parties

Time/AM

6 - 7
7 - 8
8 - 9
9-10
10-11
11 - 12
Noon

54
86
74
87
117
344

% of
Total

Time/PM

2.1%
3.3%
2.8%
3.3%
4.5%
13.2%

12 - 1 .
1-2
2 - 3
3-4
4-5
5-6
6-7
7-8

Number
of
Sampled
Parties

% of
Total

376
379
211
209
185

14.5%
14.6%
8.1%
8.0%
7.1%

180
157
142

6.9%
6.0%
5.5%

public access sites, it is of considerable importance to
distinguish between boaters and non-boaters.

Since the

Waterways Division's public boating site access program is
funded through taxes on marine fuels, users who purchase
marine fuels pay for development and use of the sites
while those users who do not purchase marine fuels do not
contribute significantly to the public access site system.
Site benefits generated by non-boaters then would reflect
benefits created for this segment of the public for which
they pay nothing (no fees and no marine fuel taxes).
As can be seen in Table 4, the number of visitors
bringing boats to the sites and those not bringing boats
is almost identical.
A Chi square test was run to test for any signifi­
cant difference in distance traveled to public access

47
Table 4.— Cross-Tabulation of People Bringing Boats to
Public Access Sites.
No Boat
Number of
Interviewed
Parties
Percentage

Boat to Site

Totals

1320

2601

1281
49.3%

100%

50.7%
Trailered

Number of
Interviewed
Parties
Percentage

Car-Top

171

1149

13%

87%

sites for the surveyed boaters and non-boaters.

The test

indicated no significant difference between the two groups.
The implications of this test on the breakdown of site
benefits will be discussed later in this chapter.
Travel Time to Destination
Site
The data gathered on the amount of travel time
that site visitors incurred in coming to the destination
site is a key variable for this study.

The information

on how far an individual would travel to use a site es­
tablished cut-off limits for analysis of both the travel
time variable and gravity variable.

Table 5 indicates the

number of parties surveyed broken out by 15 minute travel
zones.

48
Table 5.— Cross-Tabulation Travel Time to Destination
Public Access Sites.

<15

15

(15 Minute Intervals)
30
45
60
75
90

Number of
Inter­
viewed
Parties

1108

647

309

% of
Total

42.7

25.0

11.9

108

4.2

105

120

91

19

73

15

69

3.5

0.7

2.8

0.6

2.7

The jumps in percentages in the 90 and 120 minute time in­
terval groups is most likely tied to unequal geographical
distribution of access site users.

This is especially

true for the eight sites in Region II of the state.
Site Use Categories:
For the purpose of generating data on site use ben­
efits as they relate to the types of activities the visi­
tors pursue on public access sites, each visitor inter­
viewed was asked to indicate his intended primary use of
the site.

Table 6 below lists the number and percentage

of visiting parties undertaking each activity.
The "other" category for site use, (representing
over 10 percent of indicated use) consisted primarily of
visitors coming to the site to look at the lake and watch
the activity around the site.

49
Table 6.— Cross-Tabulation Number and Percentage of Pri­
mary Site Use Categories.
Number of
Interviewed
Parties
Fishing
Pleasure Boating
Swimming
Other
Water Skiing
Picnic
Sun Bathing
Scuba Diving
Hunting

758
683
605
269
190
39
28
11
4

Total %

29.3
26.4
23.4
10.4
7.3
1.5
1.1
.4
.2

Number in Visiting Party
The number of persons in the interviewed parties
provides the initial expansion in generating site visitors
during the survey period.

This information is then needed

to establish the annual number of visitors to the site.
Table 7 indicates the frequence distribution of party
sizes observed during the field observation phase of this
study.
Income Levels
In reviewing both the Texas Water Plan study on
recreational site benefits for reservoirs and the Freed
study on variables affecting Michigan public access site
visitations, the "income variable" was found significant
in explaining visitations.

Cross-tabulation of survey

data reveals the largest percentage of respondents in the

50
Table 7.— Cross-Tabulation Total Numbers and Percentages
for Party Size.

Party
Size

1
2
3
4
5
6
7
8
9+
Total

Number of
Interviewed
Parties

Total %

317
887
468
387
188
280
31
18
11
2587*

Total People
in
Interviewed
Parties

Total %

12.3
34.3
18.1
15.0
7.3
10.8
1.2
.7
.4

317
1774
1404
1548
940
1680
217
144
99

4
22
17
19
11
21
3
2
1

100.0

8123

100

14 missing responses

$10,000 to $15,000 category (30.5%).

The site visitors

with family incomes of $15,000 or less represented 51.5%
of the respondents.

Those site visitors making over

$15,000 annually represented 48.5% of all respondents.
Table 8 below lists the results of the data cross-tabula­
tion by income taxes.
Multiple Regression Analysis of the 16
Surveyed Public Access Sites
After cross-tabulation of the survey data had been
completed, the next step taken was to analyze the selected
data for the visitation equation variables, via a multiple
regression routine.

The computer routine used was one

included in the Statistical Package for Social Sciences

51
Table 8.— Cross-Tabulation Total Number and Percentages
For Income Classes.
Income Classes
(in thousands of dollars)

Number of
Parties
Interviewed

0 - 5
5-10
10 - 15
15 - 20
20 - 25
25 - 30
30 - 35
35 - 40
40 - 45
45 - 50

145
383
733
517
307
156
61
32
15
15

Total

2406*

Total %

6.0
15.0
30.5
21.5
12.8
6.5
2.5
1.3
.6
.6
100.0

195 missing responses (refusals)

(SPSS).

The application of the survey data to a multiple

regression routine produced estimators for the unknown
parameters of the site visitation model.

Once the result­

ing model was tested for accuracy in predicting visitations
(utilizing site vehicle counter data) at surveyed sites,
the model was used to create demand curves which provided
the basis for estimating dollar benefits generated by site
visitations.
The ability of the study model to accurately pre­
dict site visitations is important since it impacts the
estimation of site benefits.

As will be seen in the follow­

ing sections of this chapter, the estimation process for
predicting recreational site benefits is straight forward

52
once the parameters of the model are properly identified,
and it is producing acceptable visitation estimates.

How­

ever, if the estimated visitation figure is inaccurate,
so will be the site dollar benefit estimation.
The independent variables entered into the individ­
ual visitation estimation models for each of the surveyed
sites were:

(1) the population of the visitor's origin

"time zone," (2) travel time between the origin "time
zone" and the destination public access sites, (3) average
family income, and (4) alternate water-based recreational
opportunities around origin "time zone" (gravity).
The "gravity" variable incorporated the total lake
acres within two hours driving time of the site visitors
place of origin (time zone). This variable also included
Great Lakes shoreline miles and boatable stream miles
within the two hour driving distance of the origin "time
zone."

The equation form used to determine gravity is

given below:

X. = W

^

2

si

2

■*" i=l

log10
di

+ M,
3

2 lo910 SMe
e=l
de

+ W2

i=l

log10 Gle
de

where:
Xj = Gravity value for "time zone" j.
= Weighted value for inland lakes (value = 3).

53
S. = Surface acres of lake i (within two hours
driving time or origin zone j).
d^ = Distance between "time zone" j and lake i.
W_ = Weightedvalue for Great Lakes shoreline
miles (Value = 2).
Gle = Miles ofGreat Lakes shoreline in "time zone"
e.
de = Distance between "time zone" j and "time
zone" e.
W_ = Weighted value for boatable stream miles
(value = 1).
SMe = Miles of boatable stream miles in "time zone"
e.
de = Distance between "time zone" j and "time zone"
e.
The weights assigned to each of the water bodies
were derived from boating patterns in Michigan and outlined
21
in the 1974 Michigan Outdoor Recreation Plan.
By using
a double logrithmic transformation of the data, the com­
bined variable was entered into the visitation multiple re­
gression equation.
Independent variable 5 (destination site lake
acreage) listed under the "design of the model" section of
this dissertation, was omitted from the individual models.
Since regression equations were being developed for each
of the 16 surveyed sites, the value of the lake acreage
variable would not change so the variable was omitted.
21

Michigan Department of Natural Resources. 1974
Michigan Statewide Outdoor Recreation Plan. (Lansing,
Michigan, 1974), pp. 77-78.

54
It was included in the combined site models discussed in
detail later in this dissertation.
At this point in the study independent variable
6 (parking spaces at destination site) was dropped from
the model.

The Freed study found some significance in the

contribution of number of parking spaces to visits at pub­
lic access sites.

However, this variable accounted for
2

only a limxted amount of variance (R = .0031).

22

The se­

lection of survey sites for this "benefit estimation"
study did not show a correlation between parking spaces
and visitations (counter count data).

Because the con­

tribution the "parking spaces" variable would make in the
prediction model was highly questionable, it was dropped
from the equation.
The "Y" or dependent variable for the 16 surveyed
site equations was the number of visitors that came to
the destination site from a specific "time zone."
It should be remembered that unlike the Texas
study, the Michigan study outlined here is not using site
adjacent counties and summing county visitation estima­
tions to achieve concentric visitation time bands around
each public access site.

Rather, as indicated under the

chapter on Research Design, the Michigan Department of
Transportation "Time Zones" (508 in total) are used in
this study.

By using separate "time zones" (portions of a

22Freed, op. cit., p. 51.

55
county) a more accurate visitation prediction model should
result.

If county visitation equations are added together

to form concentric time zones, the model will only indi­
cate that from somewhere in the middle of that time zone,
X number of visitors will originate.
on the aggregation of data.

The problem centers

However, by utilizing smaller

sections of the counties (Michigan Department of Transpor­
tation "Time Zones") accurate data reflecting the charac­
teristics of the residents should improve the model pre­
dictions, since the existing geographical distribution
of site years is taken into account more precisely than
was the case in the Texas study.
Those "time zones" falling within two hours driv­
ing time of the surveyed destination sites were included
for analysis.

The two hour cut-off for driving time en­

compassed nearly 95 percent of all visitors to the sur­
veyed sites.

Though this decision likely introduced a

slight downward bias in consumer surplus value estimates,
the bias was too small to justify the computational costs
associated with going beyond the two hour limit.
A double logrithmic transformation of the data
was carried out and a quantity of 1.0 was added to each of
the 508 "mini" time zones to avoid a value of zero in pre­
dicting visitations (the logarithm of zero is undefined).
The survey data was read off the project computer tape and
entered into the SPSS multiplicative multiple regression
routine.

56
The summary results for the multiple regression
"runs" of each of the 16 surveyed sites will now be dis­
cussed.

The summary outlines the impact of the independ­

ent variables (negatively or positively) within each equa­
tion, and the overall accuracy in estimating site visita­
tions.
The multiple regression equations shown on this
and the following pages lists the independent variables
(after the coefficient for the constant); (1) population
of time zone (X^ in the individual site model, (2) travel
time (X£ in the individual site model), (3) average family
income (X^ in the individual site model) and (4) gravity
(X^ in the individual site model).

The figures in paren­

theses under each of the equation coefficients are the
standard errors of the estimates of the regression coef­
ficients.
1. AUSTIN LAKE/Kalamazoo
County
log. n (Y + 1.0) = 1.16944 + .12105* log.. n X.
±u
(.45175) (.04793)
xu x
- .52483* log.. n X, + .75229 log. n X, - .25842 log
X.
(.066619)
(.40851)
1U J (.37416)
xu
R2 = 0.34

F = 21.59*

^ Predicted Y value (unexpanded) = 139
^The model estimated number of sampled visitors
at the public access site.

57
Observed Y value (unexpanded) = 138
♦Significant at .05 level.
The regression coefficients for population and
income show that as the value of these two variables
increase in the time zones around Austin Lake, visitation
increases.

The coefficient for travel time and gravity

indicate that as the values (i.e. greater travel time,
greater amounts of lake acres) of these variables increase,
visitations to the Austin Lake site decreases.

The above

observations are expected for all of the 16 surveyed sites.
This equation was significant in explaining visitation at
the 5 percent level of significance.
2. ORCHARD LAKE/Oakland
County
log, n (Y + 1.0) = 1.7870 + .14310* log, n X..
A
(.38406)(.041881)
L

- 1.01622*
(.14252)

log, nX9
-LU ^

R2 = 0.54

- .72310 log.. n X-. + .69516 log.. n X.
(.71183)
XU
(.32077)
XU 4
F = 54.57*

Predicted Y value (unexpanded) = 2 5 7
Observed Y value (unexpanded) = 301
The regression coefficients for Orchard Lake show
that as income values increase around Orchard Lake visi­
tations to this site are decreased.

Also, as the gravity

^ *The actual number of sampled visitors.

58
(attraction away from the surveyed site) increases, visi­
tation increases.

This could be explained in part by the

popularity of the lake for boat racers;

Social interac­

tion (boat racing) could explain this variation from the
expected.
3. WOLVERINE LAKE/Oakland
County
log. n (Y + 1.0) = 1.09861 + .57847* log
X..
10
(.25720) (.022799)
XU 1
- .50516*
(.86846)

log. nX. - .68762 log. . X- - .14268 log.. n X.
XU ^ (.40912)
XU J (.17867)
XU *

R2 = .34

F = 23.61*

Predicted Y value (unexpanded) = 149
Observed Y value (unexpanded) = 56
The only regression coefficient for Wolverine Lake
that varies from the expected pattern shown at Austin Lake
is that of increasing family incomes showing decreases in
visitations to this site.

The predicted and observed

visitation figures show considerable variance for this
equation.
4. SHERMAN LAKE/Kalamazoo
County
log.. (Y + 1.0) = 1.37635 + .11441* log... Xn
1U
(.46952) (.037951)
X
X
- .68963*
(.75804)

loginX. - .42796 log... X. - .13937 log .X.
XU ^ (.37519)
XU ^ (.36233)

59
R2 = 0.40

F = 28.91*

Predicted Y value (unexpanded) = 143
Observed Y value (unexpanded) = 105
The regression coefficients for Sherman Lake
reflect expected values in relation to the explanation of
site visitations.
5. LAKE FENTON/Genesee
County
log, n (Y + 1.0) = 1.65857 + .12703* log.,n X..
XU
(.38474) (.030368)
xu x

- .89221* login X9 - 1.55103* log, n X, - .15024 log.. n X.
(.90419)
XU *
(.37244)
xu J (.24996)
XU
R2 = 0.62

F = 32.53*

Predicted Y value (unexpanded) = 200
Observed Y value (unexpanded) = 242
The regression coefficient for family income varies
from the expected with increases in family income showing
decreases in visitation to Lake Fenton (at a significant
level). The R

2

value for Lake Fenton is the highest value

observed for the 16 surveyed lakes.
6.

UNION LAKE/Branch County
log,n (Y + 1.0) = .24203 + .90904 log . X.
XU
(.28342) (.26051)
XU X

- .74536 log. n X9 + .44816 lognn X- - .79461 log.. n X.
(.039882) XU Z (.28055)
XU X (.23432)
xu

60
R2 = 0.03

F = .98

Predicted Y value (unexpanded) = 1 4 1
Observed Y value (unexpanded) = 17
Although the regression coefficients follow the
expected pattern in explaining visitations, the R 2 value
for Union Lake is extremely low.

This site gets very

little use during the year (lowest visitation figures of
all Waterways counter sites).

In the concluding section

on study recommendations, the inclusion of perceived site
attractiveness, fishing success, etc. will be discussed
to help increase the predictive accuracy of this model
for sites such as Union Lake.
7.

SWAN LAKE/Montcalm County
log.n (Y +1.0) = .68000 + .54468* log.n X.
XU
(.21329) (.19145)
XU X

- .33579*
(.42177)

loginX, - .12217 log.nX- - .62974 log.n X.
xu z (.15122)
XU
J (.17320) XU *

R2 = 0.29

F = 17.57*

Predicted Y value (unexpanded) = 117
Observed Y value (unexpanded) = 17
In predicting visitations to Swan Lake, the regres­
sion coefficient for income again shows as incomes
increase, visitations to this particular site decrease.
The overall equation is shown to be significant at the 5

61
percent level.

The predicted Y value is considerably

higher for this site, than the observed value.
8. MUSKRAT LAKE/Clinton
County
login (Y + 1.0) = 1.06884 + .76812* log.. ft X,
iU
(.33026) (.23283)
1U x
- .45909*
(.54289)

log. nX,- .079735 log. n X- - .22232 log., n X.
XU Z
(.19433)
XU J (.25956)
xu
*

R2 = 0.26

F = 19.53*

Predicted Y value (unexpanded) = 153
Observed Y value (unexpanded) = 64
The income variable regression coefficients
reflects decreased visitations as incomes increase for
the Muskrat Lake site.

This variable, however, does not

enter the equation significantly.
9. HIGGINS LAKE/Koscommon
County
log. n (Y
1U

+ 1.0) = - .084167 + .32927* log.n X.
(.73275) (.99276)

- .52559* log. n X0 + .79783 log. n X, + 1.00301 log., n X.
(.16445)
±U 2 (.45698)
1U J
(.55129)
R2 = 0.34

F = 11.59*

Predicted Y value (unexpanded) = 1 2 6
Observed Y value (unexpanded) = 238

62
Since Higgins Lake is considered one of the most
attractive lakes in the state, it appears that gravity
from other lakes does not particularly affect visitations
to this site.

As in previous site equations, only the

population and travel time variables enter the equation
significantly.
10. LAKE ST. HELEN/Roscommon
County
log.n
XU

(X +

1.0) =
1.81630 + .48841* log nX1
(.65197) (.86043)
iU 1

- 1.10503* log. n X9 + .15475 log.. n X-. + .75321 log.. n X.
(.17003)
XU z (.47105)
XU J (.48402)
1U 4
R2 = 0.47

F = 18.26*

Predicted Y value (unexpanded) = 109
Observed Y value (unexpanded) = 7 1 4
Lake St. Helen, located in Region II of the state,
owes a large number of its visitations to its location
in relation to

the city of

St. Helen.

The site islocated

at oneend of the town and is frequently used as a
"turn around" area.

car

This explains, in part, the large

variation between observed and predicted visitations.
11. CHIPPEWA LAKE/Mecosta
County
log. n (Y + 1.0) = .80991 = .29364 log.. n X..
1U
(.57675) (.57863)
XU X

63
- .63870* log,_ X0 + .29286 log,„ X- + .51661 log,n X.
(.11985)
10 2 (.37381)
10 3
(.43622)
10 4
R2 = 0.49

F = 9.54*

Predicted Y value (unexpanded) = 133
Observed Yvalue (unexpanded)
Chippewa Lake is the

=120

only site that reflects a

negative regression coefficient for population in explain­
ing visitations.

Gravity has a positive coefficient (non­

significant) which would indicate little or no effect of
this variable for this site.
12.

CLEAR LAKE/Mecosta County
log.n(Y +

1.0) = 1.6904 + .40468 log n X.
(.32580)(.38336)
1U X

- .72555* login X9
(.60238)

XU

+ .56767* log,n X- - .24133 log,„ X.
Z(.24348)
XU J
(.26947)
XU *

R2 = 0.59

F = 38.55*

Predicted Y value (unexpanded) = 90
Observed Y value (unexpanded) = 64
Clear Lake reflects the expected pattern for
regression coefficients outlined by the study sub­
hypotheses.

Increases in population and income reflect

increases in visitations.

Increases in travel time and

gravity reflect a negative impact on visitations.

64
13. WIXOM LAKE/Gladwin
County
login (Y + 1.0) = .78636 + .34095* log.. n X,
±u
(.80523) (.91176)
xu x
- .56150*
(.15337)

log. nX9 + .32705 log.. n X-. + .15573 log.. n X.
XU ^ (.51677)
XU J (.74431)
XU *

R2 = 0.30

F = 9.63*

Predicted Y value (unexpanded) = 125
Observed Y value (unexpanded) = 187
The regression coefficient for gravity reflects
little, if any influence of other water bodies related to
visitations to this site.

