National Forest Trail Users: 

Forest Service Planning for Recreation 

Northeastern Forest 

Experiment Station Opportunities 

Research Paper NE-685 

John J. Daigle 

Alan E. Watson 

Glenn E. Haas 
eographical regions have been classified into clusters of 

recreation activities: day hiking, undeveloped recreation, and camping-hiking. The 

regions are grouped as Appalachian, Pacific, Rocky Mountain, and Southwestern. 

Distance, time traveled, and time at site varied depending on the mix of activities. 

Average distance traveled remained approximately the same for all groups when 

substitute sites were available. However, visitors in the Rocky Mountain and Pacific 

regions indicated these were no alternative sites, or they were unaware of any. 

The Authors 

JOHN J. DAIGLE, outdoor recreation planner, received a bachelor's degree in 

Recreation Resources Management from the University of Maine in 1986 and a 

master's degree in Recreation Resources and Landscape Architecture from 

Colorado State University in 1990. He joined the U.S. Department of Agriculture, 

Forest Service, lntermountain Forest and Range Experiment Station in 1988 and 

transferred to the Northeastern Forest Experiment Station in 1991. Currently, Daigle 

is engaged in research on rural community development in a doctoral program at the 

University of Massachusetts. 

ALAN E. WATSON is research social scientist with the lntermountain Research 

Station, Wilderness Management Research Work Unit at the Forestry Sciences 

Laboratory on the University of Montana campus, Missoula. Dr. Watson attended the 

School of Forestry and Wildlife Resources, Virginia Polytechnic Institute and State 

University, Blacksburg, receiving bachelor's and master's degrees, and in 1983, a 

Ph.D. degree. His research interests primarily are in wilderness experience quality, 

including the influences of conflict, solitude, and visitor impacts. 

GLENN E. HAAS is professor and Chairman of the Department of Recreation 

Resources and Landscape Architecture at Colorado State University. Dr. Haas 

received his Ph.D. from Colorado State University in 1977. He has written many 

papers on recreation management, particularly on characteristics of use and users. 

Acknowledgment 

Special acknowledgment to David N. Cole, Research Ecologist, lntermountain 

Research Station; Lawrence A. Hartmann, Assistant Director, Planning and 

Application, Northeastern Forest Experiment Station of the U.S. Department of 

Agriculture, Forest Service; Stephen F. McCool, Professor, Department of Forestry, 

University of Montana; and Morgan P. Miles, Assistant Professor, Department of 

Marketing, Georgia Southern University, for helpful suggestions and review 

comments. 

Manuscript received for publication 14 August 1993 

USDA FOREST SERVICE 

5 RADNOR CORP CTR ST€ 200 

PO BOX 6775 

RADNOR, PA 19087-8775 

March 1994 
l ntroduction 

An increased use of trails in national forests concurrent with a 

decline in the number of trails available to visitors suggests 

that the U.S. Department of Agriculture (USDA), Forest 

Service must manage carefully the existing trails. The volume 

of use has nearly doubled in the past 15 years while trail 

mileage has decreased by approximately 30 percent (Wells 

Associates, Inc. 1985; U.S. General Accounting Office 1989). 

The trails provide for a variety of recreation activities. Trail 

use often involves other recreation: sightseeing, camping, 

and fishing. 

To maximize the potential of these trails, there is a need for 

better knowledge about trail users. Our current knowledge of 

trail users is very limited, despite wilderness visitors whose 

primary mode of travel involves hiking on trails. 

Characteristics of trail use and users is important information 

in understanding the needs of the public and in developing of 

management planning such as the Limits of Acceptable 

Change (Stankey and others 1985). Marketing research 

offers a strategy to identify specific visitors to national forests. 

While the USDA Forest Service traditionally has marketed 

itself to the public, it has not determined possible recreation 

marketing strategies in a national and regional context (Drake 

1988). As the Forest Service plans broader ecosystem 

management goals, information on trail use will become 

increasingly important. 

Public recreation services traditionally have been directed to 

"everyone" or the "average user" rather than,to "specific 

somebodies" (Crompton 1983). Knowledge of visitor 

characteristics permits an understanding of who, how many, 

when, where, and by what means people receive benefits. 

This information assists in identifying clientele, their behavior, 

and the causes and potential solutions to visitor-caused 

social and ecological impacts (Echelberger and Moeller 

1977; Roggenbuck and Lucas 1987). The identification of trail 

users based upon participation in recreation activities 

separates the average user into specific users. 

Several studies have demonstrated the potential of market 

segmentation in recreation planning (Havitz and Fridgen 

1985; Uysal and McDonald 1989). Rather than planning for 

some "average visitor," meaningful profiles of subgroups of 

visitors can be of immense value to decision makers. Shafer 

(1969), in his study of New York campers, concluded that the 

average camper does not exist. Campers differed not only 

from campground to campground, but also at the same 

campground from month to month. Crompton (1983) 

suggests that identifying specific groups, or target markets, 

provides the agency with a perspective on whose needs 

should be served before deciding what needs to serve. 

The purpose of this paper is to determine if market 

segmentation by activity clusters provides a meaningful 

customer description typology. If so, this typology will 

increase the understanding of the trail users and their 

interests. This knowledge should contribute to more 

responsive trail system management. 

The research reported here represents findings from four 

objectives of the original study (Daigle 1990): 

Identify an activity profile associated with national forest 

trail users in four regions of the United States. 

