Predicting Attendance At southeastern U.s. Amusement Parks Using

Predicting Attendance
At Southeastern U.S.
Amusement Parks
Using Accessibility- and
Amenity-Based Models
James Andrew McCall
Miami-Dade County Park & Recreation
Department
Miami, Florida 33128
Joe Weber*
Department of Geography
University of Alabama
Tuscaloosa, AL 35487
E-mail: [email protected]
* Corresponding author
The Geographical Bulletin 47: 45-59
©2006 by Gamma Theta Upsilon
ABSTRACT
This research examines the extent to which
traditional geographic concepts of proximity
can explain attendance to amusement parks
in the Southeastern United States. Accessibility models were calculated for nineteen
parks using both miles and driving times as
measures of distance from population centers. Results show only weak relationships
between attendance and distance from population. To test whether other factors are more
important predictors of attendance, regression analysis was carried out using variables
describing amenities and characteristics of
the parks. The results show that a high level
of explanation for attendance to parks is
provided by ticket price, parking cost, the
number of years the park has been open, and
centrality within the Southeast. Geographic
proximity to population does not appear to
be important to park attendance.
Key Words: Amusement Parks, Accessibility, Distance, Attendance
Introduction
Tourism in the Southeast has been associated with all manner of colorful and wellknown attractions and locales, including not
only Disney World but the beach resorts of
the Florida panhandle (Hollis 2004), Mammoth Cave in Kentucky (Algeo 2004), the
Smoky Mountains (Tooman 1997), assorted
roadside and urban attractions (Jakle 1985;
Hollis 1999; Newman 1999; Starnes 2003),
and amusement parks. The latter can be defined as entertainment-oriented businesses,
usually requiring admission fees, containing
attractions, rides, food, and shopping, and
other entertainment (Adams, 1991). These
amusement parks are enclosed and separated
from the tasks of everyday life, and range
in size from small family-owned business to
vast corporate parks. Theme parks involve the
application of an overall theme to a particular
amusement park.
In the early twentieth century amusement
park development was strongly tied to local
urban populations, but since the 1950s the
45
James Andrew McCall and Joe Weber
rebirth of the park industry has been tied
more to automobile travel and regional or
even international tourist travel (Adams
1991). Increasing ease of long distance travel,
and the growth of tourist complexes centered
around beaches, national parks, or large cities
has made any expectation that local populations will make up a large component of total
attendance appear doubtful. Despite this,
proximity to population remains a concern
for amusement park operators (Adams 1991).
This research will examine accessibility of
theme parks to the surrounding population
in the Southeast U.S. and relate this to attendance, with the goal of understanding the
importance of population proximity to park
attendance patterns.
THEME PARK DEVELOPMENT IN
THE SOUTHEASTERN U.S.
The origins of amusement parks have
been traced back to medieval trade fairs
that became popular for their entertain-
ments, to European pleasure gardens from
the eighteenth and nineteenth centuries,
and to the Chicago Columbian Exposition
of 1893 (Adams 1991; Younge 2002). This
Exposition included the first Midway with
food and themed entertainment as well as
an overarching theme for the entire event.
The first enclosed amusement parks with
rides, games, food, and other entertainment
appeared on Coney Island in the 1890s, and
these soon spread throughout the country
(Adams 1991). These early parks were located
within urban areas at locations well served by
streetcar or subway lines (and in fact were
often created by streetcar lines to generate
passengers), and benefited from shorter
working weeks and rising incomes during
the early 20th century. By 1920, there were
as many as 2000 amusement parks in the
U.S., but they began a steady decline through
the automobile era, Great Depression, the
Second World War, and the television era
(Adams 1991).
Figure 1: Amusement Parks in the Southeastern United States
46
Predicting Attendance At Southeastern U.S. Amusement Parks Using Accessibility- and Amenity-Based Models
The return of the amusement parks to
importance, or, in the terminology of the
destination life-cycle model, their rejuvenation (Butler 1980; Tooman 1997), is due to
Disneyland and the success of theming, with
their attention to a safe, clean environment,
and a mix of rides and attractions for all
family members (Adams 1991). New parks,
often controlled by large corporations, soon
followed, with the 1970s seeing the construction of most of the postwar theme parks now
operating. Many of these parks cater to vacationers from distant locations rather than
local residents, and are typically located in
a suburban location, adjacent to freeways.
