~I~ ~zAugust 1973
A Measure of Outdoor
Recreational Usage
Food and Resource Economics Department
Agricultural Experiment Stations
Institute of Food and Agricultural Sciences
University of Florida, Gainesville 32601
Kenneth C. Gibbs
= ~" I I I _r ___ I I _I L II
II. I I I I I~rl
Econ. Report 52
TABLE OF CONTENTS
Page
LIST OF TABLES . . . . . . .
LIST OF FIGURES . . . . .
iii
INTRODUCTION . . .
The Kissimmee River Basin .
Objectives of Study . .
Plan of This Report .
. *. ......
MODEL DEVELOPMENT . ...... . ... ..
Theoretical Model . .
Definition of Variables .
Number of Visitors (Y) .
Water Level (WL) .
Temperature (T) .
Rainfall (R) .
Wind Velocity (WV)
Dummy Variables (Di)
Overflight Counts (X)
ANALYSIS AND SOURCE OF DATA .
Traffic Counts .
Overflight Counts .
Water Level .. .
Rainfall ...
Temperature .
Wind Velocity .
. . ....... .
e
r
r
STATISTICAL MODELS AND EMPIRICAL RESULTS .
Specification of Variables . .
Lake Tohopekaliga . . .
All Lake Model . .
Overflight Predictive Model . .
Estimating Total Recreational Usage and
Kissimmee River Basin . .
. ....... 34
. . . 33
. . . 34
. . 36
. . 38
Value of the
. . 38
SUMMARY AND CONCLUSIONS . . . .
Summary . . . .
Conclusions . . . .
Limitations of Study . . .
* S S S.
r
TABLE OF CONTENTS  Continued
Page
APPENDIX . ............ ..... ... 45
REFERENCES . ..................... 50
LIST OF TABLES
Table Page
1 Lakes and respective access points used in the sample
study, the Kissimmee River Basin, 1970 ........ 14
2 Estimated number of visits at each sampled access point,
by time period, from February 1, 1970 through
June 19, 1971 in the Kissimmee River Basin .. 19
3 Total number of vehicles observed, total number of
counts, and total number of people recorded at counters
located at boat ramps (Group I) and fish camps (Group
II) in the sampled area, during the period of
January 31, 1970 to January 31, 1971 in the Kissimmee
River Basin. ..... . . . . 22
4 Determining the correction factors; number of cars per
number of counts and average number of people per car,
during the period of January 31, 1970 to
January 31, 1971 in the Kissimmee River Basin 22
5 Derived estimate of the number of visitors at each
sampled access point, in the Kissimmee River Basin,
during the period of March 1, 1970 through
March 14, 1970 . .... . . 23
6 Summary of all weighted average daily overflight
counts, by time period, for the sampled area in the
Kissimmee River Basin between January 31, 1970 and
January 31, 1971 . .. . ... 25
7 Overflight observations, on Lakes Gentry, Tohopekaliga,
and Miarian, in the Kissimmee River Basin for the time
period November 14, 1970 through December 12, 1970 26
8 Weighted average overflight counts for the time period
November 14, 1970 through December 12, 1970 of the
three sampled lakes in the Kissimmee River Basin . 27
9 Estimating the percent of recreationists utilizing the
three lakes by the use of overflight counts (Season III)
in the Kissimmee River Basin, 1970 . .... 40
LIST OF TABLES  Continued
Table Page
10 Seasonal estimates of the total number of visitors to
the Kissimmee River Basin, in 1970  based on the
estimated percentage value derived by the use of
overflight counts. . . . . 41
LIST OF FIGURES
Figure Page
1 The Kissimmee River Basin . ... . .. 3
2 Hypothesized relationship of water level to
recreationvisits, Kissimmee River Basin . . 28
3 The upper portion of the Kissimmee River Basin .. 30
A MEASURE OF OUTDOOR RECREATIONAL USAGE*
Kenneth C. Gibbs
INTRODUCTION
There exists today a need for outdoor recreation. The nature of
emotional stresses of modern life is emphasized by most experts. It is
the consensus of many that modern life is more ordered in terms of time
than was life in earlier periods [14]. It is noted that most of the
working man's leisure time in the United States today occurs at one of
three basic times: on workdays before and after work; on weekends; and
on vacations. The primary factors that restrict the individual in his
ability to take leisure whenever he wishes are the demands of his job
and the necessity of earning a living [14].
Many sociologists, psychologists, and outdoor recreation specialists
have emphasized the psychological and emotional strains which modern
urban living place upon theindividual. It is this fact that has come
This research was.conducted in cooperation with the Central and
Southern Florida Flood Control District under a grant from the Office
of Water Resources Research, U. S. Department of the Interior. The
grant was administered by the Florida Water Resources Research Center.
Kenneth C. Gibbs is assistant professor of food and resource
economics and environmental engineering at the University of Florida.
to light recently, making recreational opportunity an increasingly
important factor in water resource planning [14].
Recreation can be interpreted as an activity,voluntarily undertaken,
that is not pursuant to an occupation. It contrasts with work, which is
done primarily to earn money or otherwise to provide the necessities of
life. It also contrasts with such things as eating, housekeeping,
sleeping, and personal care [5].
In the past, recreation was taken for granted and was entirely
incidental to reservoir building, for example. Many recreational devel
opments came about more through accident than design; recreational use
was not considered a major factor. Today government water projects
attract many million visitordays of use a year. They arp bordered with
camps, parks, swimming areas, marinas, docks, launching ramps, and many
other public facilities. The agencies involved in water resources
planning have come to accept recreational use as a major factor to be
considered in water resource development projects, but the process of
determining the economic value of such use has been the subject of much
controversy [5]. The evaluation of benefits from recreation has recently
been given attention by economic researchers. It was the intent of this
study to apply a methodology to calculate the recreational use of the
Kissimmee River Basin in central Florida (see Figure 1).
The Kissimmee River Basin
The Kissimmee River Basin, as defined here, is comprised of the
following lakes, canals, and rivers;
1. Lake Mary Jane
2. Lake Hart
3. Lake Myrtle.
TITUSVILLE
COCOA
EAU GALLIE I
AlEL3BOUR
velO BE4CH
AVON PARK
SCALE
(I& 0t. 2.0
IoIII t
Figure.l.The Kissimmee River Basin, Florida
4. Lake Preston
5. East Lake Tohopekaliga
6. Lake Joel
7. (Lost) Coon Lake
8. Trout Lake
9. Lake Lizzie
10. Alligator Lake
11. Brick Lake
12. Lake Gentry
13. Canal between S63 and S63A
14. Lake Tohopekaliga
15. Cypress Lake
16. Dead River
17. Lake Hatchineka
18. Lake Kissimmee
19. Lake Rosalie
20. Tiger Lake
21. Lake Jackson
22. Lake Marian
23. Kissimmee River
24. Canal between S65E and Lake Istokpoga
25. Lake Istokpoga
Its total area encompasses over 10 million acres. Within this
region lies a complex system of waterways, locks, and levees, under the
supervision of the Central and Southern Florida Flood Control District
(FCD) [4].
The water of the river basin is mainly used for three different
purposes. First, many types of recreation activities occur on, in, or
near the river basin. Some are essentially shore activities 
picnicking, camping, swimming; others are conducted off shore,, such as
fishing, water skiing, and power boating. Throughout the river basin
one can find concession buildings, marinas, boat ramps, picnic grounds,
camping areas, nature trails, and boat docks. All of these facilities
contribute toward the accessibility of water to recreationists.