*Examine the relationship of various activity profiles in each 

region to the distance traveled in measurement of both 

hours and mileage. 

*Examine the relationship of length of stay to both activity 

profiles and distance traveled in each region. 

*Examine the relationship of availability of location with 

respect to activity profiles and distances traveled in each 

region. 

Participation in Multiple Recreation Activities 

Most studies that examine recreation opportunities focus on a 

"primary" or "singular" activity. Profiles often are developed 

that include trip characteristics and socio-economic 

information (McCollum et al. 1990). Trend studies concerning 

participation in trail and other related outdoor recreation 

activities have focused on "main" or "primary" activity (Betz 

and Cordell 1989; Hartmann et al. 1989). While this research 

is extremely useful and needs to be continued, information 

also is needed that identifies the mix of recreation activities 

that a visitor participates in while visiting a recreation area. 

The majority of outdoor recreationists participate in a variety 

of activities during a visit (Clark and Stankey 1979; Hendee 

et al. 1971 ; Williams 1988). Visitor characteristics, such as 

participation in multiple activities, may suggest a particular 

emphasis on the area's management. Certain dominant uses 

and a management desire to provide a specific opportunity 

may result in a net loss of other opportunities (Watson et al. . 

1992). Knowing the full range of experiences sought by 

visitors may help in determining appropriate management 

strategies. 

The combination of activities that recreationists would like to 

participate in and opportunities offered by the resource may 

not coincide. As the number and cultural diversity of 

recreationists who visit national forests increase, there may 

be greater need and desire for certain recreation 

opportunities. Dwyer (1992) found important differences in 

the kind and location of outdoor recreation among Blacks, 

Whites, Hispanics, and Asians. The visitor must decide upon 

activities based upon available recreation opportunites, 

personal interests, and social orientation. 

Home Range for Recreation Management 

The concept of home range has evolved from numerous 

habitat studies of animal behavior. Many wildlife texts 

associate home range with Aldo Leopold (1 933) and his 

"radius of mobility." Aldo Leopold (1 966) used an analogy to 

explain the concept by telling of his dog chasing after a rabbit 

near his farm. The rabbit knew all the hiding places, such as 

the wood pile and an old rotten log, and these allowed the 

rabbit to stay just out of the dog's reach. Familiarity of a place 
appears to be an underlying factor in Leopold's concept of 

home range: 

... the wild things that live on my farm are reluctant to 

tell me, in so many words,'how much of my 

township is included within their daily or nightly beat. 

I am curious about this, for it gives me the ratio 

between the size of their universe and the size of 

mine, and begs the much more important question, 

who is more thoroughly acquainted with the world in 

which he lives? .... 

Clark and Downing (1 985) suggest he, 

geographically bounded and behaviorally relevant area for 

individuals that influences most decisions made for a given 

activity or experience. The size of this home range may vary 

for different activities and experiences sought by individuals. 

Home range may vary in terms of time required to travel to 

the site, length of stay, and availability of alternative locations 

for participating in certain activities. 

To a degree, resource managers control the basic factors 

that determine activities and experiences available to 

recreationists. Understanding the relationship between 

available opportunities and use is important in predicting 

consequences of resource management options for the 

public (Clark and Downing 1985). "Home range" as a 

research tool allows for assessing the relative availability of, 

and demand for, different types of place-related opportunites 

for activities and experiences sought within a particular 

region. 

Recreational home range involves self-imposed geographical 

and temporal boundaries consciously or unconsciously set 

en considering recreation activities. A multitude of 

considerations such as time, money, number of people, 

weather conditions, interests of other group members, and so 

forth are part of the decision process that determines how far 

a person travels from home. The self-imposed boundaries 

are flexible when considering a combination of activities or 

even a single activity. These results represent the more 

easily measurable aspects of home range, such as distance 

traveled in miles and hours, and the length of stay, which are 

e the basic foundation in defining home range 

for various trail users. 

ublic Area Recreation Visit~r Survey 

The 1985-86 Public Area Recreation Visitors Survey 

(PARVS) was initiated to gain more knowledge about 

recreation users on both state and federally owned public 

recreation areas. Recreation users were surveyed in 

recreation areas of the U.S. Department of Agriculture, 

Forest Service, the National Park Service, the Tennessee 

Valley Authority, and the U.S. Army Corps of Engineers, plus 

12 state agencies (Cordell and others 1987). PAWS had 

three primary objectives: 

* Describe the activity patterns sf recreators onsite on public 

recreation iands. 

Obtain a description of people visiting public recreation 

areas for recreation. 

* Provide visitor expenditure data that would result in 

estimates of the regional income and employment gr 

resulting from publicly provided recreation opportunities. 

PARVS consisted of an onsite questionnaire, administered to 

randomly selected recreation site users, and a detailed 

mail-back questionnaire. In this research project, information 

obtained from onsite interviews at USDA Forest Service sites 

were utilized. The onsite questionnaire was a lengthy, but 

tightly designed survey divided into six sections; 1) interview 

number and site identification, 2) screening questions to 

identify recreation visitors, 3) onsite activity profile of current 

trip, 4) annual activity profile of recreation trips during the 

past 12 months, 5) travel time, distance, hours, origin, and 

destination, and 6) recreationist's income and trip expense. 