Since the opening of Disneyland, no new
theme park has succeeded in a traditional
urban location (Adams 1991).
Within the Southeast United States a
number of amusement parks exist (Fig. 1).
There is great diversity among these parks,
from small family-owned facilities to large
corporate structures. Six Flags Over Georgia
opened in 1967 outside of Atlanta and was
the first modern theme park in the Southeast
(Adams 1991). Parks in this area range in size
from the five-acre Family Kingdom Amusement Park in Myrtle Beach, South Carolina,
to Paramount’s colossal 400-acre King’s
Dominion amusement park in Richmond,
Virginia. There is also great variation in the
number of rides and attractions offered at
each park (Table 1). Several of the smaller
parks contain only one roller coaster and a
few adult rides. Large corporate parks owned
by Six Flags and Paramount have as many as
twelve roller coasters and as many as thirty
adult rides and attractions.
In addition, at least eighty Southeastern
amusement parks have closed their gates in
the twentieth century (Samuelson and Yegoiants 2001; Styer 2005). Even with close
proximity to urban populations, at least five
amusement parks built in Atlanta failed,
while Nashville also experienced at least five
closings. Baltimore has been historically the
worst place in the Southeast to build amusement parks, with at least twelve defunct
parks. In addition, there have been failed
attempts to create new parks, as when the
Walt Disney Company announced in 1993
plans to build Disney America in Virginia,
but abandoned the idea after strong public
opposition (Moe and Wilkie 1997).
GEOGRAPHIC ANALYSIS OF THEME
PARKS
Tourist oriented areas, including amusement and theme parks, have been examined
from a range of perspectives in recent decades
(Jakle 1985; Adams 1991; Marling 1997;
Young 2002). Theme parks, especially Disneyland and Disney World, have attracted
a great deal of attention for how they have
been designed, their impact on guests, and
for their economic and political impact on
surrounding communities (Zukin 1991;
Findlay 1992; Sorkin 1992; Francaviglia
1996; Warren, 1996; Archer 1997; Marling
1997; Foglesong 2001; Mannheim 2002;
Young and Riley 2002). Critiques of downtown commercial-entertainment districts in
large cities have used the metaphor of theme
parks to describe these areas and the way they
structure activities (Warren 1994; Hannigan
1998; Young 2002; Bryman 2004). Theming
is not limited to amusement parks or downtowns, and can be applied to entire cities
or small towns, such as the UFO-oriented
theme developed for Roswell, New Mexico
(Paradis 2002).
Rather than examining the cultural meaning and design of theme parks, the goal here
is to better understand attendance and their
relationship to local population patterns.
There is a limited amount of geographical
research and analysis of amusement park attendance patterns in the U.S., and no recent
studies have been published on amusement
park attendance. This research addresses this
shortage by examining the extent to which
traditional geographic concepts of proximity
are useful for explaining yearly attendance
to amusement parks in twelve states of the
Southeastern U.S. Given the increasing scale
of amusement parks, the greater the distance
they attempt to attract visitors from, and
the longer time these visitors will remain,
it is tempting to ask whether traditional approaches to accessibility will remain relevant
to them. An analysis of attendance at amusement parks is also useful because it provides
47
48
Nashville, TN
Charlotte, NC
Doswell, VA
Upper Marlboro, MD
Louisville, KY
New Orleans, LA
Austell, GA
Bessemer, AL
Gulf Shores, AL
Valdosta, GA
Opryland USA
Paramount’s
Carowinds
Paramount’s King’s
Dominion
Six Flags America
Six Flags Kentucky
Kingdom
Six Flags New
Orleans
Six Flags Over
Georgia
Visionland
Waterville USA
Wild Adventures
Theme Park
170
20
70
245
140
59
345
400
105
70
140
Hot Springs, AR
67
5
Magic Springs
Maggie Valley, NC
Ghost Town in the
Sky
88
Myrtle Beach, SC
Family Kingdom
Amusement Park
100
150
Pigeon Forge, TN
Dollywood
26
360
Memphis, TN
Huntington, WV
Libertyland
Williamsburg, VA
Camden Park
368
