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3:05 I LIBRARY

Michigan State
University

 

 

 

This is to certify that the
dissertation entitled

CHARACTERIZING AND MODELING THE RECREATION
USE OF DISTANCE SEGMENTED USDA FOREST SERVICE
VISITORS

presented by

Eric M. White

has been accepted towards fulfillment
of the requirements for the

Doctoral degree in Forestry

ﬂaw/U-

MajGEB‘ofessor’s Signature
4i»?1 03+ 17,, 2005’

Date

MSU is an Affirmative Action/Equal Opportunity Institution

PLACE IN RETURN BOX to remove this checkout from your record.
TO AVOID FINES return on or before date due.
MAY BE RECALLED with earlier due date if requested.

 

DATE DUE

DATE DUE

DATE DUE

 

MAY 2 7 2009

 

120908

 

 

 

 

 

 

 

 

 

 

 

 

 

m

CHARACTERIZING AND MODELING THE RECREATION USE OF
DISTANCE SEGMENTED USDA FOREST SERVICE VISITORS

By

Eric M. White

A DISSERTATION

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

DOCTOR OF PHILOSOPHY

Department of Forestry

2005

 

ABSTRACT

CHARACTERIZIN G AND MODELING THE RECREATION USE OF
DISTANCE SEGMENTED USDA FOREST SERVICE VISITORS

By

Eric M. White

The recreation behavior, consumption patterns, and activity participation of
visitors to lands managed by the USDA Forest Service (USDA FS) is highly variable. To
adequately manage and plan for recreation at the local level, USDA FS natural resource
managers must identify the types and quantity of recreation use at individual national
forests. This study presents an approach to segmenting and modeling the recreation use of
national forest visitors that informs recreation management and planning decisions.
Under the adopted segmentation framework, national forest visitors are classiﬁed into
distance-based visitor segments based upon the proximity of their home to national forest
visited. Three distance segments are recognized: Local, Mid-distance, and Long-distance.
Local visitors live very close to the national forest, Mid-distance visitors live within a
moderate drive of the forest resource, and Long-distance visitors live in the “rest of the
world”. Using visitor survey data obtained for USDA FS Regions 2 and 9 via the
National Visitor Use Monitoring (NVUM) project, visitors in the three segments are
characterized in terms of their recreation behavior, consumption patterns, and activity
participation. Statistical tests are completed to determine differences in visitor

characteristics both between study regions and between the visitor segments themselves.

 

Few statistical differences are found between study region after accounting for
differences due to visitor segmentation and trip type. Capitalizing on the segmentation
framework, recreation use models are developed to predict the forest-level recreation use
of Local and Mid-distance recreation segments. Models of Local segment recreation use
predict visitation based upon local population counts, participation rates and annual visit
frequencies. The recreation use of Mid-distance visitors is modeled via multi-site zonal
travel cost models. Separate zonal travel cost models were estimated for Mid-distance
day trips and Mid-distance overnight trips. While the parameters and coefﬁcients of the
constructed models were consistent with theory, evaluation of model prediction proved

inconclusive.

 

 

ACKNOWLEDGEMENTS

This dissertation could not have been completed without the support of many
individuals. First and foremost I would like to thank Larry Leefers for his support and
guidance as my committee chair. I have greatly enjoyed having him as a mentor and
friend. I thank him for his dedication and hard work in assisting me through the PhD
process. I would also like to thank the members of my committee, Dan Stynes, Steve
Friedman, Charles Nelson, and Don English, for their assistance. I owe a special debt of
gratitude to Daniel Stynes for hiring me on as a graduate assistant—I very much enjoyed

working with you.

My graduate assistantship was funded with support from the USDA Forest
Service. I thank Ross Arnold, USDA FS Washington Ofﬁce, for his continuous efforts to
keep the money flowing. I also thank Mike Vasievich for providing the initial link
between the NVUM project and MSU. The MSU Department of Forestry and the MSU

Graduate School also provided funding at points throughout my program.

I am also grateful for the assistance provided by Don English, Stan Zamoch and
Sue Kocis as I waded my way through the N VUM data and process. A special thanks to

Greg Alward and Susan Winter for their assistance throughout my time here at MSU.

I thank my family for their support and love, in this and in all things. Last, but
certainly not least, I thank Anita Morzillo. I look forward with great anticipation to our

adventures and our long life together.

iv

 

 

TABLE OF CONTENTS

List of Tables ..................................................................................... viii
List of Figures ..................................................................................... xii
Chapter 1. Introduction ............................................................................ 1
Research Problem ......................................................................... 3
Research Objectives ...................................................................... 9
Organization of the Dissertation ....................................................... 10
Chapter 2. Literature Review .................................................................... 12
Demand and Use Estimation in Recreation Planning ............................... 13

Methods to Estimate Recreation Demand and Use...... ....15

Structural Models of Recreation Demand and Use .................................. 17
Population-Speciﬁc Models ............................................................ 19
Travel Cost Models ...................................................................... 20
Early Travel Cost Applications ......................................................... 22
Contemporary Zonal Travel Cost Models ............................................ 24
Contemporary Recreation Models Incorporating GIS .............................. 26
Recreation Segmentation. .' ............................................................. 29
Relationship to the Current Study ...................................................... 33
Conclusion ................................................................................ 37
Chapter 3. Methods .................................................................................. 38
Conceptual Discussion of Distance-based Visitor Segmentation ................... 38
National Visitor Use Monitoring Project ............................................. 46

 

 

 

Cl.
-lL

 

NVUM Visitor Survey ......................................................... 48

Data Weighting Schemes ...................................................... 49

Study Area ................................................................................ 51
NVUM Survey Data ..................................................................... 53
Distance Segmentation of NVUM Survey Respondents ........................... 56
Characterizing Distance-based Segments ............................................... 59
Modeling Segment Visitation .......................................................... 61
General Description ............................................................ 62

Local Visitor Recreation Use Model ......................................... 68
Mid-distance Visitor Recreation Use Models ............................... 74
Evaluation of Model Predictive Ability ............................................... 80
Chapter 4. Results and Discussion ............................................................... 88
Introduction ............................................................................... 88
Characteristics of Distance Segmented Visitors ...................................... 88
Segmentation of NVUM Survey Respondents .............................. 88
Regional Comparisons within Segments ..................................... 90
Distance Segment Comparisons .............................................. 98
Summary of Distance Segment Characteristics ........................... 111
Recreation Use Models ................................................................ 112
Local Permanent Resident Model ........................................... 113

Local Seasonal Resident Model ............................................. 123
Mid—distance Recreation Use Models ....................................... 127
Evaluation of Combined Model Predictions ............................... 136

vi

Model Application to Out-of—Sample National Forests ................... 139

Discussion of Model Results .................................................. 141

Chapter 5. Summary, Conclusions, and Policy Implications ............................... 146
Introduction .............................................................................. 146
Summary ................................................................................. 146
Conclusions .............................................................................. 152

Policy Implications ..................................................................... 154
Limitations and Recommendations for Future Research .......................... 157
Appendix A ....................................................................................... 162
Introduction .............................................................................. 163

Proxy Sites ............................................................................... 163

Sample Day Selection .................................................................. 164
Survey Revisions ....................................................................... 165
NVUM Visit Estimation ............................................................... 167
Appendix B ....................................................................................... 195
Appendix C ....................................................................................... 207
Literature Cited ................................................................................... 167

vii

LIST OF TABLES

Table 1. Postulated Characteristics of Local, Mid-distance, and
Long-distance Visitors .................................................................... 42

Table 2. Administrative National Forests (NF) within USDA FS Regions 2 and 9 ...... 51

Table 3. NVUM Survey Variables ............................................................... 55
Table 4. Distance bands used in Local and Mid-distance recreation use models ......... 64
Table 5. Forest aggregation for estimating AVij .............................................................. 71
Table 6. Spatial data sources for publicly-owned land ........................................ 79

Table 7. NVUM Estimates of Local Visitor Recreation Use for USDA FS Regions
and National Forests (NF) in the Study Area .......................................... 83

Table 8. NVUM Estimates of Mid-distance Visitor Use for Regions and National
Forests (NF) in the Study Area ............................................................................ 85

Table 9. NVUM Estimates of Summed Local and Mid-distance Recreation Use .......... 86

Table 10. NVUM Estimates of the Percentage of Local Visitor use as a Function of
Summed Local and Mid-distance Use ................................................... 87

Table 11. Number of NVUM Survey Respondents by Distance Segment and
USDA FS Study Region ................................................................. 89

Table 12. Percent of Total Recreation Use by Distance Segment and
USDA FS Study Region ................................................................. 90

Table 13. Trip-type Segment Shares by Distance Segment and
USDA FS Study Region .................................................................. 92

Table 14. Mean Values for Visitor Characteristics of Interest by distance

Segment, Trip-type, and USDA FS Region ........................................... 94
Table 15. Day of National Forest Arrival by Distance Segment, Trip-type,

and USDA FS Region .................................................................... 95
Table 16. Primary Activity by Distance Segment and USDA FS Region .................. 98

viii

Table 17. Mean Values for Visitor Characteristics of Interest by Distance

Segment, and USDA FS Region ........................................................ 99
Table 18. Day of National Forest Arrival by Distance Segment and

USDA FS Region ........................................................................ 107
Table 19. Primary Activity by Distance Segment and USDA FS Region ................. 108
Table 20. Trip-type Segment Shares by Distance Segment and USDA FS

Study Region .............................................................................. 109
Table 21. Mean Values for Visitor Characteristics of Interest by

Distance Segment, Trip-type, and USDA FS Region ............................... 110
Table 22. Regional Participation Rates of Populations Residing Within

30 Miles of National Forests in the USDA FS Study Regions .................... 114
Table 23. Forest-level Distance Band Populations for NVUM forests

sampled in FY2001, FY2002, FY2003 ............................................... 115
Table 24. Distance Band Annual Visit Frequencies of Local Visitors by

Region and National Forest Aggregation Group .................................... 116
Table 25. Model and NVUM Estimates of Permanent Resident Local

Visitor Recreation Use .................................................................. 1 18
Table 26. Local Visitor Model Errors ......................................................... 119
Table 27. Local visitor Segment Shares Estimated Under Two

Alternative Weighting Schemes ....................................................... 123
Table 28. Seasonal Homes Proximate to NVUM forests sampled in

FY 2001, FY2002, and FY2003 ....................................................... 124
Table 29. Model Estimates of Seasonal Local Resident Recreation Use. . . . . . 126

Table 30. Model and NV UM Estimates of Seasonal Resident Local Visitor
Recreation Use for FY 2003 NVUM National Forests ............................. 127

Table 31. Day and Overnight Mid-distance Segment Recreation Use
Models, USDA FS Region 2 ........................................................... 130

Table 32. Day and Overnight Mid-distance Segment Recreation Use
Models, USDA FS Region 9 ........................................................... 132

ix

Table 33. Model Predictions and NVUM Estimates of Mid-distance

Segment Recreation Use, USDA FS Region 2 ...................................... 133
Table 34. Model Predictions and NVUM Estimates of Mid-distance

Segment Recreation Use, USDA FS Region 9 ...................................... 134
Table 35. Mid-distance Visitor Model Errors ................................................ 135

Table 36. Model Predictions and NVUM estimates of Local and
Mid-distance Recreation Use ......................................................... 137

Table 37. Local Percentage of Local and Mid-distance Segment Recreation
Use based upon Model Prediction and NVUM Estimates ......................... 138

Table 38. Model and NVUM Estimates of Permanent Resident Local
Visitor Recreation Use for Out-of-Sample Forests ................................. 139

Table 39. Model Predictions and NVUM Estimates of Mid-distance
Segment Recreation Use for Out-of—Sample Forests ............................... 140

Table 40. Local Percentage of Local and Mid-distance Segment Recreation
Use based upon Model Prediction and NV UM Estimates,
Out-of-Sample Forests ................................................................. 141

Table B-1. NVUM Estimates Recreation Use by Forest and Distance Bands
from 30 — 200 miles ................................................................... 174

Table B-2. NVUM Estimates of the Percentages of Day and Overnight Trips
by 30 — 200 Mile Distance Band for National Forests in Region 2. . . . . . . . .....175

Table B-3. NVUM Estimates of the Percentages of Day and Overnight Trips
by 30 - 200 Mile Distance Band for National Forests in Region 9 ............ 176

Table B-4. NVUM Estimates of Day and Overnight Recreation Use by
Distance Band for National Forests in Region 2 ................................. 177

Table B-5. NVUM Estimates of Day and Overnight Recreation Use by
Distance Band for National Forests in Region 9 ................................. 178

Table B-6. Modeled Recreation Use Estimates and Conﬁdence Intervals for
National Forests in the Study Area Sampled in
FY 2001, FY2002, FY2003 ......................................................... 179

Table B-7. Local Use Percentages of Modeled Recreation Use for
National Forests (NF) in the Study Area .......................................... 180

 

Table B-8. Mid-distance Use Percentages and Primary Purpose Percentages of
Modeled Recreation Use for National Forests (NF) in the Study Area. 181

Table C-l. Standard Errors of Annual Visit Frequency Estimates by
Region and National Forest Aggregation Group ............................... 183

Table C-2. Multi-site Zonal Travel Cost Model Inputs, USDA FS Region 2 ...... 184

Table C-3. Multi-site Zonal Travel Cost Model Inputs, USDA FS Region 9 ...... 193

xi

LIST OF FIGURES

Figure 1. USDA FS Regions 2 and 9 .......................................................
Figure 2. Distance Segmentation of USDA FS Visitors ..................................
Figure 3. Cumulative Percent of Visitation Across Visitor Origin Distance Bands......
Figure 4. River and lake barriers to travel in the study area ..............................
Figure 5. Euclidean and Barrier-adjusted Distance Estimation ..........................

Figure 6. Conceptualization of quantifying a forest’s local population ................

Figure 7. Percent of housing units classiﬁed as seasonal homes in Census 2000
for census block groups within 30 miles of national forests in USDA FS

regions 2 and 9 ........................................................................

Figure 8. Distance Segment Visit Frequency, USDA FS Region 2

(Truncated at 12 Annual Visits, Exposure Weighted) .............................

Figure 9. Distance Segment Visit Frequency, USDA FS Region 9

(Truncated at 12 Annual Visits, Exposure Weighted) ...........................

Figure 10. Duration of National Forest Visits by Distance Segments,
USDA FS Region 2 (Rounded to Nearest Hour,

Truncated at 24 hours, ExposureWtd.) .............................................

Figure 11. Duration of National Forest Visits by Distance Segments,
USDA FS Region 9 (Rounded to Nearest Hour,

Truncated at 24 hours, ExposureWtd.) .............................................

Figure 12. Number of sites (and GFA days) by Distance Visitor Segment,

USDA FS Region 2 (Figure Truncated at 5 Sites, Exposure Weighted) ......

Figure 13. Number of sites (and GFA days) by Distance Visitor Segment,

USDA FS Region 9 (Figure Truncated at 5 Sites, Exposure Weighted). . . .

Figure 14. Number of Activities by Distance Segmented, USDA FS

Region 2 (Figure Truncated at 10 Activities, Exposure Weighted) ............

Figure 15. Number of Activities by Distance Segmented, USDA FS

Region 9 (Figure Truncated at 10 Activities, Exposure Weighted). . . . . . . . .

xii

..... 9

....39

.58

....66

....67

....69

....72

..100

...100

..101

..102

...103

103

..104

105

Figure 16. People per Vehicle by Distance Segment, USDA FS
Region 2 (Exposure Weighted) ........................................................ 106

Figure 17. People per Vehicle by Distance Segment, USDA FS
Region 2 (Exposure Weighted) ........................................................ 106

xiii

CHAPTER 1

INTRODUCTION

Recreation, encompassing a wide array of activities, is a fundamental component
of American life. While many recreation pursuits take place indoors, recreation in the
outdoors is prominent. It is estimated that 98% of people in the US, 16 years of age or
older, participate in some form of outdoor recreation activity in a given year (Interagency
Survey Consortium, 2002). The rate of participation in outdoor recreation activity has
remained steady to slightly increasing since the early 1960’s (Outdoor Recreation
Resources Review Commission, 1962; National Park Service, 1986; Interagency Survey
Consortium, 1995; Interagency Survey Consortium, 2002). Individuals in the US. are
most likely to participate in relatively passive outdoor recreation activities such as
walking (82%) or taking part in family gatherings (74%). More active outdoor recreation
pursuits such as developed camping (26%), mountain biking (21%), or hunting ( 11%) are

undertaken with less propensity (Interagency Survey Consortium, 2002).

While privately-owned lands also provide recreation opportunities, publicly-
owned lands have long served a central role in the provision of places for people to
recreate. The basis for public entry to lands for the purpose of recreation was established
by the “ﬁshing and fowling” laws enacted in the colonial period (Douglass, 1999). The
establishment of public recreation areas began in 1710 with the city forest of Philadelphia

and continued in the mid 1800’s with the work of Frederick Law Olmstead and the

advent of city parks (Douglass, 1999). Public recreation on federally-owned lands in the
US. began in the latter half of the 19‘h century and expanded rapidly in the early 20th
century; concurrent with the expansion of the forest reserves managed by the then US.

Forest Service and with the establishment of the National Park Service.

In the early 21St century, the role of publicly—owned lands as the primary provider
of outdoor recreation opportunities is ﬁrmly cemented. Wellman and Propst (2004) state:
“. . .these (lands) have allowed many American to participate in
forms of recreation that honor their pioneering heritage, respond to their
desire to take risks, allow them temporarily to move from their complex
everyday environments into quieter and greener places, and offer special
opportunities for them to discover things about themselves and their
surroundings”.
Recreation opportunities are provided on lands owned by all levels of government. City-
and county-owned lands may support a limited number of outdoor recreation activities
(e.g. walking, picnicking, etc.), while state- and federally-owned land areas may provide
opportunities for a greater variety of pursuits (e. g. picnicking, camping, rock climbing,

hunting, etc.).

Federally, the USDA Forest Service (USDA PS), the National Park Service
(NPS), the Bureau of Land Management (BLM) and the US. Army Corps of Engineers
(ACOE), among others, manage land for public recreation. In 2003, NPS sites across the

nation received an estimated 266 million visits (NPS, Public Use Statistics Ofﬁce, 2004).

The ACOE receives approximately 385 million visits annually at its managed recreation
areas (U .S. ACOE, 2004). Annual visitation to USDA Forest Service lands is estimated
to be 205 million visits (English, Pers. Comm.) while the BLM reports approximately 61
million annual visits (BLM, 1999). The NPS manages 95 million acres of land and the
ACOE manages 12 million acres of land and water. Both agencies manage lands located
throughout the nation. The USDA FS and BLM manage much larger areas than the other
two agencies, 191 million acres and 262 million acres, respectively. Lands managed by
the BLM are located almost exclusively in the western contiguous US. and Alaska while
USDA FS land is concentrated in the contiguous west, but located throughout the nation.
With extensive land holdings, accessibility to much of the US. population, a variety of
natural features, and high rates of visitation, the USDA FS is an indispensable provider of

recreation opportunity in the US. and is the focus of the research presented here.

Research Problem

Characterizing recreation visitors and quantifying recreation use are central to
USDA FS planning activities (Dana, 1957; Propst, 1985; Alig and Voss, 1995).
Characterizing recreation visitors in regard to their consumption patterns and preferences
assists in identifying the types of recreation opportunities that may best meet demand,
determining what recreation facilities to develop and where, and selecting speciﬁc “on
the ground” management actions. Reliable estimates of visitation are useful for forest
plan revision, for completing economic impact analysis, in developing estimates of social

beneﬁts, and for completing beneﬁt-cost analyses. Visitor characterization and estimation

of recreation use are often completed concurrently and can be achieved via three
approaches: conducting a complete census of visitors, implementing a visitor sampling

program, or developing and applying recreation demand models.

A census of all visitors to USDA FS lands is generally accepted as impractical
and unnecessary. Attempts to determine recreation use via a census in the ﬂedgling years
of the USDA FS proved mostly futile (Waugh, 1918). The dispersed nature of recreation
and the expansive land area, accessible at a multitude of points, precludes the counting of
every USDA FS recreation visitor. Furthermore, the reliability of visitor sampling

approaches and recreation modeling makes a census unnecessary.

The USDA FS has implemented a number of projects aimed at estimating
recreation use and visitors characteristics via visitor sampling. Recent projects of note
include PARVS, CUSTOMER, and National Visitor Use Monitoring (NVUM). PARVS
and CUSTOMER were initiated in the latter half of the 1980’s while NVUM began in the
year 2000 and continues today. While PARVS and CUSTOMER surveys provided useful
information, the sample sizes were small, implementation was not standardized, and the
visitor samples were not representative of all USDA FS visitors (Alward et al., 1998). In
response to these problems, USDA FS scientists and analysts developed a national-level,
standardized method for quantifying recreation use and visitor characteristics called
National Visitor Use Monitoring (English et al., 2002). Through NVUM the USDA FS

develops national, regional, and forest-level estimates of visitor use and characteristics.

Models of recreation use can be used to characterize and quantify recreation use
under both current as well as alternative conditions and can provide analysts with a
greater understanding of the processes behind recreation use patterns and behavior.
Grant and others (1997) identified ﬁve general applications of scientiﬁc models:

1) providing a sound conceptual framework for future research,

2) evaluating alternative hypotheses about system structure or function,

3) describing system behavior under normal conditions,

4) predicting system response to speciﬁc management schemes or environmental

situations, and

5) heuristically exploring the dynamics of a system of interest.
A limited number of recreation models have been completed speciﬁcally for resources
managed by the USDA FS (e.g. English and Bowker, 1996; English and Home, 1996;
Alig and Voss, 1997; Loomis et al., 2001; Betz et al., 2003). Of these, only models
developed by English and Home (1996) were used to estimate recreation use for multiple
national forests. The remaining models were aimed at quantifying recreation use for

individual sites or for speciﬁc activities occurring on individual national forests.

For the purpose of planning and management, USDA FS recreation use has
typically been characterized (or segmented) and quantiﬁed based upon the primary
activities of recreation visitors (USDA FS recreation is also commonly characterized and
quantiﬁed by Recreation Opportunity Spectrum, ROS, class). This primary activity
approach was used in the ﬁrst census of visitors in 1916 as well as the PARVS surveys of

the 1980’s (Waugh, 1918; Alward et al., 1998). While this approach quantifies the types

of activities users are engaged in it may not provide particularly informative or reliable
information concerning behavior and/or patterns of recreation use. Speciﬁcally, the
behavior and use patterns of visitors within activity segments may be just as variable as

those of the visitor population at large.

An alternative approach to segmenting recreation visitors is one based upon the
interceding distance between visitor residence and the forest resource. Speciﬁcally, does
the visitor live very close to the forest (a local visitor), within a moderate drive from the
forest (a mid-distance visitor), or several hundred miles or more from the forest (a long-
distance visitor)? A limited number of recreation studies have shown that the intervening
distance from the visitor’s residence to the recreation destination may inﬂuence
recreation visitor consumption pattern, activity participation, recreation behavior, and trip

motivation.

Strauss and others (1993) found the proximity of hunters to the hunting
destination inﬂuenced their pattern of recreation consumption. Hunters at the Delaware
Water Gap who lived outside the local area were found to hunt almost exclusively on
Saturdays, while those from the local area hunted on days throughout the week. Though
such a pattern is intuitive, the consumption patterns of hunters are frequently
characterized without regard to hunter residence. Recreation participation by certain
demographic groups may be more likely given their proximity to the resource. Faunce
and others (1979), in their study of Maine hunters, found that participation of women

hunters was more common among resident Maine hunters than among non-resident

Maine hunters. This difference likely relates to differences in hunter motivation between

those living in the State and those living out-of-state.

In regards to recreation behavior, Stynes and White (2003) have shown that the
expenditures of USDA FS recreation visitors who live in the local forest area are almost
always less than that of non-local users within trip-type segments, regardless of
recreation activity. Finally, Etzel and Woodside (1983) found that visitors who recreated
near home were motivated more by opportunity for relaxation and recuperation while
visitors recreating farther from home expected more stimulation and entertainment in
their recreation experience. Additionally, Faunce and others (1979) found that non-
resident Maine hunters were more frequently motivated by the social interaction

component of hunting than resident hunters.

Segmenting recreation users based upon the distance from their home to the
recreation resource may provide for a greater understanding of consumption patterns,
activity participation, and recreation behavior of USDA FS visitors. This may lead to
more informed decisions by resource managers regarding the provision and management
of recreation opportunities. Assuming that visitors within distinct distance segments have
unique recreation behaviors, activity participation, and consumption patterns, quantifying
forest-level recreation use of the segments would allow individual forests to identify their

“recreation markets”.

Recreation models can be used to determine the expected forest-level recreation
use of the distance-based segments under current and alternate conditions. In addition to
predicting use, recreation models developed for individual distance segments can provide
insight into the underlying mechanisms inﬂuencing recreation use of visitors within those
distance segments. An understanding of the mechanisms inﬂuencing recreation use of
distance segment visitors can aid resource planners and managers in identifying factors

that inﬂuence the level of recreation use at an individual national forest.