Three distinct stratifications were used in identifying national 

forest trail users and in examining home range factors. First, 

a sample was partitioned into known national forest trail 

users. Visitors needed to indicate that they participated in at 

least one of the following activities on a national forest site: 

hiking, backpacking, horse riding, cross-country skiing, motor 

trail use, jogging, bicycling, or interpretive walking. Second, 

because this study examined home range factors, the 

sample excluded individuals who indicated that the visit to 

the national forest was not the primary destination for their 

trip. The final stratification merged trail users into 

geographical regions that offered similar resource - 

characteristics. 

Study Areas 

The basic sampling unit for PARVS was.a USDA Forest 

Service ranger district. From the 786 ranger districts on all 

national forests, 57 were selected for PAWS recreation-site 

interviewing (McCollum et al. 1990). For the analysis, PAWS 

study sites were divided into four geographic regions. This 

approach by geographic region attempted to group 

respondents into homogeneous populations and recreation 

opportunities in examining home range factors. A total of 38 

districts and 16 wilderness areas was included for this study. 

Four regions were defined for this study: 1) Appalachian, 2) 

Pacific, 3) Rocky Mountain, and 4) Southwestern 

(Appendices 1-41. Sample size of trail users for each area 

numbered 302,851,101 9, and 351, respectfully. 

Cluster Analysis 

National forest trail users were described based upon 

participation in clusters of recreation activities. The Wards 

minimum-variance clustering method was employed to group 

people based on activities measured on dichotomous 

nominal scales of "yes" or "no" responses for 19 selected 

activities (Appendix 5). The Wards method has a built-in 

dissimilarity coefficient called an index E, which is sensitive 

to additive and proportional translations of data profiles (SAS 

1985, 1987). The method follows a series of clustering steps 
that begins with a number of clusters equal to the number of 

objects (one object per cluster), and it begins grouping 

observations based upon similarities until one cluster 

containing all objects is formed. A total of five clusters was 

identified for the Appalachian region and six clusters in each 

of the Pacific, Rocky Mountain, and Southwestern regions. 

Three criteria recommended by Everitt and Dunn (1 983) 

were followed in identifying clusters: 1) recreation activities 

within each cluster would be collinear, or as similar as 

explained by activity cluster membership; and 3) There is not 

a significant amount of variation in length of slay explained by 

activity cluster membership. If a significant difference was 

detected (p<.05), a comparison of weighted means using the 

Student-Newman-Kuels test was used in determining where 

differences existed (Howell 1982). 

Results 

Atmalachian Reaion . . - 

possible; 2) an attempt would be made to keep each cluster 

orthogonal or as nearly independent of the others as Table 1 illustrates the distribution of individuals across activity 

possible; and 3) an attempt would be made to account for as clusters in the Appalachian region. Primary differentiating 

much variability through recreation activity as activities are listed along with the percentages of individuals 

nnrrihln participating in each activity. Eighty-eight percent of ~VYY'"'". 

Appalachian trail users successfully were distributed across 

Four steps were involved in clustering people based upon 

participation in recreation activities (Daigle 1990). First, a 

computer randomly selected a split half-sample for each 

interview site within a given region. Second, the clustering 

method was applied to one of the sample halves in each 

region. A cluster history was formulated and separate 

clusters examined to determine the extent of coliinearii and 

amount of variability. Third, logic statements were developed 

that assigned individuals to the clusters suggested by the 

Wards method. This process merely interpreted the outcome 

of the cluster analysis. These statements were developed in 

an attempt to make each cluster unique, or independent, 

from the others. The final step involved the application of the 

logic statements to the remaining half of the region level data. 

This procedure tested the success in assigning individuals to 

clusters; a high percentage of individuals assigned indicated 

high success with clustering. A success rate of 85 percent or 

higher was accepted as an indication of successful clustering. 

Home Range Factors 

In addition to describing national forest trail users based upon 

participation in clusters of recreation activities, clusters were 

compared with home range factors of distance traveled in 

miles and of hours, length of stay, and substitute location. 

The home range variables were examined for missing 

information or miscoded data. Some of the onsite interview 

forms had mileage that was missing; however, the origin and 

number of hours traveled to the site were present. A road 

atlas was used to calculate mileage from the area visited to 

the county and state from where the trip originated. In a few 

cases in which travel time was missing, mileage was used to 

estimate travel time. If no information existed to help estimate 

miles or hours traveled, a missing value was accepted for 

that particular variable. 

A weighted mean for mileage and hours to the recreation 

area, hours stayed, and distance to an alternative site were 

calculated for each activity cluster (Daigle 1990). One-way 

analysis of variance was used in comparing the dependent 

variables of home range factors to the independent variables 

of trail users. Three hypotheses were tested: 1) There is not 

a significant amount of variation in miles traveled to the site 

explained by activity cluster membership; 2) There is not a 

significant amount of variation in hours traveled to the site 

the five clusters for the split half-sample. For the entire 

combined regional sample (263), 87 percent of trail users 

were assigned to clusters. The remaining individuals not 

classified by the logic statements were dropped from further 

analyses. 

The first cluster consists of all backpackers who did not 

participate in primitive camping. More than half of the 

respondents in this profile specifically indicated having 

participated in viewing and hiking. The second cluster 

contains visitors who participated in primitive camping but not 

in developed camping. One did not have to be a backpacker 

in this cluster, though 50 percent were backpackers. The 

third cluster consists of individuals who participated in hiking 

and developed camping but did not indicate participation in 

family social and backpacking activities. Nonmotorized and 

viewing activities were indicated by approximately half the 

visitors in this cluster. The fourth cluster is comprised of 

hikers who, most likely, are day users of the recreation area. 