Second, water in the river basin is used for irrigation purposes.
Irrigation is essential even though the total amount of water from rain
fall is sufficient; the seasonal pattern of precipitation does not match
the plantgrowth requirements. In the agricultural regions within the
basin irrigation serves as insurance against the loss of crops caused
by the variability of precipitation.
And third, the Kissimmee River Basin supplies water for municipal
use. Some cities in the river basin utilize water for residential and
commercial consumption.
Objectives of Study
While it would be desirable to establish working policies that
would efficiently allocate the water supply in the Kissimmee River Basin,
it was beyond the scope of this study. Instead, this report concentrates
on the total and seasonal use and certain economic aspects of outdoor
recreation in the Kissimmee River Basin.
More specifically, the objectives of this study were: (1) to apply
a methodology to estimate the number of people who use the Kissimmee
River Basin for recreational purposes and (2) to estimate the relation
ship between recreational use and certain climatological variables,
including water level, in the Kissimmee River Basin during 1970.
Total recreational usage, in.a given time period, can be defined
as the product of the number of days a recreationist uses a recreational
site per visit and the number of visits to a recreational site, per
time period:
total
da visit total visitor days re atonal
visit time period. time period usage
1Recreation "visitordays" and recreation "visits" are defined by
the U.S. Forest Service as follows: (1) "A recreation visitorday
consists of 12 visitorhours, which may be aggregated continuously,
The analysis of calculating the days/visit will be excluded from this
study, since it is the topic of another study.
The remaining factor is to determine the number of visits in each
of the three seasons. It should be noted that it was necessary to
partition the year into seasons because it was perceived that a better
estimate of the number of visits to the river basin could be obtained.
During the year, the demand for water varies among its users. Thus, the
year was divided into three seasons based on (1) seasonal trends of
tourism in the state; (2) irrigational use of water; and (3) amount of
rainfall. The seasons are defined as:
Season I Season II Season III
February June October
March July November
April August December
May September January
Data gathered from traffic counters and from overflights of the
Kissimmee River Basin were utilized to establish the total number of
visits per season and for the year. Other variables, such as water
level, temperature, wind velocity, and rainfall, were obtained in order
to determine their relationship to visitations. This relationship is
desirable in order to predict visitations more accurately.
intermittently, or simultaneously by one or more persons. The visitor
hours contained therein must be spent by persons in any activities,
except those which are a part of, or incidental to, the pursuit of a
gainful occupation;" (2) "A recreation visit is the entry of any person
upon a site, or area of land or water, generally recognized as an
element in the recreation population. Visits must be made in order to
engage in any activities, except those which are a part of, or
incidental to, the pursuit of a gainful occupation [13].
Plan of This Report
As a means of organizing ideas and facts concerning the objectives
of this study, the report has been divided into six sections. Sections
II through VI will now be identified and discussed.
Section II includes a discussion of the theoretical predictive
model for total recreational use of the river basin with special
emphasis on water level. Each explanatory variable and its influence
on recreational use in the Kissimmee River Basin is discussed.
Section III is the largest single part in the study, and in some
respects, the most important. In most cases, the difficulties of
projecting total recreational use lie not in the lack of a suitable
methodology, but in the reliability of the data. In this section, the
discussion is limited to describing how the data was obtained and what
refinements were applied in order to adapt it to the study.
Section IV is designed to review the results of the empirical
analysis. The empirical models and the degree of accuracy obtained
from each model are discussed.
Section V is largely concerned with estimating the total number of
visitors at the Kissimmee 'River Basin in 1970. In Section VI the
results are summarized and the major conclusions and limitations
presented.
MODEL DEVELOPMENT
In this section a conceptual framework for estimating recreational
use in the Kissimmee River Basin is discussed. Certain physical vari
ables will be explained along with their hypothesized influence on
recreational use.
Theoretical Model
It was the intent of this study to estimate the number of recrea
tionists that utilized the Kissimmee River Basin during 1970. It is
desirable to establish a model to predict this value under various
conditions.
Once the coefficients of the model have been estimated, the
calculated function describes how the changes in the independent vari
ables (physical variables) affect the dependent variable (recreational
use) within the existing assumptions of the model. Using the function,
one can predict the effect on recreational use of changes in the physical
variables. Given the specified functional relationship, one can estimate
what would or will be the results if certain changes occur. Yet,
although one is dealing with future results, the answers obtained are
based upon the assumption that the existing physical relationships will
not change. The accuracy of the predictions depends upon the accuracy
of the model and the accuracy and adequacy of the observations used in
estimating the coefficients.
A necessary consideration when one is estimating recreational use
by means of regression analysis ii the selection of the variables.
Generally, due to the large number of factors affecting recreational
use, it is impossible to include, in a computationally feasible model,
all elements which affect recreational use. Therefore, the physical
variables chosen were based on accessibility of usable data and
characterized with a considerable degree of influence on recreational
use of the Kissimmee River Basin.
A multiple linear regression model was used to explain the
relationship between the number of recreational visits to the Kissimmee
River Basin and certain physical variables. The model is defined as
follows:
Y = ao + aiWL + a2WV + a3R + a4T + a.sDI + agD2 + E
where:
Y = number of visitors during a..set time period
WL = water level
WV = wind velocity
R = rainfall
T = temperature
D1 = season II
D2 = season III
a = regression coefficients
E = error term
Once the least squares estimators, ai, have been .determined, it is
possible to observe the influence of the independent variables on the
number of recreationists using the lakes.
It is also of great interest to estimate a relationship between the
total number of people visiting the area (Y) and the actual number of
people using the water for.recreational purposes (X). It is hypo
thesized that the variables are positively correlated. The derived
equation can be used to predict the total number of recreationists
during a set time period based upon the number of visitors observed
recreating during one instant in time. The. equation hypothesized in
this study is the following:
Y = Bo + BIX + E
where:
Y = number of visitors during a particular time period
X = number of recreationists as observed by an overflight2
B = regression coefficients
E = error term
Note that the observations recorded for each variable in the models were
accumulated in two week intervals.
Definition of Variables
The following sections briefly discuss each variable considered
in the theoretical models. The alternate way of.measuring certain
variables will be described and analyzed subsequently.
Number of Visitors (Y)
The dependent variable (Y) is the estimated number of people using
the lake facilities for recreational purposes. The use of a lake
facility includes such activities as launching a boat from a ramp, water
skiing, swimming, hiking, fishing, or just "passing the time. of day"
along the shoreline. Therefore, the variable Y is an estimate of the
total number of visitors that attend the lake or its immediate
surroundings to participate in a type of recreational activity, during.
a set time period.
2An overflight consisted. of using a small airplane whereby the
number of people recreating on each lake during an instant in time was
observed. These data werecollected for each lake in the river basin at
different times of the day on selected days over a period of a year.
It should be noted that a visit to the river basin was defined as
any entry of a person upon a recreational site in order to participate
in any activity other than those associated with the pursuit of a
gainful occupation. If, for example, a group of three recreationists
arrived at a site in one car, this would be recorded as three visits
regardless of how many days they stayed at the site. Thus, a visitor
can be counted one or more times during the year depending on how often
he visits the river basin throughout the year.