The USDA FS has traditionally taken a regional approach to planning for and
managing actual recreation use (e. g. 1990 RPA assessment regional recreation use
values). The assumption of regional recreation differences may result, in part, from the
perception that regional variation in the types of natural features managed yields
differences in recreation behavior and participation. Given this, testing for regional
differences in consumption patterns, activity participation, and recreation behavior of
distance segmented recreation visitors between distinct USDA FS regions is one
component of this study. USDA FS regions 2 and 9 are particularly appropriate for
comparative analysis (Figure 1). National forests within these regions are spatially

disjoint from one another and the natural features managed are, in general, quite different.

 

. l

uswa Region 2]

 

 

 

 

 

 

E] National Forests
Figure 1. USDA FS Regions 2 and EIRoaionaI Boundariu

Research Objectives

Given the above, the general objective of this study is to identify the
characteristics and model the forest-level recreation use of distance-segmented USDA FS
recreation visitors. The four speciﬁc objectives of the study are to:

1) identify the recreation behavior, activity participation, and consumption

patterns of Local, Mid-distance, and Long-distance USDA FS visitors in
USDA FS Regions 2 and 9,

2) statistically compare the recreation behavior, activity participation, and

consumption patterns of visitors to USDA FS Regions 2 and 9 within the

distance-based segments,

3) statistically compare the recreation behavior, activity participation, and
consumption patterns of Local, Mid-distance, and Long-distance visitors
within USDA FS Regions 2 and 9, and

4) model forest-level recreation use of Local and Mid-distance recreation visitors

for national forests located in USDA FS Regions 2 and 9.

Organization of the Dissertation

Chapter 2, Literature Review, is an examination of I) demand and use estimation
in forest recreation planning, 2) broad approaches employed to estimate recreation
demand and use 3) use of structural recreation demand and use models and 4) the role of
recreation segmentation in recreation planning. Also included are discussions of the
theoretical basis and evolution of travel cost modeling, descriptions of contemporary
applications of recreation modeling utilizing spatial analytical techniques, and

identiﬁcation of commonly employed recreation segmentation approaches.

Chapter 3, Methods, provides a discussion of the data and analytical techniques
used in achieving the research objectives identiﬁed above. The chapter begins with a
conceptual discussion of the distance-based segments and the relationship of the
segmentation to the recreation literature. A short description of the NVUM project,
survey data and recreation use estimates follows. Further detail on the NVUM sampling
scheme and the NVUM procedures for estimating recreation use can be found in

Appendix A. The remainder of Chapter 3 is devoted to describing the speciﬁc procedures

10

used in analyses. This section begins with delineation of the boundaries separating the
distance segments from one another. The approaches to characterizing visitors within the
distance segments and the statistical tests employed to identify differences between the
study regions and between the distance segments are then identiﬁed. The ﬁnal portion of
the Chapter relates to the development and evaluation of the Local and Mid-distance

recreation use models.

Chapter 4, Results and Discussion, is divided into two halves; the ﬁrst relates to
objectives 1 — 3 while the second relates to Objective 4. The ﬁrst half of the Chapter
identiﬁes the characteristics of the distance-based visitor segments as well as results of
statistical comparisons both between study regions and between distance segments. In the
second half of the Chapter, the constructed recreation use models are presented, and the
parameters and coefﬁcients are discussed. The predictive ability of the recreation use

models is evaluated against NVUM recreation use estimates.

In Chapter 5, Summary, Conclusion, and Policy Recommendations, a summary of
the research undertaken is presented, and conclusions related to the research objectives
are identiﬁed. Policy implications, in regard to forest planning activities, are presented

and discussed. The limitations of the research and future research needs are outlined.

11

CHAPTER 2

LITERATURE REVIEW

The impetus for this research is the need to quantify the types and numbers of
national forest recreation users for forest-level planning. The role of recreation use
estimation in recreation planning activities is identiﬁed in the ﬁrst section of this chapter.
The second section includes discussion of how recreation demand is incorporated in the
planning process and the approaches to developing recreation use estimates. The use of
recreation models, particularly the travel cost model, is described in greater detail. The
third section contains a description of traditional USDA FS approaches to segmenting
recreation visitors as well as some alternative approaches to visitor segmentation. The

chapter closes with a summary of how the literature relates to this study.

An inﬂuential report that deserves a special introductory note is “Assessing
Demand for Outdoor Recreation” published in 1975 by the National Academy of
Sciences. This report was written by the Committee on Assessment of Demand for
Outdoor Recreation Resources established by the now dissolved Bureau of Outdoor
Recreation. The committee was tasked with investigating four speciﬁc components of
recreation demand analysis: identifying the objectives of demand analysis, reviewing
systematic models of demand estimation, specifying the parameters of a recommended
demand estimation approach, and identifying the steps of to effectively estimate demand.
Two appendices were also included in that report. The ﬁrst (written by Bev Driver and

Perry Brown) examined recreation demand from a social-psychological perspective while

12

 

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the second (written by V. Kerry Smith) provided a comprehensive analysis of recreation
demand models. The report (with its appendices) is a preeminent piece of recreation

demand literature and is cited frequently within this chapter.

Demand and Use Estimation in Recreation Planning

Public recreation resources are limited. Like all scarce resources, public resources
must be allocated between competing demands. Whereas allocation of traded resources
can be determined by markets, public recreation resources generally provide non-market
public goods and services and, as such, their allocation is not as easily determined. In the
course of natural resource planning, the “demand” for recreation resources or activities as
well as the beneﬁts or values that those resource or activities provide are frequently
quantiﬁed. The Renewable Resource Planning Act of 1974 and the National Forest
Management Act of 1976 require the USDA PS to complete national-level natural
resource assessments every 10 years and to develop individual forest-level plans every 10
to 15 years. Both the national assessment and individual forest plans must incorporate
multiple uses (including recreation) of natural resources as mandated by the Multiple Use
Sustained Yield Act of 1960. At the state-level, efforts to quantify recreation demand
increased dramatically in response to the 1962 report of the Outdoor Recreation
Resources Review Commission (ORRRC) and the associated enactment of the Land and

Water Conservation Fund.

13

The speciﬁc role of quantifying recreation demand for the purpose of planning is
in “illuminating and measuring the implications of alternative planning, provision, and
management decisions” (NAS, 1975). In particular, quantifying recreation demand
provides information for three kinds of planning decisions: policy decisions, allocation
decisions, and site-speciﬁc resource provision and management. These kinds of planning
decisions correspond to three aspects of recreation demand: demand for recreation in the
context of broad social and economic policy, demand for alternative types of recreation,

and the demand for site-speciﬁc recreation resources (NAS, 1975).

The ﬁrst aspect of recreation demand focuses on the importance of outdoor
recreation opportunities as a component of overall social structure. Decisions that affect
recreation opportunities have an impact on “. . .a range of diverse elements, including
economic, industrial, and population growth. ...” (NAS, 1975). The second aspect of
demand encompasses those decisions relating to determining the type, quantity, and
location of recreation opportunities, and well as the strategy and timing for their
implementation (NAS, 1975). Assessment of this aspect of demand requires that the
planning agency recognizes what recreation opportunities are desired by users and what

management actions are needed to fulﬁll those desires.

The third aspect of demand focuses on choosing the site and selecting the kind
and quantity of recreation resources, facilities, and programs provided (N AS, 1975). This
can be achieved by identifying potential users, recognizing the alternative sites available

to them, and determining the relationship between site characteristics and the user group

14

(NAS, 1975). In all cases, recreation decisions are facilitated by quantifying the expected
mix of user segments and the activities and site characteristics that these segments desire

(Hendee, 1967; NAS, 1975; Dwyer et al., 1977; Bojanic and Wamick, 1994).

While it is mandated that recreation demand be incorporated in planning
activities, doing so is made difﬁcult, in part, by disparities in identifying what “recreation
demand” represents. In the Preface of the 1975 report of The Committee on Assessment
of Demand for Outdoor Recreation Resources (The Committee), the Associate Director
of the Bureau of Recreation described the concept of demand as “[o]ne of the most often
used, least understood, and most signiﬁcant concepts in outdoor recreation planning”
(NAS, 1975). In the vernacular sense, recreation demand may be conceptualized as the
total number of visits to a recreation resource or the expected participation in a recreation
activity during a given period. Economists, however, view recreation demand not as one
point of consumption but rather as a schedule of expected recreation use given a range of
prices or costs (Clawson and Knetsch, 1967). From the perspective of social-
psychologists, recreation demand represents the preferences or desires of individuals
regardless of whether or not those desires and preferences result in recreation

participation (Driver and Brown, 1975).

Methods to Estimate Recreation Demand and Use

The Committee, adopting a broad deﬁnition of demand, outlined four approaches

to estimating demand for outdoor recreation: application of standards, projections of use,

15

structural models, and expression of perceived wants (NAS, 1975). Standards, used to
determine the “desired” number of recreation opportunities for a given population size or
area, have been identiﬁed primarily for urban settings. The Committee suggested
standards are not particularly effective at indicating recreation demand. In part this is due
to the assumptions of applying this technique, namely that social recreation desires, the
quality and attributes of recreation sites, and population characteristics are homogenous
across areas (NAS, 1975). The second approach, projecting demand, is achieved,
generally, by extrapolating historic visitation patterns to some point in the future. This
technique intemalizes the relationship between visit volume and price for every point in
time. As such, the use of projections “. . .ignores the interaction of social and economic
conditions with recreation and the changing determinants of individual recreation
decisions, which may, in fact, cause people to behave differently in the future” (NAS,

1975).

Structural models (the third approach) estimate demand by parameterization of
the relationships between demand/supply of recreation opportunities and the factors that
inﬂuence demand. This allows the user to explicitly identify the relationships between
demand and the factors inﬂuencing recreation consumption (N AS, 1975). Structural
models allow for demand to be quantiﬁed for proposed and existing sites as well as
developing demand estimates after proposed quality improvements or degradation at
existing sites (NAS, 1975). One of the drawbacks to this method is that while
relationships are assumed to be causal they may in fact be spurious. Other limitations to

this approach include some difﬁcultly in capturing the motivation of individual

16

recreationists via independent variables and the lack of variation in independent
variables—which may lead to poor prediction (NAS, 1975). The ﬁnal method of
estimating demand is accomplished via public input on the number and types of
recreation opportunities desired. This can be accomplished through surveys, analysis of
public participation in the planning process, and/or examination of operation budgets

(NAS, 1975).

Structural Models of Recreation Demand and Use

Models of recreation demand and use can be developed along a continuum from
those that are exceedingly simplistic to those that are very complex. Moeller and
Echelberger (1974) stated the selected model “should be dynamic so that changes in
management policy, recreation supply, price, etc. can be incorporated into the model to
determine their potential impact. . .”. Structural models of recreation may be used to
estimate recreation use given supply and demand factors (e. g. Walsh et al., 1992; Hanink
and Stutts, 2002), to identify the use value of recreation resources (e. g. Bowker et al.,
1996; Fix and Loomis, 1997), or to accomplish both (Boxall et al., 1996; Loomis, 2002).
Smith (1975) identiﬁed three broad classes of models of recreation demand and use: site-

speciﬁc user models, population-speciﬁc models, and site-speciﬁc area models.

Site-speciﬁc user models depend upon data collected from individual site users.

This data may include their motivations, attitudes, or satisfaction levels (Smith, 1975).

Models of this type do not assume homogeneity in services consumed from the site to

17

recreation users. On the contrary, “(these) studies attempt to determine what individuals
reactions to heterogeneous services will be” (Smith, 1975). Fix and Loomis (1997),
Provencher and Bishop, (1997), and Loomis and others (2001) adopted the site-specific

user approach to modeling recreation behavior.

Population-speciﬁc models differ from the other two model types in that
information is collected from both participants and non-participants (Smith, 1975). These
types of models generally collect information on demand for particular services or
activities (Smith, 1975). From the information collected, participation (or participation
rate) for speciﬁc activities (or in some cases sites) can be determined for groups of users
(Smith, 1975). Smith suggests that two problems exist with this approach. First, the
observations (survey responses) are “. . .the result of the individual’s own demand and
effective supply”. The implication being that the model output incorporates many
different individual demand and supply conditions to estimate a single demand
relationship. Second, for the purpose of beneﬁt estimation, this procedure may determine
average expenditure per day while the demand curve operates on the basis of marginal
costs of production. Using this approach results in inappropriate calculations of beneﬁt
(Smith, 1975). Population-speciﬁc models include those of Cicchetti (1973), Walsh and

others (1988), Bowker and others (1999), and Romano and others (2000).

Site-speciﬁc area models are also developed from information gathered from site
users (Smith, 1975). Most important to the use of this approach is identification of the

origin of the visitor. Travel cost models, as described in Clawson and Knetsch (1967),

18

fall into this class of demand models. In general, these models relate the number of
recreation visits over a range of distances that recreationists travel (representative of cost
from an economic standpoint) to develop the recreation demand curve and expected use
for the site. Since the data collected in this method is aggregate in nature, the
characteristics of individual users are not identiﬁed; rather they are assumed to be
homogenous within zones. Models by Hellerstein (1991), Loomis and others (1995), and

Cho and others (2001) are contemporary examples of this approach.

Population-Speciﬁc Models

Cicchetti (1973) introduced a two-step population-speciﬁc model to forecast
recreation use for speciﬁc recreation activities. Estimation of the model required survey
data collected from both participants and non-participants. The ﬁrst stage of the model
predicted the probability that an individual (or individuals) would participate in a given
recreation activity while the second stage of the model predicted the expected number
recreation occurrences for a participating individual in a given year. The total recreation
use in any one activity is obtained by combining the number of participants with the
expected number of recreation occurrences per participant. Socio-economic,
demographic, and recreation supply variables were included as independent variables in
both model stages. Using estimates of future population counts and demographic and
socio-economic conditions, Cicchettii (1973) completed several example applications to
forecast future recreation use for a variety of water-based and land-based recreation

activities.

19

Travel Cost Models

In their seminal text Clawson and Knetsch (1967) discussed at length the concept
of estimating recreation demand curves, recreation use, and recreation beneﬁt, through
the use of travel cost models. Despite the prominence of the text, the authors deferred to
Hotelling (1949) and Van Doren (1960) as providing the initial impetus to using travel
cost to estimate recreation demand curves. Using distance traveled as a proxy for cost,
Clawson and Knetsch (1967) constructed recreation site demand schedules for a variety
of recreation areas. In general, origins in close proximity to the recreation site (with
associated low travel cost) contribute a greater number of visits to the sites (per capita)
than origins at a greater distance from the recreation site (higher travel cost). Total
economic beneﬁt to society is calculated as the area under the second stage demand curve
(Clawson and Knetsch, 1967). The second stage curve represents what the demand
schedule would be under increasing price. Given this, total consumer surplus can be

calculated as the entire area under the demand curve.

The travel cost approach to demand and beneﬁt estimation, as outlined by
Clawson and Knetsch (1967) and further developed by Cicchetti and others (1976) and
Cesario and Knetsch (1976), is best applied under the following conditions: 1) travel
costs are variable among users, 2) proposed changes are large enough to alter travel costs
to users, and 3) travel costs are primarily associated with the recreation site under study
(Dwyer et al., 1977). Travel cost modeling is not particularly suited for sites that attract

u331‘s primarily from areas in close proximity to the site, for sites that attract many pass-

20

thru or non-primary users, or for sites that are exceptionally large in size with multiple
entrances (Dwyer et al., 1977). Several assumptions are required when estimating
demand and beneﬁts. Speciﬁcally, recreation visits do not include trips to other recreation
destinations, the travel portion of the trip does not provide any beneﬁt to the visitor, trips
are of uniform duration, and individuals have similar travel patterns and travel means
(Loomis and Walsh, 1997). Given the conditions and assumptions, travel cost models are
better suited to modeling the recreation use of visitors who visit one recreation resource
and who travel a moderate distance to do so. The recreation use of those visitors who live
in close proximity to the recreation resource or those who visit the resource as part of a

multi-purpose trip is better modeled using a different approach.

Travel cost models can be constructed for single sites (simple travel cost models)
or for multiple sites within a region (regional multi-site travel cost models) (Dwyer et al.,
1977; Loomis and Walsh, 1997). The regional travel cost model includes multiple
recreation sites (frequently) of varying quality so the impacts of potential recreation
quality changes on an individual site can be identified. Dwyer and others (1977) stated
that regional travel cost models can be of two primary forms; speciﬁcally, a system of
linear demand equations or a single demand equation incorporating a gravity model.
Regardless of form the general procedure to construct a travel cost model is as follows
(Dwyer et al., 1977):

1) Complete a survey of visitors or households to collect visit, trip, and

demographic information,

21

2) Classify sites based upon their amenities and the recreation opportunities
provided,

3) Deﬁne the origin unit of analysis,

4) Estimate travel cost on a round trip basis,

5) Determine round-trip travel time,

6) Identify substitute recreation destinations,

7) Derive socio-economic variables of interest from necessary data, and

8) Estimate the demand model using the appropriate functional form.

Early Travel Cost Applications

The initial applications of the method outlined by Clawson and Knetsch (1967)
laid the groundwork for the development of the zonal travel cost model that we know
today. Three examples of these early applications are Burt and Brewer (1971), Cesario

and Knetsch (1976), and Cicchetti and others (1976).

Burt and Brewer (1971) presented a conceptual model for deriving the social
beneﬁt of additions to an existing set of outdoor recreation sites. This is an extension
(though the estimation of beneﬁt is approached from a somewhat different perspective) of
Clawson and Knetsch’s (1967) model of a single recreation site. The authors
subsequently applied their model to estimate the demand and beneﬁt of proposed ACOE
reservoirs in Missouri using a household survey and a system of demand equations where

the explanatory variables are travel price from visitor origins to water-resource

22

destinations and income. For the three proposed reservoirs the authors estimated an
annual visitation of 1.1 million household visit days with an associated annual beneﬁt of

$8.5 million dollars.

Cicchetti and others (1976) extended the work of Clawson and Knetsch (1967) to
explicitly incorporate the availability of substitute recreation destinations in the
calculation of social beneﬁt. The authors applied their model to a set of ski areas in
southern California to determine the net social beneﬁt associated with development of a
proposed ski area. The authors used a system of demand equations incorporating travel
cost to develop an estimate of the social beneﬁt of the proposed area. Comparing the total
social beneﬁt of the development with the costs (and using appropriate discounting), the

authors suggested the proposed development had a negative net social benefit.

In contrast to the previous example Cesario and Knetsch (1976) used a single
demand equation to estimate their travel cost model. The traditional travel cost model
proposed in Clawson and Knetsch (1967) did not include the use of a travel time variable
in predicting demand. This was due in part to the multicollinearity of travel cost and
travel time. Cesario and Knetsch (1976) suggested failing to incorporate travel time
would lead to overestimation of consumption from users at distant origins. The use of
these long-distance visitors would be overestimated as a result of failing to account for
the greater cost (value) of the increased time required to reach the recreation site. To

correct this overestimation, the authors constructed a composite variable representing

23

both travel costs and time which was then included in the model of demand. The model

was found to perform well when applied to a set of Pennsylvania State Parks.

Contemporary Zonal Travel Cost Models

In their 1986 article Ward and Loomis completed a comprehensive overview of
the evolution of travel cost modeling from the late 1970’s to the late 1980’s. The authors
described three empirical forms of travel cost modeling that continued to evolve during
the period: zonal, individual, and hybrid. The zonal approach to travel cost modeling is
described above. To overcome some of the limitations of the zonal technique the
individual travel cost model was developed. In this formulation of the travel cost model,
individual speciﬁc travel times, travel costs, and socioeconomic characteristics are
identiﬁed. Travel cost models are developed from this information for individuals (rather
than zones) where the dependent variable is the individual’s trips (rather than zonal per
capita trips). The hybrid travel cost model incorporates portions of both the individual
and zonal travel cost models. Hybrid models employ nested decision trees to estimate
individual demand models (Ward and Loomis, 1986). These models frequently take the

form of multi-nomial logit models (e. g. Provencher and Bishop, 1997).

Contemporary examples of zonal travel cost models include Hellerstein (1991),
Loomis and others (1995), and Bowker and English (1996). Hellerstein (1991) used
permit count data to construct a model of demand for the Boundary Waters Canoe Area

(BWCA). The model predicted the per capita visitation from counties surrounding

24

BWCA. To control for the constrained nature of the dependent variable (i.e. visitation to
BWCA from countyi) both Poisson and Negative Binomial models were used. For
comparison, a semi-log model was speciﬁed and ﬁtted using ordinary least squares and a
bias correction recommended by Stynes and others (1986). The author found the Poisson
model estimated via pseudo-maximum likelihood performed best. Model selection had
little inﬂuence on the coefﬁcient estimates but had signiﬁcant inﬂuence on the coefﬁcient

standard errors.

Loomis and others (1995) developed and tested the transferability of demand
models between ACOE districts. The authors constructed demand models for day users
and campers for three ACOE districts. The transferability of these models between
ACOE districts was subsequently tested. Explanatory variables included visitor
demographics (aggregated at the county level), the reservoir characteristics of the site(s)
of interest, available substitutes, and characteristics of the facilities available at the
site(s). The models were ﬁtted using a non-linear least squares model and the Heckman
two-stage model (Heckman, 1979). The authors stated that the Heckman model was
particularly suited to data with a large number of zeros in the dependent variable. While
both models performed well, the Heckman model had all the expected coefﬁcient signs.
Using the Chow test the authors found that coefﬁcient estimates were statistically
different between all models and could not be transferred across regions. Despite this, the
authors state that the models of ACOE districts in the mid-south (two of the considered
districts) were very similar to one another and suggested that geography and similarity of

demographics may greatly inﬂuence the transferability of demand models.

25

English and Bowker (1996) developed zonal travel cost models for day trip
visitors to the Chatooga River along the border of Georgia and North Carolina. While the
primary research objective was to determine the impact of alternate cost speciﬁcations on
consumer surplus estimates, their paper also serves as a good example of a contemporary
approach to model speciﬁcation when estimating zonal travel cost models. Models were
estimated using both single log ordinary least squares regression and Tobit models
estimated via maximum likelihood estimation. The OLS model was estimated with the
dependent variable number of trips +1, while the Tobit model was ﬁt with the dependent
variables number of trips and trips/capita. A value of 1 was added to the OLS formulation
to insure that zones with zero trips were included in the model. Without the adjustment

these zones would fall out of the model since the natural log of zero is undeﬁned.

Contemporary Recreation Models Incorporating GIS

The advent of Geographic Information Systems (GIS) has greatly improved the
ability of researchers to analyze recreation data in a spatial context. Several recent
recreation demand studies have explicitly incorporated the use of a GIS in the modeling
process (Alig and Voss, 1995; Lovett et al., 1997; Brainard et al., 2001; Hanink and

Stutts, 2002).

Alig and Voss (1995) attempted to model current camper use, and to predict

future camper use for the Chequamegon and Nicolet national forests in Wisconsin using

26

age-speciﬁc participation rates and demographic data. Counties were the unit of analysis
for visitor origins. The proximity and demographics of populations living around the
national forests were determined via a GIS incorporating spatial databases of county
boundaries, national forest boundaries, and US. census bureau data. Camper recreation
use was modeled as a function of the population and participation rates of those living
within 125 miles (the estimated maximum travel distance of campers) of the national
forests. The model predictions of recreation use were signiﬁcantly greater than the
observed campground visitation. The authors cited several reasons for failing to validate
the model, including the failure to incorporate substitute recreation sites and a non-
uniform pattern of population distribution. Furthermore, the authors stated that the
estimates of visitation were highly dependent upon the determination of the market area,

for which no reliable data existed.

Lovett and others (1997) were more successful in developing a recreation demand
model for woodlands in England. First, using a geographic information system (GIS) the
authors determined network distance and travel time for origin-destination pairs
identiﬁed from a sample of woodland visitors. Second, the authors developed a substitute
grid surface from a land use classiﬁcation system to determine a substitute index for each
origin, explicitly capturing the availability of substitute sites. Finally, socioeconomic and
demographic variables were associated with each origin in the study. Incorporating
population, travel time, the interaction between travel time and population, and
unemployment rate, the authors used Poisson regression techniques to specify the

recreation use model. The model successfully predicted total site visitation but performed

27

poorly at estimating visitation by origin. The authors suggested that identifying different
sub-groups within the population and specifying multiple demand functions to represent

these groups might improve model performance.

Brainard and others (2001) improved the model constructed by Lovett and others
(1997). The primary improvements to the model were the calculation of a market access
variable and the inclusion of site amenity data. Market access for each site in the study
was quantiﬁed by using a distance decay function developed from visitation count data
and an interpolated population surface estimated from the US. Census. The use of a
market access variable greatly improved the performance of the model. The authors
suggested using larger sample sizes and developing a set of user-speciﬁc models would

increase the transferability of the model.

Most recently, Hanink and Stutts (2002) developed demand models for National
Battleﬁeld Parks. The model estimated expected visitation based upon market access, the
distance to substitute battleﬁelds, and battleﬁeld amenities. Market access in this case
was determined based upon distance to metropolitan statistical areas rather than the
population of distributed areas. The speciﬁed models performed very well at predicting
total battleﬁeld visitation. Independent variables explaining a large proportion of
variation in visitation were “market potential” and “number of historic battleﬁeld

casualties”.