Respondents in this cluster indicated no participation in 

developed and undeveloped camping nor in backpacking 

activities. The final cluster contains individuals who hiked and 

used developed camping facilities. Unlike the third cluster, all 

of these individuals participated in family social and 

nonmotorized activities. None of these users participated in 

undeveloped camping or backpacking, and like the previous 

four clusters, viewing continued to be indicated by most of 

the respondents. 

Potential market segments were found to differ somewhat on 

all dependent variables tested (Table 1). There was great 

variation between groups in distance and time traveled to the 

site. The backpacking group tended to travel farther and the 

developed camping group with strong interests in family 

social activities traveled less distance to the site. However, 

this developed camping group stayed significantly longer 

than all other groups. The day hiking group stayed 

significantly shorter with a mean of 16.3 hours. The median 

for this group actually was 4.5 hours, suggesting that some 

day-use individuals probably stayed overnight or day hiked 

over several visits. While mean comparisons were not made 

for distances to alternative sites due to the small number of 

visitors who specified an alternative, most groups appeared 

to identify substitute sites at nearly the same distance from 

home as the site visited. Developed campers with family 
parison of recreatlen activity clusters and their home ranges, Appalachian Region 

Recreation home ranges 

Cluster Composition Travel milesa Travel hoursb Length of stayC Alternate sited 

Number Percent ------------------Mean------------------ 

Backpacking 52 100.0 271.7 6.2 70.1 262.7 

Viewing 39 75.0 

Hiking 30 57.7 

Undeveloped camping 0 0.0 

Primitive or undeveloped camping 31 100.0 197.3 4.6 52.3 167.2 

Viewing 24 77.0 

Hiking 22 71 .O 

Backpacking 15 48.4 

Developed camping 0 0.0 

Hiking and developed camping 43 100.0 205.2 4.6 63.9 201.8 

Viewing 25 58.1 

Nonmotorized water 20 46.5 

Family social, backpacking 0 0.0 

Day hiking 104 100.0 120.7 3.0 16.3 11 2.7 

Viewing 82 78.8 

Driving 59 56.7 

Developed, undeveloped 

camping 0 0.0 

Hiking, developed camping, family 

social and nonmotorized water 33 100.0 92.8 2.6 11 2.7 139.5 

Viewing 25 75.8 

Coliection 20 60.6 

Undeveloped camping, 

backpacking 0 0.0 

%verage miles traveled for backpacking; primitive camping; hiking and developed camping were found to be significantly 

different from day hiking; hiking, developed camping, family social and nonmotorized water (pc.01). 

b~verage hours traveled for backpacking; primitive camping; hiking and developed camping were found to be significantly 

different from day hiking; hiking, developed camping, family social and nonmotorized water (p<.01). 

'hilean length of stay for hiking, developed camping, family social and nonmotorized water was found to be significantly 

different from other cluster groups. Means for the backpacking; primitive camping; and hiking and developed camping were 

found to be significantly different from day hiking and hiking, developed camping, family social and non-motorized water (pc.01). 

dComparison of means was not calculated because of small sample sizes for this variable. 

social activities showed an approximately 45-percent 

increase in this distance, suggesting alternatives to be more 

difficult to obtain for this group. 

Pacific Region 

Table 2 indicates how individuals of the Pacific region were 

distributed across activity clusters. Eighty-five percent of the 

individual cases successfully were assigned to six clusters 

for the spit half-sample. For the entire combined regional 

sample (733), 86 percent of the trail users were assigned to 

clusters. 

The first cluster contains visitors who participated in hiking, 

fishing, and developed camping. None of these individuals 

participated in undeveloped camping or pleasure driving. The 

second cluster includes individuals very similar to the first 

except that the activity of fishing is absent. All individuals 

hiked and participated in developed camping or driving for 

pleasure. The third cluster of individuals all participated in 

driving and family social activities. None of the visitors 

participated in undeveloped camping; however, 50 percent 

indicated participating in developed camping. The fourth 

cluster contains visitors who are similar to the first three in 

that they all participated in the activity of hiking. This cluster 
Table 2.--Comparison of recreation activity clusters and thslr home ranges, Pacific Region 

Recreation home ranges 

Cluster Composition Travel milesa Travel hoursb Length of stayC Alternate sited 

Hiking, developed camping, fishing 

Viewing 

Undeveloped camping, driving 

Hiking, developed camping, no 

fishing 

Viewing 

Family social 

Fishing, undeveloped camping, 

driving 

Driving and family social 

Viewing, hiking 

Developed camping 

Undeveloped camping 

Hiking and undeveloped camping 

Viewing 

Collection 

Developed camping 

No hiking and driving 

Viewing 

Backpacking 

Undeveloped camping 

Day hiking 

Viewing 

Family social, undeveloped, 

developed camping 

Number 

84 

58 

0 

129 

84 

66 

0 

I48 

137 

73 

0 

1 29 

83 

66 

0 

109 

62 

48 

46 

134 

82 

0 

Percent 

100.0 

69.0 

0.0 

100.0 

65.1 

51.2 

0.0 

100.0 

92.6 

49.3 

0.0 

100.0 

64.3 

51.2 

0.0 

0.0 

56.9 

44.0 

42.2 

100.0 

61.2 

0.0 

aNo significant differences were found between groups for average miles traveled. 

b~verage hours traveled for driving and family social was found to be significantly different from other clusters (pe.01). C Mean length of stay for hiking, developed camping, no fishing; driving and family social were found to be significantly 

different from ail other clusters. Mean for no hiking and driving was found to be significantly different from hiking, developed 

camping, no fishing; driving and family social; and day hiking (p<.01). Mean for the activity, cluster day hiking, was found to be 

significantly different from all other clusters (p<.01). 

d~omparison of means was not calculated because of small sample sizes for this variable. 

is unique, however, in that these visitors all participated in 

undeveloped camping. The fifth cluster has individuals who 

did not participate in hiking or driving but split between 

backpacking and undeveloped camping. The final cluster had 

no overnight users. 