The differences in the pre­

dicted Y value reflects visitors coming from outside the
two hour travel zone.
14. BIG STAR LAKE/Lake
County
login (Y + 1.0) = -.10119 + .14149 log.. r X.
XU
(.58419) (.91353)
xu x
- .36688*
(.15337)

log.nX 0 + .34408 log.n X, + .15573 log.nX.
XU ^ (.51677)
XU J (.74431)

R2 = 0.16

F = 3.99*

Predicted Y value (unexpanded) = 102
Observed Y value (unexpanded) = 112
Big Star Lake, one of the eight surveyed lakes in
Region II of the state, shows only the travel time vari­
able registering significant impact in explaining site

65
visitations.

The equation is significant at the 5 percent

level of significance and shows a close fit between the
predicted and observed Y values.
15. WIGGINS LAKE/Gladwin
County
log.n (Y + 1.0) = .80029 + .38448* login X.
XU
(.66457) (.074673)
XU X

- .62961* log. _ X9 - .081092 log1n X- + .18535 log.. n X.
(.11079)

XU

R2 = 0.40

^

(.40908)

XU

J

(.60123)

XU

F = 15.57*

Predicted Y value (unexpanded) = 112
Observed Y value (unexpanded) = 161
The regression coefficient for income reflects a
slight negative impact on visitations for this lake.
Gravity is positive reflecting little effect on site vis­
itations.

The overall equation is significant at the 5

percent level of significance.
16. BIG TWIN LAKE/Kalkaska
County
log. n (Y + 1.0) = .61220 + .14730 log1n X.,
XU
(.37963) (.84699)
XU X
- .46818* log.n X9 - .51731 log1n X- + .17804 log.n X.
(.10183)
10 2 (.36743)
XU
J (.35171)
XU q
R2 = 0.26

F = 5.97*

66
Predicted Y value (unexpanded) = 52
Observed Y value (unexpanded) = 6 5
The income and gravity variables for Big Twin
Lake show regression coefficients that vary from expected
values.

The gravity coefficient reflects little effect on

visitations to surveyed site.
The importance of determining similarities in
regression equations between lake sites, centers on the
need to create equations for visitation prediction of non­
surveyed sites and yet-to-be established public access
sites.

Unlike the Texas Waterplan Study that predicted

visitations to man-made reservoirs of similar construc­
tion, the Michigan public access sites are on water bodies
vastly different in almost all respects.

Because of the

differences found in Michigan public access sites both a
single state-wide visitation equation and a number of
regional equations were developed to take into account
these site differences.
In order to create demand curves for the 16 sur­
veyed lake sites, the expansion factors for the estab­
lished visitation equations will be derived from site
counter data.
Establishing Demand Curves for
Surveyed Sites
After processing the data and arriving at esti­
mators for the parameters of the model for the 16 surveyed

67
sites, two expansion factors were required to derive total
annual visitations at each site.

It should be noted that

total annual visitations are required in order to develop
the consumer surplus associated with each site.

Since

daily observations were made only 50 percent of the time
the sites were open to use and since information was
collected from only one member of a surveyed party, the
survey data must be expanded to reflect both the number
of parties entering each site during the survey period
and the average size of each entering party.
The number of parties entering a site during the
season was taken to be that number measured by the counters
maintained by the Waterways Division at each of the sur­
veyed sites.

It was decided that counter counts, as

recorded and adjusted by Waterways personnel would be used
to expand visitation data rather than develop another mea­
sure of annual vehicle entry to the sites.

However, the

use of these figures, if later found to be in error, does
not require that new information be collected from site
users.

An error in the counter counts does not impact the

parameters of the model since these were derived without
regard to the counter information.

How this model is

used to project use will, however, need to be adjusted if
significant error is later found in counter counts.

For

example, if counter counts over estimate use by 20 percent,
then the use projected using the model will have to be

68
reduced by 20 percent.

This can be done by reducing the

expansion factor by 20 percent or reducing the final
estimate 20 percent.
In order to expand the number of surveyed vehicles
to approach the Waterways Division annual counter counts
for vehicles, the annual figure was divided by the sur­
veyed vehicles figure.

Wolverine Lake:

(example)

Waterways Vehicle
Counter Total
4,833

An example is given below:

P.A.S. Study
Vehicles Interviewed
-r

56

Site Vehicle
Expansion Factor
=

146

The vehicle expansion factors for the 16 surveyed
sites ranged from 103 to 639.

The variation between

"site specific" expansion factors can be explained in part
by:
a.

Small sample with respect to season.

b.

Counter counts 24 hour/day sites officially
open only 16 hours/day.
ing official hours.

Only interviewed dur­

Possible large non­

daylight non-boat related use.
c.

Malfunctioning counter with non-canceling error
i.e. extra counts triggered by electrical
storms is greater than missed counts caused by
mechanical failure.

69
d.

Waterways records counts from May - October.
We interviewed only June - September.

e.

Vehicles entering just to turn around, main­
tenance crews, etc. were not interviewed.
These were recorded as users by counters..

Once the total number of visitors was determined
for each of the 16 surveyed sites, "site specific" demand
curves were then created.

This process utilized the vis­

itation model previously discussed in detail.
step usedthe data collected to
parameters

in the model.

The first

quantify the unknown

Total visitation was developed

using the expansion factors given above to provide the
first point on the demand curve (i.e. use at no increase
in price).

In order to determine additional points on the

site demand curve, travel costs were increased incremen­
tally within the site visitation equation.

As the travel

costs were increased for each site, the number of visitors
would decrease.

(See Table 9.)

The cost figure for this study was derived by
utilizing a $0.20 per mile figure an average driving speed
23
of 45 miles per hour.
The total cost to operate the car
per minute was calculated at $0.15 a minute.
In Table 9, it should be noted that once travel
costs are increased to $13.50 at Wolverine Lake there were
23 The cost per mile figure was drawn from the U.S.
Congress "Travel Expense Amendments Act of 1975," Washing­
ton, D.C., March 1975.

Table 9.— Visitation Projected by Time Zone of Visitor Origin as Travel Cost
Increases (Wolverine Lake) Time Zones (15 Minute Intervals).
1

INCREASED
TRAVEL
COSTS

$ .00
$ .75
$ 1.50
$ 2.25
$ 3.00
$ 3.75
$ 4.50
$ 5.25
$ 6.00
$ 6.75
$ 7.50
$ 8.25
$ 9.00
$ 9.75
$10.50
$11.25
$12.00
$12.75
$13.50

2.81
1.13
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00

2
10.48
5.58
2.81
1.17
.50
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00

3
19.51
12.93
8.35
5.50
2.66
1.32
.53
.22
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00

4
28.17
21.06
15.48
9.72
6.36
4.02
2.42
1.15
.51
.17
.06
.00
.00
.00
.00
.00
.00
.00
.00

5
30.89
23.65
18.34
14.09
10.39
7.45
4.51
2.70
1.60
.77
.18
.02
.00
.00
.00
.00
.00
.00
.00

6
32.25
24.60
18.99
14.59
11.18
8.50
6.42
4.63
3.17
1.67
.75
.33
.07
.00
.00
.00
.00
.00
.00

7
32.78
25.15
19.22
14.74
11.28
8.54
6.46
4.80
3.46
2.40
1.50
.93
.49
.23
.13
.00
.00
.00
.00

8
32.84
25.20
19.32
14.84
11.34
8.54
6.46
4.80
3.46
2.40
1.50
.93
.67
.46
.28
.13
.07
.04
.00

o

71
no more predicted visitations.

For the 16 sites, the

point at which visitation dropped to zero ranged from
$11.25 to $15.75 of added travel cost.

Plotted demand

curves for the 16 surveyed sites are included in the next
section of this dissertation (Figures 6 though 21).
Estimation of Dollar Benefits for the
16 Surveyed Public Access Sites
In order to estimate the area of consumer surplus
under the site specific demand curve, travel costs (given
in five minute travel time increments) were multiplied by
the number of predicted visitors willing to pay the sur­
rogate charge.

Table 10 given on the following page

shows the predicted consumer surplus estimates for Wolver­
ine Lake.
Added cost/estimated visitation schedules such as
that presented in Table 10 were prepared for all 16 sur­
veyed sites.

(See Appendix D.)

The figures (6-21) fol­

lowing Table 10 illustrate the site specific demand
curves and the consumer surplus for each of the 16 sur­
veyed public access sites.

72
Table 10.— Estimated Consumer Surplus Wolverine Lake
Site Benefit Estimation (Expanded to Annual
Visitations).
Added
Cost

Estimated
Number of
Visitors

Estimated
Number of
Visitors

Consumer
Surplus

$ 8.25

421

$315.75

Consumer
Surplus

Added
Cost

$

.00

$ .00

14,863

.75

11,406

8,554.50

9.00

303

227.25

1.50

8,744

6,558.00

9.75

208

156.00

2.25

6.717

5,037.75

10.50

127

95.25

3.00

5,132

3,849.00

11.25

59

44.25

3.75

3,865

2,898.75

12.00

32

24.00

4.50

2,924

2,193.00

12.75

18

13.50

5.25

2,172

1,629.00

13.50

0

.00

6.00

1,566

1,174.50

14.25

0

.00

6.75

1,086

814,50

15.00

0

.00

7.50

688

516.00

15.75

0

.00

TOTAL CONSUMER SURPLUS = $45, 249
The consumer surplus dollar figure is equal to
$ .75 in added cost multiplied times the number of
visitors.

73

o

FIGURE 6
AUSTIN LAKE PUBLIC ACCESS SITE
DEMAND CURVE AND CONSUMER SURPLUS

o
o

-a*
CN

O

O

COST

IN

CN

O

CONSUMER SURPLUS:

W
Q
Q

$236,130

o
00

o

o

00

50. 00

150.00
3
VISITS *10

100.00

200.00

74

o

°
oo
N

FIGURE 7
ORCHARD LAKE PUBLIC ACCESS SITE
DEMAND CURVE AND CONSUMER SURPLUS

o
o
•
cn

o
o
•

o
CN

in-

S
H

o
o

•

VO

CONSUMER SURPLUS;

Eh
O
U
Q o
M o
Q
Q
CN
<1 i
—I
CA

$383,160

o
o
00

o
o

0.00

50. 00

100.00

150.00

VISITS *103

200.00

25(L 00

FIGURE 8
WOLVERINE LAKE PUBLIC ACCESS SITE
DEMAND CURVE AND CONSUMER SURPLUS

16.00

CONSUMER SURPLUS:
$45,249

12.00

ADDED

COST

IN $

20.00

24.00

28.00

75

o
00

o
o

o
o

0 .00

50. 00

100.00

150.00

VISITS *10 2

200.00

25?. 00

76

o

° J
oo
™

FIGURE 9
SHERMAN LAKE PUBLIC ACCESS SITE
DEMAND CURVE AND CONSUMER SURPLUS

o
o
•

CN

O
CN

CONSUMER SURPLUS:
$153,520

o
00

o
o

0.00

50. 00

150.00
3
VISITS *10

100.00

200.00

FIGURE 10
PENTON LAKE PUBLIC ACCESS SITE
DEMAND CURVE AND CONSUMER SURPLUS

CONSUMER SURPLUS:

$227,680

78

o

FIGURE 11

00
CM

UNION LAKE PUBLIC ACCESS SITE
DEMAND CURVE AND.CONSUMER SURPLUS

o
o

o
o
o
CM

o
o

COST

IN

<J>

CONSUMER SURPLUS:
$29,773

o
o

00

o
o

o
o
o

00

50.00

100.00

150.00

VISITS *102

200.00

25$.00

79

o

FIGURE 12
SWAN LAKE PUBLIC ACCESS SITE
DEMAND CURVE AND CONSUMER SURPLUS

oo
CN

O
O
CN

CO­

VO

COST

IN

CO

P
w
p
p

CONSUMER SURPLUS
$28,114
o
00

o
o

o

00

50. 00

100.00

150.00

VISITS *102

200.00

250.00

80

©

o

oo
CN

FIGURE 13
MUSKRAT LAKE PUBLIC ACCESS SITE
DEMAND CURVE AND CONSUMER SURPLUS

o
o
CN

o
o
o
CM

o

VD

COST

IN

in- o

P °
W o
P
•
P CN

CONSUMER SURPLUS
$36,822
O
O•
00

o
o

o
o

0.00

50.00

100.00

150.00

VISITS *10'

200.00

25 (L 00

81

o
o

FIGURE 14

00
CN

O

HIGGINS LAKE PUBLIC ACCESS SITE
DEMAND CURVE AND CONSUMER SURPLUS

o
CN

O
O
O
CN

c
o
- o
o

21

CONSUMER SURPLUS
$125,860

50.00

100.00

150.00

VISITS *10

200.00250.00

82

o

00
(N

FIGURE 15
LAKE ST. HELEN PUBLIC ACCESS SITE
DEMAND CURVE AND CONSUMER SURPLUS

O
O

O
O

o

COST

IN

o

Q
w
Q
Q
<C

CONSUMER SURPLUS:
$202,290

oo

o
o

o
o
o

00

50. 00

100.00

150.00

VISITS *103

200.00

.00

83

o

oo

FIGURE 16
CHIPPEWA LAKE PUBLIC ACCESS SITE
DEMAND CURVE AND CONSUMER SURPLUS

CM

o

o

<N

IN

CONSUMER SURPLUS:

ADDED

COST

$101,410

o

o
o
00

o
o

o
o

.00

50.00

100.00

150.00

VISITS *102

200.00

25(1. 00

FIGURE 17
CLEAR LAKE PUBLIC ACCESS SITE
DEMAND CURVE AND CONSUMER SURPLUS

16.00
12.00

CONSUMER SURPLUS:
$20,082
8-°°

ADDED

COST

IN $

20.00

24.00

28.00

84

o
o

o
o
o

0 . 00

50. 00

100.00

150.00

VISITS *102

200.00

85

o

FIGURE 18

oo

WIXOM LAKE PUBLIC ACCESS SITE
DEMAND CURVE AND CONSUMER SURPLUS
o
o

CN

O
O
O
<N

ADDED

COST

IN

W O

CONSUMER SURPLUS:
$98,665
o
00

o
o

o

o
0.00

50. 00

100.00

150.00

VISITS *102

200.00

FIGURE 19
BIG STAR LAKE PUBLIC ACCESS SITE
DEMAND CURVE AND CONSUMER SURPLUS

CONSUMER SURPLUS:
$59,134

87

o
o

FIGURE 20

03

WIGGINS LAKE PUBLIC ACCESS SITE
DEMAND CURVE AND PUBLIC SURPLUS

O

o

o
CM

COST

IN

</> O

P

o

W O
Q •
Q
CM
rij l-t

CONSUMER SURPLUS:

$67,350

o
00

o

00

50.00

100.00

150'. 00

VISITS *10

2

200.00

25^.00

88

o
o

FIGURE 21

00

rsi

BIG TWIN LAKE PUBLIC ACCESS SITE
DEMAND CURVE AND CONSUMER SURPLUS

o
o

'S’
CN

O
O
O

O
O
•

VO

I
—i

COST

IN

CM

CONSUMER SURPLUS:
$45,363

P o
pa o
a ON
§

O
O
00

O

o

o
o

0.00

50.00

100.00

150.00

VISITS *10

2

2 00.00

250.00

89
The breakdown for site visitations and consumer
surplus is given in Table 11 on the following page.

By

looking at Table on the following page it can be seen
that the total number of visitors entering the surveyed
sites was 622,737.

This figure is slightly higher than

the Waterways Division counts due to the use of a
passengers-per-vehicle expansion factor of 3.1 instead of
2.8 used by the Division.
The summed annual consumer surplus for the 16 sur­
veyed sites totalled $1,8 60,602.

This figure suggests

that each visitor would be willing to pay an average $2.99
in travel costs to utilize the site.

In other words, the

visitor would be willing to travel an additional 20 miles
to use a public access site on an average.
When the survey question on "how far would you be
willing to travel to an alternate site of similar quality"
was analysed (see Appendix B) the average willingness to
travel for all 16 sites was 39 miles.

This figure repre­

sents almost twice the value as shown by the visitation
prediction model.
The above dollar figure is higher than the one
found by Huddy in his concurrently run survey of site
users on "willingness-to-pay" for the same 16 access
24
sites.
The questions in the study survey instrument
24
.
Michael Dean Huddy, "Willingness to Pay Analysis
in Public Resource Use Considerations," (Master's degree
Plan B paper, Michigan State University, 1976), p. 45.

90
Table 11.— Estimated Annual Site Visitations and Consumer
Surplus Values Summary (16 Surveyed Sites).

Lake Site

Number of
Visitors

Orchard Lake

115,799

Lake St. Helen

Consumer
Surplus
383,160

1

114,129

202,290

4

Fenton Lake

83,391

227,680

3

Austin Lake

72,739

236,130

2

Sherman Lake

49,011

153,520

5

Higgins Lake

33,110

125,860

6

Chippewa Lake

29,894

101,410

7

Wixom Lake

23,451

98,665

8

Big Stan Lake

18,268

59,134

10

Wiggins Lake

18,035

67,350

9

Wolverine Lake

14,863

45,249

12

Big Twin Lake

13,802

45,363

11

Muskrat Lake

12,539

36,822

13

Swan Lake

9,515

28,114

15

Clear Lake

8,072

20,082

16

Union Lake

6,119

29,773

14

622,737

$1,860,602

TOTALS

$

Rank By
Consumer
Surplus

developed by Michael Huddy asked site visitors, "how much
would you be willing to pay to enter and use the public
access site facility?"

The Huddy survey data reflected a

willingness-to-pay figure of $1.39 per site visitor.

91
As discussed earlier in this dissertation under
the literature review section, the willingness-to-pay
approach is one way of estimating site benefits related
to visits.

The $1.39 average figure for willingness-to-

pay represents less than half the amount of site benefits
per visitor estimated through the use of imputed demand
curves.

A key difference between the two approaches

relates to the perceptions the visitor has toward costs
of the recreational experience under willingness to pay.
Under the imputed demand curve approach, travel costs
incurred to reach a site are known and surrogate entrance
fees are set to determine visitation patterns.

However,

under the willingness to pay approach, where the visitor
is asked directly how much he would pay to use the site,
the visitor would respond by roughly estimating the worth
of the experience to him.

The imputed demand curve

approach to estimate recreational benefits represents a
more refined method in estimation "non-market priced"
benefits than the willingness to pay approach.

The differ­

ence in estimates for site visitation benefits for these
two studies is a large one.

However, the difference is

one that can be accounted for because of recognized
variations in their basic research approaches.

92
Determination of Combined Site
Visitation Equations
In the Texas Water Plan Study of 1971, once site
specific visitation equations were established for sur­
veyed sites, the data was pooled for a regression run that
formed an aggregate predictive visitation equation.

This

model was then applied to all non-surveyed and yet to be
constructed man-made reservoirs to estimate site visita­
tions and ultimately, site benefits.
As was mentioned earlier in this dissertation, it
was not known if a single model could adequately predict
visitations to the numerous naturally existing lakes in
the state of Michigan.

This section of the dissertation

discusses the combined site visitation estimation equa­
tions developed by this study.

The equations are first

presented and then tested for accuracy in predicting site
visitations at non-surveyed sites with Waterways Division
counters on them.

In order to determine what model or

models should be used on non-surveyed and proposed sites
to project visitation at Michigan's public access sites,
the following analysis was undertaken.
The Aggregated Model
The "aggregated model" was the easiest to estab­
lish for testing purposes.

Since this model uses pooled

data from all 1§_ surveyed sites to create a single site
visitation equation, it could be applied to all lake

93
public access sites within Michigan's lower peninsula.
The single equation model shown below has added the vari­
able for lake acreage of the destination site.

This vari­

able was added because variation was created when pooling
data for multiple sites:

this variation does not exist

when looking at a single site.