28

Recreation Segmentation

To allocate recreation resources between competing recreation uses, planners
must identify the types, quantities, and locations of the recreation opportunities
demanded. In turn, decisions related to site-speciﬁc management require an
understanding of the kind and quality of recreation resources, programs, and facilities
demanded by users (N AS, 1975). Models estimating total use or demand in aggregate
provide little information in the context of these decisions. To provide more information,
expected use levels or demand is frequently estimated within user groups (user
segments). These user segments should be meaningful in that they are applicable to
planning and management decisions while at the same time explaining recreation

behavior within groups.

Segmentation is defined as the “process of dividing a large heterogeneous
population into smaller homogenous subsets” (Bojanic and Wamick, 1994). Reid (1989),
as cited in Bojanic and Wamick (1994), identiﬁed ﬁve broad approaches to
segmentation: geographic, demographic, psychographic, behavioral, and beneﬁt.
Geographic segmentation classiﬁes visitors based upon the location of their residence.
The demographic approach classiﬁes visitors based upon demographic and
socioeconomic variables such as age, gender, income, race, family status, etc.
Psychographic segmentation classiﬁes users based upon the motivations of the user while

behavioral segmentation segments visitors based upon their actions or activities. A

29

beneﬁts-based segmentation classiﬁes visitors based upon the personal beneﬁts or utility

received from recreating.

The USDA FS has a lengthy history of classifying users based upon their primary
recreation activity (one aspect of visitor recreation behavior). The ﬁrst attempt to census
visitors to USDA FS lands was mandated to be made within speciﬁc recreation activities
(Waugh, 1918). More recent survey efforts, such as the CUSTOMER survey in the
1980’s, have also attempted to estimate visitor use within speciﬁc activity groups of
interest (Alward et al., 1998). The USDA FS also uses a beneﬁts-based segmentation
approach when classifying recreation use by Recreation Opportunity Spectrum (ROS)
classes. One assumption of the ROS approach is that users of the different ROS class
areas have differing expectations and receive differing beneﬁts; that is, visitors who
recreate in primitive non-motorized areas likely receive different beneﬁts than visitors
recreating in rural areas. This approach to segmentation is complicated by visitors who
recreate within multiple ROS classes and uncertainty as to whether many visitors can

readily identify and make recreation decisions incorporating the ROS classiﬁcation.

Activity segmentation is attractive in that the relationships between activities and
management actions appear straightforward. However, users within activity groups can
still remain very heterogeneous in their recreation characteristics and behavior. Average
trip spending for USDA FS ﬁshing parties can vary from $46 to $222 depending upon
Whether the party is on a day trip from the local area or whether they live outside the

local area and are spending the night on the forest (Stynes et al., 2003). Similarly, non-

30

local snowmobile parties spend nearly 50% more in total than local parties; spending $20

more in restaurants and $15 more on gas and oil.

Faunce and others (1979) have found differences in spending, motivation, and
participation when comparing resident and non-resident Maine hunters. N on-resident
hunters spent nearly twice their resident counterparts and were more frequently motivated
by the social interaction aspect of hunting while resident hunters were more motivated by
sustenance. The participation rate of women among resident hunters was much greater
than non-resident hunters. Etzel and Woodside (1982) found differences in expenditures,
frequency of participation, family status, and expectations when comparing “near home”
and “distant” general vacation travelers. Travelers staying near home spent less on their
trip, had previously visited the area more times, had larger travel groups, and expected
rest and relaxation rather than excitement, personal growth, or intellectual stimulation

compared to distant travelers.

Some researchers have employed recreation segmentation approaches based on
factors other than primary activity. In his dissertation aimed at proﬁling recreation users,
Hendee (1967) segmented visitors into ﬁve recreation groups based upon the
recreationists’ stated recreation resource preference: national forest wilderness users
(dispersed areas), national park wildemess users (dispersed areas), national forest car
campground users, national park car campground users, and state park users. User groups
differed along socioeconomic variables including age, income, family status, and in their

attitudes toward management activities and use of natural resources.

31

Cordell (2003) has segmented the US population based upon recreation behavior
and participation patterns using data from the National Survey on Recreation and the
Environment. Eight recreationist segments are identified: inactives, passives, nature
loving drivers, nature and family, activity samplers, motor consumptives, skiers, and
enthusiasts. Inactives represent the largest segment and are least likely to participate in
outdoor recreation while enthusiasts represent the second smallest segment but have the
highest rates of outdoor recreation participation. Demographic and socioeconomic
patterns differ between groups as do attitudes toward natural resource use and
management. Identifying meaningful differences in terms of demographic characteristics

and attitudes can assist natural resource managers and policy makers in decision-making.

Stynes (1999), Stynes and others (2003), and Stynes and White (2003), for the
purpose of explaining spending, have adopted a user segmentation approach based upon
the type of trip completed and whether the visitor lives in proximity to the recreation
resource. The trip type includes day trips and overnight trips with overnight trips further
differentiated by the type of lodging used. Trip type segmentation performs better at
explaining individual spending than activity segmentation—the former explaining 18%
of variation while the latter explains only 2% (Stynes and White, 2003). In addition, party
characteristics, such as trip length, number of people, and number of children, were also

found to differ by trip type segment (Stynes and White, 2003; Stynes et al., 2003).

32

Relationship to the Current Study

Assuming there are differences between distance segments in terms of recreation
behavior, activity preference, and participation, models estimating recreation use within
segments will assist in long—term, forest-level allocation, and site-speciﬁc recreation
planning. To be most useful, the model output should produce information that can be
used to identify the level of participation in different types of activities, the types of
recreation trips undertaken, the characteristics of visitors and visitor parties, and temporal
patterns in recreation use. If possible, it would also be useful to identify what spatial
patterns of recreation use are expected. The expected recreation use of distance segments
accompanied with a characterization of the distance segments is expected to provide such

information.

Frequently, recreation use models have estimated the recreation consumption (or
demand) for a specific recreation resource conjointly for all potential recreation visitors
(or for all visitors participating in an activity of interest). However, it seems logical that
visitors located at a range of distances from the resource respond differently to the factors
that inﬂuence recreation behavior. For example, visitors in close proximity to resources
may incorporate travel cost into their recreation behavior differently than those located
farther away. In this case, using a single model relating travel cost to recreation use for all
visitors does not appear to be appropriate. Inability to replicate visitation by origin while
correctly predicting total visitation (e.g. in Lovett et al., 1997) may be a manifestation of

this functional change in relationship between travel distance and visitation.

33

The failure to incorporate substitute recreation sites in the study conducted by
Alig and Voss (1995) resulted in poor model performance, speciﬁcally overestimation of
recreation use. Selecting an approach to quantifying the availability of substitute
recreation sites for recreation use models is challenging. In part, this is due to the lack of
a clear theoretical basis for identifying substitutes and the small number of studies
investigating how recreationists View substitutes and how they decide between alternate
recreation areas. Three frequently adopted approaches to quantifying substitute
availability include 1) distance (or travel cost) to a single (or a limited number of)
alternative recreation site(s), 2) development of a substitute index incorporating distances
to multiple sites and the quality (or attractiveness) of those sites, or 3) creation of a

spatial substitute layer using a GIS.

Hellerstein (1991) and Boxall and others (1996) adopted the first approach in their
travel cost studies of the Boundary Waters Canoe Area in Minnesota and Rocky-
Clearwater forest in Alberta, respectively. This approach to quantifying substitutes seems
to be more frequently adopted when there is only one recreation resource of interest and a
nearby recreation area is clearly identiﬁable as a substitute. In the former, the substitute
site was Algonquin Provincial Park in Ontario (offering similar canoeing and
backcountry experiences) while in the latter study the substitute was Bow-Crow Forest (a
nearby Alberta Forest Service public land area). In both studies the coefﬁcient on the
substitute variable was statistically signiﬁcant and had the expected positive sign. In a

Variation of this approach some researchers have identified a number of potential

34

 

 

 

substitute sites (e. g. all state parks) and identified the minimum distance (or travel cost)

to the single nearest substitute site for each visitor origin (e. g. Fix and Loomis, 1997).

Substitute indexes were adopted in Loomis and others (1995) and Hanink and
Stutts (2002). In the former, the substitute index calculated for each visitor origin was the
ratio of lake acres (the selected measure of recreation site quality) to travel distance
summed across all identiﬁed substitute sites. In the latter, the authors used two substitute
indices, one incorporating competing site quality (the number of historic battleﬁeld
casualties) and one not incorporating competing site quality. In both of these studies,
recreation use models were estimated for multiple recreation areas and there was no
clearly identiﬁable single recreation site that could serve as a substitute. In both studies
the statistical signiﬁcance of substitute index variables were mixed depending upon the

chosen functional form of the model.

Brainard and others (2001) opted to develop a “recreation potential surface” for
use as a substitute variable in their model of demand for recreation at forestry
commission recreation sites in England. The authors ﬁrst placed a grid of sample points
at 5km spacing throughout the study area. A measure of access to publicly-accessible
forestland was then computed for each sample point. Interpolation was then used to
develop values in the intervening distance between samples, yielding a continuous
surface throughout the study area. One problem in the adopted approach is that the
modeled recreation sites are included in the calculation of the recreation potential

surface—so it is not a strict substitute surface. When incorporated in the models of

35

recreation use the recreation potential variables were statistically signiﬁcant, though the

direction of the relationship was inconsistent.

The use of a single site substitute variable seems inappropriate for the current
study since there is likely more than one substitute for any individual national forest.
However, one option is to identify a spatial database of potential substitute sites (e. g.
national parks, national forests, BLM land, etc.) throughout the study area and identify
the nearest substitute destination of each ownership type. The development of a
recreation potential surface is probably not feasible given that a substitute-only surface

would have to be computed for every origin-forest pair.

The traditional activity-based USDA FS approach to segmentation yields
information related to the number of individuals participating in a speciﬁc recreation
activity. However, within activity groups recreation behavior and participation are highly
variable. In addition, classifying visitors by primary activity can be imprecise since many
recreationists participate in multiple activities on a given recreation visit. For example,
what is the proper activity classiﬁcation for an angler who also spends two nights
camping on the national forest? A further discussion of the limits to segmenting visitors
in terms of primary activity can be found in Stynes and others (2003). Alternative
approaches to segmentation such as using distance and trip type segmentation and/or
visitor motivation have been successful at explaining variation in recreation behavior
(e.g. Faunce and others, 1979; Etzel and Woodside, 1982; Cordell, 2003; and Stynes and

White. 2003). The adopted distance segmentation (and associated recreation use models)

36

is expected to provide information for forest planning that may not be captured via

activity segmentation alone.

Conclusion

This chapter examined three areas of recreation literature relating to the objectives
of this study. The initial section of this chapter included a discussion of the role of
demand analysis in resource planning. This discussion was centered primarily on the
1975 NAS document “Assessing Demand for Outdoor Recreation” and the types of
planning questions addressed through demand analysis. The second area of focus in this
chapter was on methods commonly used to estimate recreation demand and use. Speciﬁc
emphasis was given to the travel cost model. A discussion of some contemporary
examples of recreation demand and use models was also included. The ﬁnal area of
literature examined in this chapter was approaches to segmenting recreation visitors. This
section described the traditional approach to recreation visitor segmentation, some of the
inconsistencies in recreation behavior that result from this approach, and some alternative

segmentation approaches.

37

CHAPTER 3

METHODS

The primary focus of this Chapter is a description of the methods used to achieve
the dissertation objectives identiﬁed in Chapter 1. This Chapter begins with a conceptual
discussion of the distance-based segmentation and the relationships between the
segmentation and the recreation literature. Given its importance to this study, a brief
description of the NVUM project, the sampling design, and the visitor survey procedures
follows. The remainder of the chapter is devoted to description of the speciﬁc analytical
approaches in this study. The study area is described and the NVUM survey data used in
the study are identiﬁed. Speciﬁc approaches used to delineate the distance-based
segments and the statistical tests used to compare segment visitors between USDA FS
regions and between distance segments (objectives 1 — 3) are discussed. The closing
section details construction and evaluation of the models used to predict the recreation
use of Local and Mid-distance visitors for individual national forests in the study regions,

Objective 4.

Conceptual Discussion of Distance-Based Visitor Segmentation

The distance-based approach to visitor segmentation adopted in this study
classiﬁes recreation users (and potential users) based upon the proximity of their

residence to the recreation resource. Three distance-based visitor segments are

38

recognized: Local visitors, Mid-distance visitors, and Long-distance visitors. Within
segments, visitors may be further disaggregated by recreation trip-type (e. g. day trip or
overnight trip). Visitors are classiﬁed based upon their proximity to the recreation
resource under consideration. As such, individuals in a single location may be classiﬁed
as Local visitors (or potential local visitors) to one forest and Mid-distance visitors (or
potential Mid-distance visitors) to another forest, etc. Spatially, Local visitors originate
from areas in close proximity to the forest boundary, Mid-distance visitors originate from
areas farther from the forest boundary, and Long-distance visitors originate from the “rest
of the world” (Figure 2). Conceptually, visitors within each of the distance segments
share similar recreation behaviors, recreation participation patterns, and have similar
responses to the factors that inﬂuence recreation (distance, amenities, substitutes, and

socioeconomic conditions, etc.) as other visitors within that segment.

  

 

National
Forest

    
 

 

 
 
   

Local

Mid-distance

Long—distance

Figure 2. Distance Segmentation of USDA FS Visitors.
39

The distance-based segmentation can be grounded, to some extent, in the
classiﬁcation of outdoor recreational uses and resources as described in Clawson and
others (1960) and in Clawson and Knetsch (1967). Three classes of recreation uses and
resources were identiﬁed by the authors: user-oriented, intermediate, and resource-based.
Under the Clawson and Knetsch classiﬁcation scheme, individual recreation resources
are classiﬁed into one of the categories based primarily upon the proximity of the
resource to major populations and the natural features of the resource. However, it seems
reasonable that a single national forest could represent different resource types (and
provide for multiple recreation uses) to multiple individuals. That is, visitors originating
from varying distances may recreate at a national forest as if it were a user-oriented

resource, an intermediate resource, or a resource-based recreation resource.

User-oriented areas, as outlined in Clawson and Knetsch (1967), are identified by
“their ready accessibility to users”. This proximity allows users to recreate at the resource
with high frequency and during a “variety of leisure times” (daily leisure times and/or
weekend leisure times). Visitors to these areas frequently participate in “general”
recreation activities such as swimming, picnicking, or walking. Intermediate areas are
those that are located at moderate distance from visitors. Given this distance,
recreationists are generally constrained to recreating at intermediate areas during long
day trips and weekend visits (Clawson and Knetsch, 1967). Recreation activities at
intermediate areas more frequently include camping, hunting, and ﬁshing than visitors to

user-oriented resources.

40

 

 

 

 

IS Iimit

Resource-based areas, under the Clawson and Knetsch (1967) classiﬁcation, are
farthest from populations and have “outstanding physical resources”. Recreation at these
resources generally occurs during a vacation and may occur coincident with other
activities (the resource may not be the primary purpose of the trip to the area). Individual
annual visitation rates are very low. Visitor recreation activities include those at
intermediate areas as well as more general and “interpretive” activities such as

sightseeing, nature study, and visiting historical places.

Using Clawson and Knetsch’s classiﬁcation scheme as a framework, postulated
characteristics of distance segment visitors were identiﬁed. Local visitors have either
permanent or seasonal homes in close proximity to the national forest under
consideration. Conceptually, these visitors take frequent trips with each trip typically
being of short duration—day trips or shorter (Table 1). Longer overnight trips are taken
with less frequency and may be associated with special recreation activities (e. g.
hunting). Local visitors recreate at a limited number of sites and participate in a limited
number of activities on any given trip. Expenditures on individual trips are relatively
small—though in aggregate cumulative spending by these visitors may be high.
Conceptually, Local visitors are more likely to be involved in forest planning activities
and to comment on recreation management decisions. Due to their increased visit
frequency and proximity to the forest, these visitors are most affected by natural resource
management decisions on an individual forest. The forest-level visitation of this segment
is limited mostly by the local population. For many forests these visitors will represent

the majority of forest visitation.

41

\

dist

Terr

 

Table 1. Postulated Characteristics of Local, Mid-distance, and Long-distance
Visitors.

 

Local Mid-distance Long-distance
Recreation Characteristic Visitors Visitors Visitors
.-Anaaal-IaP_Ete999991/.-----------hi_gh ............ {gadgets $9.199! ....... Yea/Jaw ..............
generally moderate
.-IFIP.PB¥?P§911--------------.----_--_.Sh91it ............ Eaters--.------_----'.--§h9tt-t.9_!99€19rat.e.-_-
pass through day
. generally day and overnight or overnight off-
..ItipIyps---.-------------..----.----s!ay._tr.irz§ ....... trips ...................... ferasttrin ............
Primary Purpose of the Trip is nearly
.Fatima!Eateriﬁaqtsatiea-runaways ......... typically. ................ irrfrsqaentlr ..........
single or
...S.i.t.e.§.Y.i§.i.t.e.€l-Rer.tIip-_-_------------.f9.W. ............. 2991.621? ................. $1951.99! fart .........
single or
.-Agtixitissrsrtﬁrz---------__------__.f.e.V.V. ............. militias ................. answerer .........
PartySlzesmall ........... medatatsjalatga ..... variable. ...............
may occur
Weekend/weekday Visitation throughout throughout the
Pattemweek ........... primarily3r9§isen€i.---wssls ...................
Trip Spending (Attributable to
Forest)10w ............. its!) ...................... metals ...............
peak and shoulder
.-.S.§§§999I-B§¥I9FﬂBEYEIEPIQP---.X‘?§:€9§P§l----§9§§9’3§ .................. peak. ...................
..IIIISIESEEP.Xi.S.i.t9¥-1.’.r.98r.?m..S.------IQYY ............. high ...................... high ....................
Likelihood of Involvement in unlikely to be
.-E9I‘E~°:t..Plﬂliiii.'}g..491i.‘.’.iii?§--------X??X.1.iIS?I)/.----9?9€19!?§?.1)f.1.ik?lx ..... 11.1%.)!st ..............
proximity of forest
to tourist
Primary Limiting Factor proximity of forest destinations, cities,
Affecting Total Group local to population thoroughfares,
Visitation population centers airports

 

Mid-distance visitors originate from beyond the local area but within a moderate
distance of the forest under consideration. Mid-distance visitors complete up to several
recreation visits to the national forest in a given year with annual visitation rates

decreasing as the distance to the forest increases (Table 1). These visitors generally have

42

 

for;

CEO."

ltt‘ft

 

teen
inilt
SL105

son

W01
of l
the
rela
tip

103

longer trip durations, a greater propensity to visit multiple sites within the national forest,
and greater likelihood to participate in multiple activities per trip than Local or Long-
distance visitors. Mid-distance visitors are more likely than Local visitors to be interested
in interpretive programs and visitor centers. Visitors from this area are more likely to
include an overnight stay in their trip than visitors from Local or Lon g-distance areas.
Visits to the forest are more likely to be secondary to some other trip purpose for these

visitors than for Local visitors.

Recreation spending by Mid-distance visitors initiates the majority of local
economic activity directly attributable to recreation on the national forest. As such, the
recreation use of these visitors can be very important to economics dependent upon
recreation spending. Conceptually, the forest-level recreation use of these visitors is
inﬂuenced by the distance from the forest to population centers, the existence of
s ubstitute recreation areas, the characteristics of the recreation resource, and the

socioeconomic characteristics of potential visitors.

Visitors originating from beyond the Mid-distance area (i.e. the “rest of the
world”) are categorized as Long-distance visitors. This group is comprised of two types
of visitors: 1) those in the forest area primarily for some purpose other than recreating on
the forest (i.e., business, visiting a nearby tourist destination, visiting friends and
relatives, traveling through the area, etc.) and 2) visitors who have traveled to the area
expressly to visit the forest. Long-distance visitors may conﬁne their recreation activity

to a select number of particularly attractive or well-known recreation sites on the forest

43

 

he:

por-

OPPI'

reprc

 

PIES:

sub:
bid
four
Lil:
fore

like

PTOx

litre

(Table 1). Regardless of trip-type, visitors in this group have annual visitation rates that
are very low. Long-distance visitor typically complete either day trips or overnight off-
forest trips. When on day trips, the visitor is frequently passing through the area. A
limited number of Long-distance visitors spend the night on the national forest. Since the
national forest may not be the primary reason for the trip away from home, much of the
spending of by these visitors may not be attributable to the national forest. Conceptually,
the number of Long-distance visitors will be inﬂuenced by the proximity of the forest to
population centers, tourist destinations, lodging, major travel routes, special recreation
opportunities, and airports. For most forests, Long-distance segment visitation will

represent a small percentage of total forest recreation use.

Some literature lends support to the link between the distance segmentation in the
present study and the resource classes of Clawson and Knetsch (1967). Dwyer and others
(1977) stated “different values may be placed on recreation participation by different
subsets of the population”. In particular the authors stated these subsets may be deﬁned
by distance from the facility (among other possibilities). Strauss and others (1993) have
found distance effects in the participation of hunters in the Delaware Water Gap.
Likewise, Nelson and Lynch (1995) found that visitors to undeveloped portions of the
forest whose primary residence was within the forest proclamation boundary were more
likely to participate in hiking/walking and less likely to participate in off-road vehicle
(ORV) use. Spending patterns of recreation visitors have been shown to be related to the
proximity of the visitor’s residence to the forest (Stynes and White, 2004). Nationally,

recreation parties originating from greater than 30 straight-line miles away from the

10::

int

115::

 

 

 

pnr.

outs:

chr

forest spend 50% more money in the local forest area per trip than parties originating

from within the local area (Stynes and White, 2004).

Finally, there is some evidence that distance to the recreation site inﬂuences
visitor decision-making. Reiling and others (1993) found that campers were indifferent
between comparable camping sites when travel times were less than two hours. However,
travel time to the sites was highly important in campground selection when traveling
greater than two hours from home. Regarding participation in speciﬁc recreation
activities, Bristow and others (1993) have found that boaters in Massachusetts depended
primarily upon local supply of boating opportunities while campers generally traveled

outside the local area for camping opportunities.

Distance-based segmentation will provide an alternate way of quantifying and
characterizing USDA FS recreation use. Whereas activity-based segmentation identiﬁes
the activity patterns of visitors, distance-based segmentation may explain the patterns of
recreation participation and consumption as well as the recreation behaviors of visitors.
Models predicting the expected recreation use of the distance-based segments will
identify the types of recreation visitors expected and may identify the “recreation
markets” of individual national forests (assuming the relationships between recreation use

and inﬂuencing factors remain the same).

45

n,
Sim.

1156

mm
lode
almo;

use. Q

 

ICCIell
manor
airs

COUlll

National Visitor Use Monitoring Project

Estimates of USDA FS visitation developed via the USDA FS National Visitor
Use Monitoring (NVUM) project and the visitor survey data collected in that process are
used in this research. NVUM began as a pilot project in 1998 and was implemented
throughout the National Forest System (NFS) in 2000. The goal of NV UM is to develop
statistically reliable national, regional, and forest-level estimates of USDA FS recreation
use (English et al., 2002). Unlike previous USDA FS visitor survey projects (e. g. PARVS
and CUSTOMER), NVUM uses a consistent visitor survey and sampling scheme
implemented on all units within the NPS. The data used in this research are drawn from
the ﬁrst four-year NVUM cycle which occurred from calendar year 2000 through ﬁscal
year 2003 (ending in September 2004). In the ﬁrst NVUM cycle, approximately M: of the

administrative forests in the NPS were sampled every year.

Concomitant with the implementation of NV UM were changes in the unit of
measure for USDA FS recreation use and the adoption of a more conservative approach
to deﬁning what “counts” as USDA FS recreation. Previously, the USDA FS relied
almost exclusively on the Recreation Visitor Day (RVD) as the unit to measure recreation
use. One RVD equals one person recreating for one 12-hour period. Under NVUM,
recreation is quantiﬁed on a “visit” basis. A visit is “one person entering and exiting a
national forest. . .for the purpose of recreation” (English et al., 2002). A visit may last just
a few minutes or several days. An individual camping for a week on a national forest is

counted as one visit while an individual staying overnight in a hotel off of national forest

46

 

 

 

Olffi

area

 

land and visiting the forest on three days is counted as three visits. Previously, USDA FS
visitation estimates included individuals traveling through national forests for purposes
other than recreation as well as individuals viewing national forest scenery from an off-
forest roadways, airplanes, ships, etc. Under NVUM, the deﬁnition of a recreation visit

has changed to exclude these cases from visit counts.

NVUM employs a double sampling approach, completing visitor counts and
administering visitor surveys at selected locations on selected days (sample days) within
individual forests. Estimates of recreation use are constructed by combining trafﬁc count
data with information obtained from visitor surveys.I Sample days are selected via a
stratiﬁed random sample from a population of site days identiﬁed for each administrative
forest by personnel from that forest. Site days are stratiﬁed by the type of recreation

location (site type) and the expected level of exiting recreation trafﬁc (site strata).