Water Level (WL)
The main physical variable of interest is WL, the water level of a
particular lake. It is assumed that recreationists react to variations
in the water level. This is important due to the fact that,if the water
level of a lake were too low or too high, the recreationist may be
unable to launch a boat from a ramp, for example. It is of great
interest to the Flood Control District to recognize how recreation
activity varies with different water levels. If these levels are deter
mined, then the FCD can allocate water among alternative users to
optimize the benefits from the use of water in the Kissimmee River Basin.
It is hypothesized that,as'the water level drops, the recreational
usage will decrease [8].
Temperature (T)
It should be noted that recreational use is affected by weather.
Most recreationists become disinterested in recreating whenever the
temperature increases to the point where it is uncomfortable. Therefore,
it is hypothesized that,as the maximum daily temperature increases, the
number of visits to the river basin will decrease.
Rainfall (R)
In most cases rainfall has a major bearing on outdoor recrea
tional activities. As rainfall increases, outdoor activities along
the shoreline and on the lake are dampened with only a few "enthu
siastic" outdoor recreationists engaging in activities. Such
recreational activities.as picnicking and sports are usually dis
rupted by thunderstorms. Thus, it is hypothesized that,as the amount
of rainfall increases, the number of visits to the Kissimnee River
Basin will decrease.
Wind Velocity (WV)
It is assumed that,as WV increases beyond a certain point, it
imposes a great restiction on the maneuvrability of sail boats and
fishing lines, and generally makes recreation less desirable. Therefore,
it was hypothesized that,as wind velocity increases, the number of
visits to the river basin will decrease. Note that it is believed that
all the physical variables have an inverse relationship with the number
of visits to the river basin.
Dummy Variables (Di)
In an effort to quantify the adjustments in recreational use during
different seasons, a zeroone dummy variable was included in the
analysis. Note that in this case Di has a value of "1" during season II;
at all other times its value is "0.'! Also, D2 has a value of "1" during
season III, and at all other times it has a value of "0." Season I has
no dummy variable; its value of "I" or "0" is implied in the regression
equation.
Overflight Counts (X)
This variable is representative of the number of people using the
lake facilities for water recreation at an instant in time rather than
during a particular time period. Since the overflight data were used to
estimate the number of recreationists actually participating in activi
ties on the water, it was hypothesized that this could be used as an
explanatory variable. It is also assumed that the overflight counts
should vary directly with Y. Therefore, the more people using the water
for recreation, the more people there will be visiting the facilities
adjacent to the water.
In the next section a detailed discussion of the methods utilized
to collect the desired data will be presented. The sampled area and
data collection points will also be defined.
ANALYSIS AND SOURCE OF DATA
The problem and objectives outlined in the first section suggested
that a method be developed to estimate total recreational usage of the
Kissimmee River Basin. In addition, the relationship between total
recreational usage of the river basin and other physical variables was
derived. A regression equation was used to accomplish this. Data
concerning each variablewere gathered during specific time periods
consisting mostly of two week intervals.
It was necessary to limit the collectionof data pertaining to
both the dependent variable Y and the independent variables (WL, T,
WV, R) to an appropriate representative segment (sampled area) of the
Kissimmee River Basin. It was possible to obtain the overflight counts
(X) for the entire river basin, but only the overflight data concerning
the three sampled lakes were utilized in the regression results.
It was decided that an adequate representative sector of the river
basin consisted of three distinct lakes (see Table 1). The degree of
usage and size of the lake was used as the criteria for distinguishing
the sampled lakes. Lake Gentry is representative of small lakes, Lake
Marian medium sized lakes, and Lake Tohopekaliga large lakes.
Table 1,Lakes and respective access points used in the sample study,
the Kissimmee River Basin, 1970
Lake Access points
Gentry 1. County boat ramp
Tohopekaliga 2. Two lane boat ramp
3. Three lane boat ramp
4. Fish camp I
5. Fish camp II
6. County boat ramp
Marian 7. Fish camp III
8. Fish camp IV
Note that the regression model estimates the total recreational
usage of the sampled lakes. In order to establish the total recreational
usage of the entire river basin, the estimates derived for the individual
lakes must be projected for the entire river basin. The procedure to
determine total recreational usage of the Kissimmee River Basin will be
discussed. Prior to this a discussion is presented to provide a de
tailed analysis of how the desired data were gathered and what kind
of refinements, if any, were necessary.
Traffic Counts
The method chosen to calculate the number of people utilizing the
facilities of a sampled lake (Y) was to conduct a traffic count survey.
The survey consisted of data gathered by eight mechanical traffic
counters at selected locations. A traffic counter was placed at all
public access points on each of the three lakes. The access points were
located at organized fish camps, county boat ramps with parking facili
ties, and at two and three lane boat ramps with no parking facilities.
The data obtained directly from the traffic counters had to be
adjusted to obtain an estimate of the number of people using the
area rather than just the number of axles that tripped the meter.
The analysis of adjusting the traffic count data was carried out in
three basic steps. First, a correction factor was determined to account
for the fact that cars are counted at least twice  once while
launching boats and once removing boats. In the cases'of the two and
three lane boat ramps, the cars were counted four times. This is due to
the fact that these ramps do not provide parking facilities within the
metered area. Thus, after the recreationist launches his boat, he must
cross the meter another time in order to park his car. When returning
to load his boat, he will again contribute two more crossings. In the
other six locations, parking is available within the metered area.
Therefore, the counts recorded at the two and three lane boat ramps with
no parking facilities were divided by four and the.counts at the
remaining six locations by two.
The second step was to calculate a correction factor to account for
trailers which contribute extra counts on the traffic meter. If these
weren't adjusted, the estimate would be biased upward. This correction
can be performed by determining the following ratio:
number of cars
Correction factor number of c
number of counts
Since the number of counts, number of cars, and number of trailers were
estimated from the sample study, the correction factor was computed
with the existing data.
The third step was to determine the average number of people per
car. This factor was established from a sample of cars chosen and the
number of people per car counted.
It should be noted that,in one location (Fish Camp I), it was
found that a portion of the people were permanent residents or lived in
rental units for a considerable length of time. Inclusion of these
people in the estimated counts would bias the estimate upward due to
their frequent trips out of the metered area. Thus, the estimate for
this traffic counter was reduced by observing the percentage of people
crossing the meter that actually utilized the recreational facilities.
This was calculated by recording the number of people entering the area
for recreational purposes versus the total number of people visiting
this location. It was estimated that only 42.5 percent of the counts
recorded on the meter were applicable to outdoor recreationists at this
area, Thus, the counts were multiplied by .425.
Once all correction factors were determined, the readings on the
traffic meter were multiplied by each factor. This operation pro
vides an estimate of the number of people using a specific access
point during a given time period.
In equation form, the analysis can best be described as follows.
The equation for the two and three lane boat ramps is:
number of
counts
estimated no. registered number average no.
of people by meter of cars of people
time period time period 4 number car
of counts
Step I Step II
Step III
The equation f(
estimated no.
of .people
time period
The equation fe
or Fish Camp I is:
number of
counts
registered
by meter 1
time period 2
Step I
or all other entrances is:
number of
counts
number
of cars
number
of counts
Step II
average no.
Sof people
car
Step III Step IV
estimated no. registered number average no.
of people = by meter of cars of people
time period time period 2 number car
of counts
Step I Step II Step III
Finally, to estimate the total number of people using an entire
lake.for a particular time period, the adjusted counts for each meter
at that lake were summed.