The "aggregated model" is
2
shown below with regression coefficients and the R value

for the equation.
constant
10g.n (Y + 1.0) = .8057
xu
(.1090)

population
+ .1377*
log.n X.
(.0110)
xu
x

travel time
family income
.8060* log.n X - .1216 log.. n X,
(.0218)
xu *
(.0805)
xu J

-

gravity
lake acres
.05016 log.n X. + .5228* log.n X(.0833)
XU 4
(.0077)
XU 3

R2 = .27

F = 168.8*

* Significant at the 5 percent level of signifi­
cance .
The Regional Models
(Michigan Department of Natural Resources Regions)
A second group of combined visitation equations looks at
predicting visitations on a regional basis.
When looking at the state

ofMichigan on a regional

basis, the state's lower peninsula has been divided in
half between Bay City on the east and Muskegon on the
west (see Figure 22).

94

•IlM M tttU IU IfM tlitlC M IM ttM ttlJIIM IfH IH ItW ltK H H IiM tlflM llltm N tH tH n N lllltlH IIM H H M tlllH M IIH H m illlH IH M H IIItM IM IIIM IM iH U lH M iln illlK lltH tlH M IIIK IM M M H im illlN IIM m m m illlH tM IM M Iflllim H IH H s

FIGURE 22
^

yC^oatme*■v .

Michigan Lower Peninsula Regions
(Location of Surveyed Sites)

I *
fMAOQvmt'
V
v i — 4|
Siam
I--- , i

IV

j

j

tuce

r~J

I

\
j mcwNAC

foriw -J'l

h j

*
O

MICHIGAN
Site Name

-T?

region

-A

'i.
/OMfl*l■
rto//'"
r o w n n o ijr

[i?

fifaL f‘Li

in

Site Number

REGION III

4f

1
<

*

__ _

)

!

t

r*

" WjS&wj 16° j

|

j

j

’

jfewiSfj
Austin Lake.................. 1
Orchard Lake................. 2
Wolverine"*Lake V
7 .'!.'3
Sherman Lake................. 4
Lake Fenton.................. 5
Union Lake................... 6
Swan Lake.................... 7
Muskrat Lake................. 8
Higgins Lake................. 9
Lake St. Helen.............. 10
Chippewa Lake............... 11
Clear Lake..................12
Wixom Lake.................. 13
Big Star Lake............... 14
Wiggins Lake................ 15
Big Twin Lake............... 16

|

_____ j m a W w g j U h ’M* V

■

j«aa#ff»»jowwvjiMco •

_________
•
‘ 9_iio

*j

i

^

[“
P 5* 1
■____ •J.4J
|
[ie*_ , U Y ^
'°cfAMi*rwAy^\MccQW^sABzuA~\MlbiM
^
/
I*
(
j
j 12 j!
i

\
\

^2

^55S8
1
7
1
p35wS»
jcWior
\« z1
. jiAPrfrl
V l7P5*- -|7 7 _i_ r
fjrr'uia" \ I
poiw "J"a/«ro*ij.v/Awiiiij
j
is . !
i &£amo i***>»** / l
iAuraami. fflAMy
;
jtA T O N ’ \7 M 4 juw#GSTr*|
• 2
Pj\
r_
jI
j ji_ ii
i »3

r

*

«V.

j

*

ngh

!
p M

0«JJKfK4l»MSJ C M

MW

(JA C KSO N "

/.JJ.1L. l

' ^ W A S H r U liw 'T cWAYHC
-

L Li

/Vf*V J J»josmfef^nTTwlljoiiIf'ltf'MWff’"ImoW y
Z
j
j
!.‘ * j
j
I /

>
■>

95
This division was utilized in the study since the Water­
ways Division (study funding agency) is in the Michigan
Department of Natural Resources and would be consistent
with Departmental planning.

As was indicated earlier in

this dissertation, Region III is the most densely popu­
lated area of the state (8,033,600) with Region II having
only 1/llth of the Region III population (729,800).
In establishing the visitation equations for each
of the two regions the data from each of the eight lake
site equations for each of the two regions were combined
to form two separate equations.

The equations are given

below:
Region II Equation.—

login (Y + 1.0)
1U

constant
population
= .5841
+ .2173* log.. n X..
(.2113)
(.0270)
xu

-

travel time
family income
.6111*login X0 + .1679 log.. n X_
(.0451)
1U *
(.1497)
±u J

+

gravity
lake acres
.3133 log,n X. + .6839 log, n X_
(.1686)
iU 4 (.0141)
xu D

R2 = .28

F = 60.4*

Region III equation on next page.

96
Region III Equation.—

log,n (Y + 1.0)
XU

-

constant
population
= 1.199
+ .1112* log,n X,
(.1328)
(.0118)
1U X

travel time
family income
.6064*log,n X, .0751 login X,
(.0268)
xuz
(.1146)
xu J
gravity
lake acres
.1269 log. n X. + .0048 log, n X,(.0988)
xu 4
(.0098)
xu 5

R2 = .37

F = 155.9*

The Subregional Models
One final model breakdown combining survey site
data, is tied to "sub-regional" areas for Region III and
a "destination lake acres" differentiation in Region II
(see Figure 23).

The above breakdown was suggested by

similarities in specific site equation coefficients.
It is believed that subregional population impact
on public access site visitations can be used to divide
up Region III of the state for model building.

The reader

must remember the access sites are "day use" facilities.
In the eastern sub-region, Detroit and its surrounding
cities influence visitations.

In the western sub-region,

the cities of Grand Rapids, Battle Creek and Kalamazoo
would generate the greatest number of visitors.

The data

97

Figure 23
Site Visitation Equations
(Sub-Regional/Lake Acres)

S^vTccn ; r
iz-^T
i
xjtootme i
r i
\''v " ‘1 L
~ocin.
.

I »A HMU

rJ
MtOQVtlTf

?

TY - /
1
!iwrfsv# i:j0sC0.1/T
«a :
I
I

|5c>nsoV!
j

r J

Ii «
« IkT sA r

.i-;'* A

v~\
/

IW fW O M fl

U

r

j

V

Vx

j y

-----------------------

1
i r■
•— O- ^

"\

>

r "JU/P

<

]

ij

- J

0/?«

o

U> ^iw-njcA/joCp.
J

S~*-'

.-o

MICHIGAN

j'"'*" 'N -V

/CMMirtei/'j

ft ) I

Q

L

L

i

j

;orjfoo.xmrji^.liiPfKA •

S
... _j._

i

^

-1

I«.’*A>ulCMhTO»pInStO(.A U.CCXMA’|
•

j
j
v»Y«f.SF|«isuiiMfjra<ofif*»|ooiMivpjjco-’ •
|

j

i

•C-L i.J^i L r

E q u a t i o n s by
Lake Acres

/••3jov"!h«(

7o s a o U

‘. c u P t

’c i n * > i

[*5riuc

j

S ili I jfcu-r ^ \
A a * w |»>WA^oojMi ;csu j i i i r m V fwatAAoj

I f A.
-froCTj

M O *T C *t* i

tern

1

Wl
iSwi*

OTTAWA ;

E q u a t i o n s by
Sub-Regions

I

•_

*

A'

\

i

'cr-tSH
r-

a i« T o £ ]i in»i. frij

► •

^

*
“

L•„k..
i

i- ^ w m

I
/ ? 4 V iX /f ffA j

'. I U H fiT V M

I .M

« JQN *

V4w ) h M rttf

/ L JJ2J I jtiwA|
__ Lj
Hff .MW1 )

fmv^xhI*eS> |if•CjM'm
/

* L o c a t i o n of

1,000 A c r e L akes

I

fc ’

—

rw A *;4^
j 6 A w rr

I

.1___

a
-*

1

98
from the equations within each of these two subregions
was pooled to form two visitation predicting models.
The differentiation between lake sites in Region
II of the state would appear to tie most closely with
lake acreage of the destination site.

Region II of the

state is not as densely populated as is Region III of the
state.

It is believed the prime attraction factor for the

Region II sites is the size of the lake.

The break down

for Region II sites was developed for lakes over 1,000
acres and those less than 1,000 acres.
The equations developed for testing are given
below:
Region III Equation - Eastern Half.— (Orchard,
Wolverine, Fenton, Muskrat Lakes)
log, n (Y + 1.0) =
xu

constant
population
1.319
+
.1147* log., n X.,
(.1755)
(.0149)
xu 1

-

travel time
family income
.6899* log., _ X_ - .5262* log n X,
(.0393)
±U *
(.1764)
iU J

-

gravity
lake acres
.1058 log, n X. - .0170 log., n X,(.1273)
XU 4 (.0126)
±U ^

R 2 = .39

F = 103.1*

Region III Equation - Western Half.— (Austin,
Sherman, Swan Lakes).

log.. (Y + 1.0) =
10

constant
population
1.110
+
.1024* log n X
(.2311)
(.0193)
1U A

99

-

travel time
family income
.5456 log. n X, + .2693 log.. n X,
(.3697)
iU ^
(.1817)
±u J

-

gravity
lake acres
.08867 log.n X0 .0225 log..n X,(.1833)
XU ^
(.0154)
1U s

R 2 = .34

F = 53.8*

Region II Equation

1,000 Acres Plus Destination

Lakes.— (Higgins, St. Helen, Wixom Lakes.)
constant
population
login (Y + 1.0) = .5305
+ .3809* log
Xn
±U
(.4397)
(.0511)
1U 1

-

travel time
family income
.7017* log.n X, +
.09447 log1n X,
(.0913)
1U ^
(.2726)
1U J

+

gravity
lake acres
.4122 log. n X. + .0695 log, _ X,.
(.3219)
XU 4
(.0488)
iU 5

R 2 = .34

F = 28.7*

Region II Equation ~ Less Than 1,000 Acre Lakes,
(Chippewa, Clear, Big Star, Wiggins, Big Twin Lakes.)
constant
population
login (Y +1.0) = .5865
+
.1268* log n Xn
10
(.2298)
(.0300)
iU x

-

travel time
family income
.5622* log n X„ +
.1397 log
X,
(.0485)
1U z
(.1708)
±u

100

+

gravity
lake acres
.2920 log,n X. + .0651 log n X,
(.1889)
4
(.0269)
•LU J

R 2 = .26

F = 34.5*

In order to test which model or model combination
(all sites, regional or sub-regional/lake acres) would
most accurately predict site visitations, four test sites
were selected.

The selected public access sites all had

Waterways Division vehicle counters at their entrances
which would allow a check of the predicting accuracy of
the three proposed models.
The number of lake sites selected for testing
was limited to sites with counters that had not been sur­
veyed.

All Region I (Upper Peninsula) sites were thrown

out for testing since the study dealt with the lower pen­
insula sites.

Also, all river, stream and Great Lakes

sites with counters in Regions II and III were thrown out
since the study dealt with lake sites only.

Of the remain­

ing sites available for testing purposes, the four selected
sites represented a range in lake acreage, and provided
testing on a geographical basis.
The sites chosen for testing the visitation esti. mation models were:

101

DNR
Region

Lake
1.
2.

Chemung
Campau

III
III

3.
4.

Houghton
Pratts

II
II

Acres

County

321
190

Livingston
Kent
Roscommon
Gladwin

19,600
180

The test for the model/s was to predict estimated
visitations as close to the actual site count data as
possible.

For each of the above test sites the initial

visitation model applied, was the single "all sites"
summed model.

The second model applied to the test sites

utilized the two "regional" equations.

The third model

(four equations) was broken down into geographic subregions for the lower half of Michigan's lower peninsula
and a lake acreage breakdown for the upper half of the
state as previously described.

Table 12 (shown on the

next page) provides the breakdown of the test results com­
paring counter count data to model predicted visitations.
As can be seen in Table 12, the sub-regional model
provided the closest projections to actual (counter counts)
use for lakes Chemung and Campau.

However, the "all sites

combined" model was the better predictor of combined
visitations to the two lakes.

102
Table 12.— Visitation Models Test Results.

Lake Site

Counter
Data

"All
Sites"
Summed
Model

Regional
Model

SubRegional
Model

Lake
Chemung

24,353

46,616

(III) 38,547

(E) 31,777

Lake
Campau

40,621

20,322

(III) 18,293

(W) 23,273

64,974

66,938

56,840

55,050

TOTALS

Lake Site

Counter
Data

"All
Sites"
Summed
Model

Houghton
Lake

32,601

25,492

(II) 52,271

65,049
(1000+
acres)

Pratts
Lake

13,977

14,013

(II) 39,035

32,670
(<1000
acres)

46,578

39,505

91,306

97,719

TOTALS

Regional
Model

Lake Acreage
Break-Down
(1000</1000>)

For Houghton and Pratts Lakes in Region II (upper
half of state), the single site estimates and the total
estimates for both sites had the closest fit for the "all
sites-summed equation" model.
Since this study was designed both to estimate
dollar benefits associated with existing public access
sites and to produce a model for estimating benefits at
yet to be established sites, the model must reflect the

103
highest possible degree of accuracy.

After extensive

deliberation between the members of the research team,
it was decided that the "all sites" summed equation would
be used in generating dollar benefits for all existing
public access sites (administered by Waterways Division)
in Michigan's lower peninsula.

Although estimates for

specific lakes could fluctuate from actual visitations,
the average for all sites would most closely approximate
reality.
None of the models discussed appears to be a
reliable predictor for individual lake visitation at
"proposed sites."

Consequently, it was concluded that

the models should not be used for this purpose without
further refinement and/or testing.

One example of a

refinement that will be investigated is that of adding a
site attractivity variable to the model.
Application of Combined Site Visitation
Equations to Create Demand Curves For
Existing Non-Surveyed Public Access
Sites
As was indicated in the previous section of this
chapter the "all sites" summed visitation equation model
was selected for use in estimating state-wide public
access site benefits.

In Michigan's lower peninsula,

there are 339 lake public access sites.

Variables for

each of the non-surveyed sites (319 total) were entered
into the "aggregated-all sites" model to create site

104
specific demand curves.

The regression coefficients for

the multiplicative multiple regression run were derived
from the summed equation with information on the five
independent variables taken from the Michigan State High­
way study (Population, Travel Time, Gravity), the Water­
ways Division (Site Lake Acres) and the 1974 Michigan
Statistical Abstract (Income).
By totalling the number of estimated visitors and
generated by consumer surplus for the 319 non-surveyed
sites and adding these figures to the surveyed and test
sites, the state-wide figures were obtained (See Table
13) projected individual lake visitations and site bene­
fits are given in Appendix E.

Table 13.- -State-Wide Lake Public Access Site Visitations
and Site Benefits (Lower Peninsula).
Number of
Lake Sites
339

Estimated
Number of Visitors
5,741,774

Estimated
Consumer Surplus
(Site Benefit)
$20,341,473

The totalled figures for visitations represents 68 percent
of the counter count annual visitations set at 8,466,390.
The number of Waterways Division administrative lake pub­
lic access sites in Michigan's lower peninsula represents
60 percent of all Waterways sites state-wide.

It was

expected that since the lakes in the lower peninsula were

105
closer to the large population centers, the percentage
of site visitors would exceed the percentage of sites
being studied.
The $20,341,473 consumer surplus generated by the
339 lake sites represents a figure of $3.54 in estimated
benefits created by each site visitor.

The individual

site benefits for the largest lakes in Michigan's lower
peninsula compare closely ($200,000 annual benefit range)
with those figures generated for the large reservoirs in
the Texas site benefit study.

It must be remembered that

the $20 million plus site benefit figure does not repre­
sent actual expenditures, but rather perceived benefits
if each site could capture the total willingness to pay
of each site visitor.
Application of the Study Model to Proposed
Public Access Sites xn Michigan's
Lower Peninsula
As was indicated in the section of this disserta­
tion on "Determination of Combined Site Visitation Equa­
tions" a decision was made to add at least one descriptive
variable to the "all-sites/summed equation" model.

It was

felt that in order to eliminate predicted site visitation
from gravitating to an average estimate, additional data
related to site attractivity must be gathered.
The existing visitation estimation model ("all­
sites" summed equation) provides an accurate average
estimate for the existing sites' visitation and benefits.

106
However, in order to improve the accuracy of this model,
a weighted site attractivity variable should be intro­
duced into the equation.

Past work on "attraction indices"

has been carried out for recreation sites in Michigan.
In the "RECSYS" systems analysis approach, used to gen­
erate demand estimates for recreation areas, part of the
model included indices explaining the attractivity of
25
those areas.
A review of the "RECSYS" report and other
similar studies will be made in order to combine the
characteristics of sites into an attractivity index appli­
cable to this study's model.

Additional information on

site attractivity, once gathered, should hopefully provide
the desired measure of accuracy in predicting site visi­
tations for the yet to be constructed lake public access
sites.
This topic will be covered further under the
chapter on "Study Recommendations."
25
Michigan Department of Commerce, "A Manual for
Program RECSYS," Outdoor Recreation Planning in Michigan,
(Lansing, Technical Report #1, 1966), pp. 38-46.

CHAPTER V
TESTING THE STUDY HYPOTHESIS

The major hypothesis for this public access site
study was stated as follows:
The monetary value of Michigan's Public Access Sites
can be determined through the use of imputed demand
curves.
Through the collection of data from the 16 survey sites,
a model was created which predicted site visitations and
computed consumer surplus for existing public access sites
in Michigan.
By altering the travel cost variable (in upward
increments) in the visitation model, site specific demand
curves were created for all 339 lower peninsula lake
sites under Waterways Division administration.

Utilizing

the combined site equation, the predicting model produced an R

2

of .27 with an F value of 168.80.

This F

value is significant at the .05 level of significance
established for this study.
This study has indicated that site specific
imputed demand curves can be utilized to predict benefits
associated with use of Michigan's inland lake public

107

108
access sites.

The acceptance of this study's model as a

predictor of site visitation related dollar benefits
rests in part with the acceptance of consumer surplus as a
measure of dollar benefits (refer to literature review
and study model sections of this dissertation).

Conse­

quently, it appears that the hypothesis has been supported
by the results of this study, given the acceptance of the
concept of consumer surplus.

However, the final accep­

tance or rejection of the study hypothesis rests with the
reviewer.
The Testing of Study
Sub-Hypotheses
In order to test this study's six sub-hypotheses,
the test results from the pooled model equation were used.
By looking at the model results for all 16 study sites,
the test results would reflect the impact of the independ­
ent variables on visitations for the broad spectrum of
sites (related to lake acres, proximity to population cen­
ters, etc. . .).

As stated earlier in this dissertation,

the sub-hypotheses deal with the selected independent
variables used in predicting visitations to Michigan
inland lake public access sites.
Sub-Hypothesis #1
The population of the origin "time zone" will
register a statistically significant positive
effect on site visitations.
By looking at the test results:

109

Variable

Origin
Zone
Population

3
Coefficient

.1377

Standard
Error-3

.1106

f

.05 Level
of
Signifi­
cance

154.877

Yes

R2

.05

It is seen that the variable of population for the
origin time zone fell within the required 5 percent level
of significance for the F test.
percent for the R

2

The contribution of 5

value was the second highest for the

selected independent variables in explaining site visita­
tions.

The Beta coefficient is positive, indicating that

as populations for time zones increase, visitations to
the public access sites increase.

The tost results indi­

cate that the population of origin time zones variable
does contribute to the explanation of site visitations
with the 5 percent level of significance.

The hypothesis

is accepted.
Sub-Hypothesis #2
Visitations to Michigan public access sites are
negatively correlated with the Travel Cost
variable.

110

Variable

Travel
Cost

3
Coefficient

Standard
Error-3

p

.05 Level
of
Signifi­
cance

-.50602

.02175

541.277

Yes

R2

.20

The test results given above demonstrate that the
Beta coefficient has a negative effect on site visitations
as travel times (travel costs) increase.