Site types incorporated in NVUM include day-use developed sites GDUDS),
ovemight-use developed sites (OUDS), Wilderness areas (WILD), and the general forest
area (GFA).2 DUDS sites have received “moderate to heavy” modiﬁcations for the
purpose of visitor convenience, education, and comfort as deﬁned in the USDA FS
Infrastructure Access (INFRA) database (English et al., 2002). These sites include picnic
areas, ﬁshing sites, interpretive sites, visitor centers, etc. OUDS sites are overnight sites
that have received “moderate to heavy modiﬁcation” as deﬁned by INFRA and are

generally developed campgrounds, cabins, hotels, resorts, etc. (English et al., 2002).

 

' Appendix A includes detailed descriptions of the NVUM protocol and use estimation procedure.
2 A ﬁfth site type, viewing corridors, exists within the N VUM sampling process. However, recreation use
on this site type does not contribute to the overall visit estimate and is not considered in this study.

47

W17.

.7.
IL

all}

3116.

03‘“

[he ;'

 

 

WILD areas are congressionally designated Wilderness areas. Sampling locations for
WILD areas are located at trailheads and access points (English et al., 2002). The GFA
encompasses the remainder of the forest area not elsewhere classiﬁed. Sample locations
for the GFA are generally at trailheads, parking lots, and NFS roads exiting the national

forest (English et al., 2002).

Site strata are based upon the “expected level of exiting visitor trafﬁc relative to
all site days in that site type” (English et al., 2002). Strata include high, moderate, low
and closed/no expected exiting recreation use. Individual forest managers classify site
days into the site strata based upon their own judgment and the recreation use patterns on

the individual forest.

NVUM Visitor Survev

Visitors (or parties) who stop at a voluntary check point established on the sample
day are questioned to determine if they are candidates to complete the NVUM visitor
survey. Visitors qualify to complete the survey if they are 1) recreating on the forest and
2) exiting the recreation site (DUDS, OUDS, WILD) or forest (GFA) for the last time
that day. If a party of visitors, rather than a single individual visitor, stops at the check
point the individual 16 years of age or older with the most recent birthday is asked to
complete the survey. The survey consists of a general questionnaire completed by all
respondents and two supplemental questionnaires (a satisfaction supplement and an

economic supplement) that are completed by subsets of survey respondents.

48

 

 

l’lSiI ‘
list.
6116.“.

llktlz.‘

“he:
“Big

I
TIMI:

Crimp}:

 

The NVUM general survey is designed to gather information relating to the

duration of time spent recreating on the forest, the number of USDA FS recreation sites
visited, participation in recreation activities, past visit frequency to the forest, the home
ZIP code of the respondent, the purpose of the trip, party characteristics, and

demographic information. The economic supplement to the general survey gathers

information on the duration of the trip away from home, annual recreation expenditures,

and trip-related expenditures made in the local area. All portions of the survey are

designed to be read to survey respondents by trained interviewers and all parts, including

the economic addition, are completed on-site during the interview.3

Data Weighting Schemes

Three weighting schemes are available for use with the NVUM survey data:
exposure weights (Exth), national forest visit expansion weights (NVEXPAND), and
visit weights (VisWt). Exposure weights adjust the sample for overrepresentation of those
visitors who visit multiple sites and/or spend multiple days in the GFA. Due to their

extensive movement and extended time in the national forest, these visitors have a greater

likelihood of being sampled. Formally, the weight is computed as

1
Ex Wt-=—,
p 1 NSj

where N 81- is the number of sites (and days in the GFA) visited by individual j. Exposure

weights for the study area visitor sample range from 0.03 to 1.

3 There is anecdotal evidence that the economic supplement was sometimes handed to the respondent for

completion rather than read aloud.

49

is:
sit:

(Er

RIC

0003

 

16V CI I
bet) a

NV’T

The NVEXPAND weights expand the visitor sample to conform to the NVUM

use estimate. The weight for an individual respondent j is the product of the number of
site visits the respondent represents (SVE,-) and the exposure weight for individual j

(Exthj):
NVEXPANDj = SVE, * Exthj.

The NVEXPAND weights for data included in this study range from 1.6 to 51,450.

Visit weights correct the sample for overrepresentation of those visitors that visit

an individual national forest many times in a given year. Similar to exposure weights,

those visiting an individual national forest many times annually have a greater likelihood
of being sampled. Weights for individual respondents are based upon the product of

Exth,- and the inverse of the reported number of annual visits for individual j

(nfv12moj+1):
VisWt - = Exth - * ‘ l *
J J (nfv12moj +1)

A limited number of individuals reported annual visitation rates greater than 365. In these

cases, the reported annual visits were truncated to 365.

This research follows the weighting approach adopted in Stynes and others

(2003). Exposure weights (Exth) are used when there is no expectation that site type or

level of site use (the factors in calculating NVEXPAND) would inﬂuence visitor

behavior. The characteristics of visits are primarily estimated using Exth’s.

NVEXPAND weights are always used when estimating total visitation or the visitation of

50

 

ad?

Reg

Met"
Nth
Pike
Rio l
Rot.
San ,
ShnE
Whit

 

 

 

PM?

a subset of visitors. The VisWt’s are used only when estimating the mean number of

annual visits.

Study Area

The study area for this research is USDA FS Regions 2 and 9 (Figure 1, Chapter
1). Region 2 is geographically located in the Rocky Mountain region of the western US.
while Region 9 encompasses the northern portion of the eastern United States. Twelve

administrative forests are located in Region 2 and 14 administrative forests are located in

Region 9 (Table 2). Both regions include a mix of “general” and “tourist destination”

forests.

Table 2. Administrative National Forests (NF) within USDA FS Regions 2 and 9.

 

 

Region 2 Region 9
Arapaho and Roosevelt NF Allegheny NF
Bighorn NF Chequamegon / Nicolet NF
Black Hills NF Chippewa NF
Grand Mesa, Uncompahgre and Gunnison NF Green Mountain And Finger Lakes NF
Medicine Bow NF Hiawatha NF
Nebraska NF Hoosier NF
Pike and San Isabel NF Huron-Manistee NF
Rio Grande NF Mark Twain NF
Routt NF Monongahela NF
San Juan NF Ottawa NF
Shoshone NF Shawnee NF
White River NF Superior NF
Wayne NF
White Mountain NF

 

In addition to national forests, Region 9 includes the Midewin National Tallgrass

Prairie (MNTP)——the ﬁrst federally-desi gnated tall grass prairie in the Nation. MNTP was

51

€51.

ICC

fag:

 

Cf.“

Uillt

 

nan,

“tie
B)" :5].
We125:
admin:
333353;:

 

Finis

Louie: I.

established in 1996 on a former ammunition plant and depot near the city of Joliet,

Illinois. To insure visitor safety, the site has, for the most part, been closed to public
recreation while the remnants of the previous ammunition production and storage
facilities are removed. Recently, a limited number of trails, recreation sites, and a visitor
center have been established. Given its limited access for recreation activity, its
uniqueness as a USDA FS public land area, and limited NVUM sample size (80

respondents), MNT P survey respondents and visit estimates have not been included in

this analysis.

In Region 2, four of the 12 administrative national forests also manage nearby

national grasslands. NVUM recreation use estimates for the forest/ grassland aggregates

are reported as a single recreation use estimate for the administrative national forest. Due
to the difﬁculties that may arise from modeling recreation use to national grasslands and
national forests in aggregate, the estimates of grassland recreation use on three of the four

administrative national forests were subtracted from the respective total administrative

national forest recreation use estimates.4 Correspondingly, surveys completed by

respondents sampled on the excluded national grasslands were removed from the survey

database.

Recreation visitors sampled on national grasslands managed by the Nebraska NF

were included in the NVUM visitor sample and the recreation use of the Nebraska NF

 

By isolating those NVUM respondents sampled on national grasslands and using the N VEXPAND
'eights a rough approximation the percentage of total administrative national forest use associated with the
Iministered national grassland can be made (English, Pers. Comm). A more comprehensive estimation of
assland recreation use would require identiﬁcation of the number of identiﬁed sitedays within stratum on
isslands relative to the number on the national forest itself. Having done this the NVEXPAND weights

uld be adjusted to identify national grassland recreation use.

52

tot
N:
in
no:
in .-

.\'\'

and its managed grasslands was modeled in aggregate. This approach was adopted for

three reasons: 1) use on Nebraska NF grasslands accounted for a signiﬁcant percentage of
total Nebraska NF recreation use, 2) a greater percentage of respondent sampled on the
Nebraska NF originated from outside the “local area”, and 3) differences between the

natural features of the national forests and national grasslands within this aggregate are

not as substantial.

Survey Data

In the ﬁrst cycle of NVUM, 16,991 visitor surveys were completed by visitors to

administrative forests in USDA FS regions 2 and 9. All respondents completed the
general survey and ‘A (4,479) completed both the general survey and the economic

addition. Stynes and others (2003) found no statistical differences between the economic
sub-sample and the general survey respondents. Variables applicable to this study
obtained directly from the survey instrument and variables computed from survey data

are shown in Table 5. All NVUM survey data were obtained from NVUM administrators
in ASCII ﬂat ﬁle format. The data had undergone a preliminary “cleaning” by USDA FS
personnel to remove inconsistencies found during the visit estimation process. Additional

cleaning activities to facilitate economic analysis were completed by MSU personnel and

are outlined in Stynes and White (2002), and Stynes and others (2003).

Several variables used in this study were computed from information provided by

JVUM survey respondents (Table 3). The duration of the forest visit (visdur) was

53

estimated as the difference between the reported ending of the forest visit (forend) and
the reported beginning of the forest visit (forarrive). This calculation was performed
using date and time functions in Microsoft Excel®. The number of sites visited (expose)
was computed as the sum of the number of developed recreation sites visited (numsites)

and the number of days spent recreating in the GFA (GFAdays). In addition to
identifying their primary recreation activity, NVUM respondents were also asked to

report all the recreation activities in which they participated. The number of activities

(numact) is the count of these reported participation activities.

The study variables fordist and triptype were constructed as part of the economic
analysis of the NV UM survey data as reported in Stynes and others (2003) and Stynes
and White (2005a). Fordist is the straight-line distance between the centroid of the

reported home ZIP code of the NVUM respondent and the nearest boundary of the visited
national forest. Approximately 15% (2,770) of non-foreign NVUM respondents in the

study area either failed to report a ZIP code or provided a ZIP code that did not
conespond to the ZIP code databases used to identify respondent origins. In these cases

fordist values are classiﬁed as “missing”. Additional discussion related to the calculation

of forest distance calculation can be found in Stynes and others (2003).

54

Table 3. NVUM Survey Variables.
Survey Computed

Variable Variable Description Variable Variable

 

regionUSDAFSRegwnl .........................
.-191991.-...--..N.21i.99a1-19.1:9§t 31911951 ........................................ 1 .........................
..iattiat.....-.M911th./.c19x11i.r.r1_e. 9.1 merrier ................................ 1 .........................
.-faranrs-.....Manthtdexltime 91.41am! at target. ........................ 1 .........................
.-19end.........Marthtdexltima 9.1 lasting. the. fares! ...................... 1 .........................
nflemo Number of times visited this NF during the
............................ Past. earl
.-1191”..§11€§-.----N}111112?£91.?91?.$:.QIJP§:-W1EP§11?§1119211991 .......... 1 .........................
..Qfééaxs.....ﬂumber 91 daysiaihegﬁé ................................. 1 .........................
.1129949....-...1191116_.Z.11?.9.9§19-9.f_amends!!! .............................. 1 .........................
.£91918?"..--.-3911191199111-1§§-19§.i§l?111.91.?.191§1g!1-99911111/.----_-----.1 .........................
peopveh Number of people in the respondent's
............................. Y ehlclel
madman/equity ................................................ 1 .........................
.-Etitantl. anarr 291129.926. -91. 111.9 11113-19. 111.9 area .................... 1 .........................
nfprime Is the national forest your primary
............................. <1 estln3t10n°1
lotyPeTr/reoflodgmsusedl .........................
.-xisdyr---------9.11111.th91.t11e.19ts§.t-xi§i.t ................................................... 1 ........
.-E’SRQ§9--------18.911199191.1?9£§?11911-§11?§ rated. ..................................................
.-namaet. - - - - - _ - 15191111291 91.a.c.t.ixi.t.i9§n.ar.1i.c.i.pat§si. in. ................................................
fordist Distance from respondent's ZIP code to the
............................. f. orestboundaryl

triptype Respondent’s trip type (i.e. day trip,

............................. 9 xemight.trimetuﬁraantrip)---__--.-----------_-_------_------1--------

.-avatrzaad. - - - - 1512111931?! 191%! 31919219399911 was!!! .................................... 1 ........

tamer! .......... 1. terms? ......................................................................... 1 ........
l

viswt expwt*(1/nfvl2mo+1)

a Year 4 data only
b Years 1 - 3 data only

 

In the course of development of recreation spending proﬁles for USDA FS
visitors, NVIJM survey respondents were classiﬁed into one of four trip types: day trips,
vernight on forest trips, overnight off forest trips, and not primary trips. Day trip visitors
e those who reported they were not spending any nights away from home as part of

:ir national forest visit. Overnight on forest visitors were those who answered yes to the

55

question “did you spend last night on the forest?”. Those visitors who reported they

would be spending at least one night away from home, not on the national forest, were

classified as overnight off-forest visitors.5 For the study presented here, the overnight
segments have been combined into a single overnight group (OVN/OVNNF). Stynes and
White (2005b) found that NVUM respondents likely had difﬁculty determining whether
they indeed “spent last night on the forest”. Not primary visitors are those who reported
that the national forest was not their primary recreation destination. Due to how the “not
primary” question was worded in the ﬁrst three N VUM years the number of not primary

visitors was likely underestimated (see Appendix A and Stynes and White 2005b).
Distance Segmentation of NVUM Survey Respondents

NVUM survey respondents were assigned to one of the three distance segments
based upon the distance between their reported home ZIP code and the forest boundary
(fordist).6 To do so, the distance boundaries (i.e. travel distances) separating Local

visitors from lVIid-distance visitors and Mid-distance visitors from Long-distance visitors
had to be identiﬁed. This was achieved via the approach described in the following

paragraphs, incorporating the NVUM data and NVUM regional visit estimates.

Regional-level visitation estimates for five-mile travel distance bands surrounding

atr'onal forests were computed using the NVUM survey data and the NVEXPAND

E

n the ﬁrst three years of NVUM, respondents were only asked to report the number of nights away from
me rather than the number of nights in the local area. As such, some visitors who were passing through
local area and but spending the night away from home outside the local area are classiﬁed as overnight

forest visitors.
his approach is consistent with the NVUM economic reports: Stynes and White (2002) and Stynes and

ers (2003).

56

 

weights. Those respondents whose primary activity was downhill skiing were excluded
from computation of these visit estimates.7 The cumulative percentage of the total
regional-level visitation was computed for each five-mile travel distance band and
depicted graphically (Figure 3).8 The percentage of total visitation associated with each
distance bands and the marked changes in the relationships between travel distance and

visitation rate changes were used to inform decisions regarding segment separation

distances.

Two important characteristics of the Local visitor segment were included in the
conceptual discussion: 1) Local visitors are the majority of total forest visitation
(particularly at the regional- and national-level) and 2) travel distance has only a minor
inﬂuence on the rate of visitation. In their economic analysis of the NVUM data, Stynes
and others (2003) utilize a 30-mile boundary to identify local national forest visitors. In
both study regions, visitation by those living within 30 miles of forest boundaries
constitute more than 50% of total regional-level visitation (Figure 3). Prior to the marked
change in slope located at approximately 30 miles, marginal visitation levels of this group
of visitors changes very slowly with increasing travel distance. Given the consistency

with the postulated Local visitor characteristics and for compatibility with NVUM

 

7 Since most downhill ski areas were NVUM proxy sites, visitor surveys were completed by downhill
skiers at a lower intensity than visitors to non-proxy sample sites (see Appendix A). Due to the high use at
ski areas and the low intensity visitor surveying, the average NVEXPAND weight for NVUM skier survey
respondents is more than 3 times greater than the average NVEXPAND weight for other survey
respondents. Given the potential for individual skier respondents to dominate any analysis of recreation use
when considered by distance band (using NVEUAND); downhill skier visits were excluded from segment
delineation and from development or evaluation of the recreation use models. Skier survey respondents
were included in analysis of visitor segment characteristic—where most analyses are based upon Exth.

8 Cumulative percentages estimated for individual forests in the study area are available from the author.

57

deﬁnition of “local visitors”, the travel distance separating Local from Mid-distance

visitors was established at 30 miles from origin to forest boundary.

 

100

90

80

70

4O

3O

Cumulative Percentage

20

10

 

 

 

 

 

 

 

 

60~

50—

 

 

 

 

 

 

 

 

i

 

 

 

é

 

N

8

I

I I I I
(a) :5 U" 0) ‘1

8 8 8 8 8
Visitor Origin Distance (Miles)

8
0

§

 

S

 

 

 

Region 2
- - - - Region 9
30 Miles
200 Miles

 

 

 

 

Figure 3. Cumulative Percent of Visitation Across Visitor Origin Distance Bands.

Conceptually, the visitation of the Mid-distance segment should be inﬂuenced

signiﬁcantly by travel distance and the total visitation of this segment should comprise

the majority of non-local visitation. Between the 30-mile and 200-mile distance bands the

inﬂuence of travel distance on marginal visitation is apparent (Figure 3). At distances of

greater than 200 miles, the cumulative visitation levels of the distance bands increase at a

relatively uniformly rate toward 100%—indicating no obvious changes in the relationship

between travel distance and marginal visitation. At the ZOO-mile band, 80% of the

58

 

 

visitation in Region 2 and 90% of the visitation in Region 9 has been accounted for.
Given these factors, 200 miles was selected as the origin to forest boundary travel

distance separating Mid-distance from Long-distance visitors.

Characterizing Distance-based Segments

Visitors within the three distance segments were characterized in regard to the
following recreation behavior, activity participation, and consumption pattern variables:
annual visit frequency, visit duration, number of sites visited per visit, number of
activities per visit, visitor primary activity, party size, visit trip type, and
weekday/weekend visit patterns (Objective 1). Statistical tests were conducted to
determine if the characteristics of distance segment visitors differed between the study
regions (Objective 2). Subsequent to these comparisons, a second group of statistical tests
were completed to identify differences between the distance segments themselves
(Objective 3). The postulated characteristics of distance segment visitors, as well as the
postulated differences between the distance segments, are listed in Table 1 (within the

conceptual discussion).

All statistical tests were completed using SPSS 12®. Selection of the appropriate
tests was based upon Howell (1997). Statistical tests were not completed if any one group
under consideration had less than 30 observations. Chi-square analysis was used in all
tests where the variable of interest was nominal in nature (e. g. trip-type). The Mann-

Whitney U test was employed when variables that were ratio in nature (e. g. annual visits)

59

were compared between two groups (e. g. study regions). When completing comparisons
of a ratio variable between more than two groups a two-step procedure was completed.
Kruskal-Wallis ANOVA (K-W ANOVA) was used to determine if there was a statistical
differences between all groups under consideration (e. g. Local, Mid—distance, and Long-
distance segments). Subsequent to a ﬁnding of statistical difference between all groups, a
series of Mann-Whitney U tests were completed between pairs of groups (e. g. Local and

Mid-distance segments) to determine speciﬁc pairwise statistical differences.

The non-parametric tests Mann-Whitney U and the K-W ANOVA were chosen
for use in this study over comparable parametric tests (ANOVA and student’s t-test) for
several reasons. First, the population distributions of the recreation characteristics under
consideration do not meet the assumption of normality required for parametric tests.9
Second, Mann-Whitney U (and the related K-W ANOVA) tests the broad hypothesis that
samples were drawn from identical populations—consistent with the objectives of this
study (Howell, 1997). Comparatively, the hypothesis of the student’s t-test is that
samples were drawn from populations with the same mean. Finally, the results of non-
parametric tests, as compared to parametric tests, are not overly inﬂuenced by the
inclusion of extreme values (e. g. annual visit rates of 365) whereas parametric tests can

be (Howell, 1997).

Analyses were completed using the following weighting schemes, outlier

removal, and data cleaning rules. With two exceptions Exth’s were used for analyses

 

9 Many argue that t-tests are robust to violation of the normality assumption if the sample size is
sufﬁciently large to yield a normal sampling distribution of the mean under the central limit theorem.

60

relating to visitor characteristics; NVEXPAND weights were employed when estimating
the frequency of primary activities while VisWt’s were used for analysis of annual visit
frequency. When completing analysis of party size, respondents reporting party sizes of
greater than 7 individuals were excluded from analysis. Similarly, respondents reporting
a trip of greater than 30 days away from home were excluded from estimation of visit
duration. Removal of these outliers is consistent with the approach adopted for economic
analysis of the NVUM data (Stynes et al., 2003). Lastly, in several cases, individual
respondents reported annual visit frequencies (nfv12mo+1) of greater than 365 visits. In

these cases, the reported number of annual visits was reduced to 365 visits.10

Modeling Segment Visitation

Objective 4 is to model the forest-level recreation use of Local and Mid-distance
visitor segments to national forests in USDA FS Regions 2 and 9. The remainder of this
chapter includes a general description of the modeling approach, a speciﬁc discussion of
the procedures used to construct the recreation models for each distance segment, and
lastly, explanation of the approach used to verify and validate the predictive ability of the

constructed models.

 

 

to
The average numbers of visits annually is not appreciably inﬂuenced by reducing the maximum number
0f annual visit to 100.

61

General Description

The models constructed here are meant to predict only the recreation use of Local
and Mid-distance visitors recreating on national forest land engaged in activities other
than downhill skiing. The recreation use of downhill skiers and visitors to national
grasslands is not modeled in this research. For both of these recreation use types, the
factors inﬂuencing recreation use are likely different from those inﬂuencing visitors
engaged in traditional outdoor activities on national forest land. Moreover, downhill skier
use was not modeled as these visitors were sampled at a very low intensity, leading to
very high individual NVEXPAND weights that may adversely inﬂuence derivation of
band-level recreation use (see footnote 7, this Chapter). Approximately 15% of NVUM
survey respondents did not provide a valid ZIP code. As the origin of these visitors could
not be determined, the recreation use associated with these visitors was also excluded

when constructing and evaluating the recreation use models.11

The forest-level recreation use of Local visitors was modeled using a simple
approach incorporating counts of the population living at several distance zones around
national forests, estimates of the percent of those individuals participating in national
forest recreation, and estimates of the number of visits completed by participating
individuals. Mid-distance recreation use was modeled via more complex multi-site zonal

travel cost models. While the recreation models are formulated differently, the basic

 

" Alternatively, it could have been assumed that the origin distribution of those providing a ZIP code was
representative of those failing to provide a valid ZIP and this use could have been included in modeled
recreation use. However, there is no basis for this assumption and it seems reasonable to expect that some
respondents may be more likely to not report a ZIP code.

62

premise of each is to model forest-level recreation use based upon information about the
populations living in zones around national forests and the patterns of national forest
recreation use by individuals in those zones. NVUM survey data and recreation use
estimates for national forests sampled in NVUM years 2, 3, and 4 were used to construct
and verify the performance of the models. Recreation use estimates for forests sampled in
NVUM year 1 were retained for model validation (ascertaining the out-of-sample
predictive ability). Retaining a portion of the data to test the out-of-sample predictive
ability of models is an accepted form of model validation (Grant et al., 1997; Haefner,

1997).

When using a zonal approach to modeling recreation use, the zonal aggregation of
the visitor origins must be deﬁned. Typically, zone deﬁnition is relatively arbitrary
(English and Bowker, 1996). In this study, twenty zones (or distance bands), spanning
travel distances from zero miles to 200 miles were delineated (Table 4). The 20 zones are
comprised of groups of origins that share a similar distance from origin to forest

boundary.12

Units of analysis for NV UM survey respondents and for population socio-
demographic characteristics represent two datasets. In the case of the former, visitor
origins are assumed to be the reported ZIP codes of the NVUM respondents. For the
latter, census block groups were selected as the origin unit of analysis. ZIP codes could

not be used in this study as the origin unit of analysis for the population data for several

 

'2 Due to large percentage errors in distance estimation, the 0 — 5 mile band was joined with the 5 — 10
mile band to form one 0 — 10 mile distance band.

63

Table 4. Distance bands used in Local
and Mid-distance recreation use models.

Distance from origin

 

to forest boundary Band Label
Local Segment Bands
Origin within forest boundary 0
0 S 10 miles 10
' 10 S 15 miles 15
15 S 20 miles 20
20 S 25 miles 25
25 S 30 miles 30
30 S 35 miles 35
Mid-distance Segment Bands
35 S 40 miles 40
40 S 45 miles 45
45 S 50 miles 50
50 S 65 miles 65
65 S 80 miles 80
80 S 95 miles 95
95 S 110 miles 110
110 S125 miles 125
125 S 140 miles 140
140 S 155 miles 155
155 S 170 miles 170
170 S 185 miles 185
185 S 200 miles 200

 

reasons: 1) comprehensive spatial databases of ZIP codes are expensive to obtain at this
scale of analysis, 2) US. Census data are not reported by postal ZIP code (Census data
are reported by ZIP code tabulation areas rather than postal ZIP codes), and 3) ZIP codes
may apply only to businesses or PO. boxes. Census block groups were selected as the
unit of analysis for population data because the 2000 US. Census data are reported by

census block group and these data are in the public domain.