1997
1987
1998
1967
2000
1990
1982
1975
1973
1972
1978
1976
1925
1961
1992
1961
1903
1975
1898
304
150
105
151
122
145
135
130
120
145
60
65
80
135
200
215
93
167
95
Park Size
(Acres) Year Opened Days Open
Lake Winnepesaukah
Amusement Park
Rossville, GA
Bowling Green, KY
Busch Gardens
Williamsburg
Location
Beech Bend Park
Amusement Park
Table 1. Characteristics of Southeastern Amusement Parks
1,250,000
175,000
400,000
2,250,000
1,095,000
1,200,000
1,550,000
2,090,500
1,850,000
2,000,000
280,000
250,000
375,000
140,000
80,000
2,300,000
105,000
2,600,000
400,000
Annual
Attendance
9
1
2
9
6
7
9
12
11
6
5
3
3
2
2
3
3
6
1
Roller
Coasters
26
6
11
21
17
19
20
18
11
12
12
7
16
6
12
11
15
8
12
Adult
Rides
yes
yes
yes
yes
no
yes
yes
yes
yes
no
yes
no
no
no
yes
yes
no
yes
no
Nearby Water
Park
yes
no
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
no
yes
yes
yes
no
Shows
19
2
5
21
16
15
24
7
30
17
0
7
0
1
0
8
1
6
0
Restaurants
6
0
4
19
12
16
10
7
2
3
1
1
0
1
0
9
1
9
0
Shops
2002
2002
2001
2002
2000
2001
2002
2002
2002
1997
2002
2002
2002
2002
2002
2002
2002
2002
2002
Year Data
Available
James Andrew McCall and Joe Weber
Predicting Attendance At Southeastern U.S. Amusement Parks Using Accessibility- and Amenity-Based Models
a perspective on the aspects and amenities at
parks that are most attractive to visitors.
DISTANCE, POPULATION, AND
ACCESSIBILITY TO THEME PARKS
Proximity to population is of fundamental
concern to theme park developers and operators, with close populations given greater
weight than more distant potential visitors.
For example, the local population might be
counted “within radiuses of 25, 50, 100, and
150 miles of a potential site” (Adams 1991,
p. 109). A market penetration model refines
this into the primary, secondary, and tertiary
markets (Miami Metrozoo 2003). The size of
each market area is based on the percentage
of residents who will visit an attraction. The
primary market for a theme park might be a
radius of 25 miles, while the secondary market
would include the population between one
and two hours driving time, and the tertiary
market consists of populations (especially in
larger cities) located between three and four
hours driving time away. The tertiary market
is however quite variable, with little or none
for a small attraction, and the entire world
for Disney World.
However, the use of discrete distance intervals can be problematic, for it has been shown
in a wide variety of situations that interaction
falls off at a non-linear rate, an effect commonly known as distance decay. This idea
has long been a part of recreation planning
(Clawson and Knetsch 1966; Hanink and
White 1999). One study concluded that,
“The average number of trips made to Six
Flags Over Georgia in 1968 decreased according to an exponential function as the travel
time to Six Flags Over Georgia increased”
(Dyer 1970, p. 33). In their study of the effects of distance on travelers’ attendance to
the Great Smoky Mountains National Park,
Cole and Mitchell (1969, p. 14) conclude
that attendance is “a negative function of
distance.” However, in another study it was
found that distance decay had only limited
power to explain attendance, in this case to
a Saudi Arabian national park, due perhaps
to the uneven local population distribution
and importance of distant urban populations
(Paul and Rimmawi 1992).
More recently, Meyer-Arendt (1997) used
a version of the traditional gravity model to
explain attendance at casinos in Mississippi.
This strongly suggests that amusement parks
can be examined in the same fashion, with
parks being patronized primarily by local
populations. Both travel costs (measured
linearly with distance) and area and amenities of water recreation sites were found to
be important (Siderelis and Moore 1998).
Travel times have been used to predict attendance at recreation sites with some success (Brainard, Lovett, and Bateman 1997).
This study used estimates of highway driving
time and the origin locations of visitors to
a park. It can be expected that incorporating information about the attractiveness of
recreation destinations as well as the presence
of competing destinations will improve such
an approach.