When potential user segments were compared, visitors from 

all clusters were found to travel similar distances to the site 

chosen. There was a significant difference in time traveled to 

the site, however. The cluster indicating high involvement 

with family social and pleasure driving types of activities took 

longer to arrive at the site from home. This isn" too 

surprising. This driving, family-social cluster appeared similar 

to the developed camping group--which also was very 

interested in family social activities--in the length of time at 

the site, The average length of stay for these two groups was 

nearly 4 days. The day hikers had dramatically shorter stays 

but relatively long lengths of stay considering they were day 

users, The median stay within this group was much lower 

than the mean. Even though this group was significantly 

lower in length of stay than the others, some group members 

may not have been day users. Visitors who identified an 
alternate site re~orted similar distances to those sites as the Rockv Mountain Reaion " 

distance from home to the interview site. Day users were the percent of trail users in me Rocfil Mountlin exception to this, reporting much longer distances to the region were assigned successfully to seven clusters for the substitute sites. 

Table 3.--Comparison of recreation activity clusters and their home ranges, Rocky Mountain Region 

Recreation home ranges 

Cluster Composition Travel milesa Travel hoursb Length of stayC Alternate sited 

Hunting 

Hiking 

Viewing 

Motorized trail use 

Motorized trail day use 

Developed and undeveloped 

camping 

Primitive camping 

Hiking, viewing 

Developed camping, hunting 

Hiking and developed camping 

Viewing 

Hunt, fish, collection, 

undeveloped camping 

Day hiking 

Viewing 

Motorized trail, hunting, fishing 

Developed and undeveloped 

camping 

Hiking, developed camping, 

collection 

Viewing 

Family social 

Undeveloped camping, hunting 

Hiking, fishing, no collection 

Viewing 

Developed camping 

Undeveloped camping, 

collection 

Motorized trail, hunting 

Number 

88 

60 

47 

0 

46 

0 

126 

91 

0 

134 

91 

0 

228 

62 

0 

0 

154 

124 

96 

0 

138 

97 

84 

0 

0 

Percent 

100.0 

68.2 

53.4 

0.0 

100.0 

0.0 

100.0 

72.2 

0.0 

100.0 

67.9 

0.0 

100.0 

56.9 

0.0 

0.0 

100.0 

80.5 

62.3 

0.0 

100.0 

70.3 

60.9 

0.0 

0.0 

a~verage miles traveled for hiking and developed camping; hiking, developed camping, and collection were found to be 

significantly different from the cluster, motorized trail day-use (pc.01). 

b~nalysis of variance test showed a significant difference existed, however, the Student-Newman-Kuels test failed to reveal 

differences (pe.01). C Mean length of stay for the activity clusters, hunting; primitive camping; hiking and developed camping; hiking, developed 

camping, collection; hiking, fishing, no collection, were found to be significantly different from motorized-trail day use; and day 

hikin (pq.01). 

&omparison of means was not calculated because of small sample sizes for this variable. 
split half-sample. For the entire combined regional sample 

14),90 percent were assigned to clusters. 

One cluster identified in this region consisted of hunters who 

did not participate in any motorized trail activities (Table 3). 

Hunting activities included a variew of types: big game, small 

game, and waterfowl. Another cluster included day users, all 

of whom participated in motorized irail activities. A third 

cluster contained undeveloped campew who did not 

participate in hunting. These individuals were much more 

involved in the activities of backpacking and fishing than the 

first cluster. A fourth cluster consisted of all hikers who 

camped at developed sites. They did not hunt, fish, or 

participate in any ty e of collection activities. Members of a 

fifth cluster, a fairly large group of 228 visitors, were day 

hikers. These individuals did not participate in hunting or any 

motorized trail activities. Another cluster consisted of 

individuals who participated in the activities of hiking and 

developed camping. Unlike the fourth cluster, the activity of 

collection (such things as berries, mushrooms, or firewood 

for home use) was indicated by all respondents. The seventh 

and final cluster consisted of hikers who participated in the , 

activity of fishing. None of these respondents indicated that 

they took part in hunting, collecting, undeveloped camping, or 

motorized activity. 

When the cluster groups were compared the average 

distances traveled were foun to be fairly similar. With a 

range of 77-1 83 mils across the group means, only one of 

the day-use groups was found to report significantly shorter 

travel distances than the two deveioped camping groups. No 

differences were found in the length of time it took visitors to 

travel to the site. Once they arrived at the site, however, the 

two predominantly day-use groups stayed significantly 

shorter lengths of time than all other groups. Relatively few 

individuals identified an alternative site for this visit. Over 30 

percent of the individuals in the hunting cluster thought there 

was no alternative site. Over 20 percent of that group didn't 

know if an alternative site existed. 

outhwestern Region 

Table 4 indicates the distribution of individuals in the 

Southwestern region across activity clusters. Ninety-five 

percent of the individual cases were assigned successfully to 

six clusters for the split half-sample. For the entire combined 

regional sample (332), 95 percent of the trail users were 

assigned to clusters. 