The results

also indicate that the F test on this variable places it
with the 5 percent level of significance for acceptance
and the 20 percent R

2

value is the highest registered by

the independent variables.

The hypothesis is accepted.

Sub-Hypothesis #3
Visitation to Michigan public access sites are
positively correlated with family income.

3
Coefficient

Family
Income

-.1216

Standard
Error-3

.08051

p

2.2822

.05 Level
of
Signifi­
cance

R

No

.0007

Ill
By looking at the test results, the Beta coeffi­
cient, unlike predicted results, shows that as family
income levels increase visitations to public access sites
decrease.

The variable did not fall within the study

established 5 percent level of significance.
A statement on positive or negative impact this
variable has on visitations can not be made by the results
of this study, since the required level of significance
was not met;
Sub-Hypothesis #4
As alternate water based opportunities (gravity)
increased around the origin time zone fewer vis­
itors are expected at the destination site.
Gravity then has a negative impact on site visitations.
Referring to the test results given below,

Variable

Gravity to
Alternate
Water
Bodies

3
Coefficient

Standard
Error-3

F

.05 Level
of
Signifi­
cance

-.05016

.08335

.3621

No

r2

.0008

the Beta coefficient indicates that as acreage and shore­
line mileage for alternate water bodies increase, visita­
tions to the study public access sites decreased.

As with

112
the income variable, however, the gravity variable failed
to fall within the accepted 5 percent level of signifi­
cance.
A statement on positive or negative effect this
variable has on visitations to Michigan public access
sites can not be made since the variable falls outside the
required level of significance for this study.
Sub-Hypothesis #5
As the lake size increases (destination site)
the number of visitors will increase.
The results given below for the lake acreage
variable indicator,

Variable

Destina­
tion
Lake Acres

3
Coefficient

Standard
Error-8

.05228

.00773

.05 Level
of
Signifi­
cance

45.758

Yes

R2

.01

the Beta coefficient is positive, which supports the sub­
hypothesis.

The variable falls within the 5 percent level

of significance also supporting the sub-hypothesis.

The

results of this study indicate that as the acreage of a
destination site increases, so do visitations.
hypothesis is accepted.

The

113
Sub-Hypothesis #6
The greater the number of parking spaces per
access site, the greater the number of expected
site visitations.
As indicated earlier in this dissertation, in the
section on Analysis of the Data, this variable was dropped
from the study visitation equation.

Because the pattern

of visitations to the 16 surveyed public access sites in
Michigan could not be correlated to the number of site
parking spaces a positive or negative test statement
could not be made.

CHAPTER VI
STUDY SUMMARY

After the 16 survey sites were selected for this
study, a 12 week schedule of personal interviews was
carried out.

Each of the 16 surveyed sites had inter­

viewers on location a total of four days each of the
three summer months of June, July and August, 1975; a
total of 12 interview days per site.
At the end of the 12 week survey period, 2601
personal interviews of site visitors had been carried out.
Cross-tabulation of data showed:

(1) 50.7 percent of

visitors brought boats to the sites (87 percent trailered),
49.3 percent did not bring boats to the site; (2) 94.1 per­
cent of all site visitors interviewed resided within two
hours driving distance of the sites; (3) average per
vehicle party size was 3.1 persons, and (4) 51.6 percent
of interviewed site visitors made $15,000 or less annually.
By establishing "site specific" visitation esti­
mation equations for the 16 surveyed sites and applying
multipliers for expansion of data to annual visits (counter
count data) and number of persons per vehicle, visitation
predicting models were generated.
114

115
Y + C = A X®1 X® 2 X®3 X®4
where
Y = Number of annual visitors to the access site
(form origin "time zone").
C = Constant used with double logrithmic trans­
forms of the data.
X^= Time zone population (origin).
X 2= Travel costs.
X^= Average family income.
X^= Gravity variable (alternative water-based
recreational opportunities— around origin
time zone.
Once the "site specific" model's were quantified,
they were utilized to generate visitation estimates at
assumed travel cost increases (i.e. site specific demand
curves).

The area under the demand curve (consumer sur­

plus) then represented the dollar benefits generated by
the site in relation to visitations.

Table 14 shows the

projected annual visits to the sites surveyed for this
study along with the estimated consumer surplus asso­
ciated with these visits.
Once the equations for the surveyed sites were
generated, the independent variables of destination site
lake acreage was added to the model.

The data from the

16 sites was then pooled creating a single multiple regres
sion equation for the study model.

The resultant single

"summed" equation, after being tested and compared with
other possible combinations, was selected for use in

116
Table 14.— Estimated Site Visitations and Consumer
Surplus.
Lake Site
Orchard Lake
Lake St. Helen
Fenton Lake
Austin Lake
Sherman Lake
Higgins Lake
Chippewa Lake
Wixom Lake
Big Star Lake
Wiggins Lake
Wolverine Lake
Big Twin Lake
Muskrat Lake
Swan Lake
Clear Lake
Union Lake
TOTALS

Number of Visits

Consumer Surplus

115,799
114,129
83,391
72,739
49,011
33,110
29,894
23,451
18,268
18,035
14,863
13,802
12,539
9,515
8,072
6,119

$

622,737

$1,860,602

383,160
202,290
227,680
236,130
153,520
125,860
101,410
98,665
59,134
67,350
45,249
45,363
36,822
28,114
20,082
29,773

predicting visitations and consumer surplus for all 339
existing lake public access sites in Michigan's lower
peninsula.
The visitation and consumer surplus totals for all
339 sites are given below.

Table 15.— State-Wide Lake Public Access Site Visitations
and Site Benefits.
Number of
Lake Sites

Estimated
Number of Visitors

Estimated
Consumer Surplus

339

3,741,744

$20,341,473

117
After completing site visitation predictions and
benefit estimations for existing sites, the model was
then reviewed for its usefulness in selecting new sites
for inclusion in the access site system.

Since the

"summed" equation model established averages over a wide
range of different types of sites for visitations to a
site, it was determined that additional work on a site
attractivity variable would be conducted.

It is hoped

that by adding an attractivity variable to the site visi­
tation equation, predicting accuracy will be improved to
the point that visits to proposed sites can be adequately
determined.

CHAPTER VII
STUDY RECOMMENDATIONS

After reviewing the various phases of this study,
a number of recommendations can be made to improve the
model building procedure and the visitation estimation
model itself.
Under the section on "Research Methods" several
suggestions for improvement can be made.

Because of the

variety in the characteristics (natural and man-made) of
the Michigan public access sites, a classification system
for sites should be developed prior to the selection of
sites to be surveyed.

The classification of existing

sites by extent of site development, natural attractive­
ness, water quality, etc... should be made to allow ade­
quate sampling of the range of site types.

By establishing

a site classification system, newly proposed sites could
be categorized by personnel in the field, and the site
visitation equation developed for that class of site could
be inputed into the computer to estimate accurately, visi­
tations and related site benefits.
The success in gathering data from the site
visitor, for this study, hinged on the design and
118

119
utilization of the survey instrument.

For this type of

study, the short personal interview provided immediate
response from the site visitor and allowed additional
substudies related to site use to be carried out at no
added cost to the granting agency.

As far as the number

of sites surveyed is concerned, re-evaluation of the sam­
ple population should be made once a clearcut site class­
ification system is established.
A follow-up to this lake study should look at the
visitation patterns for Great Lakes, rivers and streams
and Upper Peninsula public access sites to predict use and
determine the total benefits related to the Waterways
Division public access site system.

Since model building

procedures for Michigan have been developed, adaptation
to non-lake sites could be carried out with improved
efficiency in data gathering and analysis.
Under the "Analysis" section of this study, the
variable dealing with "site attractiveness" is thought to
be a key in determining why lakes within equal driving
distance from major population centers show marked differ­
ences in annual visitations.

At the time of this writing,

the components of an "attractiveness" variable have yet
to be defined.

However, the parameters of this variable

will be established, with the variable then added to the
visitation model. Additional independent variables could
be looked at in an attempt to improve the predictive

120
accuracy of this studys' model.

However high levels of

data aggregation for variables (shown in similar visita­
tion estimation studies) should be avoided.

SELECTED BIBLIOGRAPHY

SELECTED BIBLIOGRAPHY
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Recreation.
(The John Hopkins Press, Baltimore
1966).
Goldman, Thomas A. ed. Cost-Effectiveness Analysis.
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Goomber, Nicholas H. and Biswas, Asit K. Evaluation of
Environmental Intangibles. (General Press, Bronxville, New York, 1973).
Herfindahl, Orris C. and Kneese, Allen V. Economic Theory
of Natural Resources. (Charles E. Merrill Publish­
ing Company, Columbus, Ohio, 1974).
Howe, Charles W. Benefit-Cost Analysis of Water System
Planning. (Publication Press, Inc., Baltimore,
Maryland, 1971).
James, L. Douglas and Lee, Robert R. Economics of Water
Resources Planning. (McGraw-Hill Book Company.
New York, 1971).
Knetsch, Jack L. Outdoor Recreation and Water Resource
Planning. (American Geophysical Union. Washing­
ton, D.C., 1974) .
Pearce, D.W. Cost-Benefit Analysis.
Ltd., London, 1971).

(MacMillian Press

Sahni, Balbir S. Public Expenditure Analysis.
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(Rotterdam

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Brown, Gardner, Jr. "Selection of the Optimum Method for
Estimating the Demand for Non-Market Water
Resources with Incomplete Information." Final
Report of Project 161-34-10E-3996-3005 under
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123
agreement A-015-WASH (January 1, 1966-June 30,
1967). Seattle, Washington: University of
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Brown, William G., Singh A., and Castle, E. "An Economic
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Intangible Values of Natural Streams. "Application of the Uniqueness Concept."
(University of
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Florida.
Grubb, Herbert W. and Goodwin, James T. "Economic Evalua­
tion of Water Oriented Recreation in the Prelimin­
ary Texas Water Plan." Texas Water Development
Board. Report No. 84. September 1968.
James, George A. "Instructions for Using Traffic Counters
to Estimate Recreation Visits and Use on Developed
Sites." U.S. Fores Service Research Paper. SE-3.
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in New York State: Projections of Demand, Econ­
omic Value and Pricing Effects for the Period
1970-1985.
(Cornell University, 1969).

124
Krutilla, John V. "Evaluating Benefits of Environmental
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(Rec­
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(Wash­
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________. "Prospective Demand for Outdoor Recreation."
(Washington, D.C., 1962. Report No. 26).
________. "Public Expenditures for Outdoor Recreation."
(Washington, D.C., 1962. Report No. 28).
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________. "Recreation: Estimating Initial Reservoir
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126
Pearse, Peter H. "A New Approach to the Evaluation of
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Freed, Michael Dale. "Criteria for the Selection of
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(Ph.D. Michigan State University. East Lansing,
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APPENDICES

APPENDIX A
WATERWAYS DIVISION PUBLIC ACCESS
"SAMPLED SITES"

128

iff-'?

129
WATERWAYS DIVISION
PUBLIC ACCESS SITE STUDY
(1975-1976)
"Sample Sites"
(Summer 1975)
I.

II.

III.

IV.

High Population/High Lake Acreage (Measure of
Alternate Opportunities)
1. Austin Lake

Kalamazoo Co.

2. Orchard Lake

1050 Acres

47 Parking

Oakland Co.

788 Acres

63 Parking

3. Wolverine Lake

Oakland Co.

241 Acres

15 Parking

4. Sherman Lake

Kalamazoo Co.

120 Acres

36 Parking

High Population/Low Acreage
1. Lake Fenton

Genesee Co.

845 Acres

15 Parking

2. Union Lake

Branch Co.

518 Acres

5 Parking

3. Swan Lake

Moncalm Co.

127 Acres

25 Parking

4. Muskrat Lake

Clinton Co.

39 Acres

40 Parking

Low Population/High Lake Acreage
1. Higgins Lake

Roscommon Co.

9900 Acres

50 Parking

2. Lake St. Helen

Roscommon Co.

2400 Acres

15 Parking

3. Chippewa Lake

Mecosta Co.

770 Acres

30 Parking

4. Clear Lake

Mocosta Co.

130 Acres

5 Parking

1980 Acres

60 Parking

Low Population/Low Lake Acreage
1. Wixon Lake

Gladwin Co.

2. Big Star Lake

Lake Co.

912 Acres

80 Parking

3. Wiggins Lake

Gladwin Co.

345 Acres

15 Parking

4. Big Twin Lake

Kalkaska Co.

215 Acres

5 Parking

APPENDIX B
STUDY SURVEY INSTRUMENT

130

131

Recreation Research & Planning
Unit Study
Michigan State University
MICHIGAN
PUBLIC ACCESS SITES
Site Visitation Information
HOTE:

Please check a single resnonse to each of the five ouestions below.
(Your response remains completely anonymous according to strict
University research codes.)

1- Primary use of the site:
1.
2.
3.
4.
5.
II.

Pleasure boating
Fishing
Hunting
Swimming
Skiing _ _ _ _ _

6.
7.
8.
9.

Humber of people in your immediate
1

Scuba/Skin Diving
Sun Bathinq_ _ _ _ _
Pi c n i c _ _ _ _ _
other_ _ _ _ _

family:

3_

1.
2.
3.
4.
5.
6.
7.
3.
9.

7

5 m

m

8

6_ _ _ _ _ _ _ _ _ _

Education level of the head

'

(Check one)

4

2

III.

(Check one)

"

9 and over

of household:

(Check one)

Elementary school ____
Junior high
Some high school ~~
High school
Some college (includes associate degree)
BS/BA
MS/MA
MD/DDS
PhD

IV. Annual family income/before taxes: (Check one)
1.
2.
3.
4.
5.
6.

Less than $5,000
5,000-10,000
10,001-15,000
15,001-20,000
20,001-25,000
25,001-30,000_____

7.
8.
9.
10.
11.

30,001-35,000
35,001-40,000
40,001-45,000
45,001-50,000
nVer 50,000

V. If this access site was not available for use, how many miles would you
be willing to travel'to utilize a site of similar quality? _ _ _ _ _ (Mr1te
in number of miles)
THANKS FOR YOUR COOPERATION! THIS INFORMATION "ILL HELP THE MICHIGAN ST«TF
WATERWAYS COMMISSION PLAN FOR BETTER PUBLIC ACCESS SITES THROUGHOUT THE STATE.

MICHIGAN
PUBLIC ACCESS SITES
Site Visitation Information
Date:

/

/I975
Mo. Day Year

Site No.

Day: M T " TH F S S

Resoondent No.

(circle one)
Meather Conditions:

(Check one)

. Sunny
Partly Cloudy
____
Cloudy ____
Raining _ _ _ _ _
Time of Interview:
6:00
7:01
8:01
9:01
10:01

-

(Check one)

7:00
8:00
9:00
10:00
11:00

11:01
PM 12:01
1:01
2:01
3:01

-

12:00
1:00
2:00
3:00
4:00

4:01
5:01
6:01
7:01

Did this group of individuals bring a boat to the site:
*yes
*!fyes:

no
trailered

Number in party:

City of Residence:

car top

____

_________

County

Distance traveled to the PAS:
Travel time:

Hrs.

State

miles
Min.

Do you currently reside on a l a k e _ _ _ _ _ yes

no.

-

5:00
6:00
7:00
8:00

APPENDIX C
SURVEY DAILY TABULATION SHEET
(BOATS TO SITE/COUNTER CHECK)

133

134

DAILY INFORMATION SHEET
Date:
Day:

/

/

Site Number

M T W TH F S S

Time Shift:
(Circle one)

AM

/

PM

Counter count:
Enter site __________
Leave site ____ _____
Number of vehicles bringing boats onto the site:
l±±i for initial count)

End of time shift - total vehicle/boat count:

(mark -

APPENDIX D
CROSS TABULATION OF SURVEY INFORMATION
FOR THE 16 SURVEY SITES

135

a

C R 0 S S T A 3 U L

LAKE
* * * * * *

♦

JAYOFWK

*

*

A T I 3 N
0 r
BY DUOFWK
*

*

TABLE A1
Cross Tabulation For 16 Survey Sites

♦ * * *

PAGE

1 OF

2

COUNT
k OW pot

LAKE

MONOAT

COL POT
TOT PCI
-- — — —
1

AUSTIN
~

2
ORCHAR)
3
WOLVERINE
■

~

5
FENTON
~

a
UNION
7
SWAN
-

3
MUSKRAT
COLUMN
TOTAL
(CONTINUED)

136

SHERMAN

THURSDAY FRIDAY
SATURDAY SJNOAY
P.OH
TOTAL
1
A I
5
6 I
7 I
------- - ------- -I------ -I — — — -I
10
5 I
16
52 I
55 I
138
7.2
3.6 I 11.6
37.7 I 39. 9 I
5.3
A.l
2.2 I
2.A
7.6 I
7.2 I
•A
.2 I
.5
2.0 I
2.1 I
- - - - - - - - - - - - - - - -I- - - - - - - - ------I — — — -I
33
58 I
7b
70 I
6A I
301
11 . u
19.3 I 25.2
23.3 I 21.3 I 11.6
13.7
25.2 I 11.3
10.2 I
8.3 I
1.3
2.2 I
2.9
2.7 I
2. 5 I
------- - ------- -I- ----------—
-I
-I
o
3 I
10
1A I
23 I
56
1U.7
5. A I 17.9
25.0 I Al.l I
2.2
2.5
1.3 I
1.5
2.0 I
3.0 I
.2
.1 I
.A
.5 I
.9 I
-- — — - ------ -I- ------- ------ -I ------ -I
12
22 I
23
2A I
2A I
105
11.A
21.0 I 21.9
22.9 I 22.9 I
A •0
5.0
9.6 I
3. A
3.5 I
3.1 I
.5
.8 I
.9
.9 I
.9 I
- - - - - - - ------- -I- - - - - - - - - - - - - - -I------I
28
7 I
A3
fa3 I
96 I
2 A2
11.fa
2.9 I 17.8
28.1 I 39.7 I
9.3
11 . 6
3.0 I
6.A
9.9 I 12. 5 I
1.1
.3 I
1.7
2.6 I
3.7 I
— - - - - - - ------ -I-I - - - - - — -I
3
(. I
A
3 I
7 I
17
17.6
0 I 23.5
17.6 I A1.2 I
.7
1.2
0 I
.6
.A I
.9 I
.1
0 I
.2
.1 I
.3 I
--------- “ - - - - - - - -I- ------ --------- -I -------- -I
1
2 I
A
3 I
? I
17
5.9
11.8 I 23.5
A7.1 I 11.8 I
.7
.A
.9 I
.6
1.2 I
.3 I
.0
.1 I
.2
.3 I
.1 I
- - - - - - - - --------- -I- ------ --------- -I
-I
1 I
6
7
15 I
35 I
6A
9.A
1.6 I 10.9
23.A I 5a . 7 I
2.5
2.5
.A I
1. j
2.2 I
A. 6 r
.2
.U
I
.3
.6 I
1. 3 i
—
— - ------- -I- ------I
-i
2A1
230
o7 5
688
757
2601
9.3
8.8
2o ■0
26. 5
29. 5
1CU.0

1

lake

* * * * .