However, there are increasing reasons to
suspect that traditional proximity-based approaches might no longer be valid in the case
of theme parks. The growth of commercial
air travel and the construction of the Interstate Highway System have shrunk travel
times between cities, resulting in space-time
convergence (Janelle 1968). This has been
mapped for the Southeast U.S., showing
greatly reduced driving times between 1950
and 1975 (Carstensen 1981). In response to
faster speeds of travel (resulting in greater accessibility to major cities), amusement parks
in larger cities may experience increased patronage due to a wider area from which to
draw visitors. Similar issues apply to tourist
attractions that increasingly function on a
global scale (Williams 1998).
An important issue with tourism is that
travel can be an important and valued part
of the vacation experience, and so cannot
necessarily be expected to show a distance
decay effect for all forms of travel (Hanink
and White 1999). In the case of theme parks,
which may be infrequently visited or are
part of a larger vacation, this appears to be
a strong possibility. While this is very likely
the case with Disney World and adjacent
parks, it may also be the case for many other
Southeastern parks. A shrinking importance
for distance could also be the result of the
49
James Andrew McCall and Joe Weber
increasing role of new and larger attractions
at parks. Theme parks increasingly compete
by building new rides, especially thrill rides
such as roller coasters (Newman 2004).
These new rides are essential for attracting
repeat customers (Braun and Soskin 1999),
whose loyalties may not be based on proximity. These conflicting possibilities for the
importance of proximity to population will
be tested with data about amusement park
attendance in the Southeast.
STUDY AREA AND DATA
This study examines amusement parks
located in the Southeastern United States,
excluding those in Florida. Amusement parks
were identified as entertainment-oriented attractions that charged admission and had at
least one roller coaster. There are nineteen
amusement parks distributed throughout the
twelve states of this area, with the majority
of the parks located near large cities (Table
1). This is not surprising given the emphasis
Figure 2: Attendance at Amusement Parks
Figure 2: Attendance at Amusement Parks
50
on proximity to population. Two parks are
located adjacent to the Great Smoky Mountains National Park, which is the most visited
National Park in the country and clearly an
ideal place to attract tourists.
Since Florida has many large amusement
parks, it may seem unusual that it is not included. However, several of Florida’s amusement parks, especially Walt Disney World
in Orlando, are attractive globally, whereas
most parks in the study are attractive only
regionally. Consequently, the inclusion of
amusement parks in Florida would greatly
skew the results. In addition, three parks were
excluded because they offer free admission
and are located along public piers that also
contain restaurants, clubs, and shopping
attractions. Yearly attendance estimates for
these piers were reported in local papers, but
it is impossible to know how many of the
people that visited the pier actually visited
the amusement park. Finally, one additional
park was excluded because no attendance es-
Predicting Attendance At Southeastern U.S. Amusement Parks Using Accessibility- and Amenity-Based Models
timates were found and park officials refused
to release attendance data. While Opryland is
no longer open, it represents a modern and
recently operating theme park and so was
included in the study.
For each park, the attendance data used
in the analysis were for the most recent year
available. Figure 2 shows attendance at parks
in the study area. Tourist attendance is volatile over time, and related to many factors
that extend beyond the attributes of the destination (Frechtling 2001). This represents
the effective demand, and not the potential
demand for these destinations, represented
by the total population interested in attending such a park (Hall and Page 1999). While
there are several variables that could be used
to represent attendance, here the number of
visitors per year is used.
Finding attendance data for amusement
parks can be quite troublesome, as “many
amusement parks by policy do not reveal or
report attendance figures even to industry
associations . . .” (Adams 1991, 168). The
following sources were used to compile attendance data: Amusement Business (ten parks),
articles in local papers furnished by local
visitors’ centers and tourism development
councils (seven parks), and phone interviews
with public relations personnel (two parks).
ACCESSIBILITY OF
SOUTHEASTERN PARKS
Accessibility is a fundamental concept in
geography, which in its traditional proximitybased form relates a set of origin locations to
one or more destination locations (Pirie 1979;
Kwan and Weber 2003). A number of different
accessibility measures exist, depending on how
they take into account distance and whether
they incorporate the size of the destination.
The Shimbel or network accessibility measure,
which was first introduced to geography by
a pioneering analysis of accessibility in the
Southeast (Garrison 1960), measures the
highway distance from each city to all other
cities. Lower total mileage values represent
greater accessibility to other cities. This has
been a common means for measuring accessibility among cities (Gauthier 1968; Murayama
1994; and Spence and Linneker 1994).