The first cluster includes visitors who participated in 

developed camping, but not pleasure driving or fishing. The 

second cluster consisted of day users who hiked and 

participated in pleasure driving activities. Mpne of these 

individuals fished. Visitors in the third cluster camped at 

developed sites and all reported participating in pleasure 

driing activities. None of these people fished. The fourth 

cluster was a group of people who fished and camped at 

developed sites. The fifth cluster contained day hikers who 

did not participate in pleasure driving activities. The final 

escribed as undeveloped campers. 

Members of two out of thee developed camping segments, 

and the undeveloped segment, traveled significantly farther 

to the site than the day users or the one developed camping 

group. The day users showed corresponding shorter travel 

times to the sites, as well as significantly shorter lengths of 

stay. The two developed camping groups most similar in 

travel distance also had the longest mean length of stay at 

the sites. The other developed camping group and the 

undeveloped camping group were intermediate in length of 

stay, shorter than the developed camping groups, but longer 

than the day-use groups. Only 13-52 percent of the 

individuals in various clusters indicated an alternative site 

and the distance to it. 

eseareh Implications 

The use of cluster analysis methods to identify activity groups 

based on dichotomous nominal scales needs further 

verification. The Wards clustering method could be compared 

with other clustering methods to evaluate the effectiveness of 

classifying groups of visitors on this type of scale response. 

Conceptually linked activities such as social learning, passive 

free-play, appreciative-symbolic, and extractive-symbolic 

could be used in trying to explain clustered individuals 

(Hendee 1971, McCool 1978). However, clustering of 

individuals on Likert-type scales, which indicate the extent of 

participation in certain recreation activities, may provide more 

reliable clustering results than the Wards method. Dimanche 

et al. (1991) have proposed that a measure of involvement or 

participation also could increase the accuracy and 

applicability of segment definitions. Methods that assess 

desired opportunities in addition to activities participated in 

also may improve the success of marketing strategies based 

upon targeting segments, 

The use of clustering activities illustrated some limitations 

when describing cluster groups. Conditional statements 

developed from suggested clustered groups were limited to a 

number of activities. A number of key activities distinguished 

among the groups while other activities made up the rest of 

the group in lower percentages. Consequently, these 

conditional statements developed from clustering resulted in 

a net loss of valid trail users ranging from 5 to 14 percent 

depending upon region. The separation of certain activity 

clusters like backpackers with no indication of participating in 

undeveloped camping in the Appalachian region suggests 

that perception of some activities may differ or automatically 

include other activities. This presents a two-edged sword in 

that they are different based upon stated activities by cluster 

identification, yet some visitors consider them similar. 

However, if specific activity or primary activity were 

examined, visitors may be misclassified or assumptions may 

be made that merit further in-depth evaluation. 

Another limitation to clustering based on activities is the 

exclusion of other visitor data. Individuals who participated in 

day hiking sometimes had a very long length of slay, 

suggesting that groups made multiple trips during their visit to 

the nationa! forest or perhaps were campers. Social 

meanings and cultural significance influencing choice of 
Table 4.--Comparison of recreation activity clusters and their home ranges, Southwestern Region - 

Recreation home ranges 

Cluster Composition Travel miiesa Travel hoursb Length of stayC Alternate sited 

Hiking and developed camping 

Viewing 

Undeveloped camping, driving, 

fishing 

Day users - driving and hiking 

Hiking 

Viewing 

Developed and undeveloped 

camping, fishing 

Driving, developed camping 

Hiking 

Viewing 

Family social 

Fishing 

Fishing, deveioped camping 

Hiking 

Viewing 

Family social 

Undeveloped camping, driving 

Day hiking, no driving 

Hiking 

Viewing 

Developed and undeveloped 

camping, fishing, driving 

Primitive camping 

Hiking 

Viewing 

Developed camping 

Number 

60 

38 

0 

46 

44 

40 

0 

30 

28 

25 

21 

0 

69 

61 

51 

42 

0 

85 

74 

59 

0 

42 

33 

3 1 

0 

Percent 

100.0 

63.3 

0.0 

100.0 

95.7 

87.0 

0.0 

100.0 

93.3 

83.3 

70.0 

0.0 

100.0 

88.4 

73.9 

60.9 

0.0 

100.0 

87.1 

69.4 

0.0 

100.0 

78.6 

73.8 

0.0 

aAverage miles traveled for the activity clusters driving, developed camping; fishing, developed camping; primitive camping, 

were found to be significantly different from hiking and developed camping; day users - driving and hiking; and day hiking, no 

driving (p<.01). 

b~verage hours traveled for the activity clusters driving, developed camping; fishing, developed camping; primitive camping, 

were found to be significantly different from day users - driving and hiking; and day hiking, no driving (p<.01). 

'Mean length of stay for the activity clusters driving, developed camping; fishing, developed camping were found to be 

significantly different from day users - driving and hiking; day hiking, no driving; primitive camping. Mean for the activity cluster 

primitive camping was found to be significantly different from day users - driving and hiking; day hiking, no driving. Mean for the 

activity cluster hiking and developed camping was found to be significantly different from day users - driving and hiking; day 

hikin , no driving (p<.01). 