The adjusted values of visitation at each access point, for all
time periods, are shown in Table 2. The derivation of the values in
[.425]
Table 2 can be exemplified by applying the above equations to the
traffic survey data taken only during the period of March 1, 1970, to
March 14, 1970. This is done merely to illustrate the procedure.
First, as shown in Table 3, the total number of vehicles observed, total
number of counts contributed by the vehicles, and the total number of
people are recorded. It is necessary to calculate these values since
they are utilized to derive the desired correction factors. It should
be noted that,due to the lack of data, it was assumed that the locations
could be combined into two groups. The two lane, three lane, and county
boat ramps comprised group I, and the other four entrances constituted
group II. In Table 4 the number of cars per number of counts and the
average number of people per car, for each access point, are shown.
Table 5 derives a numerical estimate of the number of visits at each
sampled access point during the desired time period. The total number
of people visiting Lake Gentry during the period March 1, 1970 to
March 14, 1970 was 1293.0; Lake Tohopekaliga attracted 4562.0 (this
value is the sum of counters 2 through 6), and Lake Marian had 3638.0
visitors. The values for the remaining time periods listed in Table 2
can be derived similarly. For all values of total traffic counts
recorded for each time period at each sampled access point see Appendix
Table A1.
Overflight Counts
The method for determining the number of visitors actually utilizing
the water in the river basin for recreation was to employ overflight
counts of the lakes during a particular time of day. There were a total
of 48 flights during a period of one year. These values will be referred
Table 2.Estimated number of visits at each sampled access point, by time period, from February 1, 1970
through June 19, 1971 in the Kissimmee River Basin
Time period  1970
Access point 131 207 214 301 314 327 411 425 519 531 614 629 713 727
to to to to to to to to to to to to to to
207 214 301 314 327 411 425 519 531 614 629 713 727 811
1. Lake Gentry 336 898 2272 1293 696 1212 1816 1916 1050 2118a 1324 1402 1560a 1176a
(county boat ramp)
2. bake Tohopekaliga 86. 310 836a 358 976 566a 636 701 289 408 183 112 117 93
(two lane boat ramp)
3. Lake Tohopekaliga 526 63 1292 947 1002 934a 982 945a 550 773 777 724 662 635a
(three lane boat ramp)
4. Lake Tohopekaliga 107 231 537 332 83 290a 419 294a 142 103 292 184 261 205
(Fish Camp I)
5. Lake Tohopekaliga 371 447 1433 1008 1246 1252 1182 1345 829 1087 830 912 1141a 452
(Fish Camp II)
6. Lake Tohopekaliga 340 680 2216 1620 1132a 892 798 552 368 676 94 453 328 312
(county boat ramp)
7. Lake Marian 271 452 1697 3046 1229 815 744 823 381 826a 212 639 609a 460a
(FishCamp III)
8. Lake Marian 186 222 473 591 720 702 206 81 64 444a 339a 278 220 246a
(Fish Camp IV)
Continued
Table 2.Estimated number of visits at each sampled access point, by time period, from February 1, 1970
through June 19, 1.971 in the Kissimmee River BasinContinued
Time period  1970 1971
811 824 906 919 930 1014 1031 1114 1203 1212 1226 108 123
to to to to to to to to to to to to to
824 906 919 930 1014 1031 1114 1203 1212 1226 108 123 131
1. Lake Gentry 1530 760 1824 1012 1686 1042 224 400 320a 404 356 424 234
(county boat ramp)
two2. Lake Tohopekaram 284a 162 273a 266a 338a 1052 184 402 76 222 251a 111 81
(two lane boat ramp
3. Lthreake Tohopekaiga 602 559 537 449 632 916 754 1193 524 828 845 897 589
(three lane boat ramp)
4. Lake Tohopekaliga 202 262 293a 295a 246 290 195 262 114 146 141 183 101
(Fis.h Camp I)
5. Lake Tohopekaliga 1369 834 1366 1302a 1022 1286 1328 1754 609 1032 805 731 743
(Fish Camp II)
6. Lake Tohopekaliga 342 304 254 396 504 590 478 928 440 534 656 840 748
(county boat ramp)
7. Lake Marian 780 448 534 452 701 590 638 887 398 441 617 507 797
(Fish Camp III)
8. Lake Marian 181 234 1320 339 398 529 572 874 351 600 565 516 370
(Fish Camp IV)
Continued
Table 2.Estimated number of visits at each sampled access point, by time period, from February 1,
through June 19, 1971 in the Kissimmee River BasinContinued
1970
Time period  1971
Access point 131 214 227 313 331 419 430 522 529
to to to to to to to to to
214 227 313 331 419 430 522 529 519
1. Lake Gentryb
(county boat ramp)
2. Lake Tohopekaliga 168 100 81 2 9 12 12 13 23
(two lane boat ramp)
3. Lake Tohopekaliga 83 82 87 55 50 53 25 47
(three lane boat ramp) 281 83 82 87 55 50 53 25 47
4. Lake Tohopekaliga 1123 1381 2177 203 330 135 10 81 102
(Fish Camp I)
5. Lake Tohopekaliga 1020 1273 1273 2168. 1374 1061 434 250 807
(Fish Camp II)
6. Lake Tohopekaliga 188 1000 52 45 28 17 304 64 209
(county boat ramp)
7.. Lake Marian
(Fish Camp III)
8. Lake Marian
(Fish Camp IV)
9. Lake Tohopekaliga
(South Lake Tohopekaliga) 199 314 322 102 157 104 87 42 85
See page 24 for footnotes.
Table 3.Total number of vehicles observed, total .number of counts, and
total number of people recorded at counters located at boat
ramps (Group I) and fish camps (Group II) in the sampled area,
during the period of January 31, 1970 to January 31, 1971 in
the Kissimmee River Basin
p Total number Total number Total number
Group of vehicles of counts of people
I 47 109.5 88
II 53 81.5 102
Table 4.Determining the correction factors; number of cars per number
of counts and average number of people per car, during the
period of January 31, 1970 to January 31, 1971 in the
Kissimmee River Basin
Acces pin Number of cars Average number
Access pointer number of people
of counts per car
1. Lake Gentry 47 429 88 .
(county boat ramp) 109.5 47
2. Lake Tohopekaliga 47 429 88
(two lane boat ramp) 109.5 47
3. Lake Tohopekaliga 47 429 88 8
(three lane boat ramp) 109.5 47
4. Lake Tohopekaliga 53 50 102
(Fish Camp I) 81.5 53
5. Lake Tohopekaliga 53 .650 102
(Fish Camp II) 81.5 53
6. Lake Tohopekaliga 47 4 88
(county boat ramp) 109.5 47
7. Lake MIarian 53 650 02 1.92
(Fish Camp III) 81.5 53
8. Lake Marian 53 102 1
(Fish Camp IV) 81.5 .650 53
Table 5.Derived estimate of the number of visitors
Basin, during the period of March 1, 1970
at each sampled access
through March 14, 1970
point, in the Kissimmee River
Total Correction Correction factor: Correction Estimated
traffic factor: to to account for factor: to number
counts account for trailers determine of
Access point recorded duplicate contributing number of people
for counts due to extra counts people per car for
time no parking. number of cars number of people time
period facilities number of counts number of cars period
1. Lake Gentry 3224 .50 .429 1.87 1293.0
(county boat ramp)
2. Lake Tohopekaliga 1790 .25 .429 1.87 358.3
(two lane boat ramp)
3. Lake Tohopekaliga 4723 .25 .429 1.87 947.2
(three lane boat ramp)
4. Lake Tohopekaliga 1523 .50 .650 (1.92) (425)a 332.2
(Fish Camp I)
5. Lake Tohopekaliga 1616 .50 .650 1.92 1008.3
(Fish Camp II)
6. Lake Tohopekaliga 4039 .50 .429 1.87 1620.0
(county boat ramp)
7. Lake Marian 4882 .50 .650 1.92 3046.5
(Fish Camp III)
8. Lake Marian 948 .50 .650 1.92 591.5
(Fish Camp IV)
aAdditional correction factor accounts for
(at Fish Camp I) to the traffic survey.