C R 0 3 S T A i J L A T I 3 N
0
BY
3FH.K
* * ♦ * * * * * * * » + * * * * * * * * * * * * * * 3Af
* * * *

THURSDAY FRIJAY

3

PAGE

2 OF

2

SATURDAY SUNGAY

kOW
TOTAL
A
7
6
7 I
........ -------- ________ _______ -I
26
51
fc3
77 I
233
i.; .9
21. A
23.6
32. A I
9.2
11.3
7.6
9.9
10. C I
1.0
2•u
2.6
3. C I
-------- -------- ________ _______ -I
63
31A
153
71A
10 5 I
11.6
AA. t
22.1
1A. 7 I 27.5
0 0.1
Ab. 5
23.0
13. 7 I
3.2
12.1
6.1
A.C I
-------- —-- ---- ________ _______ -I
5
4l
97 I
120
.:1. '
A.2
3A.2
39.2 I
A.6
2.2
3.3
6.0
6. 1 I
•2
.a
1.6
1.8 I
— ---- ________ _______ -I
8
i.
20
17 I
6A
12.6
IS .5
31.3
26. b I
2.5
3.5
1.5
2.9
2.2 I
•3
.A
.3
.7 I
........ ________ ________ __
-I
1
31
51
83 I
137
•5
15.6
27,3
AA. A I
7.2
•A
•5
7.6
10.8 I
•u
1.2
2.0
3.2 I
“I
it*
7
2
2-:
AS
37 I
112
BIG STAR
6.3
1.8
17.9
61.1
33. C r
k.3
2.9
.9
3.G
6.7
A. 8 i
•3
.1
.8
1.8
1. A i
— ........ ........ ________ ________ _______ -i
15
2u
A
29
35
73 r
161
HIGGINS
12.t*
2.5
18. „
21.7
A 5. 3 t
6.2
6.3
1.7
4.3
5.1
9.5 i
.8
.2
1.1
1.3
2.8 t
- ........ -------________ ------ -i
lb
lu
3
15
15
22 i
65
BIG THIN
15.L
A.b
23.1
23.1
33. 8 i
2.5
a .1
1.3
2.2
2.2
2.9 i
•A
•1
•3
.6
.8 i
- ........ ........ ________ ________ _______ _i
COLUMN
2t*l
23C
675
688
767
2601
TOTAL
9.3
8.8
26. A
26.5
29.5
1S0.0

NUHBEP OF MISSING OBSERVATIONS =

*

137

OAYOFHK
COJNT I
ROM PCT IiON GAY
COL POT
TOT PCT
i
........
-------LAKE
3
lb
HIGGINS
6.7
6.6
■o
• ........
12
5**
ST HELEN
7.6
22.•*
2.1
........
11
5
CHIPPEHA
t*.Z
2.1
.2
- ---- --12
9
CLEAR
1<*.1
3.7
.3
........
13
21
HIXON
11.2
6.7
.6

G R 3 S 3 T A 3 U L A T I 3 N
3Y TIME

LAKE

TIME
7-3
1

LAKE

2 I
3
A I
5
------- -I- ------- ------ -I -- ----7 I
9 I
6
6.5 I
A. 3
5.1 I
A. 3
16 .5 I
8.1
8.0 I
5. 1
.3 I
.2
.3 I
.2

8
5.8
i<*.8
•3

2

17
5 *o
31.5
.7

26
8.6
3u .2
1.0

I
I
I
I

15
5.3
21.6
■5

15
5.0
17.2
.6

I
I
I
I

16
5.3
13.7
.6

5.9
5.2
.7

3
5 .**
5*6
•1

2
3.6
2.3
.1

I
I
I
I

3
5. A
A. 1
.1

0
0
0
0

I
I
I
I

A
7.1
3. A
.2

(f

3.3
7 .A
.2

A
3.3
A.7
.2

I
I
I
I

1
1.0
1. A
.0

A
3.3
A.6
.2

I
I
I
I

6
2.5
11.1
.2

7
2.9
3.1
.3

I
I
1
I

3
3.7
12.2
.3

10
A a1
11.5
.A

0

ORCHARD

3
WOLVERINE

k

SHERMAN

5
FENTON

b

UNION

7

SHAN

Q

0

0

0

0
u

0
0

I
I
I
I

0
0

3
16.7
3.5
.1

I
I
I
I

u

0
8

2
3.1
3.7
.1

MUSKRAT
“
COLUMN
TOTAL

-----2.1

0

0
u

t
0
u

0

A I
3
6.3 I
A.7
A.7 I
A .1
.2 I
.1
----------- -I7
-*
36
3.3
2.8

2 -3

6 I
7 I
8 I
------ -I ------ -I- _____ -I1^ I
I
2! I
6.5 I 13 •u I 16.7 I
2.6 I
A.8 I
6.1 I
.3 I
.7 I
.9 I

1
AUSTIN

(CONTINUED)

XSi

6-7

PAGE

TABLE A2
Cross Tabulation For 16 Survey Sites
Number and Percentage of Interviews By Time of Day
3-9
9-10
10-11
12-1
1-2
H
1
H

COUNT
ROW POT
COL FCT
TOT PCT

C r
it

3-L
9
11
8.0
5.2
.A

ROW
TOTAL

I
10
-I —
I
1A
I 10.1
I
6.7
I
.5

I
I
I
I
I

113
5.3

T
A

A1
13.5
10.9
1.6

I
I
I
I

37
12.2
9.3
1. A

I
I
I
I

2A
7.9
11.A
.9

I
I
I
I

19
6.3
9.1
.7

I
I
I
I

A
7.1
1.2
.2

I
I
I
I

6
10.7
1.6
.2

I
I
I
I

9
16.1
2.A
.3

I
I
I
I

5
8.9
2.A
.2

I
I
I
I

5
8.9
2.A
.2

I
I
I
I

A
3.8
3. A
.2

A
3.8
1.2
.2

I
I
I
I

17
16.2
A.5
.7

I
I
I
I

1A
13.3
3.7
.5

I
I
I
I

10
9.5
A.7
.A

I
I
I
I

13
12.A
6.2

I
I
I

135
A. 0

I
I
I
I

10
A. 1
8.5
.A

1A
5.8
A «1
.5

I
I
I
I

A3
19. 8
12.8
1.8

I
I
I
I

26
10.7
6.9
1.0

I
I
I
I

20
8.3
9.5
.3

I
I
I
I

20
8.3
9.6
.8

I
I
I
I

2A2
9.3

1
5.9
1.1
.3

I
I
I
I

1
5.9
.9
.li

2
11.8

3
17.6
.8
.1

I
I
I
I

5
29.A
1.3
.2

I
I
I
I

2
.9
.1

I
I
I
1

1
5.9
.5
.0

I
I
I
I

17
.7

.1

I
I
I
I

2
11.1
2.3
.1

I
I
I
I

3
16.7
2.6
.1

2
11.1
. .6
.1

I
I
I
I

2
11.1
.5
.1

I
I
I
I

1
5.6
.3
.0

I
I
I
I

0
0
0
0

I
I
I
I

1
5.6
.5
.0

I
I
I
I

13
.7

A
6.3
1.2
.2

I
I
I
I
-I

9
1A.1
2.A
.3

A
6.3
1.9
.2

I
I
I
I

6A
2.5

2
3.1
2.3
.1

I
3
I
A. 7
I
2. 6
I
.1
----- -I ---->17
117
3.3
A.5

IS

.6

------

-

3AA
13.2

I
I
I

1 OF

11.3

I
7 I
7 I
I 10.9 I 10.9 I
I
1.3 I
3.3 I
I
.3 I
.3 1
____ -I-I-I —
37 n
379
211
1A.5
1A.6
8.1

209
3.0

303
11.6

56

2.2

26S1
103.0

o
V
o

LAKE
* * * * * *

S T A 3 J L A T I D N
BT T H E
♦ * * * * * * * * * * * ♦ * * * * * * * * * * * * * * *

0F
*

*

*

*

*

*

* • * *

PAGE

2 OF

TIME
COUNT
ROM PCT
COL PCT
TOT PCT

:>-o

6-7

7-8

ROH
TOTAL

11

12

15

1*

I

12
3.7
b .5
.5

6
%.3
3.3
•2

5
3.6
3.2
•2

A
2.9
2.a
.2

133
5.3

p

20
0.3
10.B
•a

20
6.6
11.1

19
b* 3
12.1
.7

15
5.U
1G.6

30 3
11.6

LAKE
AUSTIN

ORCHARD

.
a

.6

3

HOLVERINE

10 5
A. 0

5

26
10.7
lH.l
1.0

17
7.G
9.%
.7

19
7.9
12.1
.7

10
A.l
7.0
.A

2A2
9.3

b

0
0
u
0

1
5.9
•6
.0

1
5.9
.6
.u

Q
a
0
0

17
.7

7

2
11.1
1.1
.1

2
11.1
1.1
.1

3
U
C
u

0
0
0
0

18
.7

9.%
3.2
•2

%
6.3
2.2
.2

6
9. A
3.a
.2

3
A.7
2.1
.1

6A
2.5

IBS
7.1

iac
6.9

157
5. 0

1A2
5.5

2601
100.0

FENTON

UNION

SHAN

8
MUSKRAT

COLUMN
TOTAL
(CONTINUED)

56
2.2

6

139

SHERMAN

2
i
3
9
3.6
1.8
1A. 3
7.1
1.1
•6
5 .1
2.3
•1
.G
.3
.2
- -------- -------- -------- ------A
%
12
9
5
6.6
11.%
3.8
A.3
6.5
5. u
2.5
3.5
•5
.3
.2
.2

C R J S 3 T A 3 J L A T I I N
6Y
TIME

lake

0 -

?flGE

T

•>

i

5

I

4

I

5

I

6

I

7

I

3

I

9

I

10

I

1
I
I
I

u

5
2.1
5.8
.2

i
i
i
i

5
2.1
b.3

8
3.4
9.2
•3

i
i
i
i

23
9.7
6.7
•9

I
I
I
I

35
16.7
9.3
1.3

I
I
I
I

39
16 •6
10 •3
1.5

I
I
I
I

26
10.9
12.3
l.C

I
I

20
8.6
9.6
.8

I
I
I
I

238
9.2

8.5
•4

I
I
I
I

I

•2

I
I
I
I

ia

u
u

I
I
I
X

I
I
I
I

3
1.1
1 Ltd
<3

I
I
X
I

10
1 •4
11.6
•4

i
i
i
i

13
2.5
24.3
•7

I
I
I
I

16
2.2
18.4
.6

I
I
I
I

32
4.5
27.4
1.2

r
I
I
I

222
31.1
66 .5
8.5

I
I
I
I

96
12.6
23.9
3.5

I
I
I
I

96
13.2
26.8
3.6

I
I
I
I

37
5.2
17.5
1.6

I
I
I
I

62
5.9
20.1
1.6

I
I
I
I

713
27.6

111
I
I
I

1
.3
1.9
(i

X
I
I
I

1
.8
1.2
.0

I
X
i
i

1

3
2.5
3.4
.1

I
I
I
I

4
3. 4
3.4
2

I
I
I
I

7
5.9
2.0
.3

I
I
I
I

26
23.2
5.6
.9

I
I
I
I

26
20.2
6.3
.9

I
I
I
I

16
13.6
7.5
.6

I
I
I
I

13
10.9
6.2
.5

I
I
I
I

119
6.6

1.4
.w

I
X
I
I

12

1
I
I
I

1
1.6
i .9
•c

X
1
I
I

1
1.6
1.2
.0

i
i
i
i

2
3.2
2.7
•1

I
I
I
I

1
1.6
1.1
.0

I
r
i
i

3
4. 8
2. 6
.1

I

5

10
15.9
2.7
.6

I
I
I
I

9
16.3
2.6
.3

I
I
I
I

3
6.8
1.6
.1

I
I
I
I

12.7
3.8
.3

I
I
I
I

53
2.6

I
I

7.9
1.5
.2

I
I
I
I

B

t

I
I
I
I

3
1 .6
9*o
.1

X
£
I
I

6
3.2
7.0
.2

X
I
I
I

0

I
I
I
I

9
4.8
10.3
•3

i
i

3
1.6
2.6
.1

9
6.3
2.6
.3

I
I
I

23
12.3
5.1
.9

I
1
I
I

60
21.6
10.6
1.5

I
I
I
I

19
10.2
9.0
.7

I
I
I
I

16
8.5
7.7
.6

I
I
I
I

187
7.2

i
i

I
I
I
I

T

3.2
3.1
.2

I
I
X
I

o
C
0
Q

I
I
X
I

3
2.7
3.5
.1

X
I
I
I

1
.9
1.4
•0

X
I
I
I

5
4.5
5.7
.2

X
X

5
4.5
4. 3
.2

I
I
X
r

7
6.3
2.0
.3

I
I
I
I

17
15.2
6.5
.7

I
I
I
I

17
15.2
6.5
.7

I
I
I
I

7
6.3
3.3
.3

I
I
I
I

9
8.0
6.3
.3

I
I
I
X

112
6.3

I
X
I

I
I
I
X

4
2.5
4.7
.2

I
I
I
I

2
1.2
2.7
•1

I
I
I
I

2
1.2
2.3
.1

i

i
i
i

10
6. 2
8.5
•4

i
i
r
i

9
5.6
2.6
.3

I
I
I
I

25
15.5
6.6
1.0

I
I
I
I

21
13.0
5.5
.8

I
I
I
I

19
11.8
9.0
.7

I
I
I
I

20
12.6
9.6
.8

I
I
I
X

161
6.2

£

1
•6
1.9
.0

I
I
I
I

£
Q
J
u

I
X
I
£

1
1.5
1.2
.0

X
X
I
I

1
1.5
1. *
»-

I
X
X
I

2
3.1
2.3
.1

i
i
i
i

3
4.6
2. 6
.1

r

5
7.7
1.5
.2

I
I
I
I

8
12.3
2.1
.3

I
I
I
I

13
2G.0
3.6
.5

I
I
I
I

5
7.7
2.6
.2

I
I
I
I

6
6.2
1.9
.2

I
I
I
I

65
2.5

7-8

9-9

LAKE
9
HIGGINS

1C
ST HELEN

CLEAR

13
HIKON

14
815 STAR

15
WIGGINS

15
815 TWIN

COLUMN
TOTAL
{CONTINUE

A

TIME
I
Ib-7
I
I
i

COUNT
*0W PCT
COL PCT
TOT PCT

CHIPPEWA

3 OF

j

54
2.1

86
3.3

1L-ii

9-10

•3

74
2.8

87
3.3

i

i

12-1

1-12

2

117
4.5

i

i
r

369
13.2

1-2

376
16.5

3- 6

2-3

379
16.6

211
8.1

T

ROW
TOTAL

209
8.0

2601
100.C

■ v-iSl

LAKE

C R 0 3 S T A a J L A T
07
► * * * * * *■ * * » u * * ♦ * * * * * * * * * * • .

OF

pAGE ^ QF ^

TIME
COUNT
ROM PCT *•-5
COL PCT
TOT PCT
11
— ------ -------—
AK£
9
HIGGINS
8 .A
1J .8
•8
- -------lu
27
ST HELEN
3.8
1A.6
l.C
- -------11
lb
CHIPPtHA
8.A
3.A
•A
- -------12
7
CLEAR
11.1
3.8
.3
13
W1XON

li+
BIG STAR

13
WIGGINS

16
BIG THIN

COLUMN
TOTAL

,
s

5-6

6 -7

7-8"

13
I
lu I
--------- —A— --------- ------ -I
1.5
18 I
16 I
7.6 I
5.5
6.7 I
1 ti.0 I
3.3
11.3 I
.7 I
.5
.6 I
---------I- --------- ------ -I
45
S/
I
35 I
6.3 I
5.2
9.9 I
25.0 I 23.6
29.6 I
1.7 I
1.9
1.3 I
------ — I- ------I
5 I
li
9 I
<*.2 I
5.0
3.9 I
2.8 I
3.8
2.B I
.2 I
.2
.2 I
------ -I- ------I
3 I
3 I
12.7 I
3.2
9.8 I
9.9 I
1.3
2.1 I
.3 I
.1
.1 I

909
TOTAL

12

238
9.2

713
27.9

119
9. b

53
2.9

15
19 I
11
13 I
18 7
8 «u
7.5 I
5.9
7.0 I
7.2
8*1
7.8 I
7.0
9.2 I
•6
.5 I
.9
.5 I
- -------- ------ -I- ——---- -----— -I
■i
11 I
5
19 I
112
7.1
9.8 I
7.1
12.5 I
9.3
<*■3
6.1 I
5.1
9.9 I
.3
.9 I
.3
.5 I
— -------- -- ---- -I- ------ ------ -I
12
19 I
lu
12 I
151
7.5
B.7 I
6.2
7.5 I
6.2
6.5
7.8 I
6.9
8.5 I
.5
.5 I
.9
.5 I
- ---— -— ------ -I- ------ ------ -I
6
5
9 I
8
I
55
9.2
7.7 I 12.3
6.2 I
2.5
3.2
2.8 I
5.1
2.8 I
.2
.2 I
.3
.2 I
--—
—
- ——-----------I-I
135
180
15 7
192
2601
7.1
6.9
5.0
5.5
1UC.0

N U H8ER OF M I S S I N G O B S E RVATIONS =

3

I-1
(->

G R 0 S S T A 3 J L A T ION
BT BOAT

LAKE

0 -

TABLE A3

PAGE

1 OF

2

BOAT

LAKE

COUNT
ROM POT
COL POT
TOT POT
------1

AUSTIN

2

Cross Tabulation For 16 Survey Sites
Number and Percentage of People Bringing Boats To Public Access Sites
NO BOAT TRAILERS CAR TOP
ROW
0
TOTAL
1
2
3
-------- ------19
11£
9
138
13.8
79.7
6.5
5.3
1.8
9. b
5.3
.7
9.2
.3
ZZ

3

8
19.3
.6
.3

90
71.9
3.5
1.5

3
19.3
9.7
.3

56
2.2

■*

59
5 j .9
9,3
2.1

38
35.8
3.3
1.5

19
13.2
3.Z
.5

116
9.1

13

6
2.5
3.5
.2

292
9.3

1.0
.5

223
92.1
19.9
8.6

6

6
35.3
.5
•Z

9
52.9
.8
.3

2
11. 3
1.2

17
.7

7

1
5.9
.1
.0

9
23.5
.3
.2

12
70.6
7.0
.5

17
.7

8

12
18.8
.9
.5

29
95.3
2.5
1.1

23
35.9
13.5
.9

69
2.5

COLUMN
TOTAL

1283
99.3

1199
99.1

171
6.5

26L3
1 Ou .93

7 .2

WOLVtRINE

SHERMAN

5
FENTON

} .9

UNION

SHAN

MUSKRAT

(CONTINU-U)

i

142

23
7.6
13.5
.9

3C9
11.7

1.7
.8

259
85.2
22.5
lG.u

ORCHARD

C R O S S

T A 3 J L A T I 3 N
Of
13 AT

LAXE
* * * 9BOAT
COUNT
RON POT
COL PCT
TOT PCT

NO BOAT
1

LAKE
9

136
57.1
10 .6
5.2

lu

539
31.9
95.5
22.9

11

99
79. u
7.3
3.6

HIGGINS

ST HELEN

CHIPPENA
-

---------------

TRAILER £ CAR TCP
RQ VJ
TOTAL
2 i
3 I
_T _
“I*
99 I
3 I
238
91. b I
i >j X
9.1
3.6 I
1.3 I
3.8 I
.1 I
.T a
A
lu 6 I
23 I
713
19.9 i
3.2 I 27.9
9.2 I 13.5 I
9.1 I
.9 I
—
**Ti—
22 I
3 I
119
13.5 I
2.5 I
9.6
1.9 I
1.3 I
.8 I
.1 I
------------ -I-—
-X
is I
13 I
69
2 3.9 I 21.3 I
2.5
1.3 I
7.6 I
.6 I
.5 I