'Comparison of means war not calculated because of small sample sizes for this variable. 
activities are masked (McCool 1978). The limited information 

on alternative sites could not be correlated directly with 

specific activities or special attributes associated with the 

trails at the national forest. Conditional statements made 

before clustering activities may be necessary to identify 

certain visitor characteristics. 

Management Implications 

Despite some of the limitations, cluster analysis to identify 

market groups of trail users appears to be a valuable 

segmenting procedure. Visitors should no longer be 

considered just hikers or campers. The complex assortment 

of activities they participate in can be sorted and described in 

meaningful ways with implications for management. 

Information on travel and trip characteristics of these visitors 

has potential implications to management and planning policy 

on both a micro- and macrolevel in the USDA Forest Service. 

Understanding the characteristics of the various cluster 

groups gives insight into the combinations of services that 

national forest visitors need. Strategies used to develop 

policies should utilize marketing techniques that target these 

clusters. 

While the regions differed somewhat in the number of activity 

clusters defined, many similarities of groups of activities were 

evident across all four regions. In each of the four 

geographical regions at least one day-hiking and one 

undeveloped camping cluster was found. At least two 

developed camping and hiking activity clusters were evident 

in each region with various amounts of participation in other 

activities such as viewing, family social activities, 

nonmotorized water, and collecting. Hunting and motorized 

trail use activities clustered naturally in the Rocky Mountain 

region. For one cluster in the Pacific region and for two 

clusters in the Southwestern region, everyone participated in 

driving for pleasure. Collecting and family social activities 

were participated in at higher rates by individuals in the 

undeveloped and developed camping activity clusters in all 

four regions. 

When activity clusters were compared in each region, 

average distances traveled to the sites generally were 

different. These differences were not necessarily consistent 

across regions, though day users (motorized and 

nonmotorized) tended to drive shorter distances and have 

corresponding short drive times, Campers drove farther for 

participation and took longer to get there, particularly those 

developed campers who report participation in pleasure 

driving. Generally, it appears that average driving distances 

are shortest for the Pacific Region of the country (grand 

mean of about 125 miles) and longest for the Appalachian 

Region (grand mean of about 170 miles). The overall mean 

driving times for the Appalachian and Rocky Mountain 

Regions appears similar (around 4 hours) and the 

Southwestern and Pacific Regions average about 3.5 hours 

travel time across all visitors interviewed. These results 

suggest an opposing view that travel to the more numerous 

and small sites in the East are shorter than travel to western 

locations (Lucas 1980; Watson et al. 1986). 

Length of stay in all four regions varied significantly for the 

different activity clusters. Length of stay, as would be 

expected, tended to be much shorter for day-use clusters. 

The mean length of stay, however, tended to be very long for 

day-use clusters, suggesting that several visitors probably 

were campers or considered their excursions on the trail as 

only day use. There was some variation in camping clusters, 

with individuals who participated in undeveloped camping 

having shorter mean lengths of stay for the Southwestern, 

Appalachian, and Pacific regions. They were not 

distinguishable in the Rocky Mountain region. The 

information examined on alternative sites provides a unique 

perspective in looking at various activity clusters. In general, 

the mean distances to the alternative site were comparable to 

distances traveled to originally selected recreation sites. 

Some exceptions included the day- hiking activity cluster in 

the Pacific region and undeveloped camping cluster in the 

Rocky Mountain region. Day hikers in the Pacific region 

would have to travel 4 to 5 times the mean distance to actual 

sites visited, and Rocky Mountain campers would have to 

travel twice the distance to a substituted undeveloped site. 

The ability to identify visitor characteristics and label them 

into workable groups is the primary advantage of using a 

cluster analysis technique. The number of activities found to 

be participated in by members in each activity cluster 

supports previous studies. Provided the opportunity, an 

individual typically will participate in a variety of activities 

during an outdoor recreation visit. To know the various mixes 

of activities sought by current visitors is important information 

for planning, developing, and monitoring trail resources. 

Identification of different types of users could point out 

potential conflicts or problems caused by continual or 

increased use by specific activity clusters. Knowing the status 

of users and changes occurring within activity clusters would 

be a crucial aspect in monitoring to assure quality recreation 

experiences and opportunities. 
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George H.; McCool, Stephen F., comps. Proceedings of 

the symposium on recreation choice behavior; 5 984 March 

22-23; Misssula, MT. Gen. Tech. Rep INT-184. Ogden, 

UT: US. Department of Agriculture, Forest Service, 

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Appendix 1 

Appalachian Region 

New Hampshire 

Androscoggin District, White Mountain National Forest 

Tennessee 

Citico Creek Wilderness, Cherokee national Forest 

North Carolina Vermont 

Cheoha District, Nantahala National Forest Bristol Cliffs Wilderness, Green Mountain National Forest 

Joyce Kilmer-Slickrock Wilderness, Nantahala-Cherokee 

National Forest West Virginia 

Tellicao District, Cherokee National Forest Greenbriar District, Monongahela National Forest 