permanent and rental residents contributing extra counts
24
to as "overflight counts." While the overflight data were available for
all lakes in the river basin, only the three sampled lakes were utilized
in this portion of the study. The raw data actually consisted of a count
of the number.of people sighted on boats and shorelines of each lake in
the basin. In order to sum the individual overflights to obtain an esti
mate of the use over a period of time, the data were adjusted. The
adjustment was necessary since there are more weekdays than weekend days
or holidays in a given time period. This adjustment was not applied to
the traffic count data since it was collected over several weeks rather
than at one point in time.
The general procedure for adjusting the overflight data was charac
terized as follows. The overflight data during a particular time period
were divided into weekday and weekend counts. Then each set was summed
and the daily average was computed. Finally, both sets of counts were
added together to derive a weighted average of the total visitors per
day for that time period. The counts were weighted, depending on how
many weekdays or weekend days and holidays there were in that time period.
This analysis was carried out individually for each lake. Note that
some of the time periods used in calculating the daily average overflight
counts were different than the time periods used in the traffic survey.
This was due to the lack of sufficient overflight data during the desig
nated time periods in the traffic study. For a complete listing
concerning the adjusted overflight data for all time.periods, see Table 6.
alt was necessary to estimate the number of visits by the use of a
seasonal weighted average due to lack of data during that time period.
No data were collected from Lakes Gentry and Marian during the
time periods of 131 to 619 for 1971.
~*1
Table 6.Summary of all weighted average daily overflight counts, by
time period, for the sampled area in the Kissimmee River
Basin between January 31, 1970 and January 31, 1971
Time period
Gentry
13170
30170
32770
42570
53170
62970
72770
82470
93070
101470
111470
121270
30170
32770
42570
53170
62970
72770
82470
93070
101470
111470
121270
13170
3.90
1.65
1.29
0.92
2.10
0.00
0.00
1.90
1.57
12.05
11.20
3.41
Lakes
Tohopekaliga
150.90
101.70
96.60
105.20
171.70
139.80
83.90
65.50
65.00
120.9
98.3
84.7
The procedure used to establish the adjusted overflight data can
be illustrated by considering the time period November 14, 1.970 to
December 12, 1970, as an example. During that time period five over
flight observations for each lake were made, three of them on a weekday
and two on a weekend. This is summarized in Table 7. For all overflight
observations by time period, see Appendix Table A2.
In order to utilize these data, an adjustment must be made for the
fact that out of the five observations, two were weekend values and
Marian
52.00
33.90
18.80
15.07
6.82
17.56
10.85
18.06
6.71
6.31
11.28
33.40

Table 7.Overflight observations, on Lakes Gentry, Tohopekaliga, and
Marian, in the Kissimmee River Basin for the time period
November 14, 1970 through December 12, 1970
No. of people No. of people No. of people
Date of observed on observed on observed on
observation Lake Gentry Lake Tohopekaliga Lake Marian
Weekday Weekend Weekday Weekend Weekday Weekend
111570 18 162 8
112170 11 245 20
112570 3 12 4
120270 16 83 12
121170 10 50 14
three were weekday observations. For example, Lake Marian's weekend
observations consisted of 28 counts and the weekday total was 30. Since
two weekend values were observed during that time period, the average
count for the weekend was 14 visitors per day.. There were three observa
tions taken during the weekdays between November 14, 1970 and
December 12, 1970; therefore, the average overflight count for the
weekdays was 10 visitors per day. Next, both averages must be weighted
to account for the fact that there are more weekdays than weekends and
holidays in the time period. Thus, since 19 out of the 28 days in that
time period were weekdays, the average weekday value was multiplied by
19/28. The weighted average weekday value was (19/28) (10) or 6.78
visitors per day. The average weekend .value was multiplied by 9/28
since there were nine weekends days in the time period. Therefore, the
weighted average weekend value was (9/28) (14) or 4.50 visitors per
day. To derive the total weighted average daily count, the individual
weighted average counts per day were summed. .The total weighted average
was 11.28 visitors per day. Therefore, during this time period, an
average of 11.28 visitors per day was estimated by the overflights at
Lake Marian. The total weighted average of the overflight counts for
Lakes Gentry, Tohopekaliga, and Marian during November 1970 through
December 12, 1970 is presented in Table 8. Note that Lake Gentry
averaged 11.20 visitors per day and Lake Tohopekaliga 98.3 visitors
per day.
Table 8.Weighted average overflight counts for the time period
November 14, 1970 through December 12, 1970 of the three
sampled lakes in the Kissimmee River Basin
Weighted average for Total
weighted
Lake Weekend Weekday average of
overflight overflight overflight
counts counts :counts
Gentry 9/28 (14.5) = 4.66 19/28 (9.66) = 6.55 11.20
Tohopekaliga 9/28 (203.5) = 65.40 19/28 (48.3) = 32.9 98.30
Marian 9/28 (14.0) = 4.50 19/28 (10.0) = 6.78 11.28
Water Level
The primary physical variable of interest is water level (WL).
Water level is of interest since it is desirable to know how recreation
activity varies with variations in water level in order to efficiently
manage the water in a river basin. It was hypothesized that, as the
water level decreases or increases, recreational use of the lake
decreases (see Figure 2 );it was only possible to test the former half
Number of
visits
to the
Kissi mee i
River
Basin
Low High:
" water water
level level
Water level
Figure 2.Hypothesized relationship of water level to recreationvisits, Kissimmee River Basin
of the hypothesis due to the lack of data during flood conditions. It
was possible to obtain from the Central and Southern Florida Flood
Control District daily measurements in feet above sea level of the
water at the nearest lock of each of the three lakes sampled. Speci
fically, for Lake Gentry, the nearest water level data collecting center
was at gate S63. In the case of Lake Tohopekaliga, it was gate S61,
and gate S65 was the closest to Lake Marian (refer to Figure 3) [12].
Rainfall
The next physical variable of interest was rainfall. Initially, a
measurement of rainfall at each individual sampled lake was desired.
Instead, a daily rainfall count at the nearest lock, for each sampled
lake, was the measurement obtainable. For Lake Gentry, it was lock
S63; Lake Tohopekaliga, lock S61; and Lake Marian, lock S65 [12].
Rainfall was measured by two.different methods.
The first procedure to account for rainfall entailed measuring
rainfall in inches per day during each time period at each of the
three locks. The second method consisted of determining the total
number of days that rain occurred Within the time period at each lock.
Thus, if during a particular time period, rainfall was recorded on three
different days, then the value "three" was utilized. It is believed
that the first method of measuring rainfall would prove to be more
significant in the regression equation since recreationists are affected
by the amount of rainfall rather than the mere presence of rainfall.
Note that in the Kissimmee River Basin rainfall occurs most frequently
during seasons I and II but not for long durations. It should also be
noted that water level data and all rainfall data were collected.at the
same locations.