12

36
56.3
2.3
1 .9

13

66
35.3
5.1
2.5

108
57.8
9.9
9.1

I
I
I
I

13
7.:
7 •b
.5

I
I
I
I

197
7.2

61

59.5
9.3
2.3

99
39.3
3.8
1.7

I
I
I
I

7
6.3
9.1
.3

I
I
I
I

112
9.3

15

121
75.2
9.9
9.6

3C
18.6
2.6
1.2

10
6.2
5.8
.9

I
I
I
I

161
6.2

16

56
76.9
3.9
1.9

13
20.0
1.1
.5

2
3.1
1.2
•X

I
I
I
X

1233
99.3

1199
99.1

CLEAR

— T—

NIXON

19
BIG STAR

— T —

HIGGINS

I
I
I
I
_ T a

BIG THIN

COLUMN
TOTAL

NUHBER OF HISSING O B S E RVATIONS =

I
I
I
1
“I“

171
6.6

bS

2.5

26L3
100.0

0 -

R 0 S
LAKE

TABLE A4
Cross Tabulation For 16 Survey Sites
Travel Time To Destination Public Access Sites

HIN15

LAKE

COUNT
RON POT
COL PCT
TOT PCT
------1

AUSTIN

2
ORCHAKO

3
WOLVERINE

9
SHERMAN

5
FENTON

6
UNION

7
SHAN

MUSKRAT

1

I

2

3

I

9

5

I

6

7

I

8

I

9

I

39
92.8
5.3
2.3

bo
97.8
10.2
2.5

I
I
I
I

lu
7.2
3.2
.9

2
1.9
1.9
.1

I
I
I
I

0
0
0
0

G
0
0
3

I
I
I
I

I*

0
0
0

X
I
I

0
C

I
I

a

i

a

i

C

I

0
0
0
0

I
I
I
I

52
17.1
9.7
2.0

1^ 6
39.9
16.9
9.1

I
I
I
1

93
3j .□
0j .1
5.b

39
11.2
31.5
1.3

I
I
I
I

13
9.3
19. 3
.5

1
.3
5.3
.0

I
I
I
I

3
1*0

a

i

a

i

0
0

I
I

0
C
0

I
I
I

a

i

0
0
0
0

I
I
I
I

23
91.1
2.1
.9

9
16.1
1.9
.3

I
I
I
I

11
19.0
3.5
.9

9
16.1
8.3
.3

I
I
I
I

3
5.9
3. 3
.1

b
0
0
0

I
I
I
I

i
1.8
1.9
.c

a

i

5
3
0

I
I
I

I
0

I
I

fl
0

I
I

a

i

a

i

0

I

0

I

13
17.0
1.6
.7

39
5 5.7
9.1
2.3

I
I
I
I

22
2u. 6
7.1
.3

2
1.9
1.9
.1

I
I
I
I

3
2.8
3. 3
.1

0
0
0
0

I
I
I
I

1
.9
1.9
.0

J
0

I
I

a

i

3

I

C
0
G
0

I
I
I
I

0
C
0
0

I
I
I
I

5u
20.7
9.5
1.9

119
97.1
1 7.6
9.9

I
I
I
I

52
21.5
16. 8
C..b

9
3.7
8.3
.3

I
I
I
I

7
2.9
7.7
.3

5
2.1
26.3
.2

I
I
I
I

2
.8
2.7
•1

1

I
I
I

C
0
0
0

I
I
I
I

0
0
0
0

I
I
I
I

6
35.3
.5
.2

6
35.3
.9
.2

I
I
I
I

3
17.6
1.0
.1

0
0
0
0

I
I
I
I

0
0
0
0

0
0
0
0

I
I
I
I

I
I
I
I

0
0
0
0

I
I
I
I

2
11.1
.3
.1

I
I
I
I

2
11.1
.5
.1

2
11.1
1.9
.1

I
I
I
I

2
11.1
2.2
.1

0
0
G
0

I
I
I
I

1
5*6

0
0
0
C

I
I
I
I

8

18
30
28.1
96.9
1.6
9.6
.7
1.2
-------- ------11^3
U-.7
92.7
25.0

I
19
I 21.9
I
9.5
I
.5
L------Jb 9
11.9

1
1.6
.9
.0

I
1
I
1.6
I
1. 1
I
.0
----- -I- ----1 -a
91
9.2
3. 5

U
U

.1

•k

6.7
• a

i

U
0

a

i

a

i

c

0
0

I
I

0
0
0
0

.C

0
0
0
0

I
I
I
I

0
0
C
G

I
I
I
Z

C
0
0

I
I
I

a

i

a
0
u

0
0
0

I
X
I

a

i

C
C
0
0

I
I
I
I

0
Q
G
0

I
I
I
I

0

u

1
t-4

COLUMN
TOTAL

ROW
TOTAL

u

99.9
.7
.3
8

T A 3 U L A T I 3 N
•3V MIN15

19
.7

73
2.8

15
•6

69
2.7

3
•1

2593
100.0

C R 0 S 3 T A 8 J L A T I 3 N
er m i n i s

LAKE

OF

MINIS
COUNT
•ROW PCT
COL PCT
TOT PCT
LAKE

3

HIGGINS

lu
ST HELEN

11

CHIPPEWA

13
HIXON

1A
BIS STAR

15
HIGGINS

16
BIG TWIN

COLUMN
TOTAL

A I
5 1
6
7
---- - -I- --- -- -I- --- --- ------A I
1 I
9
3
1. 7 I
.A I
3.8
1.3
A. A t
5.3 I 12.3
21 •0
.2 I
.0 I
.3
•1
—— — — -I- ----- -I- ---- —
-------j I
? I
3
0
.7 I
.3 I
.A
0
5.5 I 1 u •5 I
A .1
a
.2 I
.1 I
•1
«i

ROW
TOTAL
8
- - - - - - -

6
2.6
8.7
•&
7
1.0
10.1
•3

I
9
-IG
I
I
0
I
Q
1
0
-II
1
I
.1
I 33.3
1
.0

23A

9.Q

713
27.5

15
12.6
1.4
•b

23
19.3
3.6
•9

I
I
I
1

7
5.4
2,3
*3

5
A.2
A.6
.2

A
3.A
A. A
.2

I
I
I
I

3
2.5
15.8
.1

I
I
I
I

17
1A.3
23.3
.7

2
1.7
13.3
•1

14
11.8
20.3
*5

I
I
I
I

1
•8
33.3
.0

A .6

24
36.1
2.2
.9

12
19.0
1.9
•5

I
I
I
I

5
«♦,8
1* i»
.1

2
3.2
1.9
.1

A
&. 3
A. A
.2

r
i
i
t

0
0
0
0

I
I
I
I

u
G
g
c

0
0
0
0

5
7.9
7.2
.2

I
I
I
I

>
0
0
0

2.A

18
9.7
1 &
.7
4
3.7
,4
.2

3b
19.5
5.6
1.4
.......
3
2.8
•5
.1

r
I
I
I

22.7
13.5
1.6

I
I
I
I

1
.9
*3
.0

42

25
13.5
23.1
1.0
------J
2.5
2.8
.1

26 i
2 I
7
2
1
6 I
1A, 1 i
1.1 I
3.8
1.1
3,2 I
.5
28.6 i lu .5 I
9.6
13.3
8.7 I 33.3
1. 0 i
.1 I
•3
•1
.2 I
.0
------ -i- ----- -I- ------- - - - - - - - — -— -«T5 i
3 I
24
7
24 I
0
A. 6 i
2.8 I 22.0
6.4
22.0 1
0
5.5 i 15.8 I 32.9
46.7
34.8 1
o
.2 i
.1 I
•9
.3
.9 I
0

78
39 I
1A
5
12 i
2 I
5
0
48.4
24.2 I
3.7
3.1
7.5 i
1.2 I
3.1
0
7.o
6.0 I
A.5
A.6
13.2 i 10.5 I
6.8
0
3.0
1.5 I
.5
.2
.5 i
.1 I
•2
0
—— . .. . . .......
------- ------ -i-I- ------- -------26
18
J
I
2
2 i
C I
a
4 u .b
26.1 I
3.1
0
3.1 i
0 I
G
Q
2.3
2.8 I
.o
0
2.2 i
0 I
G
0
1. u
•7 I
.1
a
.1 i
0 I
0
0
....... ....... -I--— ---- -------- ------ -i-I- ------- ------11JS
647
3j 9
1.3
91
19
73
15
25.0
<♦5.7
11.9
A.2
3.5
.7
2.6
- .6

s
3.1
7.2
•2
2
3.1
2.9
.1

1

Q

I

0

I
I
-II
I
I
1

119

63

135
7.1

109
A. 2

161

6.2

Q

0
o
0
o
o

6A
2.5

-I69
2.7

3
•1

2593
100.0

145

CLEAR

o
1 1
2
3
....... ....... -j........ -- — -—
127
*9 I
1*
3
54 •o
2*,.9 I
o •u
1.3
11*5
7.6 I
4.5
2.8
4.9
1.9 1
.5
.1
.— — ... . ....... -I-....... -- — -—
582
75 I
19
o
61.6
1 u •5 I
2.7
.8
52.5
11.6 I
5.1
5.6
22.4
2.9 I
.7
.2

C R 0 S S T A 3 J L A T I D N
0 87
SITEUSE

LAKE

TABLE A5

SIT EuSE
COUNT
ROW PCT
COL PCT
TOT Per

3L BOAT

2

6
HOLNERIN

5

b
UNION

7
SHAN

SCU3A

SUN 8ATH PI CNIC
c.
7
B

i*

I

5

6

71
29.o
5.9
1. 5

61.*

0
6
w
L

8
5.3
1.3
.3

I
I
I
I

9
6.5
<♦. 7
.3

0
0
0
G

7
5.1
25.C
•3

167
55.5
2^.5
6.5

57
13.9
7.5
2.2

u
-

1.3
.7
•2

I
I
I
I

53
2C. 9
33.2
2. G

2
.7
13.2
.1

C

1H
25. u
2. 0
.5

35
62.5
<*.6
l.L

u

1
1.3
.2
.3

I
I
I
I

*
7.1
2.1
.2

lu
9.6
1.5

tC
38.5
5.3
1. 5

c
V

38
36.5
6.3
1.5

I
I
I
I

G
3.8
2. 1
.2

137
53.1
LU. 1
5.3
“
2
11.6
•3
•1
0
U
U
G

(J
MUSKRAT

S<I

3

.* ♦

FENTON

SHIM

0.3
.6
.2
“ -------COLUMN
o33
TJTAu
2 o .4

9. L
2.7

u

C
u

U

0

53
k
2 2.5
7.G
2.G
“—
— *■ ------------ ~
9
52.9
u
1.2
i
•3
G
16
9<*.l
2.1
.6
55
37.3
7.3
2.1
75 8
29.3

s
L
I
G
L
ID
u
w
—- —- —- ———•2

2
.8
.3
•1
-------0
0
0
Q

I
35
I
1G. 3
I
18. L
X
l.<»
I —......
I
1
I
5.9
I
.5
X
.0

3
0
0
0

I
I
I
I

u
0
J
G

1
J
I
C
I
G
I
G
j ————————
19 u
7.3

6C5
23.*

0
0
0
Q

2
0

i
G
G

fl

i

j

C

3
2.9
27.3
• 1

G
C

0
G
G
G
-------G

0
0
0

0

G
0

I

G
-------C
G
u
C

Q
G
0
0

Q
0
Q
0

G
0
0
G

u

11
•d

G
0
c
-------28
1.1

OTHER

ROH
TOTAL
9

0
a
a
Q

3
2.2
1.1
.1

t*

i+
1.3
1.5
.2

11.5

0
G
0
0

2
3.6
,7
.1

2.2

6
5.8
15. <*
.2

3
2.9
1.1
.1

1
• <*
2.6
•0
-------0
0
a
a

8
3. L
3• C
.3

1.3
1G.3
.2

a
a

0
0
0
0
G
3
- ———————
39
1.5

138
5.3

301

55

ISA
A.3

236

9.1

5
29.4
1.9
.2

17

1
5.9
• <*
.0

17
.7

6.3
1.5
•2

2.A

.7

53

269
lt.<*

2587

1G0.0

146

SHERMAN

HUNT
2

1

ORCHARD

FISH

1

LAKE
AUSTIN

Cross Tabulation For 16 Survey Sites
Number and Percentage of Primary Site Use Categories

C R 0 S S T A 3 J L A T I 3 N
0 ar s i t e u s e

LAKE

SITEUSE
COJNT
I
RUM PCT I PL 3JAT
FISH
COL PCT I
TOT PCT I
I I

LAKE
9
HIGGINS

1
I
I
I

HUNT
E l

SHIM
3 1

S<I
A

I

SCUBA
5 1

SUN BATH PICNIC
OTHER
E
6 1
7 1
8 1
9 1

ROH
TOTAL

1----1--------1--------1-------- X--------1-------- 1--------X-------- 1-------- 1
92
I
AC I
C l
A3 I
10 I
A
I
5 1
2 1
A2 I
38.7
13.5
3.6

I 16.8
I
5.3
I
1.5

-I....... 1..... —

I
I
X

3

i
u

1
X
I

18.1
7.1
1.7

I
I
I

A. 2
£
5.3
I
. <. 1

1.7
36.A
.2

I
I
1

2.1
17.9
.2

I
I
1

.8
1 17.6
5.1
I 15.6
. 1 1
1.6

I
I
I

238
9.2

i— ..... I-------- 1------- 1-------- 1— ------I-------- 1-------- j

1.
ST HELEN

CHIPPEHA

1
69
I
1A5
I
I I
383
I
16 I
2
1
11
I
8 1
78
I
713
I
9.7
I
2u.3
I
. 1 1
53.7 I
2.2
I
.3
1
1.5
I
1.1
I
1C. 9
I
27.6
I
10.1
X 1 9 . 1 X 2 j .C X 6 3 . 3
X
8.A £
13.2
X 33.3
X 20.5
I29.0
X
I
2.7
I
5.6
I
.u I
1A.3
I
.6
1
.1 1
.A X
.3
1
3.C
I
- I ----------------1----------------1---------------1-----------------1--------------- £----------------1--------------- 1 ----------------1--------------- 1
11
I
la I
18 X
U I
35 1
6 1
0
1
L I
A I
21
I 12C
I
13.3
I 15.0
I
0 I
A5. 3 I
5.0
I
0 1
C l
3.3
I
17.5 I
A.6

I
2.3 I
2.A I
u I
9.1 I
3.2 I
0
1
0 1 1C.3 I
I
.6 1
.7 1
0 I
2.1 I
.2 I
0
1
0 1
.2
1
-I........I... — I......... I-....... I........I........ I........ I........I—

7.B I
.8 1
..... I

12

HKON

BIG STAR

I
3 1
26 X
l
I
8 1
A X
0 1
0 1
A X
l A l
I
12.3
X Au. 6
I
£ I
12.5
I
6.3 £
0
1
0 1
6.3
I
21.9
I
I
1.2
I
3.A I
0 1
1.3
I
2.1 I
0
1
0 1 10.3
I
5.2 I
X
.3
I
l.u
I
0 1
.3
1
.2 1
O X
0 1
.2
1
.5 1
- 1 --------------- 1 ----------------1---------------1 -----------------1 --------------- 1 --------------- 1 --------------- 1 ----------------1--------------- 1
13
I
53 I
88
I
I I
13
I
15
I
0
1
2
1
A I
11
I
I
28.3
I a7 • 1
I
.5 1
7.0 I
8.0
£
0 1
1.1 I
2.1
I
5.9 I
I
7.8
I
11.6
I
25.t
I
2.1
I
7.9
I
C l
7.1
I
10.3
I
A.l
I
I
2.0
I
3.A X
.0
1
.5
1
.6
1
0
1
. 1 1
.2
1
.A I
------1-------- £-------- 1 --------1--------1--------1------ --I-------- 1 ------- 1
1A
I
35
I
25
I
0
1
3 1
16
I
0
1
I I
2 1
26
I
I
31.8
I
22.7
I
0
1
A.5 I
1A. 5 I
0
1
.9
I
1.8
I
23.6
I
I
5.1
I
3.3
I
0
1
.3
1
9.A I
0
1
3.6
I
5.1
I
9.7 I
I
1•A I
1.0
I
0 1
.2
1
.6 1
0
1
.0
X
. 1 1
1.0 I

5A
2.5

187
7.2

110
A.3

-x------ 1------1------1------1------1------x------1------x----- 1

15

I
29 I
6A I
I I
21
I
5
1
0
1
I I
3 1
36
I
160
X
18.1
I AO . 0
I
.6 1
13.1 I
3.1
I
u l
.6 1
1.9
I
22.5 I
6.2
I
A.2 I
8.A I
25.0
I
3.5
I
2.6 I
0
I
3.6
I
7.7
I
13.A I
I
1.1
I
2.5
I
.0
1
.8
1
.2 1
0
1
.0
1
. 1 1
1 ■A I
- I ---------------- 1----------------1----------------X----------------X----------------1--------------- 1----------------1----------------1--------------- 1
16
X
7 1
16
I
I I
2A I
2
1
0
1
I I
I I
11
I
63
BIG THIN
I 11.1
I
25.A I
1.6
I
38.1
I
3.2
I
0
1
1.6
I
1.6
1 17.5
I
2.A
I
1.0
I
2.1
I
25.0
I
A.S
I
1.1
I
0
I
3.6
I
2.6
I
A.l I
I
.3
1
.6
1
.0
1
.9
1
. 1 1
0
1
,C
I
.0
1
.A X
- X ----------------x----------------1----------------£ ----------------1----------------1--------------- 1 ----------------1 ----------------1--------------- j
COLUMN
683
758
A
6t5
190
11
28
39
269
2537
TOTAL
2o. A
29.3
.2
23.A
7.3
.A
1.1
1.5
1C.A
100. 0

HIGGINS

NUMBER OF MISSING O B S E R VATIONS =

22

147

CLEAR

R O S S T A 8 U L

LAKE

* * * ♦ • *
COUNT
ROW PCT
COL PCT

ROW
1

I

2

3

9

I

5

6

7

8

9

t

i

23
16.3
7.3
.9

I
I
I
I

55
90.1
0.2
2.1

27
19.7
5.8
1 .u

16
11. 7
9.1
.6

I
I
I
I

8
5. 8
9. 3
.3

5
3.6
1.8
.2

1
' .7
3.2
.0

2
1.5
11.1
.1

0
0
0
0

13 ?
5.3

1C9
39.9
11.7
9.0

6b
21.9
19.1
2.6

bO
19.9
15.5
2.3

I
I
I
I

29
9. 6
15. 9
1.1

10
3.3
3.6
.9

2
.7
6.5
.1

0
3
0
0

2
.7
18.2
.1

3u2
11.7

27
3. E
1.0

li
17.9
2.1
.9

10
17.9
2.6
.9

I
I
I
I

0
0
C
0

2
3.6
.7
.1

1
1.8
3.2
.0

0
0
0
0

0
0
0
0

I
7
I
I
6.9
I
I
2.2
I
I
.3
I
“ I ” . . . . . . -I —
5
I
17
I
I
7.0
I
I
5.9
I
I
.7
I

95
99.1
5.1
1.7
—
—
89
36.8
10.0
3.9

lb
15. 7
3.9
.6
---------------55
22.7
11. 8
2.1

16
15.7
9.1
.6

10
9.8
5.3
.9

2
2.0
.7
.1

5
9.9
15.1
.2

21
8.7
11.2
. 8

11
9.5
3.9
.9

2
.8
6.5
.1

2
.8
11.1
.1

1
.9
9.1
.0

6

i
i
2

I
I
I
£

29
9*6
9.1
1.1

I
I
I
I

3

I
I
I
I

6
10.7
1.9
.2

I
I
I
I

ORCHARD

WOLVERINE

7
SHAN

B
MUSKRAT

------ — — —

—

--------

1
1.0
5.6
.0
_____ — -

0
0
0
0

102

3.9

292
9. 9

9
23.5
1.3
.2

I
I
I
I

6
35.3
.7
.2

17.6
•5
.1

2
11.8
.5
.1

I
I
I
I

1
5.9
.5
.0

1
5.9
.9
.0

L
0
0
0

0
g
0
0

0
o
a
a

17
.7

I
I
I
£

2
11.1
.6
.1

I
I
I
I

9
50.0
1.0
.3

6
33.3
1.3
.2

1
5.6
.3
.0

I
I
I
I

u
0
0
0

0
0
0
0

0
t

0

0
0
0
0

is

£
£

5
7.8
1.6
.2

I
I
1
I

37
57.8
9.2
1.9

13
20.3
2.8
.5

7
10.9
1.8

I
I
I
I

1
1.6
.5
.0

0
0
0

887
39.3

96 8
13.1

38 7
15.0

188
7.3

£

(CONTINUEJ)

3

I
I
I
I
------ - I 99
I
18.2
I
11.9
I
1.7
I

56
2.2

£
£
£
£

£
COLUMN
TOTAL

h 8 .2

ill
12.3

.3

c

c
a

0

0

0

i

Q

c
c
u

1.6
5.6
.0

280
10 . 8

31
1.2

13
.7

0
0

g

.7

69
2.5

0

li

2537

.9

100.0

148

4

UNION

2

TOTAL

i

AUSTIN

FENTON

1 OF

pcr

LAKE

SHERMAN

PAGE

TABLE A6
Cross Tabulation For 16 Survey Sites
Total Numbers and Percentages for Party Size

PARTY

ror

0r

A T I 0 N
3Y PARTY

C R O S S T A B J L A

LAKE

* * * * .