64

The impact of using two datasets is limited as both NV UM origins and population
origins are ultimately aggregated by distance bands measured from the respective origin
centroid to the forest boundary. Additionally, the recreation use models for Local and
Mid-distance visitor segments are estimated incorporating observations from all forests in
the respective study regions—further lessening the impact of any errors at the origin
level. Despite these controlling factors, this difference remains a potential measurement

error in constructing the recreation use models.

The distances from origins to destinations for the NVUM survey respondents
were calculated from the NVUM variable “fordist”. This straight-line distance was
calculated in ArcView3.2a using the “distance by ID” extension (Jenness, 2004) and
spatial databases of the geographic centroids of ZIP codes reported by NVUM
respondents and the national forest boundaries. The projection for this calculation was
Albers Equal Area Conic, distance units equal to miles. “Fordist” was constructed to
classify visitors into local and non-local groups for NVUM economic analysis and has
been used frequently in analysis of the NVUM data. For a detailed discussion of the

fordist variable and its calculation see Stynes and others (2003).

The distances from census block group origins to forest destinations were
estimated using a straight-line calculation that incorporated an adjustment for barriers to
travel (i.e. large lakes, major rivers). This procedure reduced the potential for assigning to
the wrong zones the band population estimates of individual forests. First, straight-line

distances were computed for all origin/destination pairs (census block group centroid to

65

national forest boundary) with an intervening distance of less than 200 miles. Second,
straight-line routes crossing major lakes and rivers within the study area (Figure 4) were
identiﬁed. Third, the barrier-adjusted Euclidean distance (BED) was calculated for each
of these origin/destination pairs using the procedure below. As with NV UM distance, the

analysis projection was Albers Equal Area Conic, distance units equal to miles.

 

'4 # A
la... M
‘" 1""ng

     

 

Figure 4. River and lake barriers to travel in the study area.
(Source: ESRI, Redlands, CA)

3 crossing the

The latitudes and longitudes of all road bridges and car ferriesl
barriers shown in Figure 4 were identiﬁed using Delorrne Street Atlas 2004 were

imported to ArcView 3.2a and converted to the analysis projection. Points allowing

 

'3 The car ferries crossing the Great Lakes were not included in the constructed bridge and ferry database
since they charge signiﬁcantly greater fees than the other general river and/or publicly subsidized ferries.

66

travelers to circumvent the Great Lakes (e. g. a point south of the southern tip of Lake
Michigan) were digitized directly in ArcView 3.2a. Barrier-adjusted Euclidean distance
was computed by ﬁrst identifying the barrier crossing nearest the intersection of the
Euclidean route and the barrier. Next, the Euclidean distances from the origin to

the crossing identiﬁed in the previous step and from the crossing to the nearest boundary
on the destination forest were determined. Lastly, these two distances were combined to

form BED (Figure 5).

Origin

 

O

1% National Forest

A/ Transportation Barrier
II Bridge
\ Euclidean Distance
'\

 

Barrier-adj usted Distance

Figure 5. Euclidean and Barrier-adjusted Distance Estimation.

Mountainous areas are often perceived as barriers to travel. However, in the
context of this analysis, mountainous regions do generally not meet the deﬁnition of
barriers, namely “completely inhibiting travel”. While often circuitous, travel through

mountainous regions is generally possible. Individual mountains may serve as barriers to

67

travel; however, identifying individual barrier mountains located within the study area as

well as the appropriate bypass points is beyond the scope of this analysis.

Locgl Visitor Recreation Use Model

The Local population is comprised of those individuals living within 30 miles of
the forest boundary. Total Local segment visitation includes the recreation use of
permanent as well as seasonal Local residents. The recreation use of these two groups
was modeled separately due primarily to differences in how the US. Census Bureau
quantiﬁes permanent and seasonal residents. Permanent residents are quantiﬁed on a
person basis while the seasonal population is quantiﬁed on a household basis. The
permanent resident Local model is presented ﬁrst followed by the seasonal resident Local

model.

Conceptually, the visitation of the permanent Local visitor segment is a function
of the local population, the rate of national forest recreation participation, and the annual
visit frequencies of Local visitors. The number of Local segment visits to an individual
national forest (Tqu) is computed as

I
TVLj = 201019,-j * Pan,- * AVij),
i =1
where Popij is the local population within each distance band i of forest j, Part, is the

percentage of the population in distance band i that participates in national forest

recreation estimated at the regional-level, and AVij is the number of annual visits of

68

participating recreation users in distance band i for forest j. The populations located in
ﬁve distance bands around each national forest in the study regions (Popij) were estimated
from the 2000 US. Census data, spatial databases of census block group geographic
centroids (U .S. Census, 2004b) and national forest boundaries (USDA FS, 2000) (Figure

6).

   
 
 

. Census Block Centroids
- National Forest
[:| Distance Bands

Figure 6. Conceptualization of quantifying a forest’s local population.

In some portions of the study area (particularly in Region 2) local populations are
located in proximity to multiple administrative national forests. Conceptually, recreation
use by this segment is a function of convenient access to the natural resource. Given this,

it is assumed that visitors located within 30 miles of multiple national forests would

69

choose to recreate at the closest. To that end, census block groups located proximate to
more than one national forest were included only in the population counts of the closest
national forest. In cases where census block group centroids were located at equal band
distance from more than one administrative national forest the block group population
was split evenly between forests. In addition to other national forests, some census block
groups are proximate to recreation opportunities managed by other public land agencies.
In these cases, it is assumed that the availability of substitutes is accounted for in the

annual visitation rates of NVUM respondents sampled on that forest.

AVij was estimated from the NV UM survey data using the nfv12mo variable,
weighting cases with the visit weights (VisWt). Since NVUM respondents were asked to
not include the current visit, one visit was added to the reported nfv12mo to compute
nfv12mo+1. To preserve local differences, AVij values were obtained by aggregating
respondents across forests located in proximity to one another (Table 5). If less than 30
NVUM cases occurred within a band after forest aggregation, the regional AV, was

substituted for AVij.

The participation rates of Local visitors were determined at the regional-level
based upon the NVUM use estimate for Local visitors, the annual visit frequency of
Local visitors, and the local permanent population. Formally, the participation rates of

local visitors within band i (Parti) were estimated as

_ TVi
(POPi * AVI) ’

 

Part,-

70

where Pop, is the study region population of band i, TV, is the NVUM regional use
estimate for visitors from distance band i and AV, is the average number of annual visits

by visitors from band i estimated at the regional-level using the approach outlined above.

Table 5. Forest aggregation for estimating AV”.

 

 

Reg'gn 2 Region 9
Bighorn National Forest Hoosier National Forest

Shoshone National Forest

Black Hills National Forest
Nebraska National Forest

Arapaho and Roosevelt National Forests
Medicine Bow National Forest
Pike and San Isabel National Forests

Grand Mesa, Uncompahgre and
Gunnison National Forests

Rio Grande National Forest

Routt National Forest

San Juan National Forest

White River National Forest

Mark Twain National Forest
Shawnee National Forest

Allegheny National Forest
Monongahela National Forest
Wayne National Forest

Green Mountain And Finger Lakes
National Forests
White Mountain National Forest

Hiawatha National Forest
Huron-Manistee National Forest
Ottawa National Forest

Chippewa National Forest

Chequamegon / Nicolet National
Forest

Superior National Forest

 

As constructed, a principal assumption of the permanent resident Local model is
that individuals within USDA FS regions participate in national forest recreation with the
same proclivity as other individuals located at equal distance from forests in that region.
It is assumed that much of the forest—level variation in recreation use due to climate,
locally-popular recreation pursuits, and natural resource features is captured in the annual

visit frequencies.

71

In areas where seasonal homeownership is common, permanent resident
population ﬁgures underestimate the effective local population. Based on data obtained in
the 2000 Census, approximately 570,000 seasonal homes are located within 30 miles of
national forests in the study area—80,000 in Region 2 and 490,000 in Region 9.
Seasonal homes located near national forest local areas are most common in the Great
Lakes and northeastern portions of Region 9 and along the front range of the Rocky
Mountains in Region 2 (Figure 7). Given the potential level of recreation use by seasonal
homeowners and their family and friends, it is important to include seasonal homes in

any model of national forest visitation.

»~..L ....

 

Percent Seasonal Homes
- 67 - 100 Percent
- 34 - 66 Percent

iii... 1 - 33 Percent

No Seasonal
Homes

I: Regional Boundaries

 

Figure 7. Percent of housing units classiﬁed as seasonal homes in Census 2000 for
census block groups within 30 miles of national forests in USDA FS regions 2 and 9.

72

Unfortunately, very little information concerning seasonal homeowners can be
gathered from the NV UM survey data. There is no way to identify seasonal homeowners
from surveys completed in NVUM years 1 — 3 and only a partial enumeration of seasonal
homeowners is feasible using Year 4 NVUM survey data. As such, it was impossible to
estimate participation rates and annual visit frequencies by distance band (as in the
permanent resident model) for those owning seasonal homes in the local area using the
NVUM survey data. Lacking this ability, it was assumed that the participation rates and
annual visit frequencies of seasonal local residents were the same as permanent local

residents.

The US. Census Bureau reports the number of seasonal housing units. Thus,
multiplying participation rates by the number of households will underestimate total
seasonal homeowner visitation as there are frequently multiple individuals visiting any
single seasonal home. To correct for this, the average party size of seasonal homeowner
respondents (2.6 individuals) was identiﬁed from partial sample of seasonal homeowners
available in the Year 4 NVUM data.14 The resulting model to predict the total visitation
of seasonal homeowners in the local area of forest j (T VLSj) is

I
TVLSJ- = 25H,-j * Pan,- *AVij * PS,
i=1
where SH,, is the number of seasonal homes in band i of forest j, Part, is as deﬁned above,
AV,, is the annual visit frequency of local permanent residents in band i of forest j, and

PS is the average party size of seasonal homeowner parties. The numbers of seasonal

 

14 Due to small sample size, mean party size was estimated from all seasonal homeowners sampled
nationally using Exth.

73

homes located in the five distance bands around each national forest in the study regions
(SI-Iij) were estimated from the 2000 US. Census data following the same approach used
for permanent local residents. As in the permanent resident model, census block groups
proximate to multiple national forests contributed to the local seasonal population of only

the closest national forest.

Mid-distance Visitor Recreiion Use Models

Of the three distance segments, the recreation use of Mid-distance segment
visitors best matches the assumptions and conditions for successful application of a travel
cost model. Visits completed by individuals within this segment are likely to be single
destination trips, it is likely that these visitors receive no beneﬁts during travel to the
recreation location, and sufﬁcient variation in travel cost exists within the segment
(Loomis and Walsh, 1997). It is difﬁcult to apply a travel cost model to model the
recreation use of the Local segment since distance band travel distances are very similar
within that segment (due to their proximity to the forest and the selection of a 30—mile
boundary). Use of a travel cost model for Long-distance recreation use is probably
inappropriate given that these visitors likely receive some beneﬁt from traveling to the

area and trips completed by this segment are more likely to include multiple destinations.

Mid-distance visitors may complete day trips to the national forest or trips to the

national forest that include overnight stays either on or off the forest. Additionally, these

visitors may complete national forest visits that are secondary to some other trip purpose.

74

A confounding issue in developing travel cost models is variation in time spent at the
recreation destination. Variation in on-site time may change the relationships between
travel cost and recreation use (Loomis and Walsh, 1997). Speciﬁcally, visitors who spend
a greater amount of time at the recreation area may be willing to incur greater costs than
those spending a shorter time. In addition to travel cost, the relationships between
recreation participation and the other factors inﬂuencing recreation (e. g. resources
characteristics, substitutes, etc.) may vary depending upon the type of trip and time spent
on site. With these considerations in mind separate travel cost models were developed for
Mid-distance segment day trips and Mid-distance segment overnight trips within each
study region. Consistent with theory, “not primary” Mid-distance recreation use was

excluded from the constructed zonal travel cost models.

Functional forms commonly chosen for use in zonal travel cost models include
single—log ordinary least squares (OLS), the Tobit model, and the Heckman two-stage
model (e.g. Loomis et al., 1995). The single log OLS model is attractive because 1)
interpretation of the independent variable coefﬁcients estimated via OLS is
straightforward and 2) use of a single-log dependent variable for visits yields non-
negative model predictions of recreation use. A drawback to the single log OLS is that
the model cannot be estimated with zero-value observations as the natural log of zero is
undeﬁned. The typical solution to this problem is to add one visit (or a small value) to all
observations to insure computability (e. g. English and Bowker, 1996). In the Tobit
model and two-stage Heckman model, recreation use is modeled as both a function of the

likelihood of participation as well as the number of visits of participating populations.

75

The Tobit model incorporates both the likelihood of participation and level of recreation
use in one model while participation and recreation use level is modeled separately in the
Heckman two-stage model. Both model formulations explicitly incorporate zero-value

observations.

The multi-site travel cost models were estimated for each trip-type/region
combination following the general approach to empirical speciﬁcation adopted by
English and Bowker (1996). Models for day trips and overnight trips were estimated
using both single log ordinary least squares (OLS) and Tobit formulations. Models were
estimated with several different formulations of the dependent variable. Dependent
variables used in the OLS formulation included ln(distance band visits,,) and ln(distance
band visits/population (1,000’s)). Dependent variables in the Tobit model formulation
were distance band VISItSij and distance band visits,,/capita. In all cases, “distance band
visits” is the number of day or overnight trips originating from distance band i of forest j.
The selected dependent variables were chosen for consistency with theory and for model
computability. All models were estimated using Eviews 4.1® (Quantitative Micro

Software LLC, Irvine, CA).

When using a natural logarithm dependent variable, English and Bowker (1996)
added one visit to each observation to insure model computability. Attempts to make a
similar adjustment in this study resulted in poorly performing models that yielded
predictions of forest-level recreation use that were unrealistically low. Given this result,
this adjustment was not made and zero visit observations were dropped from the OLS

formulations. The impact of excluding zero visit observations from the OLS models

76

should be minimal as the range of observed number of visits is large and there are a
number of observations with low recreation use (e. g. < 250 visits). Zero visit
observations are included in the Tobit model formulation and comparison of the
predictive ability of between the two study models should help to identify the impact of

dropping zero visit observations in the single-log OLS model.

Dependent variables for the Mid-distance segment models were constructed using
the NVUM estimates of the number of forest-level day and overnight visits originating
from the Mid-distance segment distance bands identiﬁed previously. In the ﬁrst cycle of
NVUM only respondents to the economic survey can be classiﬁed by trip-type. As such,
a three-step process was required to estimate band-level recreation use for the trip-types
under consideration. First, the forest—level recreation use of all trip types (day, overnight,
not-primary trips) in each Mid-distance segment distance band was estimated for all
forests in the study area (Appendix Table B-l). Second, the percentage of day, overnight,
and not primary recreation visits for each forest—level distance band was determined from
the NVUM economic subsample and the NVEXPAND weights (Appendix Tables B-2
and B-3). In several cases no economic subsample observations occurred in a given
forest/distance band combination. In these cases, the regional trip-type percentages for
the distance band were substituted. Third, forest-level visit estimates by trip-type and
band were computed as the product of the forest-level visit estimate obtained in step one

and the trip-type percentages obtained in step two (Appendix Tables B-4 and B-5).

77

The forest-level recreation use of Mid-distance day trip visitors (NDDJ) and Mid-
distance overnight trip visitors (MDO,) in Region 2 was modeled as:

MDDj =F(P0p,-j,D-- I SNF-- SBLM SNPS

w a" 11* .7. RUCU’AJ’UJ')

ijs

MDOJ- = F(Pop,j,D 1,-J-,SNF~ SBLM

,1, ,j,SNPS,-j,RUC-- Aj,UJ-),

i!" U’
respectively. Likewise, the forest-level recreation use of Mid-distance day trip visitors
(MDD,) and Mid-distance overnight trip visitors (MDO,) in Region 9 was modeled as:

I SNF-

MDDJ- =F(P0p,-j,D ,1,

,j, ,j, SNPSij,RUC,-J-,Aj,UJ-),

SNF--

MDOj=F(P0p,J-,Dij,l ,J,

SNPSU- ,RUC--

will».

ij .
respectively. Independent variables include the population in band i of forest j (Pop,,), the
maximum distance from band i to forest j (D,,), the median income of band i of forest j
(I,,), the distance to the nearest other national forest land of band i of forest j (SNF,,), the
straight-line distance to the nearest BLM land of band i of forest j (SBLM,,), the straight-
line distance to the nearest NPS land of band i of forest j (SBLM,,), the Rural Urban
Continuum (RUC) code of band i of forest j (RUC,,), the acreage of forest j (Aj), and the
number of major separate units on forest j (U j). The independent variables for each
distance band are computed by aggregating the respective values of all census block
groups located within each distance band. Additionally, band values for variables I,,,
SNF,,, SBLM,,, SNPS,,, and RUC,, were computed by weighting each census block group

observation by the census block group population—forming population-weighted band

values for these variables.

The sources of data for the independent variables are as follows. Census block

group population estimates and median income ﬁgures were obtained from the US.

78

Census Bureau (2004a). Distances from each census block group centroid to the nearest
other national forest land15 , the nearest BLM land, and the nearest NPS land were
determined using ArcView 3.2a based upon the spatial datasets obtained from the sources
identiﬁed in Table 6. The RUC codes for each census block group were determined from
the database of county-level RUC codes developed by Beale (2004). Each census block
group is located within a single county. RUC codes quantify the rurality of individual
counties based upon: 1) whether the county is classified as a metropolitan area by the
Ofﬁce of Management and Budget, 2) whether it is adjacent to a metropolitan area if a
non-metropolitan area, and 3) the county’s urban population. Increasing RUC values
indicate increasing rurality.16 The acreages of individual national forests were obtained
from the USDA FS 2004 Land Areas Report (USDA FS, 2004). The number of separate
units was determined based upon a subjective count of the number of major spatial units
managed by each forest. For example, the number of separate units managed by the
Hiawatha NF is two while the number of separate units managed by the Arapaho-

Roosevelt NF is one.

Table 6. Spatial data sources for publicly-owned land.

 

 

Land Agency Data Source

USDA Forest Service USDA FS, 2000

National Park Service NPS, 2002

Bureau of Land Management National Atlas of the US, 2005

 

-_

'5 National forests located in regions other than the study region were included in this computation.
RUC code was included in the models as a continuous variable. Inclusion of RUC as a dummy variable
(1 = metropolitan area) yielded equivalent results.

79

Evaluation of Model Predictive Ability

The predictive ability of Local and Mid-distance recreation use models were
evaluated in three tiers. First, the model predictions of forest-level recreation use for each
segment (i.e. Local and Mid-distance segments) were compared to the NVUM estimates
of forest-level recreation use for those segments. Second, the summed model predictions
of forest-level Local and Mid-distance recreation use were compared to the NVUM
estimates of Local and Mid-distance recreation use. Finally, the forest-level percentages
of Local recreation use (as a function of predicted Local and Mid-distance recreation use)
were compared to the forest-level NVUM estimates of the percentage of Local recreation

use (as a function of the NVUM estimates of Local and Mid-distance recreation use).

NVUM offers a clear advantage over previous USDA visitor sampling programs
in that it is implemented on all forests in the NPS via a consistent approach. Additionally,
the visitor survey data collected via the NVUM process represent the most
comprehensive USDA FS visitor survey dataset available to date. However, the dispersed
nature of USDA FS recreation use, the large land areas managed, and the number of
access points to individual national forests make USDA FS visitor sampling challenging.
Ultimately, the NVUM recreation use ﬁgures are estimates of actual visitation, the
accuracy of which is dependent upon the appropriateness of the sampling design and

application of the visitor sampling protocols.

80

The accuracy of NV UM forest-level recreation use estimates for particular groups
of recreationists (e. g. Locals, snowmobilers, visitors from a given distance band, etc) are
particularly dependent upon the ability of NVUM to obtain surveys from visitors in those
groups and the appropriateness of weights to expand those samples of visitors to total
recreation use. Failure to obtain surveys from visitors in certain visitor groups may lead
to recreation use estimates that appear unreasonably low. For example, since no
snowmobilers were surveyed on the Huron-Manistee NF (located in the northern lower
peninsula of Michigan) the NVUM recreation use estimate for that national forest
indicates there was no snowmobile recreation use during the NVUM sample year.
Conversely, obtaining a large number of surveys from individuals in a given visitor group
may lead to recreation use estimates that seem questionably high. Biases in the obtaining
representative visitor samples could inﬂuence the estimates of distance-based segment

use for individual national forests.

Stynes and others (2003) have raised concerns as to whether the NVEXPAND
weights (employed to estimate total recreation use) are appropriate when applied to
visitor characteristics (including group membership). Of particular concern is the
potential for individual respondents with high NVEXPAND weights to exert undue
inﬂuence over results of analyses of visitor characteristics (including the recreation use of
visitor groups) (Stynes et al., 2003). The inﬂuence that individual observations with large
NVEXPAND weights have over the results increases as sample sizes decrease. The

potential impacts from observations with large NVEXPAND weights (if they occur) will

81

primarily inﬂuence estimation of the recreation use models—when a small number of

cases are used to identify recreation use by distance band.

Despite the potential limitations, N VUM recreation use estimates are the only
comprehensive set of USDA FS recreation use ﬁgures obtained via a consistent national-
level program. As such, the NVUM use ﬁgures are deemed the best ﬁgures to evaluate
the ability of the constructed models to predict distance-segment recreation use. In all
evaluations, statistical uncertainty in the NVUM use ﬁgures is reﬂected in the 80%

conﬁdence intervals constructed around the estimates.

NVUM estimates of forest-level Local recreation use range from 57,000 to 2
million visits in Region 2 and from 79,000 to 727,000 visits in Region 9 (Table 7). The
forest-level estimates of Local visitor recreation use were obtained via a multi-step
process. This is required because the published NVUM estimates of forest-level
recreation use differ from recreation use estimates obtained by using the N VUM survey
data and NVEXPAND weights alone. First the forest-level recreation use associated with
visitors of known origin not engaged in downhill skiing and recreating on national forest
land (termed “modeled recreation use”) were estimated for each study area forest
(Appendix Table B-6). These ﬁgures were estimated by multiplying the published
NVUM estimates of forest-level recreation use by the NVEXPAND weighted percentage
of NVUM survey respondents meeting the modeled recreation use criteria on each forest.

Next, the forest-level modeled recreation use ﬁgures were multiplied by the

82

NVEXPAND weighted percentage of NVUM survey respondents classiﬁed in the Local

visitor segment on each forest (Appendix Table B-7).

Table 7. NVUM Estimates of Local Visitor Recreation Use for USDA FS Regions
and National Forests (NF) in the Study Area.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

NVUM Estimate Lower Estimate Upper Estimate
Bighorn NF 273,995 198,388 355,876
Black Hills NF 506,796 398,248 622,623
Grand Mesa, U.,G. NF 1,388,635 953,697 1,848,310
Medicine Bow NF 288,978 236,296 345,489
Nebraska NF 56,678 40,789 74,836
Pike San Isabel NF 2,045,153 1,714,877 2,389,610
Routt NF 587,221 501,804 679,250
Shoshone NF 258,705 214,246 306,760
White River NF 1,279,373 1,171,184 1,392,239
Region 2 6,821,822 6,333,542 7,321,118
Allegheny NF 555,824 381,840 744,479
Chequamegon-Nicolet NF 595,152 389,868 822,667
Chippewa NF 726,966 592,578 872,045
Hoosier NF 365,668 295,388 440,773
Huron-Manistee NF 212,354 129,357 307,506
Mark Twain NF 412,738 350,419 477,656
Monongahela NF 262,522 214,339 314,594
Ottawa NF 78,848 61,991 97,583
Shawnee NF 316,315 270,318 365,126
Wayne NF 248,914 200,208 301,341
Region 9 4,102,540 3,734,356 4,480,997

 

In addition to statistical uncertainty in the forest-level estimates of total recreation

use, there is also statistical uncertainty in the forest-level estimates of Local recreation

use. The error in this use estimate is a combination of error in estimating total recreation

use (estimated by NVUM personnel) and error in estimating the percentage of forest-

level recreation use associated with Local visitors. Conﬁdence intervals (at the 80%

83

level) around the percentages of forest-level Local visitor recreation use were computed

as

pil.28l7*0'p,

where p = the sample estimate of the percentage and 6,, is the standard error of the Local

_ [pa-p)
O'p— —N .

The upper estimates of forest-level Local recreation use (Table 7) were obtained by

percentage:

multiplying the upper estimates of total recreation use (Appendix Table B-6) by the upper
estimates of the forest-level Local percentage (Appendix Table B-7). Likewise, the lower
estimates of forest-level Local recreation use (Table 7) were obtained by multiplying the
lower estimates of modeled recreation use (Appendix Table B-6) by the lower estimates

of the forest-level Local percentage (Appendix Table B-7).

Forest-level estimates of Mid-distance recreation use were computed following
the same approach as above with one addition. The models developed to predict Mid-
distance recreation use exclude “not primary” recreation use. To be consistent, estimates
of forest-level Mid-distance recreation use also exclude not-primary recreation use (Table
8). Speciﬁcally, forest-level estimates of modeled recreation use (Appendix Table B-6)
were multiplied by forest-level estimates of the percentage of Mid-distance use and by
the forest-level estimates of Mid-distance “primary purpose” recreation use (Appendix
Table B-8). Upper and lower percentage estimates were not constructed around the

percentages of “primary purpose” recreation use.