4tfw
L. MYRTLE
0m
HAINES
CTY
L.
ROSALIE
WLALE
"WaLEO
cQi
SCALE
Figure 3.The upper portion of the Kissimmee River Basin
422
Temperature
Another variable which posed some measurement problems was
temperature. It was impossible to attain an accurate reading at each
lake. The intention was to derive an average of the highest daily
temperature at each lake by considering the highest daily temperature
at each access point of the lake. The only data available concerning
daily temperature wereobtained from the U.S. Department of Commerce 
National Oceanic and Atmospheric Administration. The nearest location
for gathering the highest daily temperature readings for Lake Gentry
and Lake Tohopekaliga was at the Kissimmee Climatological Station;
for Lake Marian the nearest was the Indian Lake Estates Climatological
Station (see Figure 2) [12]. Note that only the extreme point of highest
daily temperature was considered since it was assumed that extreme
temperature readings were the only readings that would influence
recreational usage of the river basin. The other extreme point, the.
lowest daily temperature, was not considered relevant due to the general
climate in this part of Florida and the type of recreational activities
predominating in the area.
Wind Velocity
The data available at each lake concerning wind velocity werenon
existent. The desired value was an average of readings at several
locations at each lake of the highest mile per hour reading recorded
during the day. But the only attainable data for the entire river basin
came from the Herndon Airport in Orlando, Florida [12]. It recorded the
highest mileper hour reading, for each day, in the Orlando area. Thus,
the same measurement had to be used for each of the three lakes.
Three distinct procedures were established to utilize the wind
velocity values obtained from the Herndon Airport. The first method
consisted of determining the number of days the wind velocity was higher
than the highest mile per hour average for the year during a particular
time period. The second method entailed calculating the number of days
the wind velocity was higher than the highest mile per hour average
during the particular time period in question. For example, if the
highest m.p.h. average for a specific time period was 18.0 m.p.h. and
four days in that time period received a reading greater than 18.0 m.p.h.,
then the number of days the wind velocity was higher than the highest
m.p.h. average was four. Note that the basic difference between methods
one and two is that the first procedure refers to the highest mile per
hour average during the year while the second refers to the time period.
The third method was to compute the average highest mile per hour wind
velocity for each time period. Again, it was presupposed that only
extreme wind velocity readings would affect recreational activity and,
therefore, would be pertinent to this study.
'The empirical results of this study and an examination of the
relationship between the number of visits to the river basin and the
physical variables will be presented in the next section.
STATISTICAL MODELS AND EMPIRICAL RESULTS
A recreational use equation wias estimated for Lake Tohopekaliga3
and for a combination of the three lakes. Also, a predictive equation
3Lake Tohopekaliga was singled out due to the nature of the data
obtained during a drawdown of the lake.
was computed to estimate recreational use of a lake based upon the
overflight counts. The selection of the final equations was based upon
the extent to which it was believed to describe observed conditions and
upon statistical .indicators of significance. The statistical criteria
included "t" values for individual variables and R2 and "F" values for
the function.
The "t" value associated with each regression coefficient was used
to determine the status of the variable in the regression equation. If
the coefficient was statistically different from zero at a probability
level of .05 or less, the regression coefficient was considered
significant.
One of the basic assumptions of a linear regression model is that
no multicollinearity prevails among the independent variables. A simple
correlation coefficient matrix was developed for each regression
equation in order to investigate if linear dependence existed among
the explanatory variables. In this study interdependency occurred most
often among the physical variables. This was expected since, for
example, rainfall varies inversely with temperature and directly with
wind velocity.
If an estimate of recreational use was desired, and not its rela
tionship to other variables, then these regression. equations are not
needed. The procedures to calculate Y from the traffic counts are all
that would be relevant. This study was especially interested in the
relationship between water level and recreational use due to an interest
in allocation.
SThe remaining portions 'of this section will discuss each estimated
equation separately: recreational usage of Lake Tohopekaliga; the
general regression equation which can be adapted to more than one lake
in the river basin; and the overflight predictive equation.
Specification of Variables
The variables used in the regression equations are summarized below.
Other variables previously defined that do not appear in the final
equations are not listed.
Y represents the estimated number of people using the lake
facilities (traffic count survey) per day
X represents the average daily estimate of people using the
lake for recreation (overflight counts)
WL represents the average water level of the lakes, in feet
above sea level
T represents average of highest daily temperatures
Da represents the dummy variable for season II
Dz represents the dummy variable for season III
R . represents the average amount of rainfall in inches per day,
during a particular time period
Li represents the dummy variable for the ith lake
Lake Tohopekaliga
The estimated equation for Lake Tohopekaliga is as follows:
Y = 550.10 + 23.01 WL*** 160.88 R 4.90 T** 38.73 02
(4.10) (111.20) (1.45) (24.66)
R2 = .675 d.f. = 29 F = 15.07
The asterisks (*) in the estimated equation indicate the level of
significance of the regression coefficients. One (*), two (**), and
three (***) represent .10, .05, and .01 significance levels, respectively.
A coefficient without an asterisk is statistically different from zero
at a significance level greater than 10 percent. The standard error of
the regression coefficients is entered in parentheses under each
coefficient.
The level of significance of the regression coefficients was .05 or
lower for water level and temperature. However, two variables (R and D2)
were included in the final function for which the coefficients were
significant only at the 16 and 13 percent levels, respectively. Wind
velocity was not included in the final equation due to the high degree
of correlation with rainfall (simple correlation coefficient of .67).
The same variation in Y was being explained by both the rainfall and
wind variables. For all practical purposes the variable R (rainfall)
could also have been eliminated from the model without having any signi
ficant effect on the regression equation. The coefficient of determination,
R2, was calculated to be .675. Thus, the independent variables explain
67.5 percent of the variation in Y, while 22.5 percent of the deviation
in Y remains unexplained.
The calculated Fvalue was 15.07. The table Fvalue with 6 and 27
degrees of freedom was 3.56. Therefore, since the calculated Fvalue
was greater than the table Fvalue, the independent variables have a
significant effect upon recreational use of the lake.
The findings indicate that for Lake Tohopekaliga temperature and
rainfall have an inverse effect on recreational use. This result
substantiates the original hypothesis. That is, as the values of the
physical variables increase, recreational use of the lake decreases.
The results also point out that there was very little, if any,
difference in total recreational usage between season I and season II,
but there existed, on the average, a decrease of 38.78 visitors to the
lakes between season II and season III (which was significant at only the
13 percent level).
The water level hypothesis was also reinforced by the results. It
was observed that,if the water level decreased by one foot, the
recreational use of the lake would decrease by 23.01 visitors. The large
negative intercept (550.1) can be contributed to the fact that WL (water
level) was measured in feet above sea level rather than the actual water
depth of the lake.
All Lake Model
This regression equation was estimated in order that the recreational
use of the lake could be established from one equation rather than the
individual equations presented earlier. The main difference between the
.general model and the individual lake model is that a dummy variable.Li
was introduced as an independent variable in the general model. As in
the case of the seasonal variables, a zeroone dummy variable was used
to differentiate among the different types of lakes. The variable Li
possesses a value of "1" whenever the data being examined was collected
from the lake in question and a zero at all other times. Note that Lake
Tohopekaliga has no dummy variable since its value of "1" or "O" is
implied in the regression equation. When estimating recreational usage
of lakes, other than those sampled by the traffic survey, such charac
teristics as sizeof lake., distance from population centers, and depth.
of water should be considered in determining the proper value for L.