T I D N
Y PARTY
PAGE

2 OF

PARTY
CO'JNT
ROM PCT
COL PCT
TOT PCT

ROM
TOTAL

I

2

I

3

4

5

I

6

I

7

I

8

9

34
m .**
1J . 7
1. 3

I
i
I
I

92
39. 0
10.4
3.6

I
I
I
I

50
21.2
10.7
1.9

42
17.8
10.9
1.6

9
3.8
4. a
.3

I
I
I
I

7
3.0
2.5
.3

I
I
I
I

1
.4
3.2
.0

I
I
I
I

0
0
0

1
.4
9.1
.0

93
13.1
29.3
3.6

I
I
I
I

178
25.1
2b.1
6.9

I
I
I
I

96
13.5
20.5
3.7

68
9.6
17.6
2.6

51
7.2
27.1
2.0

I
I
I
I

208
29.3
74. 3
8.0

I
I
I
I

8

1.1
25.8
.3

I
I
I
I

4
.6
22.2
.2

4
.6
36.4
.2

13
1u• 9
4.1
.5

I
I
I
I

38
31.9
4.3
1.5

I
I
I
I

17
14.3
3.o
.7

16
13. 4
4.1
.6

12
10.1
6.4
.5

I
I
I
I

10
8.4
3.6
.4

I
I
I
I

6
5.0
19.4
.2

I
I
I
I

5
4.2
27.3
.2

2
1.7
18.2
.1

17
27.9
5.4
.7

I
I
I
I

27
44.3
3. 0
1.0

I
I
I
I

8
13.1
1.7
.5

3
4.9
.3
.1

3
4.9
1. 6
.1

I
I
I
I

3
4.9
1.1
.1

I
I
I
I

0

0

0

I
I
I
I

0
0
a
0

19
10.2
6 «u
.7

I
I
I
I

66
35.3
7.4

33
17.5
7.1
1.3

40
21.4
10 . 3
1.5

18
9.6
9.6
.7

I
I
I
I

a

4.3
2.9
.3

I
I
I
I

2
1.1
6.5
.1

I
I
I
I

0
0

2. 6

I
I
I
I

l->
13.A
4.7
.6

I
I
I
I

43
38.4
4.8
1. 7

I
I
I
I

19
17.0
4.1
. 7

23
20.5
5.9
.9

6
5.4
3.2
.2

I
I
I
I

5
4.5
1.8
.2

I
I
I
I

1
.9
3.2
. b-

I
I
I
I

0
0
0
0

24
14.9
7.6
.9

I
I
I
I

51
31.7
5.7
2. 0

I
I
I
I

35
21.7
7.5
1.4

29
18.3
7.5
1.1

13
8.1
6.9
.5

I
I
I
I

6
3.7
2.1
.2

I
I
I

1
.6
3.2
.0

I
I
I
I

2
1.2
11.1
.1

lake

9
HIGGINS

lu
ST HEL-N

11
CHIPPEWA

12
clear

13

HIXON

1-.
BIG STAR

15
HIGGINS

16

BIG THIN

9
14.3
2.8
.3

I
20 I
14
I
31.7
I
22.2
I
2.3
I
3.0
I
.8
I
.5
■ * -------- -------1 ----------------- • I - - —- - - - COLUMN
8 >17
317
468
TOTAL
12.3
34.3
13.1

NUMBER OF MISSING O B S E R VATIONS =

22

10
15.9
2.6

I

I
2 I
I
3.2
I
I
.7
I
.4
I
.1
I
------------------ --------------- - I - ------------- - I 3«7
138
23C
7. 3
15.0
10.8
6

9.5
3.2
.2

C
G

1
1.6
3.2
.0

0

0
0
0

G
0

I
1
I
1.6
I
5.6
.0
---- - II — ____
31
15
1.2
.7

i

.5
9.1
.0
0
0
0

236

9.1

710
27.1.

119
4.5

61
2.A

187
7.2

112
4.3

0

0
0
0
G
0
0
0
0
11
.4

161
6.2

63

2.4

2597
100.0

149

1

t i 0

S T A 8 J L A T I 3 N

lake

rfT

* * * * * * * * *

0-

INCOME
PAGE

TABLE A7

INCOME

LAKE

COUNT
ROM PCT I j - 5
COL PCT
TOT PCT 1
-

A U S T IN

ORCHARD

WOLVERINE

FENTON

TOTAL
(CONTINUED)

I

U

I

*

I

5

I

6

I

7

I

8

I

9

I

10

23
lb.9
6.0
1.0

I
I
I
I

05
33.1
6.1
1.9

I
I
I
I

35
25.7
6.8
1.5

I
I
I
I

18
13.2
5.9
.7

I
I
I
X

5
3.7
3.2
.2

I
X
I
I

2
1.5
3.3
.1

I
I
I
I

0
0
u
0

I
I
I
I

0
0
G
G

I
I
I
I

3
0
0
G

I
X
I
I

1
•3
.7
.u

I
I
I
I

9
3.0
2.3
.0

I
I
I
I

65
21.0
3.9
2.7

I
I
I
I

61
20.1
11.8
2.5

I
X
I
I

51
2G.1
19. 9
2. 5

X
I
I
I

05
10.8
28.8
1.9

I
I
I
I

15
0.9
20.6
.6

I
I
I
I

13
<».3
»&
.5

I
I
I
I

12
3.9
80.0
.5

I
I
I
I

2
.7
13.3
.1

I
I
I
I

1
1.8
.7
.0

I
I
I
I

5
8.9
1.3
.2

I
I
I
X

19
33.9
2.6
.3

I
I
I
X

12
21.N
2.3
.5

I
I
I
X

8
10. 3
2.6
.3

I
I
I
I

5
8.9
3.2
.2

I
I
I
I

2
3.6
3.3
.1

I
I
I
I

1
1.8
3.1
.8

I
I
I
I

G
0
0
0

I
I
I
I

0
0
0
0

I
I
I
I

10
9.5
6.9
.<«

I
I
I
I

23
21.9
6.0
1.0

I
1
I
I

36
30.3
0.9
1.5

I
I
I
I

21
2b •0
0.1
.9

I
I
I
I

10
9.5
3. 3
.0

I
I
I
I

3
2.9
1.9
.1

I
I
I
I

1
l.C
1.6
.0

I
I
I
I

1
l.Q
3.1
.0

I
I
I
I

G
G
0
0

I
I
I
I

0
0
0
0

I
I

a
3.3

2G
8.3
5.2
.8

7i
29.6
9.7
*5• L'

X
I
X

I
I
I

03
17.9
10. G

I

52
21.7
10.1
2.2

X

1. 8

X
X
I
I

20
10.0
15.0
l.G

I
I
X
X

9
3.7
10.8
.0

I
I
I

5
2.1
15.6
.2

t
I
i
I

a

5.5

I
I
I

I

I
I

a
0

I
I
I
1

2
.8
13.3
.1

b

I
I
X
I

2
11.8
.0
.1

X
I
I
I

1
5.9
.3
.0

I
I
I
I

0
0
0
0

I
I
X
I

0
G
0
0

I
I
I
I

0
0
0
0

I
I
I
I

c
c
0
0

I
I
I
X

1
5.9
6.7
.0

17
.7

I
I
I
X

1
5.9
.2
.0

I

1
5.9
.3
.0

I
I
I
I

G

I
I

0
0
0

I
I
I
I

0
Q

I
I
I
I

0
0
0
0

X
I
I
I

Q
0
0
0

I
I
I
I

0
0
0
0

17
.7

I
X
I

15
23.0
2.9
.6

I
X
I
I

10
15.6

I
I

X
I
X
I

0
0
0
Q

i
I
I

a
0
C
0

I
I
I
I

0
c
0
0

61*
2.7

.3

I

2 I
11.8 .I
l.<* I
.1 I

3

I

I
I

7

I
I
I
I

I

I
I
I

91.2
1.8
.3

I
I
I
1

4
6.3
2.8
.2

I
I

12
18.8
3.1
.5

lo5
6.0

I

1

I
I
I

5
29.<*
1.3
.2

17.6
2.1
.1

I

COLUMN

2

<*.1
.2

I

MUSKRAT

ROW
TOTAL

I
I
I
I
I

1

SMAN

<*5-50

l

I
I
I
I

I
I
I
I

UNION

1 OF

333

15.9

I
I

I
I

I
I

35.3
.8
.2
5

29.0
.7

.2
23
35.9
3.1
1*3
733
30.5

I

517
21.5

X

3. 3

.0
30 7
12. 8

I

I

0
0
0
0
156
6.5

G

0

X
X

0
G

I
I

w
Q

61
2.5

I

32
1.3

I

G

15
•6

15
•6

136
5.7

30!*
12.6

56
2. 3

105
<*.<*

2<*0
10.0

2006

100.0

150

SHERMAN

6

Cross Tabulation For 16 Survey Sites
Total Numbers and Percentages for Income Classes
5-iu
1L -15
15-2l
20 -25
25-3 l
30-35

T A B U L A T I O N
3X
INCOME

0 r

pAG£ , QF

I NO0 ME
COUNT
ROW POT
COL PCT
TOT PCT
LAKE

il

Z
1•5

AUSTIN

ROM
TOTAL
136
5.7

* •d

ORCHARD

o •o

30 A
1Z. 6

A7 • 6

.8

3
5.A
7.1

HOLVERI NE

56
Z.3

.1

FENTON

Z.5
1A. 3
.Z

ZAO
10. G

UNION

17
.7

SHAN

17
.7

6A
2.7

MUSKRAT

COLJMN
TOTAL
(CONTINUE 0)

Z

2AC6

1.7

lu u •0

a

151

105
A.A

SHERMAN

C R 0 S S T A 3 U L 4 T I D N
0*
ijy INCOME

t-AKE

pfl&E 3 OF I*
INCO.it

COUNT
ROH PCT
COL PCT
TOT PCT

0- 5

5-1C

10-15

15- 2 l

22 - 25

ST MELON

li
CHXPPExA

CLEAR

12

13
HIXON

BIG STAR

15

0

I

5

6

7

I

8

9

10

50
22.7
10.0
2.2

I
I
I
I

01
17.2
13.0
1. 7

16
6.7
10.3
.7

5
2.1
8.2
.2

I
I
I
I

1
.0
3.1
.0

1
.0
6.7
.0

2
.8
13. 3
.1

I
06
26
15
I
I
8. 8
5.0
2.9
I
I
15. 0
16.7
20.6
I
I
1.9
1.1
.6 I
------- - I — ----------- --------------- -------------fa
21 I
1 I
17.5
I
5. 0
0.2
.8
I
0.1
I
2. u
3.2
1.6
I
.3
I
.2
.2
.0
1

5
1.0
15.6
.2

C
0
0
0

5
1.0
33.3
.2

*♦3
3*2
if 9. 7
1. 3
------- — “ 12
l u .u
3.3
•5

111
153
21.1
23.1
2 9. u
2 u. 9
*4.6
6.0
------------- - ------- ------3 ‘I
32
26.7
31. 7
8.0
5.2
1.3
1.6

118
22.5
22.8
0.9

_____

1
.8
3.1
.0

.8
6.7
.0

1
.8
6.7
.0

9
1*4.5
6.2
•*

13
21. 0
3. 0
.5

21
33. 9
2.9
.9

10
16.1
1.9
.0

I
I
I
I

5
8.1
1.6
.2

1
1.6
.6
.0

1
1.6
1.6
.1

I
I
I
I

0
C
3
0

1
1.6
6.7
.0

1
1.6
6.7
.0

9
*4.8
6. 2
. *4

20
12.8
6.3
1. j

70
39.5
10. 1
3.1

01
21.9
7.9
1.7

I
I
I
I

20
12. 8
7.8
1. 0

9
0.8
5. 8
.0

3
1.6
0.9
.1

I
I
I
I

3
1.6
9.0
.1

0
0
0
0

0
0

7

19
17.1
5.0
.8

37
33.3
5• u

1.5

27
20.3
5.2
1.1

I
I
I
I

10
9. S
3. 3
.0

8
7.2
5.1
.3

3
2.7
0.9
.1

I
I
I
I

3
0
0
0

0
0
a
G

0
0
0
0

37
23.1
9.7
1. 5

05
28. 1
5.1
1.9

35
21.9
6.8
1.5

I
I
I

0.0
0.5

3
1.9
0.9
.1

I
I
I
I

2
1.2
6.3
.1

C
0
0
0

1
.6

I

16
1L. 0
5.2
. 7

13
21. 3
1.8
.5

12
18.8
2.3
.5

I

7

2

1C. 9
2. 3

3.1

1
1.6

•2

20
37.5
6.3
1. 0

1.3
.1

l.b
.C

I
I
I
I

0
0
0
0

0
0
0
0

0
0
0
3

1*45
6*o

383
15. 9

733
3. . 5

517
21.5

156
6.5

61
2.5

32
1.3

15
.6

15
.6

13
8.1

*4
2.6

(CONTINUED)

ROM
TOTAL

82
3+. 5
11.2
3.0

6.3

COLUMN
TOTAL

05- 50

19
8. u
5• u
•8

•7

8IG THIN

0C- 05

13
5.5
9.1
*5

9.3
16

35-0,.

3

t) .3
<4.8
.3

HIGGINS

3L- 35

2

LAKE

HIGG XN3

25-30

1

I
I
I

.3
30 7

It. 3

7
.3

0
0

6.7
.0

2035
i o c .o

r

G R O S S T A 3 U
LAKE
•

*

*

*

l

A T I 3N
0 r
BY H30ME
* * * *

PAGE

OF

INCOME

COJNT
ROW PCT
COL PCT
TOT PCT
LAKE

ROW
TOTAL

5 0+

k

HIGGINS

1.7
9.5
•Z

IQ

236
9.9

525

ST HELEN

21.8

7.3

11
CHIPPEHA

c
1.7

1Zii
5. li

4.8

•1
bZ
2.6

13

187
7.8

CLEAR

HIXON

I1*

153

12

111

BIG STAR

<*•6

1

HIGGINS

16 b

6.7

.o
2 .<»