84

Table 8. NVUM Estimates of Mid-distance Visitor Use for Regions and National

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Forests (NF) in the Study Area.ll

NVUM Estimate Lower Estimate Upper Estimate
Bighorn NF 99,692 68,193 135,990
Black Hills NF 27,680 15,875 42,481
Grand Mesa, U.,G. NF 312,161 199,280 442,999
Medicine Bow NF 177,220 142,030 215,708
Nebraska NF 32,674 22,516 44,637
Pike San Isabel NF 179,644 130,947 235,966
Routt NF 392,194 328,547 461,412
Shoshone NF 53,634 39,561 69,781
White River NF 702,753 631,665 777,533
Region 2 1,969,848 1,790,973 2,156,547
Allegheny NF 340,138 227,368 466,716
Chequamegon-Nicolet NF 833,607 556,978 1,131,889
Chippewa NF 442,223 349,658 544,799
Hoosier NF 126,631 94,776 162,927
Huron-Manistee NF 456,977 292,176 634,072
Mark Twain NF 55,123 41,207 71,043
Monongahela NF 246,185 203,838 291,264
Ottawa NF 32,836 25,618 40,898
Shawnee NF 133,763 109,090 160,889
Wayne NF 130,716 100,655 164,355
lLegion 9 3,000,003 2,720,308 3,288,796

 

a Day and overnight primary purpose visits only

 

The NVUM estimates of forest-level total Local and Mid-distance recreation use

(Table 9) were calculated as the sum of the NVUM estimates of Local and Mid-distance

recreation use (the “NVUM estimate” columns only) in Tables 9 and 10. The upper and

lower estimates around these values ﬁgures were calculated using the forest-level 80%

error rates reported by N VUM personnel. Finally, the NVUM estimates of the forest-

level percentages of Local use (Table 10) were obtained by dividing the NVUM

estimates of Local use (Table 7) by the NVUM estimates of summed Local and Mid-

85

estimates.

distance recreation use (Table 9). No conﬁdence intervals were constructed around these

Table 9. NVUM Estimates of Summed Local and Mid-distance Recreation Use.“

NVUM Estimate Lower Estimate Upper Estimate

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Bighorn NF 99,692 68,193 135,990
Black Hills NF 27,680 15,875 42,481
Grand Mesa, U.,G. NF 312,161 199,280 442,999
Medicine Bow NF 177,220 142,030 215,708
Nebraska NF 32,674 22,516 44,637
Pike San Isabel NF 179,644 130,947 235,966
Routt NF 392,194 328,547 461,412
Shoshone NF 53,634 39,561 69,781
White River NF 702,753 631,665 777,533
Region 2 1,969,848 1,790,973 2,156,547
Allegheny NF 340,138 227,368 466,716
Chequamegon-Nicolet NF 833,607 556,978 1,131,889
Chippewa NF 442,223 349,658 544,799
Hoosier NF 126,631 94,776 162,927
Huron-Manistee NF 456,977 292,176 634,072
Mark Twain NF 55,123 41,207 71,043
Monongahela NF 246,185 203,838 291,264
Ottawa NF 32,836 25,618 40,898
Shawnee NF 133,763 109,090 160,889
Wayne NF 130,716 100,655 164,355
Region 9 3,000,003 2,720,308 3,288,796

 

a Mid-distance recreation use includes only primary purpose visits.

86

Table 10. NVUM Estimates of the Percentage
of Local Visitor use as a Function of Summed

Local and Mid-distance Use.

 

NVUM
Bighorn NF 73%
Black Hills NF 95%
Grand Mesa, U.,G. NF 82%
Medicine Bow NF 62%
Nebraska NF 63%
Pike San Isabel NF 92%
Routt NF 60%
Shoshone NF 83%
White River NF 65%
Region 2 78%
Allegheny NF 62%
Chequamegon-Nicolet NF 42%
Chippewa NF 62%
Hoosier NF 74%
Huron-Manistee NF 32%
Mark Twain NF 88%
Monongahela NF 52%
Ottawa NF 71%
Shawnee NF 70%
Wayne NF 66%
Region 9 58%

 

87

CHAPTER 4

RESULTS AND DISCUSSION

Introduction

Results of analyses undertaken to achieve the objectives identiﬁed in Chapter 1
are detailed in this chapter. The ﬁrst half is devoted to characterizing the recreation
characteristics of Local, Mid-distance, and Long-distance visitors (Objective 1). Included
in this portion of the chapter are the results from statistical comparisons between study
regions and between the distance segments (Objectives 2 and 3). The second half of the
chapter focuses on the models developed to predict the recreation use of Local and Mid-
distance visitors in USDA FS Regions 2 and 9 (Objective 4). The parameters and

performance of the constructed models are described in detail.

Characteristics of Distance Segmented Visitors

Segmentgtion of NVUM Survev Respondents

Of the 19,146 respondents sampled under NVUM in USDA FS regions 2 and 9,

39% (7,378) were classified as Local visitors, 27% (5,210) as Mid-distance visitors, and

20% (3,788) as Long-distance visitors (Table 11). Greater percentages of survey

respondents in Region 2 are classiﬁed as Local or Long-distance visitors compared to

88

Region 9. Correspondingly, a greater percentage of survey respondents are Mid-distance
visitors in Region 9. Nationwide, approximately 14% of NVUM survey respondents
failed to report the ZIP code of their permanent residence or reported an invalid ZIP code.
Consistent with this, 14% of non-foreign NVUM survey respondents (2,770) sampled in
the study area failed to report a valid ZIP code (or reported an invalid ZIP code). These
respondents are categorized as “missing” and excluded from further analysis. NVUM
survey respondents originating from foreign countries are classiﬁed as Long-distance

visitors.

Table 11. Number of NVUM Survey Respondents by Distance Segment and
USDA FS Study Region.

 

Region 2 Region 9 Total
Distance Full Economic Full Economic Full Economic
Segment Sample Sample Sample Sample Sample Sample
Missing 1,304 79 1,466 62 2,770 141
Local 4,246 1,151 3,132 789 7,378 1,940
Mid-dist. 1,938 530 3,272 878 5,210 1,408
Long-dist. 2,705 691 1,083 Zﬂ 3,788 220
Total 10,193 2,451 8,953 2,028 19,146 4,479

 

Objectives 1 through 3 relate to characterizing visitors within the distance
segments and completing statistical comparisons. Statistical comparisons are completed
between study regions, within segments, and between the distance segments themselves
within study regions. Regional comparisons of distance segments are presented ﬁrst

followed by comparisons between segments.

89

Regional Comparisons within Segments

Local visitors comprise the largest share of total recreation use in both regions—
53% in Region 2 and 49% in Region 9 (Table 12).1 In Region 9, 42% of total recreation
use is associated with Mid-distance visitors, substantially greater than the 21% in Region
2. Conversely, Long-distance visitors account for 27% of all use in Region 2 and only 9%

of visitation to Region 9 national forests.

Table 12. Percent of Total Recreation Use by
Distance Segment and USDA FS Study Regjon.a

Region 2 Region 9

 

Local 53% 49%
Mid-distance 21% 42%
Lon g-distance 27% 9_%
Total 100% 100%
' Estimated based upon the full NVUM survey sample and
NVEXPAND weights.

 

Differences between regions in the percentages of Mid-distance and Long-
distance visitors may be explained, in part, by three factors. First, the populations
surrounding national forests in Region 9 are greater and more uniformly spatially
distributed than populations around Region 2 national forests—thereby creating a greater
number of potential Mid-distance visitors. Second, the unique natural features located in
and around Region 2 national forests, particularly those located in Colorado, attract a
greater number recreation users traveling more than 200 miles from home. In particular,

opportunities for downhill skiing attract a signiﬁcant number of these visitors. Downhill

 

’ Removing skiers, Local visitation represents 63% of use in Region 2 and 50% of use in Region 9. These
non-skier shares are consistent with Figure 3 in Chapter 3.

90

skiers account for approximately 10.7 million visits to Region 2 national forests with
more than 40% of these visitors traveling greater than 200 miles to reach the forest. In
Region 9, only 320,000 visits are made primarily for downhill skiing; of those visits, only
4% resulted in travel of more than 200 miles from home. Third, Region 2 forests likely
receive more visitors traveling through the area on their way elsewhere (e. g. traveling to

the western US.) than Region 9 forests.

Three types of recreation trips are recognized in this study: day trips (Day), trips
involving an overnight stay either on the national forest or off the national forest
(OVN/OVNNF), and trips where the primary purpose was something other than
recreating on the national forest (Not Primary). Aggregating across distance segments, a
signiﬁcant difference exists between study regions in frequency of trip-type (Table 13).2
Within distance segments, a statistical difference betweens study regions in trip type

frequency exists only for the Mid-distance segment.

In both regions, 72% of Local visits are day trips. Overnight trips represent about
25% of visits while “not primary” trips comprise only a very small percent of all visits.
Trip-type propensity is nearly identical between study regions. Mid-distance recreation
use most frequently involves an overnight stay away from home (Table 13). However,
approximately 20% of Mid—distance visits in each study region are day trips. Compared

to Local visits, Mid-distance visits are more frequently “not primary” trips.

 

2 Detailed statistical tables for all statistical tests in this Chapter are available from the author.

91

Table 13. Trip-type Segment Shares by Distance Segment and USDA FS Study

 

 

Region.“
Day OVN/OVNNF" Not Primary‘ Total P-valued

Local Region 2 72% 24% 3% 100% 0.77
Region 9 72% 25% 4% 100%

Mid-dist. Region 2 24% 66% 11% 100% 0.01
Region 9 17% 70% 14% 100%

Long-dist. Region 2 8% 59% 34% 100% 0.60
Region 9 6% 60% 34% 100%

Total Region 2 46% 42% 12% 100% 0.00
Reg’on 9 40% 48% 12% 100%

 

3‘ Estimated based upon the economic NVUM economic sample and Exth.
b Recreation trips involving an overnight stay in the local forest area or on the national forest.
c Trips not made primarily to recreate on the national forest.
d Statistical comparisons between regions within distance segments were completed using contingency table

analysis.

The majority of Long-distance visits in both regions are classiﬁed as Overnight

trips. In both study regions, “Not Primary” trips comprise 34% of Long-distance visitor

recreation use. In NVUM year 4, survey respondents were asked to identify the primary

reason of their trip away from home.3 In Region 2, 74% of “Not Primary” Long-distance

visitors reported “visiting other recreation areas” as the primary purpose of being away

from home. “Not Primary” Long-distance visitors in Region 9 more commonly cited

“business and family” (35%) as their primary trip purpose than the same visitors in

Region 2.

Given differences (both statistical and practical) between study regions in the

distribution of trip-types within the three distance segments, statistical tests of regional

 

3 NVUM survey forms are available from Susan Kocis, Field Coordinator National Visitor Use Monitoring
Program, USDA Forest Service.

92

differences in segment characteristics were completed incorporating trip-types using only
the NVUM economic sub-sample (Tables 14 and 15). For the most part, after controlling
for differences in distance segment and trip type, visitors to national forests in USDA FS

Regions 2 and 9 have recreation behaviors that are not statistically different.

The greatest number of statistical differences between study regions occurs in the
Local visitor segment. Local visitors in the two regions differ in the number of annual
visits, the duration of the national forest visit, the number of people per vehicle and
national forest arrival day. Across all trip-types, Local visitors in Region 2 complete
fewer annual visits than those in Region 9 (Table 14).4 Region 2 Local visitors on day
trips spend a longer period on the national forest and recreate in smaller parties. On
overnight trips, Local visitors in Region 2 spend a shorter period of time on-forest than
their Region 9 counterparts.5 lastly, Local day trip visitors in Region 2 tend to visit more

frequently throughout the week than their Region 9 counterparts (Table 15).

The difference in annual visitation rates between study regions is slight. This
difference may be largely driven by the spatial arrangement of populations around
national forests in the study regions. In Region 9, a slightly greater percentage of total
Local segment use is associated with visitors living within 10 miles of the national forest

boundary than in Region 2 (76% compared to 68%).6 Assuming that those living farther

 

’ Annual visit median values for Local visitors in Regions 2 and 9 are 2.0 and 3.0, respectively

5 On-forest duration statistics are likely inﬂuenced by the ability of NV UM survey respondents to identify
and differentiate lands managed by the USDA FS.

6 Based upon NVUM sample of Local visitors and NVEXPAND weights.

93

Table 14. Mean Values for Visitor Characteristics of Interest by distance Segment,
Trip-type, and USDA FS RegLon.

 

 

Visit
Distance Study Annual duration Number Number of People!
_§e_gment Trip type Region visits“ (hours)" of Sites Activities Vehicle

Local Day 2 3.0“ 1. ° 32° 21°
Day 9 2.6“ 1.1“ 31° 23°
OVN/OVNNF 2 240°l 1.3° 4.6“ 25°
OVN/OVNNF 9 40.2d 13° 4.6“ 23°
Not primary 2 26f 1.3f 4.2f 22°
Not primary 9 3.2“ 1.1f 34" 22°
All trip-types 2 6.1‘1
All trip-types 9 6.0d

Mid-dist. Day 2 4.3“ 1.1“ 2.9“ 2.4“
Day 9 4.2“ 1.2c 3.1“ 2.2“
OVN/OVNNF 2 222° 13° 42° 25°
OVN/OVNNF 9 280° 1.4“ 4.4° 25°
Not primary 2 3.0“ 1.3“ 4.2“ 2.6“
Not primary 9 3.6“ 1.3“ 3.6“ 2.8“
All trip-types 2 2.6“

All trip-types 9 1.6“

Long-dist. Day 2 20" 1.3r 3.4f 2.4f
Day 9 1.9f 1.3f 3.8f 1.8f
OVN/OVNNF 2 55° 1.5“ 39° 2.8d
OVN/OVNNF 9 8.0“ 1.o° 42° 2.5‘1
Not primary 2 30° 15° 4.8“ 25°
Not primary 9 2.2“ 1.4“ 3.6“ 2.4“
All trip-types 2 1.6“

All trip-types 9 1.8“

 

Note: Statistical tests completed using Mann-Whitney U, Exth.
' Not computed within trip-types since variable corresponds to previous trips of unknown type. VisExpwt
used in statistical analyses,
“ Median values reported,
“ P-value > 0.05, no statistical difference between regions,
“ P—value < 0.05, statistical tests between regions,

“ P-value < 0.01, statistical tests between regions,

f Number of cases in at least one group is less than 30, statistical test not completed.

94

Table 15. Day of National Forest Arrival by Distance Segment, Trip-type, and
USDA FS Region.“

 

Distance P-
iegment Trip-type Sun. Mon. Tues. Wed. Thurs. Fri. Sat. value
Local Day2 29% 13% 7% 8% 10% 8% 24% 0.001

Day“ 34% 10% 5% 6% 6% 8% 33%

OVN/

OVNNF“ 16% 7% 7% 6% 6% 25% 32% 0.265

OVN/

OVNNF" 13% 5% 5% 9% 12% 30% 26%
Mid-dist. Day“ 40% 8% 3% 6% 10% 5% 27% 0.553

Day9 37% 10% 4% 4% 4% 7% 35%

OVN/

ovr~n~n=2 17% 4% 7% 5% 10% 27% 30% 0.402

OVN/

OVNNF’ 12% 5% 4% 6% 11% 30% 31%

Not

primary“ 9% 5% 7% 14% 7% 26% 33% 0.064

Not

primary“ 28% 8% 8% 3% 8% 14% 31%
Long-dist. OVN/

OVNNF“ 15% 13% 15% 14% 8% 15% 20% 0.051

OVN/

OVNNF“ 14% 10% 4% 16% 14% 19% 23%

Not

primary“ 8% 11% 20% 20% 14% 16% 11% 0.170

Not

primary“ 10% 10% 13% 11% 13% 18% 25%

 

“ Segment, trip-type combinations with less than 30 cases in at least one region are excluded. Statistical
analysis completed using contingency table analysis and Exth. Comparisons between regions using
exposure weights assumes there is no bias in site stratiﬁcation or the propensity for visitor sampling to
occur on a given day(s) between regions.

2 Region 2.

9 Region 9.

95

from national forests visit with less frequency, it is reasonable that Region 2 Local

visitors, overall, would have lower mean annual visitation rates.

Mid-distance visitors in regions 2 and 9 are not statistically different (within trip-
types) from one another in terms of annual visit frequency, on-forest duration, number of
sites visited, number of recreation activities participated in during recreation visits, or
party size (Table 14).7 Likewise, there are no statistical differences in arrival patterns
(Table 15). Statistical similarity between study regions (within this distance segment)
contrasts to the commonly held perception that recreation visitor behavior (in terms of
these characteristics) differs by USDA FS region. This ﬁnding is particularly important,
as these visitors (non-locals, whose primary trip purpose is generally visiting the national
forest) are frequently the focus of economic impact and resource valuation studies as well

as analyses of management alternatives and strategies.

Across all trip-types, Long-distance segment visitors in Region 2 complete fewer
national forest visits annually than comparable visitors in Region 9 (Table 14). A greater
percent of Region 2 Long-distance visitors (68%) complete just one annual visit
compared to Region 9 Long-distance visitors (58%).8 It is unclear how many of the
Long-distance visitors, in either region, are completing their ﬁrst trip to the national

forest.

 

7 Comparisons not incorporating trip-type also resulted in no statistical differences in Activity visitor
characteristics between study regions.
8 Based upon the NVUM survey sample, weighted by VisExpwt.

96

The prevalence of large parties among Long-distance overnight visitor groups in
Region 2 leads to the statistical difference in party size between regions (Table 14). In
Region 9, only 1% of recreation use in this segment/trip-type combination is associated
with parties of 5 — 7 people. In Region 2, large parties represent 14% of use in this
segment. Visitors in large parties in Region 2 primarily participate in general tourism-
type activities such as “driving for pleasure” (52%) “nature study” (17%) or “viewing
nature” (14%).9 These large parties likely typify general tourist visitors drawn to Region
2 national forests by unique natural features as well as their proximity to urban centers

and large national parks.

Within distance segments, the primary recreation activities of recreation users are
largely similar between the study regions (Table 16). Across all distance segments, a
substantially greater percentage of recreation use in Region 2 is associated with downhill
skiing. This is expected given the renown and sheer number of ski resorts located in the
national forests of Region 2. Within distance segments, Local visitors in Region 2 are
more commonly engaged in downhill skiing or biking and less commonly in hunting or
ﬁshing than their Region 9 counterparts. Likewise 45% of Mid—distance visitors in
Region 2 are downhill skiers compared to only 13% in Region 9. Additionally, Mid-
distance visitors in Region 2 are more frequently biking and less frequently ﬁshing,
hiking, or cross-country skiing than the same visitors in Region 9. Similarly, a greater
percentage of Region 2 Long-distance visitors are engaged in downhill skiing and fewer

are hiking or ﬁshing than in Region 9.

 

9 Based upon NVUM survey respondents reporting between 5 - 7 people per vehicle, weighted by
NVEXPAND

97

Table 16. Primary Activity by Distance Seggm and USDA FS Region.“

 

Local Mid-distance Long-distance
Region Region Region Region Region Region
2 9 2 9 2 9

Developed Camping 2% 2% 3% 5% 2% 3%
Prim. Camping &

Backpacking 2% 1% 1% 4% 0% 3%
Resort 0% 1% 0% 1% 0% 4%
Picnic 2% 3% 2% 0% 1% 1%
Nature-related 8% 7% 5% 7% 10% 13%
General/Relaxing 5% 5% 4% 6% , 4% 7%
Fishing 7% 13% 3% 9% 3% 7%
Hunting 5% 13% 6% 9% 3% 5%
OHV use 3% 3% 4% 5% 1% 2%
Driving 3% 6% 2% 2% 4% 1%
Snowmobile 4% 3% 2% 3% 1% 5%
Boating 2% 1% 1% 1% 1% 1%
Hiking 19% 16% 7% 13% 11% 25%
Biking 7% 2% 6% 1% 6% 0%
Downhill skiing 14% 7% 45% 13% 45% 4%
Cross-country skiing 5% 7% 1% 8% 0% 3%
Other non-motorized 4% 3% 2% 2% 1% 2%
Other 3% 4% 1% 1% 1% 4%
No primary Activity 4% 1% 2% 3% 2% 3%
Multiple primary

activities 2% 2% 3% 7% 2% 5%
Total 100% 100% 100% 100 % 100% 100%

 

“ Estimated from full NVUM sample, weighted by NVEXPAND.

DisLance Segment Comparisons

Distance segment visitors were characterized in terms of annual visitation, visit
duration, number of sites visited, number of activities, and party size (Table 17).
Aggregating across trip-types, Local visitors complete a large number of national forest
visits with most visits lasting less than four hours. These visitors recreate at a limited
number of sites, generally participating in a moderate number of recreation activities in

small recreation parties. Mid-distance visitors generally visit a given national forest up to

98

several times a year, generally spending more than six hours on the national forest. Mid-
distance visitors recreate at a greater number of sites, participate in more activities, and
have larger recreation parties than Local visitors. Visitors traveling greater than 200 miles
to the forest have very low annual visitation rates with on-forest durations of most visits
being less than 5 hours. These visitors recreate at the greatest number of sites, participate

in the greatest number of activities, and recreate in the largest visitor parties.

Table 17. Mean Values for Visitor Characteristics of Interest by Distance Segment,
and USDA FS Region.

Visit
Study Distance Annual duration Number Number of People!
Region Sggment visits“ (hours)" of Sites Activities Vehicle

 

2 Local 6.1I 3.8‘ 1.1I 3.1I 2.11
Mid-dist. 2.61 6.62 1.32 2.82 2.42
Long-dist. 1.62 4.23 1.43 3.03 2.93
9 Local 6.01 3.41 1.21 2.91 1.91
Mid-dist. 1.61 7.32 1.22 3.42 2.42
Long-dist. 1.82 4.01 1.57- 3.92 2.52

 

Note: K-W ANOVA was used to identify statistical differences between all distance segments within
regions. Mann-Whitney U was used to identify speciﬁc differences between segments. Analyses were
completed using the exposure weights.

“ Weighted by VisExpwt,

“ Median values reported.

1' 7" 3 Statistically different subgroups within regions.

There is a clear difference between distance segments in the number of annual
visits reported (Figures 8 and 9). More than half of the Long-distance segment visitors
sampled complete only one annual visit to the national forest—approximately 95%
complete no more than three annual visits. Approximately 40% and 30% of Mid-distance
visitors and Local visitors report only one annual visit, respectively. While both of these
segments visit more often than Long-distance visitors, Local visitors more frequently

complete a greater number of annual visits than Mid-distance visitors. The 95th percentile

99

 

 

 

 

 

 

:3
l
l

 

' n ' —o—Local
—l— Mid-Dist.

- -A- - Long-Dist

 

 

 

 

 

 

 

 

ii

 

 

N
ix?

Percent of Respondents
s
I
l
I

 

 

 

—L
53

 

 

 

0%

 

5 6 7 8 9 10 11 12
AnnualVlslts

 

Figure 8. Distance Segment Visit Frequency, USDA FS Region 2 (Truncated at
12 Annual Visits, Exposure Weighted).

 

 

 

 

 

 

 

 

 

 

70%
60% T
A.
r. 50% __._'._% e—
' s
“g ' s
C 400k q : I ___E44ﬁ—e—- ﬂ!---—-* * ”v— +Local
3. I _._ Mid-Dist.
. .‘p -Long-Dist.

 

 

 

 

 

 

 

 

 

0123456789101112
AnnualVlsits

 

Figure 9. Distance Segment Visit Frequency, USDA FS Region 9 (Truncated at
12 Annual Visits, Exposure Weighted).

100

 

for Mid-distance visitors is seven annual visits. Comparatively, the 95‘h percentile for

Local recreation visitors is 20 annual visits.

Within distance segments, substantial variability exists in the duration of on-forest

visits. At most, the modes of visit duration for each distance segment are reported by only

19% of respondents (Figures 10 and 11). Though variability is wide, the majority of

respondents report visits of 10 hours or less. Local visitors are most likely to have on-

forest durations of 1 to 5 hours. Forest visits of greater than 5 hours are reported more

frequently by Mid-distance and Long-distance visitors. In both regions, Long-distance

visitors are more likely than visitors from the other two segments to have national forest

visit durations of one hour or less.

 

18%

16%

14%

12%

10%

8%

6%

Percent of Respondents

4%

2%

0%

 

 

 

 

 

 

 

 

. _, _2 _.—Local
_I— Mid-Dist.

 

 

 

 

 

 

 

‘ - * -Long-Dist.
I ‘.

 

 

 

 

 

 

 

 

 

 

0 5 10 15 20
Visit Duration

 

Figure 10. Duration of National Forest Visits by Distance Segments, USDA FS
Region 2 (Rounded to Nearest Hour, Truncated at 24 hours, ExposureWtd.).

101

 

 

 

 

 

 

 

 

—o—Local
—.— Mid-Dist.
- g- - Long-Dist.