The estimated regression equation for the All Lake Model is as
follows:
Y = 766.2 + 22.07 WL*** 85.98 R 1.80 T** 43.08 D2**
(3.737) (54.35) (0.865) (14.39)
82.80 Li*** 314.28 L2*
(14.37) (35.56)
R2 = .687 d.f. = 77 F = 28.14
where the variables are as defined on page 34.
The strongest correlation among the independent variables occurred
between the variables L2 (dummy variable for Lake Gentry) and WL (water
level). This interdependence can be justified since the water level
did not vary a great deal within the lakes, with the exception of Lake
Tohopekaliga, but rather varied between lakes. Again, wind velocity
was not utilized.
The calculated R2 was .687 and the calculated Fvalue was 28.14,
which is significant at the 1 percent level. The statistical results
support the original hypothesis in that recreational use of the lake
varies directly with the water level and inversely with the physical
variables temperature and rainfall. As water level (WL) increases by
one foot, recreational use of the lake is estimated to increase by 22.07
visitors and that between season II and season III recreational usage
of the lake will decrease by 43.08 visitors due to the seasonal trends.
There was no evident seasonal trend during the months of February through
September. This conclusion.differs from the original hypothesis that the
year be divided into three seasons rather than two. Also, the variables
L2 and L2 behaved as predicted. That is, the total number of visitors
recorded decreased from Lake Tohopekaliga to Lake Marian and from Lake
Marian to Lake Gentry by 82.81 and 314.28 visitors, respectively.
Overflight Predictive Model
This equation was derived to estimate the total number of visitors
at a lake by the use of overflight counts. Data from all sampled lakes
were utilized. The regression equation was estimated as follows:
Y = 98.73 + 1.189 X*** 27.594 Di* 33.968 D2**
(.129) (15.32) (16.69)
R2 =.554 d.f. = 80 F = 33.05
The overflight counts (X) varied directly with recreational use.
Thus, the more people observed by the overflight, the greater the number
of recreationists using the facilities. This result is in direct
accordance with the original predictions. The dummy variables Di and 02
also behaved as expected. In this relationship recreational use
decreased from season I to II and from II to III.
The relationship established in this equation enables the estima
tion of recreational use by engaging an overflight. That is, for each
person observed, it is estimated that 1.2 recreationists would
utilize the facilities. For example, in season I (Di = 0 = O),if
30,000 people were observed by the overflights, an estimate of total
usage (based on this relationship) of 35,769 is obtained.. All that is
necessary to utilize this relationship is to substitute a zero or one
for the appropriate season and evaluate the overflights.
Estimating Total Recreational Usage of the Kissimmee River.Basin
The preceding models refer to the total number of.people using the
sampled lakes. It is of great importance to estimate the total number
 ~~I"*U*~~"Y~U~'~
of people using the entire river basin during a particular time period.
The main problem encountered in establishing this value was that the
traffic meter data were applicable only to three lakes. Therefore, it
could not be used directly to estimate the number of recreationists
for the entire river basin. The relationship between the number of
people using the three lakes and the total number of people using the
river basin was established. This was accomplished by determining the
percentage of people in the entire river basin.that utilize the three
lakes.
The method to calculate the percentage of people using the three
sampled lakes was to utilize the overflight counts. This was estab
lished by first determining the number of people, as computed by the
overflights, for the three lakes, during each seasonal period. Next,
it was necessary to calculate the number of people, as estimated by
the overflights, for the entire river basin during each season.
Finally, the percent of people in the entire river basin using the three
lakes was computed as the ratio of the number of people using the three
lakes to the total usage.
Once the proportion (,) is determined, the total number of people
using the river basin.daily (YR) can be calculated by multiplying the
total number of people using the three lakes by the factor .
Thus:
Y3 1 r
R 
i= 1
where i refers to each of the three lakes.
A numerical example of'the above analysis as related to the months
of October through January, 197071, is included in Table 9.
Table 9.Estimating the percent of recreationists utilizing the three
lakes by the use of overflight counts (season III) in the
Kissimmee River Basin, 1970
Lakes People Percent of recreationists
per day utilizing the three lakes
Lake Gentry 18.84
Lake Tohopekaliga 183.75
Lake Marian 34.12
Total of three lakes 236.71
236.71
Total for river basin 1026.65 26.65 = .231
1026. 65
During the same months the traffic survey counts were distributed
among the three lakes as follows:
Lake Gentry = 5,090 visitors
Lake Tohopekaliga = 27,568 visitors
Lake Marian = 10,351 visitors
Total = 43,009 visitors
To estimate the total number of recreationists for the year the seasonal
totals were combined (.see Table 10). When employing the overflight method,
it is assumed that the proportion of the number of people actually parti
cipating in water oriented recreation (as observed by the overflights) to
the total number of recreationists utilizing the lake facilities "is con
stant among the lakes. Thus, if the overflight counts actually represent
40 percent of all recreationists at Lake Marian, it should also represent
40 percent of all visitors at Lake Gentry, and so forth.4
4Other methods could be utilized to estimate the number of visits
for the entire river basin. 'For example, the proportion of access points
on the three sampled lakes to the basin could be used. This was applied
in [I].
Table 10.Seasonal estimates of the total number of visitors to the Kissimmee River Basin, in 1970 
based on the estimated percentage value derived by the use of overflight counts
Number of Percentage
Season Lake visitors (Yi) (P) calculated Y. 1/P YR
at lakes sampled for season
Feb.May Gentry 11,488 .375 30,635
Tohopekaliga 34,526 .375 92,069
Marian 13,280 .375 35,413
Total 59,294 158,117
Jun.Sept. Gentry 11,896 .326 34,491
Tohopekaliga 23,592 .326 72,368
Marian 8,508 .326 26,098:
Total 43,996 134,957
Oct.Jan. Gentry 5,090 .231 22,035
Tohopekaliga 27,568 .231 119,342
Marian 10,351 .231 44,809
Total 43,009 186,186
Note: EYR = 479,260 estimated total number of visitors
to the Kissimmee River Basin, in 1970.
SUMMARY AND CONCLUSIONS
Summary
The present study was undertaken in order to explore the magnitude
of recreation and to determine the climatological elements that have an
influence upon outdoor recreation in the Kissimmee River Basin. Thus,
to fulfill the investigation, the research was oriented toward the
solution of two objectives: (1) to apply a methodology to determine
the number of people utilizing the Kissimmee River Basin for recrea
tional purposes and (2) to estimate the relationship between
recreational use and certain climatological variables, including water
level, in the Kissimmee River Basin during 1970.
In order to determine the number of people recreating in the river
basin, a sample of three lakes was chosen from the Kissimmee River
Basin. These were Lake Gentry, which was representative of small
lakes; Lake Marian, medium sized lakes; and Lake Tohopekaliga, large
lakes. A traffic counter was placed at all public access points on each
of the three lakes. The data obtained from the traffic counter had to
be adjusted in order to obtain an estimate of the number of people using
the area rather than just the number of vehicles recorded on the meter.
Once the annual estimate of the number of visits by recreationists to
the sampled area was obtained, it was projected for the entire river
basin. Thus, it established the total usage of the Kissimmee River
Basin for recreational purposes during 1970.