Another set of accessibility models have
been termed population potential or Hansen
measures, first developed in the 1940s (Pooler
1987) and suggested as an accessibility measure by Hansen (1959). These measures take
into account not only the distance from the
origin to destinations, but the size of destinations. In this case, the closer a park is to more
and larger population centers, the higher its
accessibility. The population potential model
can be written as follows:
Ai = k
Pj
D ij
b
where accessibility of park i is equal to the
sum of the population of each center (Pj) divided by distance from the park to that center
(Dij b). k represents a calibration constant.
Distance is raised to a power represented
by b, which allows for non-linear distance
decay. This is similar to the formulation of
distance decay in gravity models for spatial
interaction, with higher b values meaning
that accessibility will fall off more rapidly
with distance.
This population potential accessibility
measure has been used in many recreation
applications, including accessibility to golf
courses (Mitchelson and Lazaro 2004), and
the accessibility of national parks to population (Hanink and White 1999; Hanink
and Stutts 2002). It has been suggested for
evaluating the accessibility of wilderness areas
to population, although because support for
wilderness areas tends to be greatest among
those living farthest away, multiplying the
attractiveness of the wilderness by distance
could actually give a more suitable measure
for the value of wilderness to society (Hanink
1995).
The population potential model was calculated using ArcView 3.2 Geographic Information System (GIS). County centroids
were used to represent population centers,
using population values from the 2000
Census. A short script was written to allow
ArcView to calculate population potential
models between each amusement park and
every county in the study area. To account
for visitors to parks from outside the study
51
James Andrew McCall and Joe Weber
area, a 200-mile buffer was created around
the entire study area, and counties inside this
buffer added to those in the Southeast.
Both highway mileage and driving time
were used to represent distance. Doing so
allows some indication of how households
choose parks. While travel times to parks can
be important given long trips, mileage may
be of more importance to households on long
vacations. In both cases the 2003 National
Transportation Atlas Database (Bureau of
Transport Statistics 2003), which includes
GIS representation of all major highways
in the Southeast, was used for the highway
network. Because there is no highway driving
time data present in this dataset, travel times
were estimated for each highway link. To do
this, average driving speeds were estimated
for different classes of roads, a common approach at the interurban level (Carstensen
1981; Brainard, Lovett, and Bateman 1997).
Average speeds of 62.3 mph were computed
for Interstates and 52.1 mph for other major
highways based on average values for these
highways throughout the Southeast (Rand
McNally 2004). These average speeds were
converted to driving times and assigned to all
links in the street network database.
In addition to calculating population
potential using travel times and miles, accessibility was measured using different
distance decay values. In the case of gravity
Table 2. Results of Correlation Analysis
Measure of
Distance
Beta Value
Correlation
Coefficient
Miles
1
0.485a
Miles
1.5
0.457a
Miles
2
0.397
Miles
2.5
0.362
Miles
3
0.347
Travel Time
1
0.488a
Travel Time
1.5
0.456a
Travel Time
2
0.397
a Statistically Significant at p <=.05
52
models there has been considerable discussion about the proper b value (Pooler 1987;
Mikkonen and Luoma 1999). Because there
is no expectation about exactly at what rate
proximity to population will decrease as a
potential influence on park attendance, b
parameters ranging from 1 to 3 were used
for mileage, and 1 to 2 with travel time. If
distance is cubed instead of being squared in
the model, lower predictions of attendance
should result.
EXPLAINING AMUSEMENT PARK
ATTENDANCE
The accessibility results were tested to see
whether variations in attendance were strongly related. Pearson correlation analysis was
used to test the accuracy of the population
potential accessibility model predictions with
the actual yearly attendance data for each
park. Network distances and travel times
were used, with several beta values (Table 2).
The best results were obtained with linear distance, which shows very clearly that distance
decay is not as great a factor for amusement
park attendance as it is for other kinds of
trips (Hanson and Schwab 1995). However,
because amusement park trips are likely to
be rare for most households, it should not
be surprising that other considerations are
far more important. For travel times a beta
exponent of 1 again provided the most accurate prediction of attendance to amusement
parks in the Southeast. The fact that travel
time provides a higher level of explanation
than distance must be due to the unevenness of travel times on the highway system,
as expected from space-time convergence
(Carstensen 1981).