 

 

 

 

 

 

Percent of Respondents

 

 

 

 

 

 

 

 

0 5 10 15 20
Visit Duration

 

 

 

Figure 11. Duration of National Forest Visits by Distance Segments, USDA FS
Region 9 (Rounded to Nearest Hour, Truncated at 24 hours, ExposureWtd.).

Compared to the divergent patterns between segments in annual visits and visit
duration, the numbers of sites visited by visitors within each distance segment are quite
similar (Figures 12 and 13). Approximately 90% of Local visitors in both regions visit
only one recreation site or recreate only one day in the GFA. The percentages of single
site visitors in the Mid-distance and Long-distance visitor segments are slightly lower. In
Region 2, Long-distance visitors are more likely to visit a greater number of sites than
Mid-distance visitors. In Region 9, propensity for visiting a large number of sites is

similar between the two segments.

102

 

 

1 00%
90%

80% -

70%
60%
50%
40%
30%

Perwnt of Respondents

20%
1 0%
0%

 

 

 

 

 

 

 

—0— Local

 

   

—.— Mid-Dist.
- -A- - Long-Dist.

 

 

 

 

 

 

 

I
I
I

I
I

 

I

“HAM-=4

 

 

0

3 4 5

Number of Sites

 

Figure 12. Number of sites (and GFA days) by Distance Visitor Segment,
USDA FS Region 2 (Figure Truncated at 5 Sites, Exposure Weighted).

 

 

1 00%
90%

80%-

70%
60%
50%
40%
30%

Percent of Respondents

20%
1 0%
0%

 

 

 

 

 

—0— Local
—I— Mid-Dist.

 

//”

- 1- - Long-Dist.

 

 

 

 

 

 

 

i
1L

 

 

 

Number of Sites

 

Figure 13. Number of sites (and GFA days) by Distance Visitor Segment,
USDA FS Region 9 (Figure Truncated at 5 Sites, Exposure Weighted).

103

 

Mid-distance activity participation is somewhat bi-modal (Figures 14 and 15).
Visitors are most likely to participate either in a single recreation activity or in 3 to 5
activities. More than four recreation activities are completed with decreased propensity—
though such a pattern is most common among Long-distance visitors in Region 2 and
among Mid-distance visitors in Region 9. Local visitors less frequently participate in
multiple activities than visitors in other visitor segments. Local visitors may be
characterized as “focused visitors”, coming to the forest and participating only in one or
two speciﬁc activities, while Mid-distance and Long-distance visitors more frequently

participate in multiple recreation activities during a single visit.

 

 

 

 

 

 

 

 

 

 

 

 

 

  

 

 

 

 

25%

20% .
i
l: 15% Local
3 —-— Mid-Dist.
E 10% - *- - Long-Dist.
s

5% -

0%

Number of Activities

 

 

 

Figure 14. Number of Activities by Distance Segmented, USDA FS Region 2
(Figure Truncated at 10 Activities, Exposure Weighted).

104

 

 

25%

8

 

 

 

+Loca|
_.— Mid-Dist.
- -A- - Long-Dist.

 

 

10%

 

 

Percent of Respondents
9

5%

 

 

 

0%

 

 

Number of Activities

 

 

 

Figure 15. Number of Activities by Distance Segmented, USDA FS Region 9
(Figure Truncated at 10 Activities, Exposure Weighted).

The patterns of party size are moderately bi-modal (Figures 16 and 17). In both
regions, Long-distance and Mid-distance visitors are most likely to recreate in either two
or four person parties. Three person parties or parties with more than four persons are less
common among these visitor segments. Local visitors are most likely to recreate in two
person parties and nearly equally likely to recreate in either three or four person parties.
Party sizes of more than four people are less common among this segment. Large parties

of recreation users are most common in the Lon g-distance visitor segment.

105

 

 

Percent of Respondents

 

 

 

 

 

 

 

 

 

 

 

 

 

—o—Local
—-— Mid-Dist.
- ‘- - Long-Dist.

 

 

 

 

 

 

 

3 4 5
People per Vehicle

 

Figure 16. People per Vehicle by Distance Segment, USDA FS Region 2

(Exposure Weighted).

 

 

60%

Si

5

5

Percent of Respondents
5

—L
23

0%

 

 

 

 

 

 

 

 

 

 

—o—Local
—|— Mid-Dist.
- *- - Long-Dist.

 

 

 

 

 

People per Vehicle

 

Figure 17 . People per Vehicle by Distance Segment, USDA FS Region 2

(Exposure Weighted).

106

 

 

 

 

Across all distance segments, the greatest numbers of recreation visitors first
arrive at the national forest on Friday, Saturday, or Sunday (Table 18). Arrivals on
Monday, Tuesday, Wednesday, or Thursday are more common by Long-distance visitors
than other distance segments. This pattern is consistent with the assumption that Long-
distance recreation visitors are either on vacation or in the area for business, both likely to
include weekday travel. The arrival patterns of Local and Mid-distance visitors are
comparable—though Local visitors are slightly more likely to arrive at the national forest
during the workweek. This similarity is contrary to the postulated characteristics

identiﬁed in Chapter 3.

Table 18. Day of National Forest Arrival by Distance Segment and USDA FS
Region.“

 

 

Study Distance P-
lggion Segment Sun. Mon. Tues. Wed. Thurs. Fri. Sat. Total value
Region
2 Local 25% 11% 7% 8% 9% 15% 25% 100% 0.000
Mid-dist. 21% 7% 7% 6% 10% 23% 26% 100%
Long-
dist. 15% 12% 16% 14% 11% 15% 17% 100%
Region
9 Local 27% 8% 5% 7% 8% 15% 31% 100% 0.000
Mid-dist. 19% 6% 5% 6% 9% 24% 30% 100%
Long-
dist. 15% 10% 11% 11% 11% 20% 23% 100%

 

“ Statistical analysis completed using contingency table analysis and Exth’s. Comparisons between
regions using exposure weights assumes there is no bias in site stratiﬁcation or the propensity for visitor
sampling to occur on a given day(s) between regions.

In Region 2, greater percentages of Local visitors engage in hunting, fishing, and
hiking than visitors within other Region 2 distance segments (Table 19). More of these

visitors recreate on the national forest with no particular primary activity. Local visitors

107

in Region 9 are more commonly hunting, ﬁshing, or “driving for pleasure” than other
distance segments in the region. Mid-distance visitors in both regions are more likely to
be camping, operating OHV’s, and downhill skiing than others. In both regions, Long-
distance visitors are more commonly completing “nature-related” activities. In Region 2,
these visitors more frequently are “driving for pleasure” than other segments while in
Region 9 they are more commonly hiking, snowmobiling, or using resorts than other

segments.

Table 19. Primary Activity by Distance Segment and USDA FS Region.“

 

Region 2 Region 9
Mid- Long- Mid- Long-
Local dist. dist. Local dist. dist.
Developed Camping 2% 3% 2% 2% 5% 3%
Primitive Camping &
Backpacking 2% 1% 0% 1% 4% 3%
Resort 0% 0% 0% 1% 1% 4%
Picnic 2% 2% 1% 3% 0% 1%
Nature-related 8% 5% 10% 7% 7% 13%
General/Relaxing 5% 4% 4% 5% 6% 7%
Fishing 7% 3% 3% 13% 9% 7%
Hunting 5% 6% 3% 13% 9% 5%
OHV use 3% 4% 1% 3% 5% 2%
Driving 3% 2% 4% 6% 2% 1%
Snowmobile 4% 2% 1% 3% 3% 5%
Boating 2% 1% 1% 1% 1% 1%
Hiking 19% 7% 11% 16% 13% 25%
Biking 7% 6% 6% 2% 1% 0%
Downhill skiing 14% 45% 45% 7% 13% 4%
Cross-country skiing 5% 1% 0% 7% 8% 3%
Other non-motorized 4% 2% 1% 3% 2% 2%
Other 3% 1% 1% 4% 1% 4%
No primary activity 4% 2% 2% 1% 3% 3%
Multiple primary
activities 2% 3% 2% 2% 7% 5%
MI 100% 100% 100% 100% 100% 100%

 

ﬂoured from full NVUM sample, weighted by NVEXPAND.

108

The most marked differences between distance segments occur in trip-type
frequency (Table 20). Local visitors generally recreate on day trips and less commonly on
overnight trips. “Not primary” trips are rare for this visitor segment. Mid-distance visitors
most commonly complete overnight trips, recreating less commonly on day trips and
infrequently on “Not primary” trips. Very few Long-distance visitors complete primary
purpose day trips, instead taking a large number of “Not primary”. These differences are

marked and consistent with the expectations identiﬁed in Chapter 3.

Table 20. Trip-type Segment Shares by Distance Segment and USDA FS Study

 

 

_Rr_:_glon.d
Day OVN/OVNNF“ Not Primary“ Total
Region2 Local“ 72% 24% 3% 100%
Mid-dist.“ 24% 66% 11% 100%
Long-dist.c 8% 59% 33% 100%
Region9 Local“ 72% 25% 4% 100%
Mid-dist.“ 17% 70% 14% 100%
Long-dist.“ 6% 62% 33% 22%

 

“‘ b' “ Statistically different subgroups within regions.

“Estimated based upon the NVUM economic survey subsample. Exth’s applied. Statistical comparisons
between regions within distance segments were completed using contingency table analysis.

“ Recreation trips involving an overnight stay in the local forest area or on the national forest.

Trips not made primarily to recreate on the national forest.

Within trip-types, the characteristics of visitors in distance segments are less
frequently statistically different (Table 21). Statistical differences between distance
segments remain for visit duration and the number of people per vehicle when accounting
for trip-type. For the most part, patterns of site use and the number of activities are

statistically similar across segments within trip-types.

109

Table 21. Mean Values for Visitor Characteristics of Interest by Distance Segment,
Trip-type, and USDA FS Region.

 

Visit Number
Distance duration Number of People!
Trip Type Reg‘Lon Segment (hours)“ of Sites Activities Vehicle
Day Region 2 Local 30‘ 1.1‘ 3.2‘ 21‘
Mid-dist. 4.32 1.1‘ 29‘ 2.42
Long-dist. 20° 1.3‘ 3.4‘ 24"2
Region 9 Local 2.6‘ 1.1‘ 3.1‘ 2.3‘
Mid-dist. 4.22 1.2‘ 3.1‘ 22'
Long-dist. 1.9“l 1.3al 3.8a 1.8“
OVN/OVNNF Region 2 Local 24.0‘ 1.3‘ 4.6‘ 2.5‘
Mid-dist. 22.2‘ 1.3‘ 4.22 2.5‘
Long-dist. 5.52 1.5‘ 3.92 2.82
Region 9 Local 40.2‘ 1.3‘ 4.6‘ 2.3‘
Mid-dist. 28.02 1.4‘ 4.4‘ 2.52
Long-dist. 8.03 1.6‘ 4.2‘ 2.5‘2
Not primary Region 2 Local 26° 13‘ 4.2‘ 2.2‘
Mid-dist. 3.0‘ 1.3‘ 4.2‘ 26‘
Long-dist. 3.0‘ 1.5‘ 4.8‘ 25‘
Region 9 Local 3.2“ 1.1“ 3.4“ 2.2“
Mid-dist. 3.6‘ 1.3‘ 3.6‘ 28‘
Long-dist. 2.22 1.4‘ 3.6‘ 2.4‘

 

Note: Statistical tests completed using Mann-Whitney U incorporating the exposure weights.
“ Median values
“ Not computed within trip-types since variable corresponds to previous trips of unknown type. VisExpwt
used in statistical analyses,
:“ 3 Statistically different subgroups within regions.

In both study regions, Local visitors on day trips spend a shorter period of time
on-forest than Mid-distance visitors on day trips. This pattern is consistent with the
postulated characteristics identiﬁed in Chapter 3. Considering overnight visits, the on-
forest durations of Local, Mid-distance, and Long-distance visitors follow a continuum
from longest forest duration to shortest forest duration, respectively. In Region 2, only the

visit duration of Long-distance OVN/OVNNF visitors is statistically different from the

110

other segments while all OVN/OVNNF segments are statistically different from one

another in Region 9.

Average party sizes range from 2.1 to 2.8 individuals for all segment trip-type
combinations (excluding those with less than 30 cases). The party sizes of Local day trip
groups in Region 2 are statistically different from that of parties in the Mid-distance
segment in that Region. Similarly, Local visitor groups on overnight trips are smaller
than Long-distance segment visitor parties. In Region 9, the size of overnight Local
visitor parties is statistically different from that of Mid-distance visitor parties on

ovemight trips.

Summgy of Distance Segment Characteristics

The characteristics of visitors within the three distance visitor segments are
largely consistent with those postulated (Chapter 3, Table 1). Local visitors complete
multiple trips annually, primarily on short-duration day trips. Local visitors typically visit
a small number of sites and participate in few activities on any single visit. When on
overnight trips, Local visitors spend a substantial time on-forest and participate in a larger
number of activities. Contrary to expectations outlined in Chapter 3, Local visitors
primarily ﬁrst arrive at the national forest during the weekend rather than the weekday—

though Monday arrivals are relatively common among Local visitors.

Mid-distance visitors recreate on a given national forest less frequently than Local

visitors (though the difference is not statistically signiﬁcant) and more frequently than

111

Long-distance visitors. When completing a national forest recreation visit, Mid-distance
visitors typically spend the night away from home. Among distance segments, these
visitors are least likely to spend only a short period of time (less than 5 hours) on-forest
during their recreation visit. In aggregate, these visitors recreate on more sites, participate

in more activities, and are in larger recreation parties than Local visitors.

The characteristics unique to Long-distance visitors are low annual visit
frequencies and a propensity for national forest visits of very short duration. More than
50% of Long-distance visitors in both regions complete only one annual national forest
visit and more than 95% complete less than four visits annually. Approximately 50% all
Long-distance visitors spend less than four hours on the national forest. In Region 9,
nearly 20% stay for less than one hour. By far, this distance segment has the greatest
percentage of visitors classiﬁed as “Not Primary”, more than 20%. Among distance
segments, those traveling greater than 200 miles visit the greatest number of sites,
participate in the greatest number of activities, and recreate in the largest parties.
Compared to Local and Mid-distance visitors, Long-distance visitors are most likely to

arrive at the national forest during the workweek.

Recreation Use Models

Objective 3 of this study is to develop models to predict the annual forest-level

recreation use of Local and Mid-distance segment visitors. Recreation use estimates for

national forests sampled under NVUM in FY2001, FY2002, and FY2003 were used to

112

construct recreation use models and to verify mode] operation. Recreation use estimates
from forests sampled in calendar year 2000 are used to validate model performance
(ascertain the model’s out-of—sample predictive ability). In this section, the Local
recreation use models are presented ﬁrst followed by models constructed to predict the

recreation use of Mid-distance visitors.

Local Permanent Resident Model

Construction of the models predicting the recreation use associated with
permanent Local area residents required estimation of regional-level estimates of the
percent of the population participating in national forest recreation, identiﬁcation of
forest-level Local populations, and estimation of annual visitation rates of those
participating in national forest recreation, all by distance band. Evaluation of the Local
model predictions required development of the NVUM estimates of forest-level Local

segment recreation USC.

Regional-level participation rates of Local populations living in distance bands
surrounding national forests were estimated using the formula developed in Chapter 3.
This formula incorporated band-level populations, NVUM use estimates, and annual
visitation rates, aggregated within region (Table 22). For the most part, the empirically
estimated regional participation rates decrease with increasing distance, as expected. One

notable exception to this pattern is a substantial increase in participation in the 25-30 mile

113

distance band in Region 2. This increase in percent participating is a manifestation of the

difference in spatial units of analysis between the NVUM data and the Census data.'0

Table 22. Regional Participation Rates of Populations Residing Within 30 Miles
of National Forests in the USDA FS Study Regions.

 

Distance Band
0 0-10 10-15 15-20 20-25 25-30
Region NVUM Use
2 Estimate 1,723,5 16 3,476,959 585,723 647,619 181,577 206,428
Population 134,035 1,076,205 467,167 680,461 5 1 1,090 140,893
Annual Visits ﬁ._8 M 4._8_ 4; 3+6 3g
Percent
Participating 87% 44% 26% 21% 10% 41%
Region NVUM Use
9 Estimate 1,166,158 2,259,467 270,956 90,953 15 1,668 164,1 13
Population 385,018 2,235,003 1,282,808 1,280,169 1,197,016 1,587,431
Annual Visits L8 7_.0_ $9 2L6 43 3,4
Percent
g Partﬂating 39% 15% 5% 2% 3% 3%

 

Across all bands, the participation rates of those living in Region 2 are greater
than those of the Region 9 population. Within bands, the participation rates of those
living within national forest boundaries (0 Band) are substantially greater than

pOpulations living outside national forest boundaries. The occurrence of very high

—~

10 Aiarge number of ZIP code origins in the 25 - 30 mile distance band (also the 20 - 25 mile band) in
Region 2 are located in the Denver metropolitan area. However, the census block groups in the 25-30 mile
band are located, largely, on the outskirts of the Denver metropolitan area (near the Denver airport) in an
area With a small population. In the future, it may be advantageous to aggregate the 20-25 mile distance
band and the 25-30 mile distance band to smooth this estimate.

114

participation rates among those living within national forest boundaries is logical as the

national forest is “at the doorstep” of this population.

Within regions, Local populations are highly variable (Table 23). In Region 2,

only 30,000 people live within 30 miles of the Routt NF while 2.1 million people live

near the Pike San Isabel NF. The Local population of the Hiawatha NF is only 93,000

residents while the Local population of the Mark Twain NF is 1.8 Million people. Local

populations around Region 9 national forests are typically greater than those of Region 2.

Table 23. Forest-level Distance Band Populations for NVUM forests sampled in

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

FY2001, FY2002, FY2003.“
Distance Band
0 0-10 10-15 15-20 20-25 25-30

_R_ggion 2

Mom NF 1,322 11,684 23,162 4,963 4,301 2,490
Black Hills NF 24,371 114,182 8,413 5,013 1,987 0
Grand Mesa, U.,G. NF’s 17,937 56,119 81,250 38,399 5,994 625
Medicine Bow NF 0 34,325 15,070 60,605 41,851 39,216
Nebraska NF 4,507 34,338 13,114 10,936 13,147 10,845
Pike San Isabel NF 33,071 755,161 322,666 531,226 408,905 55,697
Routt NF 4,621 16,262 3,370 6,140 0 0
Shoshone NF 3,011 11,345 17,917 5,476 8,398 23,219
White River NF 46,517 55,709 1,833 5,110 1,150 625
Region 9
Allegheny NF 20,332 113,825 120,692 108,484 74,660 79,758
Chequamegon-Nicolet NF 24,468 94,671 64,2 85 96,731 72,5 86 65,320
Chippewa NF 14,866 50,161 30,452 15,733 17,668 11,460
Hoosier NF 26,027 264,409 163,230 122,646 141,626 269,580
Huron-Manistee NF 88,017 380,337 105,201 164,850 180,481 279,015
Mark Twain NF 86,030 559,874 269,382 382,272 290,601 244,049
Monongahela NF 31,414 104,822 65,629 67,451 96,558 248,230
Ottawa NF 10,431 37,426 6,552 18,601 13,230 6,734
Shawnee NF 26,819 249,541 11 1,037 96,633 67,285 84,795
Wayne NF 56.614 379,938 349,274 208,574 238,802 297,367

 

“ Estimated using Barrier-adjusted Band distance. Census block groups applied only to the nearest forest.

115

The mean numbers of annual visits completed by visitors living within Local
distance bands (Table 24) were estimated for the national forest aggregates. The number
of annual visits declines with increasing distance from forest up to 15 miles from the
forest boundary. From 15 to 30 miles the annual visit frequencies are relatively constant.
The annual visit estimates of Region 2 Local visitors are, for the most part, greater than

those reported by Region 9 Local visitors.

Table 24. Distance Band Annual Visit Frequencies of Local Visitors by
Region and National Forest Aggﬂrgation Group.“

 

Distance Band
Aggregation
Group“ 0 0-10 10-15 15-20 20-25 25-30
Region 2 1 14.1 10.7 4.8“ 6.3 3.6 “ 3.5 “
2 10.7 6.7 6.3 4.5 “ 3.6 “ 3.5 “
3 11.0 5.7 3.3 2.7 3.0 3.3
4 16.7 11.7 6.4 5.9 3.6 “ 3.6

Regional Avg. 14.8 7.3 4.8 4.5 3.6 3.5

 

Region 9 l 6.8 6.6 3.1 3.5 4.7 4.2
2 9.3 7.1 4.9 3.6 c 4.8 3.2
3 6.7 6.4 4.3 3.6 “ 2.9 3.4 “
4 5.6 4.4 4.0 “ 3.6 “ 4.9 “ 2.6
5 12.3 9.2 4.2 3.6 4.9 3.4
Regional Avg. 7.8 7.0 4.0 3.6 4.9 3.4
“ Estimated from the NVUM survey and weighted by VisExpwt. Standard errors available in
Table 01

“ Aggregation Groups deﬁned below.

Region 2: l Bighorn NF, Shoshone NF; 2 Black Hills NF, Nebraska NF; 3 Arapaho and
Roosevelt National Forests, Medicine Bow NF, Pike and San Isabel National Forests; 4 Grand
Mesa, Uncompahgre, and Gunnison National Forests, Rio Grande NF, Routt NF, San Juan NF,
White River NF.

Region 9: l Hoosier NF, Mark Twain NF, Shawnee NF; 2 Alleghany NF, Monongahela NF,
Wayne NF; 3 Green Mountain and Finger Lakes National Forests, White Mountain NF; 4
Hiawatha NF, Huron-Manistee NF, Ottawa NF; 5 Chippewa NF, Chequamegon/Nicolet NF,
Superior NF.

“ Number of cases is less than 30. Region-level average substituted.

116

Population counts of those living in distance bands around national forests were
combined with regional-level participation rates and estimates of annual visit frequencies
to predict forest-level recreation use by those living within 30 miles of the national forest

boundary (I able 25). Model predictions of forest-level Local visitor recreation use range

from 112,000 visits to 3 million visits for national forests in Region 2 and from 51,500

visits to 903,000 visits for national forests in Region 9.

In Region 2, only one forest prediction of Local visitor use (Black Hills NF) is
consistent with the NVUM Local visit estimate. The regional summation of forest-level
predictions in Region 2 falls below the Region 2 NVUM Local visitor estimate. Of the
eight Region 2 national forests where model predictions are outside the NVUM Use
estimates, the model underestimates Local use for six national forests and over-estimates
Local use for two national forests. Model predictions for three of the six under-predicted
national forests were within 90,000 visits of lower NVUM Local visit estimate. The two

forests over-predictions were well above the NVUM upper estimates of Local segment
visits.

The Local visitor model performs slightly better in Region 9 than in Region 2. In three

cases, the predicted Local visitor use falls within the conﬁdence intervals of the NVUM
Local visit estimates. Although not within the conﬁdence intervals, the predicted Local
visitor use for Ottawa NF is very close to lower NVUM estimate. The regional

summation of predicted forest-level Local visitor use falls within the conﬁdence interval .

of the NVUM Region 9 Local use estimate. Of the seven Region 9 national forests where

117

 

model predictions are outside the 80% NV UM confidence interval, the recreation use of

Local visitors is under-predicted on four national forests and over- predicted on three

national forests.

Table 25. Model and NVUM Estimates of Permanent Resident Local Visitor

Recreation Use.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Local Visitor Lower Upper
Use NVUM Use NVUM NVUM
Prediction Estimate Estimate Estimate
Bighorn NF 112,122 273,995 198.388 355.876
Black Hills NF 583,526“ 506,796 398.248 622.623
Grand Mesa, U.,G. NF 736,311 1,388,635 953,697 1,848,310
Medicine Bow NF 200,592 288,978 236,296 345,489
Nebraska NF 195,758 56,678 40,789 74,836
Pike San Isabel NF 3,004,975 2,045,153 1,714,877 2,389,610
Routt NF 164,189 587,221 501,804 679,250
Shoshone NF 157,285 258,705 214,246 306,760
White River NF 973,054 1,279,373 1,171,184 1,392,239
Total 6,127,811 6,821,822 6,333,542 7,321,118
Alfgheny NF 246,456 555,824 381,840 744,479
Chequamegon-Nicolet
NF 280,043 595,152 389,868 822,667
Chippewa NF 149,101 726,966 592,578 872,045
Hoosier NF 410,796“ 365,668 295,388 440,773
Huron-Manistee NF 512,529 212,354 129,357 307,506
Mark Twain NF 903,695 412,738 350,419 477.656
Monogahela NF 279,677“ 262,522 214,339 314,594
Ottawa NF 51,438 78,848 61,991 97.583
Shawnee NF 354,919‘ 316,315 270.318 365.126
Wame NF 759,006 248,914 200,208 301,341
Total 3,947,661‘ 4,102,540 3i34,356 4,480,997

 

* Model prediction falls within the 80% conﬁdence interval of the NVUM Local visitor use estimate.

118

The absolute values of raw errors in model predictions range from 76,000 visits
to 960,000 visits in Region 2 and from 17,000 visits to 510,000 visits in Region 9 (Table
26). Six of the nine national forests in Region 2 have percentage errors (percentage error
= absolute raw error/NV UM Local visit estimate) of less than 50%. Two forest-level
percentage errors are below 25%. The absolute value of the raw error between the
summation of the Region 2 Local model predictions and the NVUM regional estimate

was approximately 700,000 visits, corresponding to a percent error of 10%.