To fulfill the second objective, two linear regression models were
constructed. The first model was used to estimate the relationship
between the number of people using the recreational facilities and the
climatological variables: temperature, rainfall, wind velocity, and
water level. The second model derived a relationship between the number
of people utilizing the lake (Y) and the overflight counts (X). During
the gathering of all the data, the measurements were taken between
specific time intervals and averaged for that time period. In the case
of the overflights, the data had to be adjusted due to the fact that
there are more weekdays than weekend days or holidays during the year.
Conclusions
While the significance of the variables differ among the
regression equations, the All Lake Model is perhaps the most appropriate
equation to use when analyzing the recreational use of any lake in the
Kissimmee River Basin based on climatological factors.
The relationship between the overflight data and recreational use
of a lake was significant at the 1 percent level in the overflight
equation. It is suggested that this equation be used whenever esti
mating the total number of visitors at a lake during a time period
based on the overflight counts alone.
In two cases (Lake Tohopekaliga and All Lake Model) a statistically
significant relationship between the water level and recreational use
existed. It can be stated that recreational use varies directly with
the water level of the lake. While it was only possible to test the
hypothesis that as the water level decreased recreational use of the
lake decreased, due to insufficient data under flood conditions, the
results did substantiate that hypothesis in two cases. Separate
regressions were not estimated for the water levels of Lakes Marian
and Gentry due to the fact that the data were gathered during a period
when the water level deviated very little. Thus, it was impossible to
measure how recreation use varied with fluctuations in the water level
on these lakes.
Temperature had a significant impact on recreation in the
regression equations for Lake Tohopekaliga and the All Lake Model.
Rainfall was not significant in either of the equations.
While it was hypothesized that there existed three distinct
seasonal trends in thetotal usage of the lakes, only two seasons were
apparent. The first season ranged from February through September, the
second from October through January. The overflight model was the only
case where three seasons were observed.
The final total estimate of the number of visitors was estimated to
be 479,260 people.
Limitations of Study
There were three basic limitations that affected the results of the
study. First, it was assumed that the traffic counts were adjusted
properly. If further research were carried out, it is essential that a
sampling plan be devised to determine the number of times a vehicle
contributes extra counts. Data should be collected so that this factor
can be computed for each individual access point sampled.
Another limitation was that some data such as temperature and wind
velocity were not available at desired locations (Lake Tohopekaliga,
Lake Marian, and Lake Gentry); thus, it was necessary to assume that the
data were a substitute for the actual values.
APPENDIX
 ~~~~`^~~~~~' ~~~~rr;~
Table A1.Total traffic counts recorded at each sampled access point by time period, from February 1, 1970
through January 31, 1971 in the Kissimmee River Basin
Time period  1970
Access point 201 207 214 301 314 327 411 425 519
to to to to to to to to to
207 214 301 314 327 411 425 519 531
cou. L ake Gentrymp) 838 2241 5665 3224 1736 3024 4528 4778 2620
(county boat ramp)
2. Lake Tohopekaliga 420 1547 a 1790 4871 a 3172 3500 1445
(two lane boat ramp)
3. Lake Tohopekaliga 2623 3306 6447 4723 4999 a 4898 a 2744
(three lane boat ramp)
4. Lake Tohopekaliga 404 872 2023 1253 315 a 1580 a 538
(Fish Camp I)
5. Lake Tohopekaliga
(Fish Camp IIealiga 595 717 2297 1616 1998 2007 1895 2157 1329
6. Lake Tohopekaliga 851 1697 5528 4039 a 2224 1994 1378 918
(county boat ramp)
7. Lake Marian 435 725 2720 4882 1971 1307 1193 1319 612
(Fish Camp III)
8. Lake Marian
(Fis. ame Mrin 293 356 759 948 1155 1125 331 130 103
(FContinuedsh Camp IV)
Continued
aData not obtained due to mechanical
difficulties with the traffic meter.
Table Al.Total traffic counts recorded at each sampled access point by time period, from February 1, 1970
through January 31, 1971 in the Kissimmee River BasinContinued
Time period  1970
Access point 531 614 629 713 727 811 824 906 919
to to to to to to to to to
614 629 713 727 811 824 906 919 930
.1. Lake Gentry a 3303 3498 a a 3819 1895 4549 2527
(county boat ramnp)
2. Lake Tohopekaliga 2035 916 559 586 467 a 808 a a
(two lane boat ramp)
3. Lake Tohopekaliga 3855 3879 3611 3304 a 3002 2791 2681 2239
(three lane boat ramp)
4. Lake Tohopekaliga 391 1100 693 985 730 764 986 151 a
(Fish Camp I)
5. Lake Tohopekaliga 1743 1331 1462 a 725 2194 1338 2190 a
(Fish Camp II)
6.cou Lake Tohopekaliga 1688 238 a 821 782 857 758 634 992
(county boat ramp)
7. Lake Marian a 340 a a a 1251 718 857 725
(Fish Camp III)
8. Lake Marian a a 447 353 a 291 376 2116 544
(Fish Camp.IV)
adata not obtained due to mechanical difficulties with the traffic meter.
Continued
Table Al.Total traffic counts recorded at each sampled access point by time
through January 31, 1971 in the Kissirmee River BasinContinued
period, from February 1, 1970
Time period  1970
Access point 930 1014 1031 1114 1203 1212 1226 108 123
to to to to to to to to to
1014 1031 1114 1203 1212 1226 108 123 131
1. Lake Gentry
(county boat ramp) 4204 2598 562 1000 a 1012 892 1061 585
2. Lake Tohopekaliga
(two lane boat ramp) a 5248 922 2009 381 1110 a 557 406
(twLeake Tohopekal iga
3. Lake Tohopeka p)ga 3152' 4570 3762 5951 2617 4129 4218 4475 2937
(three lane boat ramp)
Fish. Lake Tohopekaliga 926 1094 734 990 431 551 533 688 380
(Fish Camp I)
5. La Toopekaliga 1638 2062 2129 2811 977 1655 1291 1172 1192
(Fish Camp II.)
cou Lake Tohopekaliga 1261 1475 1195 2314 1098 1334 1636 2095 1868
(county boat ramp)
7. Lake idarian
(Fish Camp III) 1124 946 1024 1423 639 708 990 814 1278
Fisiake Mparian 639 849 917 1402 564 962 907 828 594
(Fishata not obtained due to mechanical difficultiesIV)with the traffic meters.
a~ata not obtained due to mechanical difficultieswith the traffic meters.
Table A2.Average daily overflight count, in the Kissimmee River Basin, for each
February 1, 1970 through January 31, 1971
time period, from
No. of people observed No. of people observed .No. of people observed
Time period on Lake Gentry on Lake Tohopekaliga on Lake Marian
Weekday Weekend Weekday Weekend Weekday Weekend
20170 to 30170 3 6.33 150 153 59 36.6
30170 to 32770 .75 4 75.5 173 34 34
32770 to 42570 1 2 83.5 126 13 32
42570 to 53170 0 3 43.7 244 2.7 43
53170 to 62970 3 0 36 429 4.5 12
62970 to 72770 0 0 82 284 11 34
72770 to 82470 0 0 107.2 26 15.2 0
82470 to 93070 0 6 19.3 175 1.6 57
93070 to 101470 1 3 27 160 3 16
101470 to 111470 16 1 124 112 4 13
111470 to 121270 9.66 14.5 48.3 203.5 10 14.0
121270 to 13170 0 11 72 113.5 28 45.5
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