Although proximity to population provides a moderate level of explanation for attendance, a variety of other factors can also
be expected to contribute to attendance at
amusement parks. These could include an
amusement park’s location near physically
attractive features like beaches or mountains;
the possibility of attending an adjacent water
park, restaurants, or shopping center; elaborate marketing schemes by parks and local
tourism developers; incentives to bring large
institutionalized tourist groups to the park;
Predicting Attendance At Southeastern U.S. Amusement Parks Using Accessibility- and Amenity-Based Models
cheaper admission rates for senior citizens
and for stays longer than one day; and the
appeal of amusement parks as a perfect family vacation destination. These are a more
restricted set of variables than might be used
for more general tourism studies, but include
destination attractiveness, distance, and price
(Frechtling 2001). And like national parks,
amusement parks include a wide range of
features that appeal to different segments
of the tourist market, making it difficult to
select variables (Hanink and White 1999).
Although most of these parks do not reach
the level of publicity of Florida’s large theme
parks, it may be that they draw on populations well beyond those found locally. Strong
loyalties or preferences to particular parks
might also account for attendance patterns
that are not based on distance.
These possibilities were explored using
stepwise multiple regression analysis. Although tourist attendance is commonly modeled and predicted using time series analysis
(Frechtling 2001), a regression analysis with
a range of geographical data is used here,
since the goal is not to predict attendance
based on past patterns, but instead to relate
attendance to cross-sectional explanatory
variables. As the attributes of visitors, or
their origin locations, are not available, the
analysis necessarily focuses on destination
attributes (Table 1). Yearly attendance at
each of the nineteen amusement parks in
the study area was the dependent variable,
with 34 independent variables describing
amenities and attractive characteristics of the
parks (Table 3). Independent variables were
obtained from the websites and brochures for
each individual park. The 34 independent
variables for each park fit into five general
groups: 1) General park information; 2) Park
admission price information; 3) Accessibility;
4) Park ride information; and 5) Park attractions and amenities. The original continuous
data were also recoded to create seventeen
categorical variables to better isolate parks
with similar features.
As with national parks (Hanink and White
1999), it is expected that older theme parks
will be more widely known. Larger parks
should offer more attractions, while those
open for longer periods during the year
should attract more tourists. Ownership by
a larger corporation such as Six Flags would
likely be associated with a park being better known and as a indicator of quality and
satisfaction. As all of these parks are seasonal,
the number of days each operated per year
was included, with the expectation that
longer seasons should be related to higher
attendance. Price information includes adult,
child, senior admissions, season pass, and
daily parking fees. The lower these prices,
the greater the expected attendance.
Variables were also included describing attractions present in the parks. The number of
roller coasters, rides for adults and children,
presence of a nearby water park, shows, restaurants, and shops were included. Adding
new rides and attractions is essential to allowing a park to stand out from the competition,
and make the park more appealing as a family
destination and as a repeat destination (Newman 2004; Cobb 2005). It can be expected
that the presence of more of these amenities
and features will have a positive effect on
attendance. The population potential accessibility measures discussed earlier were
included in the regression model. Shimbel
accessibility was also included, representing
the total distance from each park to all county
centroids. A high accessibility value for this
measure indicates a park is less central, as it
has a higher total mileage from the park to
all county centroids, and vice versa.
Stepwise regression produced a model that
used five variables to explain attendance to
amusement parks in the Southeastern U.S.
(Table 4). These variables are the adult-one
day admission price for each park, daily
parking fees, year the park opened, a coding
for the year parks opened, and the network
accessibility of each park. Although the
limited number of parks in the Southeast
allows for a small number of observations,
the overall model was significant and the
model has a very high level of explanation
for park attendance (R2 = 0.953). However,
interpretation of the explanatory variables is
not straightforward.