15
BIG THIN

6A

1
2•*

2.7

1.5

.0

COLJHN
TOTAL

<•2
1.7

2 ‘»i6

lu o .8

NUMBER OF MISSING O B S E RVATIONS =

2o3

APPENDIX E
CONSUMER SURPLUS FIGURES FOR THE
16 SURVEY PUBLIC ACCESS SITES

154

•

155

TABLE A8
CONSUMER SURPLUS
AUSTIN LAKE SITE BENEFIT ,
ESTIMATION
COST

ESTIMATED
NUMBER OF VISITORS

COST

$ .00

72,739

$ 8.25

4,398

.75

55,683

9.00

3,130

1.50

42,669

9.75

2,298

2.25

33,101

10.50

1,763

3.00

25,692

11.25

1,347

3.75

19,849

12.00

1,070

4.50

14,936

12.75

872

5.25

11,568

13.50

713

6.00

9,013

14.25

614

6.75

6,993

15.00

495

7.50

5,507

15.75

396

CONSUMER SURPLUS = $236,130

ESTIMATED
NUMBER OF VISITORS

156

TABLE A9
CONSUMER SURPLUS
ORCHARD LAKE SITE BENEFIT ESTIMATION
COST

ESTIMATED
NUMBER OF VISITORS

$ .00

115,799

$ 8.25

8,092

.75

86,512

9.00

6,204

1.50

66,673

9.75

4,798

2.25

52,305

10.50

3,532

3.00

41,371

11.25

2,580

3.75

32,909

12.00

1,684

4.50

26,189

12.75

871

5.25

20,726

13.50

386

6.00

16,438

14.25

238

6.75

13,119

15.00

0

7.50

10,376

15.75

0

CONSUMER SURPLUS = $383,160

COST

ESTIMATED
NUMBER OF VISITORS

157
TABLE A10
CONSUMER SURPLUS
WOLVERINE LAKE SITE BENEFIT ESTIMATION
COST
$

ESTIMATED
NUMBER OF VISITORS

COST

ESTIMATED
NUMBER OF VISITORS

.00

14,863

$8.25

421

.75

11,406

9.00

303

1.50

8,744

9.75

208

2.25

6,717

10.50

127

3.00

5,132

11.25

59

3.75

3,865

12.00

32

4.50

2,924

12.75

18

5.25

2,172

13.50

0

6.00

1,566

14.25

0

6.75

1,086

15.00

0

7.50

688

15.75

0

CONSUMER SURPLUS = $45,249

TABLE All
CONSUMER SURPLUS
SHERMAN LAKE SITE BENEFIT ESTIMATION
COST

ESTIMATED
NUMBER OF VISITORS

COST

$ .00

49,011

$ 8.25

2,364

.75

37,329

9.00

1,568

1.50

28,576

9.75

1,153

2.25

21,853

10.50

830

3.00

16,837

11.25

554

3.75

12,950

12.00

369

4.50

9,975

12.75

265

5.25

7,484

13.50

185

6.00

5,651

14.25

150

6.75

4,278

15.00

104

7.50

3,206

15.75

0

CONSUMER SURPLUS = $153,520

ESTIMATED
NUMBER OF VISITORS

159
TABLE A12
CONSUMER SURPLUS
FENTON LAKE SITE BENEFIT ESTIMATION
COST

ESTIMATED
NUMBER OF VISITORS

COST

$ .00

83,391

.75

61,527

9.00

954

1.50

45,388

9.75

542

2.25

33,302

10.50

353

3.00

24,161

11.25

224

3.75

17,482

12.00

141

4.50

12,534

12.75

94

5.25

8,894

13.50

35

6.00

6,185

14.25

0

6.75

4,052

15.00

0

7.50

2,603

15.75

0

CONSUMER SURPLUS = $227,680

$ 8.25

ESTIMATED
NUMBER OF VISITORS
1,708

160
TABLE A13
CONSUMER SURPLUS
UNION LAKE SITE BENEFIT ESTIMATION
COST

ESTIMATED
NUMBER OF VISITORS

COST

ESTIMATED
NUMBER OF VISITORS

$ .00

6,119

$ 8.25

937

.75

5,415

9.00

735

1.50

4,758

9.75

549

2.25

4,147

10.50

383

3.00

3,541

11.25

280

3.75

3,049

12.00

181

4.50

2,583

12.75

114

5.25

2,221

13.50

67

6.00

1,822

14.25

36

6.75

1,527

15.00

21

7.50

1,196

15.75

10

CONSUMER SURPLUS = $29,773

161

TABLE Al4
CONSUMER SURPLUS
SWAN LAKE SITE BENEFIT ESTIMATION
COST

ESTIMATED
NUMBER OF VISITORS

COST

ESTIMATED
NUMBER OF VISITORS

$ .00

9,515

$ 8.25

158

.75

7,350

9.00

93

1.50

5,682

9.75

65

2.25

4,337

10.50

50

3.00

3,229

11.25

29

3.75

2,381

12.00

14

4.50

1,705

12.75

7

5.25

1,187

13.50

0

6.00

834

14.25

0

6.75

511

15.00

0

7.50

338

15.75

0

CONSUMER SURPLUS = $28,114

TABLE A15
CONSUMER SURPLUS
MUSKRAT LAKE SITE BENEFIT ESTIMATION
COST

ESTIMATED
NUMBER OF VISITORS

COST

ESTIMATED
NUMBER OF VISITORS

$ .00

12,539

$ 8.25

303

.75

9,502

9.00

188

1.50

7,199

9.75

119

2.25

5,515

10.50

92

3.00

4,161

11.25

78

3.75

3,092

12.00

64

4.50

2,188

12.75

55

5.25

1,578

13.50

41

6.00

1,106

14.25

32

6.75

766

15.00

0

7.50

477

15.75

0

CONSUMER SURPLUS = $36,822

163

TABLE A16
CONSUMER SURPLUS
HIGGINS LAKE SITE BENEFIT ESTIMATION
COST

ESTIMATED
NUMBER OF VISITORS

COST

ESTIMATED
NUMBER OF VISITORS

$ .00

33,110

$ 8.25

2,358

.75

28,953

9.00

1,805

1.50

22,052

9.75

1,144

2.25

18,194

10.50

788

3.00

13,822

11.25

534

3.75

11,541

12.00

438

4.50

10,085

12.75

248

5.25

6,806

13.50

222

6.00

5,821

14.25

203

6.75

4,995

15.00

184

7.50

4,201

15.75

165

CONSUMER SURPLUS = $125,860

164.
TABLE A17
CONSUMER SURPLUS
LAKE ST. HELEN SITE BENEFIT ESTIMATION
COST

ESTIMATED
NUMBER OF VISITORS

COST

ESTIMATED
NUMBER OF VISITORS

$ .00

114,129

$ 8.25

1,719

.75

53,269

9.00

1,154

1.50

33,607

9.75

729

2.25

22,329

10.50

408

3.00

15,136

11.25

278

3.75

10,007

12.00

182

4.50

7,170

12.75

122

5.25

5,529

15.50

61

6.00

4,123

14.25

17

6.75

3,133

15.00

0

7.50

2,370

15.75

0

CONSUMER SURPLUS = $202,290

165

TABLE A18
CONSUMER SURPLUS
CHIPPEWA LAKE SITE BENEFIT ESTIMATION
COST

ESTIMATED
NUMBER OF VISITORS

$ .00

29,894

$ 8.25

1,719

.75

23,827

9.00

1,154

1.50

18,836

9.75

729

2.25

14,878

10.50

408

3.00

11,536

11.25

278

3.75

9,253

12.00

182

4.50

7,170

12.75

122

5.25

5,529

13.50

61

6.00

4,123

14.25

17

6.75

3,133

15.00

0

7.50

2,370

15.75

0

CONSUMER SURPLUS = $101,410

COST

ESTIMATED
NUMBER OF VISITORS

166
TABLE A19
CONSUMER SURPLUS
CLEAR LAKE SITE BENEFIT ESTIMATION
COST

ESTIMATED
NUMBER OF VISITORS

COST

ESTIMATED
NUMBER OF VISITORS

$ .00

8,072

$ 8.25

140

.75

5,415

9.00

89

1.50

3,841

9.75

51

2.25

2,752

10.50

19

3.00

1,989

11.25

0

3.75

1,459

12.00

0

4.50

1,082

12.75

0

5.25

776

13.50

0

6.00

527

14.25

0

6.75

342

15.00

0

7.50

220

15.75

0

CONSUMER SURPLUS = $20,082

167
TABLE A20
CONSUMER SURPLUS
WIXON LAKE SITE BENEFIT ESTIMATION
COST

COST

ESTIMATED
NUMBER OF VISITORS

$ .00

23,451

$ 8.25

2,252

.75

20,050

9.00

1,388

1.50

14,784

9.75

1,241

2.25

12,998

10.50

966

3.00

11,220

11.25

142

3.75

9,961

12.00

49

4.50

8,658

12.75,

35

5.25

7,780

13.50

22

6.00

7,093

14.25

9

6.75

6,073

15.00

0

7.50

3,382

15.75

0

CONSUMER SURPLUS = $98,665

ESTIMATED
NUMBER OF VISITORS

168
Cl

TABLE A21
CONSUMER SURPLUS
BIG STAR LAKE ESTIMATED SITE BENEFITS
COST

ESTIMATED
NUMBER OF VISITORS

$ .00

18,268

$ 8.25

551

.75

15,743

9.00

363

1.50

12,904

9.75

293

2.25

9,800

10.50

237

3.00

6,905

11.25

188

3.75

5,015

12.00

35

4.50

3,494

12.75

0

5.25

1,834

13.50

0

6.00

1,416

14.25

0

6.75

1,067

15.00

0

7.50

732

15.75

0

CONSUMER SURPLUS = $59,134

COST

ESTIMATED
NUMBER OF VISITORS

169
TABLE

A22

CONSUMER SURPLUS

COST

ESTIMATED
NUMBER OF VISITORS

$ .00

18,035

$ 8.25

1,210

.75

13,174

9.00

758

1.50

11,083

9.75

567

2.25

9,507

10.50

95

3.00

8,238

11.25

44

3.75

7,210

12.00

20

4.50

6,230

12.75

0

5.25

5,516

13.50

0

c
n
•
o
o

WIGGINS LAKE SITE BENEFIT ESTIMATION

3,508

14.25

0

6.75

2,750

15.00

0

7.50

1,857

15.75

0

CONSUMER SURPLUS = $67,350

COST

ESTIMATED
NUMBER OF VISITORS

170
TABLE: A23
CONSUMER SURPLUS
BIG TWIN LAKE SITE BENEFIT :ESTIMATION
COST

ESTIMATED
NUMBER OF VISITORS

COST

ESTIMATED
NUMBER OF VISITORS

$ .00

13,802

$ 8.25

684

.75

10,797

9.00

415

1.50

8,440

9.75

293

2.25

6,571

10.50

171

3.00

5,142

11.25

134

3.75

3,957

12.00

110

4.50

3,066

12.75

73

5.25

2,394

13.50

49

6.00

1,857

14.25

24

6.75

1,441

15.00

0

7.50

1,063

15.75

0

CONSUMER SURPLUS = $45,363

APPENDIX F
LOWER PENINSULA LAKE SITES:
ESTIMATED VISITATIONS AND DOLLAR BENEFITS

171

172

APPENDIX F
ESTIMATED VISITATIONS AND CONSUMER SURPLUS
MICHIGAN LOWER PENINSULA LAKE PUBLIC ACCESS SITES
(Waterways Division Administered)
CONSUMER
SURPLUS

COUNTY

LAKE

(1975)
VISITS

Allegan

Big Lake
Duck Lake
Green
Selkirk
Pike
Miner
Swan
L. Sixteen
Sheffer

18,237
20,236
4,498
16,746
15,025
23,888
22,712
12,603
12,395

64,582
70,707
10,043
57,660
46,433
89,799
83,367
39,728
35,534

Alpena

Fletcher Pond

9,344

37,493

Antrim

Ellsworth
Clam
Intermediate
L. Bellaire
Intermediate
St. Clair
Green
L. of the Woods
Torch
Wilson
Elk
Birch

4,753
6,367
8,496
8,786
8,496
4,503
21,975
7,307
12,870
4,640
11,947
6,100

13,627
21,277
30,699
32,088
30,699
12,527
82,198
19,905
55,712
13,126
48,719
20,075

Barry

Middle
Jordan
Fine
Clear
Carter
Duncan
Long
Bristol
Leach
Thornapple

20,569
22,907
25,775
22,446
16,396
18,552
11,135
21,028
20,307
5,917

67,381
73,319
85,414
71,815
49,774
63,033
48,587
66,154
66,262
20,498

Benzie

Platte
Upper Herring
Brooks

11,715
7,823
2,658

44,186
26,078
6,414

173

APPENDIX F (Continued)

COUNTY

LAKE

Benzie
(Continued)

Turtle
Lower Herring
Davis
Stevens
Herendeene

Berrien

VISITS

CONSUMER
SURPLUS

5,307
7,500
3,224
5,502
5,281

14,199
24,558
8,272
14,963
14,095

Paw Paw-W
Paw Paw-E
Black

29,489
29,489
11,979

120,562
120,562
34,770

Branch

Randall
Coldwater
Marble
L. of the Woods
Gilead
Cary
L . George
Matteson
Lavine
Middle
Union
Craig

24,643
27,942
28,249
27,055
15,176
17,800
23,658
17,771
14,633
16,595
6,119
21,953

73,637
98,713
51,863
84,287
43,534
45,803
69,444
54,002
39,530
44,392
29,773
62,263

Calhoun

Nottawa
Goguac
Lanes
Duck
Warner
Upper Brace
Lee •
Prairie
Winnepeg

25,093
39,958
24,775
40,453
19,112
29,591
16,885
23,120
24,813

73,903
132,134
65,295
131,997
55,438
83,028
48,906
65,753
66,385

Cass

Fish
Magician
Paradise
Diamond
Hemlock
Donnell
Stone
Driskel1s
Juno

22,287
21,849
16,830
26,097
15,582
17,791
18,771
12,089
15,430

77,112
80,182
54,730
101,633
45,860
59,522
61,864
33,158
52,128

174

APPENDIX F (Continued)

COUNTY

LAKE

VISITS

CONSUMER
SURPLUS

Cass
(Continued)

Harwood
Corey
Long

15,509
20,832
17,270

48,268
75,577
56,903

Charlevoix

Susan
Six Mile
Dutchman Bay
Thumb
Ironton
Deer

7,208
7,383
14,874
6,747
13,390
7,661

21,554
23,340
64,176
21,212
56,723
24,618

Cheboygan

Mullett-N
Cochran
Munro
Silver
Long
Lancaster
Mullett-E

8,659
4,058
2,951
4,921
10,722
1,758
11,553

35,301
9,799
8,998
13,254
31,426
4,299
47,403

Clare

Long
Five
Cranberry
Windover
Crooked
Little Long
Perch
L . George
Nestor
Lilly

6,294
14,637
9,019
8,036
12,122
7,157
8,036
10,270
7,288
11,455

19,538
45,213
24,842
21,083
36,854
18,059
21,083
29,458
21,322
34,120

Clinton

Muskrat

12,539

36,822

Crawford

Horseshoe
Bluegill
Guthrie
Section One
Kyle
K P
Lake Margrethe

3,848
3,999
5,440
4,156
3,450
5,154
15,171

8,862
9,329
14,126
9,828
7,687
13,174
48,870

Emmet

Lake Paradise
Round

3,628
8,611

12,012
27,477

175

APPENDIX F (Continued)

COUNTY

LAKE

VISITS

CONSUMER
SURPLUS

Emmet
(Continued)

Pickerel
Crooked

8,905
10,144

30,698
37,865

Genesee

Lobdell
Lake Fenton

51,790
83,391

228,008
227,680

Gladwin

Pratts
Wiggins
Lake Four
Wixom

14,013
18,035
9,862
23,451

41,317
67,350
25,044
98,665

Grand Traverse

Fish
Silver
Ellis
Cedar
L . Skegemog
Fife
Bass
Green
Cedar Hedge
Bass

4,314
9,731
6,108
9,585
12,438
8,836
8,813
10,585
8,004
5,418

11,324
34,285
17,263
31,741
46,065
38,386
29,740
38,329
25,736
15,211

Hillsdale

Hemlock
Cub
Bear
Bird
Long Lake North
Round
Long Lake South

12.428
19,859
19,503
19,366
13,761
10,621
12.428

33.417
53,729
52,335
51,803
38,535
26,837
33.417

Ionia

Morrison
Long
Woodard

25,814
22,216
22,541

86,090
76,230
65,535

Iosco

Floyd
Cedar
Tawas
Londo

5,662
7,288
11,403
7,678

15,512
21,322
43,794
24,374

Isabella

Littlefield Lake

14,872

46,090

176

APPENDIX F (Continued)
CONSUMER
SURPLUS

COUNTY

LAKE

VISITS

Jackson

Center
Crispell

49,205
26,688

192,300
83,839

Kalamazoo

Barton
Sherman
Long
Eagle
LeFever
Paw Paw
Rupert
Austin

24,671
49,011
38,260
23,617
11,003
19,560
21,061
72,739

85,291
153,520
144,917
84,462
29,698
62,516
64,699
236,130

Kalkaska

Blue
Starvation
Bear
Cub
Indian
Big Twin
Cranberry

5,766
5,992
7,410
4,824
5,098
13,802
3,290

14,881
15,720
21,069
11,608
12,540
45,363
7,127

Kent

Murray
Campau
Bass
Camp
Big Pine
Campbell
Lincoln
Lime

22,556
20,322
16,400
19,284
20,890
15,676
19,769
10,926

81,062
71,108
52,661
65,919
73,210
50,140
67,052
33,440

Lake

Big Star
North
Harper
Switzer
Reed
Paradise

18,268
6,202
7,811
4,184
6,101
5,879

59,134
15,593
21,744
8,953
15,248
14,494

Lapeer

L. Nepessing

46,449

192,755

Leelanau

L . Leelanau-W
L. Leelanau-E
Cedar
L. Leelanau-S
L. Leelanau-N

6,721
12,700
8,100
6,721
6,721

27,043
53,484
29,628
27,044
27,043

177
APPENDIX F (Continued)

COUNTY

LAKE

Leelanau
(Continued)

Glen
Lime
Armstrong
School

Lenawee

VISITS

CONSUMER
SURPLUS

8,697
5,904
3,155
4,531

34,770
20,152
8,038
13,712

Sand
Allens
Devils

35,239
21,521
41,632

136,832
67,295
150,272

Livingston

L . Chemung
Crooked
Woodland
Whitmore

46,616
52,891
54,132
48,640

204,321
241,154
248,440
223,526

Manistee

Bear
Portage
Stronach

7,139
7,340
9,387

258,236
26,898
32,549

Mason

Gun
Ford
Hackert
Plinness

6,877
6,656
6,938
6,363

22,831
219,255
22,006
19,628

Mecosta

L. Mecosta
Chippewa
Pretty
Townline
Clear
Hillsview
Brochway
Jehnson
Lower Evans
Big Evans
Upper Evans
Winchester
Bergess

13,975
29,894
11,149
12,573
8,072
11,543
7,172
10,820
18,013
18,013
18,013
18,013
24,068

41,734
101,410
30,838
35,723
20,082
32,742
18,347
30,253
60,524
60,524
60,524
60,524
84,677

Missaukee

Sapphire

8,416

24,377

Montcalm

L . Montcalm
Crystal
Horseshoe

10,070
22,154
11,206

27,254
68,897
31,205

178
APPENDIX F (Continued)

COUNTY

LAKE

VISITS

CONSUMER
SURPLUS

Montcalm
(Continued)

Nevins
Dickerson
Clifford
Derby
Swan
Little Whitefish
Muskellunge
Half Moon
Tamarack
Rainbow
Cowden
Loon

12,227
17,704
16,725
14,769
9,515
16,780
15,636
12,012
15,469
16,445
15,464
11,410

36,218
58,096
54,002
46,050
28,114
52,909
48,037
35,383
46,798
51,470
47,330
33,052

Montgomery

Rush
Grass
Crooked
Avalon
Gaylanta
Sage Lake Flooding
Long
DeCheau
Crooked '

4,423
4,485
3,111
4,463
3,764
3,303
4,266
2,734
3,111

14,388
14,703
8,694
14,587
11,353
9,446
13,616
7,276
8,694

Newago

Brooks
Diamond
Pickerel
Hess
Bills
Englewright
Robinson

15,963
13,787
16,235
21,024
14,790
11,133
12,950

54,940
43,920
56,296
81,186
49,219
33,623
40,217

Oakland

Squaw
Lakeville
L. Orion
Oakland
Loon
Maceday
Crescent
Orchard
Union
Long
Wolverine

39,197
47,377
47,785
53,087
53,054
57,735
48,849
115,799
58,515
49,582
14,863

181,027
231,404
234,720
255,389
255,182
284,700
226,742
383,160
296,276
240,157
45,249

179
APPENDIX F (Continued)
CONSUMER
SURPLUS

COUNTY

LAKE

VISITS

Oakland
(Continued)

Cedar Island
White
Pontiac Lake N
Big Lake
Tipsico

48,974
51,351
61,352
47,901
49,994

236,404
249,318
307,862
219,153
226,179

Oceana

Crystal
McLaren

8,145
12,418

23,000
42,154

Ogemaw

Clear
Hardwood
Sage
Horseshoe
George
Bush
Tee
L. George
Peach
Au Sable
Rifle
Long

6,664
7,358
10,214
4,581
7,354
4,881
7,147
6,808
9,099
7,484.
6,780
6,287

19,985
22,592
35,826
11,770
20,174
12,410
19,434
20,558
26,988
23,320
20,446
17,891

Osceola

Hicks
McCoy
Wells
Todd
Diamond

8,286
3,744
5,080
5,684
5,403

22,172
8,844
13,311
15,311
14,395

Oscoda

Tea

4,005

12,398

Otsego

Dixon
Big
Brandford
L. Manuka
Heart
Opal
Big Bass
L. Twenty Seven
Emerald
West Twin

7,195
7,968
7,546
6,884
5,616
6,448
5,708
7,886
5,372
5,996

17,855
20,600
20,129
17,732
13,407
16,217
13,707
20,297
12,619
21,386

Ottawa

Petty's Bayou

3 3,814

143,292

180
APPENDIX F (Continued)

COUNTY

LAKE

VISITS

CONSUMER
SURPLUS

Presque Isle

Lost
Long
L. Emma
L. Nettie
Little Tomahawk
Grand
L. Ferdelman
Bear Den
Lake May

3,567
23,132
3,921
4,242
3,072
11,241
2,925
3,266
3,832

11,129
72,927
12,970
14,445
7,531
49,236
8,187
8,196
12,488

Roscommon

L. St. Helen
Houghton Lake-W
Houghton Lake-E
Higgins Lake-W

114,129
25,492
25,492
33,110

202,290
97,848
97,848
125,860

St. Joseph

Pleasant
Klinger
Fishers
Clear
Fish
Thompson
Palmer
Long
Noah
Lee
Sturgeon

27,419
23,598
28,446
26,978
19,889
17,370
26,855
19,147
19,288
12,039
23,743

90,159
84,163
95,322
87,981
60,066
49,914
89,865
58,802
53,967
30,496
75,587

Van Buren

Clear
Round
Gravel
Saddle
Cedar
Brandywine
Van Auken
Three Mile
Huzzy
L. Cora
Wolf
L. Eleven
Fish
Scott
Rush
Hall

16,915
18,708
23,567
23,862
23,138
17,383
21,674
24,067
18,015
23,634
18,411
16,116
17,096
18,770
21,512
15,786

53,804
63,729
83,844
86,330
81,623
55,850
77,964
83,684
56,978
84,442
60,325
59,335
54,590
61,924
73,563
46,701

181

APPENDIX F (Continued)
CONSUMER
SURPLUS

COUNTY

LAKE

VISITS

Van Buren
(Continued)

Lake of the Woods
Shafer
Eagle
Reynolds
School Section
L. Fourteen
Three Legged
Jeptha
Bankson

22,917
20,062
21,483
20,374
19,844
16,915
13,763
15,967
21,866

80,488
66,376
73,350
67,898
63,559
53,804
40,508
49,701
75,223

Wexford

Berry

7,927

21,126