Appendix 2 

Pacific Region 

California 

Goosenest District, Klamath National Forest 

Mt. Shasta Wilderness, Shasta-Trinity National Forest 

Mammoth District, lnyo National Forest 

Minarets District, Sierra National Forest 

Monterey District, Los Padres National Forest 

San Gorgonio Wilderness, San Bernardino National Forest 

Valyermo District, Tahoe Basin Management Unit 

Oregon 

Ashland District, Rogue River National Forest 

Big Summit District, Ochoco~National Forest 

Klamath District, Winema National Forest 

McKenzie District, Willamette National Forest 

Wenaha-Tucannon Wilderness, Umatilla National Forest 

Unity District, Wallowa-Whitman National Forest 

Oakridge District, Wlllamette National Forest 

Washington 

Cle Elum District, Wenatchee National Forest 

Colonel Bob Wilderness, Olympia National Forest 

White River District, Mt. Baker-Snoqualmie National Forest 

Appendix 3 

Rocky Mountain Region 

Colorado 

Blanco District, White River National Forest 

Dillon District, White River National Forest 

Pine District, San Juan National Forest 

La Garita Wilderness, Gunnison-Rio Grande National 

Forest Indian Peaks Wilderness, Arapaho- Roosevelt 

National 

Forest 

South Platte District, Pike-San Isabel National Forest 

Idaho 

New Meadows District, Payette National Forest 

Elk City District, Nez Perce National Forest 

Priest Lake District, ldaho Panhandle National Forest 

Salmon River District, Nez Perce National Forest 

Teton Basin District, TargeeNational Forest 

Montana 

Ashland District, Custer National Forest 

Beartooth District, Custer National Forest 

Great Bear Wilderness, Flathead National Forest 

Hungry Horse District, Flathead National Forest 

Utah 

Cedar City District, Dixie National Forest 

Flaming Gorge District, Ashley National Forest 

Logan District, Wasatch-Cashe National Forest 

Wyoming 

Jedediah Smith Wilderness, Targee National Forest 

Big Penny District, Bridger-Teton National Forest 

Tensleep District, Bighorn National Forest 
Appendix 4 

Southwestern Region 

Arizona 

Bear Wallow Wilderness, Apache-Sitgreaves National 

Forest Payson District, Tonto National Forest 

Pusch Ridge Wilderness, Coronda National Forest 

Kachina Peaks Wilderness, Coconino National Forest 

Sierra Vista District, Coronado National Forest 

Springerville District, Apache-Sitgreaves National Forest 

Appendix 5 

PARVS Activities Included in the 

Cluster Analysis: 

Hiking 

Hiking 

Walking 

Interpretive walks 

Ranger talks 

Self guided booklet 

Motorized trail 

Snowmobile 

Off road vehicle 

Backpacking 

Jogging 

Bicycling 

Horseback riding 

Cross country skiing 

Flshing 

Cold freshwater 

Warm water 

Anadromous 

Developed camping 

Undeveloped camping 

New Mexico 

Dome Wilderness, Sante Fe National Forest 

Glenwood District, Gila National Forest 

Mimbres District, Gila National Forest 

Espanola District, Santa Fe National Forest 

Viewing 

Photography 

Sightseeing 

Information center 

Historic areas 

Wildlife observation 

Nature study 

Family social 

Picnicing 

Family gathering 

Campfire program 

Attend special event 

Nonmotorized water activities 

Canoeing 

Sailing 

Outdoor pool 

Swimming 

Motorized water activities 

Motorboat 

Water skiing 

Other motorized crafts 

Winter Activities 

Ice skating 

Sledding 

Downhill skiing 

Other winter activities 

Hunting 

Big game hunting 

Waterfowl hunting 

Small game hunting 

Other hunting 

Collecting 

Firewood 

Berries 

Driving 

AU. 5. GOVERNMENT PRINTING OFFICE: 1994 - 550- 1 12 / 800 12 
Daigle, John J.; Watson,. Alan E.; Haas, Glenn E. 1994. National forest trail 

users: planning for recreation opportunities. Research Paper NE-685. 

Radnor, PA: US. Department of Agriculture, Forest Service, Northeastern 

Forest Experiment Station. 13 p. 

National forest trail users in four geographical regions of the United States are 

described based on participation in clusters of recreation activities. Visitors 

are classified into day hiking, undeveloped recreation, and two developed 

camping and hiking activity clusters for the Appalachian, Pacific, Rocky 

Mountain, and Southwestern regions. Distance and time traveled to national 

forest sites from home varied for activity clusters. Length of time at the site 

varied across activity clusters. Recreation activities combined with home 

range allows for assessing relative availability of, and demand for, different 

types of place-related opportunities and experiences users seek within a 

particular region. 

KEYWORDS: cluster analysis, home range, visitor management, recreation 

activities, market segmentation. 
Headquarters of the Northeastern Forest Experiment Station is In Radnor, Pennsylvania. Field laboratories are maintained at: 

Amherst, Massachusetts, in cooperation with the University of Massachusetts 

Burlington, Vermont, in cooperation with the University of Vermont 

Delaware, ,Ohio 

Durham, New Hampshire, in cooperation with the University of New Hampshire 

Hamden, Connecticut, in cooperation with Yale University 

Morgantown, West Virginia, in cooperation with West Virginia University 

Orono, Maine, in cooperation with the University of Maine 

Parsons, West Virginia 

Princeton, West Virginia 

Syracuse, New York, in cooperation with the State University of New York, 

College of Environmental Sciences and Forestry at Syracuse University 

University Park, Pennsylvania, in cooperation with The Pennsylvania State University 

Warren, Pennsylvania 

Persons of any race, color, national origin, sex, age, religion, or with any handicapping condition are welcome to use and enjoy all facilities, programs, and services of 

the USDA. Discrimination in any form is strictly against agency policy and should be 

reported to the Secretary of Agriculture, Washington, DC 20250. 

"Caring for the Land and Serving People Through Research" 

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