Surprisingly, variables describing the attractions and amenities offered in the park
53
James Andrew McCall and Joe Weber
Table 3. Regression Variables
Variable
Park Size
Description
Acres
CODE Park Size
Year Opened
CODE Year Opened
Length of Operating Season
CODE Operating Season
CODE Owned by Corporation
CODE Owned by Six Flags
Adult 1-Day Admission
<100 acres=1, 101-199=2, 200-299=3, ≥ 300=4
Year
Before 1945=1, 1946-1977=2, 1978-2003=3
Days of Operation per Year
1-99 days=1, 100-150=2, 151-199=3, ≥ 200=4
NO=0, YES=1
NO=0, YES=1
$
CODE Adult 1-Day Admission
Child 1-Day Admission
CODE Child 1-Day Admission
Senior 1-Day Admission
CODE Senior 1-Day Admission
Season Pass Price
CODE Season Pass
Parking Fees/Day
CODE Parking Fees/Day
Population Potential Accessibility
Network Accessibility
Number of Roller Coasters
CODE # of Roller Coasters
Number of Adult Rides
0$=0, 1-19=1, 20-29=2, 30-39=3, 40-49=4, ≥ 50=5
$
0$=0, 1-16=1, 17-26=2, 27-34=3, ≥ 35=4
$
0$=0, 1-16=1, 17-26=2, 27-34=3, ≥ 35=4
$
0$=0, 1-49=1, 50-79=2, 80-105=3, ≥ 106=4
$
0$=0, 1-4=1, 5-8=2, 9-12=3
CODE # of Adult Rides
Number of Child Rides
CODE # of Child Rides
CODE Nearby Water Park
Shows
CODE Shows
Restaurants
CODE Restaurants
Shops
CODE Shops
CODE Conventions, Catering
1-5=1, 6-10=2,11-15=3, 16-20=4, 21-25=5, ≥ 26=6
1=1, 2-5=2, 6-8=3, ≥ 9=4
1-5=1, 6-10=2,11-15=3, 16-20=4, 21-25=5, ≥ 26=6
NO=0, YES=1, OCEANFRONT=2
Number of shows in park
NO=0, YES=1
Number of restuarants and food stands in park
0=0, 1-5=1, 6-10=2, 11-15=3, ≥ 16=4
Number of shops in park
0=0, 1-5=1, 6-10=2, ≥ 11=3
0=NO, YES=1
do not show up. Instead, the most important
variable in the regression analysis is the adult
one-day admission charge. Adult one-day admission price is directly related with yearly
attendance at each park. This must indicate
54
a relationship between willingness to pay a
high fee and satisfaction from previous visits (or expectations prior to a visit). Higher
priced parks therefore reflect high demand
based on their attractiveness. This is surpris-
Predicting Attendance At Southeastern U.S. Amusement Parks Using Accessibility- and Amenity-Based Models
Table 4. Regression Results for Amusement Parks
Dependent Variable
Attendance
Independent Variable
Constant
Adult Admission
CODE Parking
Year Opened
CODE Year opened
Accessibility
ing because the size of parks or the number of
rides does not appear in the final model, and
a categorical variable representing whether
parks are part of large corporate chains also
does not seem to be related to attendance.
Whatever park characteristics represent their
attractiveness to visitors is therefore not represented in the variables. A related variable is
the daily parking fee charged by parks, which
again show that the prices charged by parks
are directly related to its yearly attendance.
This variable must also be a reflection of past
demand, and not a predictor of future attendance.
Contradictory results are found for the
year that parks are opened. Newer parks are
associated with higher attendance values.
This may reflect novelty or publicity given
to newer parks, or perhaps the fact that newer
parks are larger. However, when parks are
coded by their opening date, an inverse relationship is found between attendance and
age. This must reflect the grouping of parks
into the three age categories (opened before
1945, 1946-1977, and after 1977) and large
attendances at parks opened in the 1960s and
1970s. Patterns exist here that have clearly
not been fully identified.
Finally, the results for Shimbel accessibility
show that parks with higher centrality have
higher attendance. Several amusement parks
near the center of the study area (including
Six Flags Over Georgia and Dollywood)
tend to have high yearly attendances, though
clearly parks in Virginia and Maryland also
have substantial numbers of visitors.
Standardized
Coefficient
-23626393
0.658
0.352
0.419
-0.548
-0.185
Significance
0
0.002
0.018
0.004
0.032
R2
0.953
CONCLUSIONS
This study has attempted to explain yearly
attendance at nineteen amusement parks in
the Southeastern U.S. From a geographical
perspective, use of accessibility models is appealing for explaining attendance to parks
based on their distances from residents of the
study area. The use of distance as an influence
on attendance or travel behavior is an important concept in geography, and is supported by
work on national parks, which has shown that
attendance varies with distance from the park
(Cole and Mitchell 1969; Hanink and Stutts
2002). Yet this is not necessarily the case, as
unlike other results with national parks, attendance at amusement parks does not strongly
decrease as a negative function of distance.
Parks that are closer to larger populations do
not necessarily have higher attendance than
those in more remote locations.
It is undoubtedly the case that attendance
levels at amusement parks have likely been
augmented by space-time convergence
within the Southeastern U.S. resulting from
improvements in transportation systems, and
centrality within the region is also associated
with higher numbers of visitors. However, it
does not stem fully from proximity to population. Neither does the size or number of
rides in the parks explain attendance, though
newer parks do tend to have larger crowds.
Instead, the adult admission price and parking fees are the primary factors found in this
research for explaining attendance at Southeastern amusement parks.
55
James Andrew McCall and Joe Weber
These findings are interesting because they
must be a reaction to other factors that have
previously created high attendance. This has
been shown for amusement parks in central
Florida, where parks steadily increased their
admissions through the 1980s, when they
saw steady attendance increases (Braun and
Soskin 1999). During this period Disney
World’s ticket price rose faster than other
parks as it was seen as the best park in the
area and so could charge a premium. Because
families may choose park destinations based
on attractions and amenities, and the total
cost of travel to parks may make the admission cost relatively unimportant, amusement
parks have no incentive to reduce cost to encourage larger crowds (Braun, Soskin, and
Cernicky 1992).
This study does not take into account the
potential for competition between amusement parks for the same customers. Competition does not have any significant effects on
national park attendance (Hanink and White
1999), but does have some for national
battlefields (Hanink and Stutts 2002). It is
possible that there is significant competition
among parks, so that the population potential measure is not appropriately specified.
While accessibility in the sense of proximity of parks to population does not seem to
be important, proximity of parks to nearby
parks (or other recreation activities) could be
of greater significance. The crucial issue is the
extent to which parks offer a similar range of
attractions. If parks differ considerably, then
competition among them should be taken
into consideration (Hanink and Stutts 2002).
However, it may also be that people perceive
each park as a unique destination, in which
case competition is not an issue.
This research also did not test for the importance of large tourist attractions (such as
found at Myrtle Beach or the Smoky Mountains) adjacent to amusement parks, except
for a dummy variable that describes whether
there is a nearby water park. The high level of
explanation produced by the final regression
models suggests that agglomeration effects
are not important, or have otherwise been
exploited by the parks in their admission
56
and parking prices. Also, while Dollywood,
which is adjacent to Great Smoky Mountains
National Park and the tourist attractions of
Gatlinburg and Pigeon Forge, does have high
attendance, the Shimbel accessibility measure
relates this attendance to centrality within
the Southeast rather than proximity to those
other tourist attractions.
It would be interesting to analyze all
amusement parks in the U.S. to determine if
these findings remain consistent. Population
potential predictions of attendance for Florida’s amusement parks would likely be less
accurate because of the enormous actual attendance figures generated by colossal resorts
like Walt Disney World, Universal Studios
Orlando, and Busch Gardens Tampa Bay,
which draw visitors from around the world.
However, a regression analysis of variables
most related to attendance for amusement
parks in Florida may have similar results.
Walt Disney World, Universal Studios Orlando, and Busch Gardens Tampa Bay all
charge high adult admission prices and high
parking fees, while all ranking within the top
eleven in yearly attendance figures for North
American amusement parks.
Although the results of this study suggest
that traditional concepts of proximity may
not explain attendance to amusement parks,
the results for national parks shows this need
not be the case with other tourist-oriented
facilities. It would be interesting to test the
population potential model with attendance
to beaches, ski resorts, concerts, and other
tourist destinations to see if results similar
to these for amusement parks hold true. It
would also be useful to examine more sophisticated interaction models to better understand the importance attached to distance.
Identifying home locations of visitors would
be essential, as assessments of the accuracy of
beta parameter exponents for distance in the
model can potentially be improved.
ACKNOWLEDGEMENTS
The authors would like to thank Gerald
Webster, James Salem, and several anonymous reviewers for their helpful comments.
Predicting Attendance At Southeastern U.S. Amusement Parks Using Accessibility- and Amenity-Based Models
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