Table 26. Local Visitor Model Errors.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Absolute Errora Percentage Error”
Bighorn NF 161,873 59%
Black Hills NF 76,731 15%
Grand Mesa, U.,G. NF’s 652,324 47%
Medicine Bow NF 88,386 31%
Nebraska NF 139,080 245%
Pike San Isabel NF 959,821 47%
Routt NF 423,032 72%
Shoshone NF 101,420 39%
White River NF 306,319 24%
Region 2 694,011 10%
Allegheny NF 309,368 56%
Chequamegon-Nicolet NF 315,109 53%
Chippewa NF 577,864 79%
Hoosier NF 45,128 12%
Huron-Manistee NF 300,175 141%
Mark Twain NF 490,958 119%
Monongahela NF 17,156 7%
Ottawa NF 27,410 35%
Shawnee NF 38,604 12%
Wayne NF 510,092 205%
Region 9 154,879 4%

 

a abs(Model prediction — NVUM Local visit estimate)
b (absolute error/NVUM Local visit estimate)*100

119

In Region 2, the predicted Local visitor use for the Nebraska NF (and
administered grasslands) was vastly different from the NVUM estimate of Local use. In
fact, the predicted use of Local visitors was greater than the NVUM estimate of all
recreation use on the Nebraska NF (127,000 total visits). Such a large overestimation is
indicative that either the regional participation rates or aggregate annual visit frequencies

are inappropriate for the Nebraska NF.

The Region 2 participation rates are largely influenced by national forests located
in the Rocky Mountain region of Colorado and Wyoming. The outdoor recreation
resources and services available on these mountain national forests likely differ from
those of the Nebraska NF. In addition, the recreation behavior and participation patterns
of those living around the Nebraska NF may be more similar to populations proximate to
Region 9 national forests given the Nebraska NF’s distance from the mountain national
forests. Substituting the Region 9 participation rates for Region 2 participation rates in
the Nebraska NF Local visitor model yields a prediction of 59,881—nearly identical and
within the confidence intervals to the NVUM estimate. Given this result, Region 9
participation rates appear more appropriate for the Nebraska NF than Region 2

participation rates.

Errors are generally greater in Region 9 than in Region 2. Only four of ten
national forests in Region 9 have percent errors of less than 50%. Percent errors for the
Huron-Manistee NF, the Mark Twain NF, and the Wayne NF are all above 100%. On

those Region 9 national forests where the model predictions are within the NVUM Local

120

visitor use estimates (Shawnee NF, Hoosier NF, Monongahela NF), both absolute and
percent errors are quite small. Summing all Region 9 national forest model predictions,
the regional prediction of Local use has an absolute raw error of 155,000 visits, a percent

error of only 4%.

The national forests in the study area with the greatest population sizes are the
Pike San Isabel, the Mark Twain NF, and the Wayne NF, respectively. The predicted
Local use levels for each of these forests are significantly greater than the NVUM
estimate. The percent errors for the Wayne NF and Mark Twain NF are among the
highest in Region 9. In all three cases, the prediction from the Local visitor model is
greater than the NVUM estimate of total recreation use for the national forest.
Commonly, the participation rates of large population areas are less than those where
population numbers are small. Overestimation for these national forests is indicative of
the need to develop adjusted participation rates applicable to forests with large population
areas. Such participation rates could be developed by pooling visit estimates among

forests with large populations and re-estimating participation rates.

When there are statistical differences between the two, Local model predictions
tend to be lower than the NVUM Local visit estimates. Of the 15 cases where statistical
differences exist, the model predictions are lower than the NVUM estimates in 10 cases.
Non-random error in specifying the model inputs or over-estimation of Local visitor use
from NVUM may cause the consistent underestimation of Local segment visits.

Concerning the former, the model assumes that all residents within a given distance band

121

participate with the same proclivity. If a disproportionate share of the population within

the distance band is located closer to the forest than the Mid-distance, visitation may be

underestimated. This may be particularly applicable to the zero — 10-mile band where a
large percentage of residents may reside directly adjacent to the forest boundary. Based

upon their proximity, these residents may have much higher participation rates than other

individuals in the distance band living farther away from the national forest.

Consistent underestimation may also be indicative of errors in the NV UM Local
use estimate. In this study, the NVUM estimate of Local use is developed from the visitor
survey data and the NVEXPAND weights. Stynes and others (2003) have questioned

whether the N VEXPAND weights are appropriate for estimating visitor characteristics

and trip types from the NVUM survey data. Use of the NVEXPAND weights assumes

that the NVUM approach to stratifying recreation sites captures variability in visitor

origins and/or visitor characteristics. The exposure weights, which assume the NVUM

stratiﬁcation does not explain variability in visitor characteristics, have been adopted by

Stynes and others (2003) when estimating visitor characteristics and, in some instances,

when estimating visitor shares. For national forests in the study area, use of exposure

weights yield lower Local visitor shares in many cases (Table 27). In several cases the

differences between forest-level estimates of Local segment shares under the alternate
weighting schemes are greater than 10 percentage points—particularly in Region 2.

Consistently high NVUM estimates of Local use could lead to the pattern of under-

estimation found in the Local visitor model.

122

Table 27. Local visitor Segment Shares Estimated Under
Two Alternative WeigﬂLSchemes.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

NVEXPAND Exth
Bighorn NF 52% 31%
Black Hills NF 69% 59%
Grand Mesa, U.,G. NPS 67% 55%
Medicine Bow NF 51% 51%
Nebraska NF 45% 30%
Pike San Isabel NF 73% 75%
Routt NF 39% 38%
Shoshone NF ' 53% 49%
White River NF 44% 44%
Region 2 58% 51 %
Allegheny NF 57% 54%
Chequamegon-Nicolet NF 38% 38%
Chippewa NF 55% 46%
Hoosier NF 71% 67%
Huron-Manistee NF 30% 38%
Mark Twain NF 84% 70%
Monongahela NF 38% 35%
Ottawa NF 34% 26%
Shawnee NF 62% 61%
Wayne NF 64% 69%
Region 9 51 % 49%

 

Local Seasonal Resident Model

The number of seasonal homes located within the Local areas of national forests
in the study was identiﬁed from US. Census data (Table 28). In Region 2, Pike San
Isabel NF followed by the White River NF and Grand Mesa, Uncompahgre, and
Gunnison NF have the greatest number of seasonal homes close to the national forest.
The Nebraska NF has the lowest number of seasonal homes in the local area of the
National Forest. The Huron-Manistee NF, in Region 9, has more than 100,000 seasonal

homes located in its local area—by far the largest number of seasonal homes among

123

national forests in this study. Elsewhere in Region 9, the Chequamegon—Nicolet NF, the
Alleghany NF, and the Mark Twain NF all have 30,000 or more seasonal homes located

within 30 miles of the forest. The Shawnee NF has the fewest number of seasonal homes

proximate to the national forest.

Table 28. Seasonal Homes Proximate to NVUM forests sampled in FY2001,

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

FY 2002, and FY 2003.a
Distance Band
0 0-10 10-15 15-20 20-25 25-30
_R__e_gion 2

Bighorn NF 313 598 442 24 271 57

Black Hills NF 1,523 838 33 112 30 0

Grand Mesa, U.,G. NF’s 4,014 2,004 145 66 17 2

Medicine Bow NF 0 1,450 327 636 121 535
Nebraska NF 99 586 173 103 210 115
Pike San Isabel NF 5,429 7,964 920 1,690 259 138
Routt NF 1,174 976 391 33 0 0
Shoshone NF 506 513 868 973 785 564
White River NF 7,632 1,847 36 60 25 2

Region 9

Allegheny NF 9,982 8,212 2,099 4,472 4,976 6,192
Chequamegon-Nicolet NF 12,674 22,831 1 1,998 10,662 5,170 5,697
Chippewa NF 4,948 9,541 3,250 3,467 3,635 2,597
Hoosier NF 804 2,366 1,431 1,805 2,342 1,022
Huron-Manistee NF 25,106 34,947 12,328 14,021 10,335 11,473
Mark Twain NF 5,256 9,585 2,893 5,355 2,873 8,256
Monongahela NF 5,624 4,004 2,179 5,062 4,448 3,049
Ottawa NF 3,688 8,654 2,241 1,089 1,632 183
Shawnee NF 649 1,375 432 853 485 772
Wayne NF 746 3,853 2,995 2,931 2,338 1,964

 

‘ Estimated using Barrier-adjusted Band distance. The number of seasonal households within census
block groups counted only for the nearest forest.

Due to data limitations, it was assumed that seasonal homeowners in the Local
area visit with the same propensity and with the same annual frequency as permanent

local residents. An estimate of the average party size of seasonal homeowner parties

originating from seasonal homes was identiﬁed using the NVUM FY 2003 national-level

124

survey data. Nationally, seasonal homeowner parties average 2.6 individuals per party.
For the Local visitor seasonal model it is assumed that recreation parties associated with

seasonal households in all Local distance bands are comprised of 2.6 individuals.

The numbers of visits associated with seasonal homes in the local areas of
national forests were estimated from the counts of seasonal homes, participation rates,
annual visit frequencies, and party sizes (Table 29). The predicted number of national
forest visits associated with seasonal homes is highly variable. In Region 2, only 8,500
visits to the Nebraska NF are associated with seasonal homes while more than 310,000
visits are predicted for the White River NF. The model predicts only 8,600 seasonal home
visits to the Shawnee NF and nearly 250,000 seasonal home-related visits to the
Chequamegon-Nicolet NF. Although there are many more seasonal homes in Region 9,
visitation associated with seasonal homes is nearly equal between the two regions—due

to the greater assumed participation rates and visit frequencies of Region 2 residents.

It is difficult to verify the performance of the seasonal homeowner Local visitor
model. The NVUM visitor surveys used in the first three years of NVUM did not clearly
identify seasonal homeowners, precluding direct estimation of seasonal homeowner
recreation use for forests sampled during these years. In the Year 4 survey, respondents
were asked to report the type of local lodging used, including owned-homes, if they were
“staying away from home on this trip”. The percent of survey respondents selecting
“owned home” can be used to develop the NVUM estimate of seasonal homeowner use.

However, this is likely not a complete enumeration of seasonal homeowner use as it is

125

reasonable to expect that many seasonal homeowners may not have answered the
question since they were indeed staying at a home they owned (i.e. “not staying away

from home”).

Table 29. Model Estimates of Seasonal

 

 

 

Local Resident Recreation Use.

Forest Model Estimate
Bighorn NF 19,347
Black Hills NF 43,890
Grand Mesa, U.,G. NF’s 179,194
Medicine Bow NF 13,190
Nebraska NF 8,517
Pike San Isabel NF 192,660
Routt NF 59,184
Shoshone NF 31,566
White River NF 313,134
Total 860,682
Allegheny NF 121,326
Chequamegon-Nicolet NF 248,499
Chippewa NF 98,865
Hoosier NF 13,466
Huron-Manistee NF 214,88 1
Mark Twain NF 65,970
Monongahela NF 68,254
Ottawa NF 37,187
Shawnee NF 8,648
Wayge NF 21,1 18
Total 829,960

 

Three of the seven forest-level model predictions (all in Region 9) for Year 4
national forests fall within the conﬁdence intervals of NVUM seasonal homeowner
recreation use estimates (Table 30). Seasonal resident model predictions for Region 2
were all greater than the NVUM estimates. However, it is unlikely that the NVUM

estimate of no seasonal homeowner visits to the Shoshone NF is correct given the sheer

126

number of seasonal homes proximate to the NF. For this NF (at least), the NVUM

estimate likely underestimates true seasonal homeowner recreation use.

Table 30. Model and NVUM Estimates of Seasonal Resident Local Visitor
Recreation Use for FY 2003 NVUM National Forests.

 

 

Count of Local NVUM Lower Upper

Seasonal Visitor Use Use NVUM NVUM

Homes Prediction Estimate Estimate Estimate
Black Hills NF 2,536 43,890 3,930 0 13,175
Grand Mesa,

U.,G.

NF’s 6,248 179,194 17,474 5,227 54,177
Shoshone NF 16,399 31,566 0 0 0
Hoosier NF 9,770 13,466‘ 6,742 217 15,299
Monongahela

NF 24,366 68,254‘ 49,532 31,063 71,708
Ottawa NF 17,487 37,137" 47,669 32,694 65,358
Wayne NF 14,827 21,118 3,394 0 8,642

 

1' Model prediction falls within the 80% conﬁdence interval of the NVUM Local visitor use estimate.

Mid-distance Recreation Use Models

The recreation use of day trip and overnight trip Mid-distance visitors was
modeled separately. Day trips are those where the recreation visitor does not spend a
night away from their home. Overnight trips include those visits involving an overnight
stay on the NF as well as those including a stay overnight off-forest.ll The models
developed for Mid-distance visitation predict only the recreation use where the primary
trip purpose is recreation at the NF. “Non-primary” trips are excluded, as these trips are

likely a function of factors other than those typically included in travel cost models.

 

n In the ﬁrst three years of NVUM survey, respondents were asked to report the length of their trip away
from home rather than the number of nights in the local forest area. Given this, some modeled overnight
trips will include nights spent away from home but not in the local forest area.

127

The multi-site zonal travel cost models of Mid-distance segment recreation use
were estimated using both single-log models estimated via ordinary least squares (OLS)
and Tobit models estimated via maximum likelihood estimation (MLE) for each study
region. The single-log OLS models were estimated with the dependent variables day and
overnight visits and day and overnight visits per thousand population. Tobit models were
estimated with the dependent variables day and overnight visits and day and overnight
visits per capita. Eviews 4.1 (Quantitative Micro Software LLC, Irvine, CA) was used to
estimate all models as well as to forecast forest-level Mid-distance recreation use based
upon the ﬁtted models. When forecasting, Eviews 4.1 software corrects for the bias
resulting from the retransforrnation of log estimates. Stynes and others (1986) identiﬁed
bias in coefﬁcients and use estimates when using log formulations of travel cost models.

White’s heteroskedasity test was completed on all OLS models. In all cases, the null

hypothesis of homogeneity could not be rejected.

The “best performing” single-log OLS and Tobit models in each region were
selected to predict forest-level day and overnight Mid—distance segment recreation use.
Models were selected based upon 1) consistency of the coefﬁcient estimates with theory
and 2) the smallest mean absolute percentage errors between model prediction and
observed values of band-level recreation use. The number of observations used for
estimating the day and overnight single-log OLS differ from the number of observations
used to estimate the Tobit models since zero-visit observations are excluded from the

single-log formulations. The greatest number of zero-visit observations occurs for day

128

trip recreation use and, correspondingly, these models have the smallest number of

observations.

Preliminary models constructed using alternate dependent variable formulations
were compared to one another, in part, based upon their mean absolute percent errors
(MAPE). Despite the selection of ﬁnal models that had the smallest MAPE values (in

addition to coefﬁcient estimates consistent with theory) the ﬁnal model MAPE values are
still moderately large for Region 2 models and very large for Region 9 models. When
interpreting these MAPE values it is important to recognize that the model MAPE’s
depict the differences between observed dependent variable (e. g. day trips/capita) and the
predicted dependent variable—rather than observed total recreation use versus predicted
total recreation use. Given that the dependent variables used in estimating the models are
generally small values (e. g. visits per capita), moderate divergences from these values

can result in large MAPE values.

In Region 2, day trips are largely dependent upon travel distance. In both model
formulations, day trips decrease with increasing distance, all else being equal, as
expected (Table 31). In the OLS formulation, the coefﬁcient on RUC is also statistically
signiﬁcant. Increases in RUC code (increasingly rural areas) leads to fewer day trip visits
originating from that distance band, all else remaining the same. The coefﬁcients on
median income, distance to substitutes, and recreation site acreage and number of units

were not statistically different from zero.

129

Table 31. Day and Overnight Mid-distance Segment Recreation Use Models, USDA

 

 

 

FS Region 2.a
OLS Model Dep. Variable: ln(Distance Band Visits/ Population(l,000s)),
Variable Day T-Value OVN T-Value
Constant 12.546 353"" 6.464 3.72‘"
Population (1,000’s) 0.000 -0.40 0.000 -1.12
Distance -0.054 4.19“" -0.015 4.95""
Med. Income (1,000’s) -0.046 -1.09 0.009 0.39
Nearest Other NF Land 0025 -0.92 -0.010 -0.98
Nearest BLM Land 0.011 0.36 0.012 1.29
Nearest NPS Land 0.007 0.44 ~0.001 -0.14
RUC Code -0.514 -1.80* -0.122 086
Forest Acreage (1,000’s) 0.000 -0.68 0.000 -0.95 ‘
Number of Separate Forest Units -0510 -129 -0312 -203”
N 33 95
Adjusted R-squared 0.35 0.31
F-Value 2.91" 562“"
Mean Absolute Percentage Error 38% 121%
Tobit Model Dep. Variable: Distance Band Visits/Capita
Variable Day Z-Value OVN Z-Value
Constant 0.704 1.19 0. 842 2.19"
Population (1,000’s) 0.000 0.72 0. 000 -0.71
Distance -0005 -205" -0. 003 395“"
Med. Income (1,000’s) -0.007 -1.01 -0.004 -0.80
Nearest Other NF Land -0004 -073 -0.005 -186"
Nearest BLM Land -0.002 -0.46 0.003 1.43
Nearest NPS Land 0.001 0.43 0. 000 -0.03
RUC Code 0047 -1.06 -0. 053 -1.74"
Forest Acreage (1,000’s) 0.000 0.74 0. 000 -0.39
Number of Separate Forest Units -0.031 -0.50 -0. 055 -1.44
N 124 124
Tobit o 0.17 0.22
Log likelihood —10.92 -26.64
Mean Absolute Percentage Error 7% 17%

 

a Model dependent variables and inputs shown 111 Table C- 2

Lp-value < O. 10
M*p-value < 0. 05
p-value < 0. 01

130

Considering overnight recreation use, coefﬁcients for travel distance and the
number of separate forest units were statistically different from zero in the OLS overnight
model while coefﬁcients on travel distance, distance to nearest substitute national forest,
and RUC code were statistically different from zero in the Tobit formulation. In both
models, as expected, increasing distance leads to decreases in overnight recreation use,
all else remaining equal. In the OLS formulation, a greater number of separate forest
units yields lower overnight recreation use. In the Tobit model, origins more rural in
character (increasing RUC code) are associated with lower overnight recreation use. The
coefﬁcient on distance to nearest substitute USDA FS land is contrary to expectations.

However, given the density of USDA FS land in and around Region 2, a complementary

rather than substitute relationship between national forests is conceivable.

In Region 9, travel distance is the only statistically signiﬁcant predictor of day trip
recreation use (Table 32). Increasing travel distance leads to signiﬁcant decreases in day
trip recreation use, all else being equal. In the overnight recreation use models, the
coefﬁcients on population, travel distance, distance to nearest substitute national forest,
and forest acreage and number of separate units are all statistically different from zero in
the OLS formulation. The signs of all statistically significant coefﬁcients are consistent
with expectations. In the Tobit model, travel distance, RUC code, and forest acreage and
number of units are all statistically signiﬁcant predictors of overnight recreation use. The

signs of these statistically signiﬁcant coefﬁcients are consistent with expectations.

131

Table 32. Day and Overnight Mid-distance Segment Recreation Use Models,
USDA FS Region 9.a

OLS Model Dep. Variable: ln(Distance Band Visits/Population(l,000’s))

 

 

Variable DaL T-Value OVN T-Value
Constant 1.174 0.52 3.680 2.34”—
Population (1,000’s) 0.000 -1.18 0.000 -170"
Distance 0,024 4.77"" -0020 639*"
Med. Income (1,000’s) 0.059 1.29 0.004 0.15
Nearest Other NF Land 0.004 0.88 0.006 2.13"
Nearest NPS Land -0.009 -0.94 -0.005 -0.85
RUC Code 0.336 1.42 0.058 0.34
Forest Acreage (1,000’s) 0.000 0.03 0.002 3.87"“
Number of separate Forest Units -0.084 -0.74 -0.405 -5.23m
N 69 131
Adjusted R-squared 0.45 0.56
F-Value 808"" 21.31""
Mean Absolute Percentage Error 110% 252%
Tobit Model Dep. Variable: Distance Band Visits
Variable Day Z-Value OVN Z-Value
Constant -4,877 .3 -0.24 30,5360 1.31
Population (1,000’s) 0.3 0.15 -2.1 -0.89
Distance -1432 340"” -112.8 -2.51"
Med. Income (1,000’8) 349.9 0.91 234.0 0.55
Nearest Other NF Land -7 .8 -0. 19 68.3 1.53
Nearest NPS Land 4.6 0.06 -3.0 -0.04
RUC Code -866.5 -041 -7,620.8 3.18""
Forest Acreage (1,000’s) 5.7 1.08 18.1 3.12"“
Number of separate Forest Units 40.4 0.04 -3,113.1 276'"
N 140 140
Tobit a 17,096 20,842
Log likelihood -874.41 -1,506
Mean Absolute Percentage Error 713% 1,182%

 

a Model dependent variables and inputs shown in Table C-3
‘ p-vaiue < 0.10

" p-value < 0.05

"I p-value < 0.01

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In Region 2, model predictions from either the OLS or Tobit formulations fall
within the NVUM estimates for eight of the nine national forests used in model
development (Table 33). In four cases predictions from both formulations fall within the
conﬁdence intervals. The summation of predicted Region 2 forest-level Mid-distance
recreation use also falls within the NVUM regional Mid-distance segment use estimate.

Use predictions are generally consistent between the two model formulations, though
large differences do exist for the Nebraska NF and the White River NF. In all cases
(except the Rout NF) where model predictions fall outside the NVUM confidence

intervals, model predictions fall below the NVUM estimates.

Table 33. Model Predictions and NVUM Estimates of Mid-distance Segment

Recreation Use, USDA FS Region 2.‘I
Lower Upper
NVUM NVUM NVUM

OLS Models Tobit Models Estimate Estimate Estimate

 

Bighorn NF 43,177 38,775 99,692 68,193 135,990
Black Hills NF 25,121" 19,713" 27,680 15,875 42,481
Grand Mesa, U.,G.

NF’S 138,352 290,152' 312,161 199,280 442,999
Medicine Bow NF 167,741" 208,947" 177,220 142,030 215,708
Nebraska NF 39,783" 5,552 32,674 22,516 44,637
Pike San Isabel NF 156,519“ 199,490‘ 179,644 130,947 235,966
Routt NF 419,313‘ 539,219 392,194 328,547 461,412
Shoshone NF 57,379" 64,966‘ 53,634 39,561 69,781

White River NF 370,492 642,862‘ 702,753 631,665 777,533
Rggm 2 1,417,876 2,009,675" 1,969,848 1,790,973 2,156,547

 

‘ Combined recreation use of Day and Overnight trips. NVUM estimates exclude recreation use where the

NF is not the primary trip purpose.
* Model prediction falls within the 80% conﬁdence interval of the NVUM Mid-Dist. visitor use estimate.

There was less consistency between model predictions and NVUM estimates of

NIid-distance recreation use in Region 9 (Table 34). In only four of ten cases were forest-

level predictions from either model formulation within the NVUM conﬁdence intervals.

133

Though not within the conﬁdence interval, the OLS prediction for the Wayne NF was

within approximately 500 visits of the lower NVUM estimate. In only one case, the

Huron-Manistee NF, did model predictions from both the OLS and Tobit models fall

within the NVUM estimate. The regional summation of predicted forest-level Mid-

distance recreation was outside the NVUM conﬁdence interval. In Region 9, model

predictions under the two formulations were frequently disparate. Predictions under the

OLS formulation typically were below the NVUM estimates while Tobit model

predictions tended to be greater than the NVUM estimates. In particular, Mid-distance

segment use predictions for the Ottawa NF and the Mark Twain NF were drastically

greater than the NVUM estimates.

Table 34. Model Predictions and NVUM Estimates of Mid-distance Segment

Recreation Use, USDA FS Region 9.a

 

Lower

Upper

 

Tobit NVUM NVUM NVUM
OLS Models Models Estimate Estimate Estimate
Allegheny NF 175,380 388,120' 340,138 227,368 466,716
Chequamegon-Nicolet
NF 252,227 535,414 833,607 556,978 1,131,889
Chippewa NF 80,425 303,915 442,223 349,658 544,799
Hoosier NF 129,161" 288,652 126,631 94,776 162,927
Huron-Manistee NF 350,460‘ 517,792‘ 456,977 292,176 634,072
Mark Twain NF 50,163~~ 278,779 55,123 41,207 71,043
Monongahela NF 304,444 394,705 246,185 203,838 291,264
Ottawa NF 85,188 303,324 32,836 25,618 40,898
Shawnee NF 73,230 207,301 133,763 109,090 160,889
Wayne NF 100,167 229,221 130,716 100,655 164,355
Region 9 1,600,844 3,447,222 3,000,003 2,720,308 3,288,796

 

' Combined recreation use of Day and Overnight trips. NVUM estimates exclude recreation use where the

NF is not the primary trip purpose.

* Model prediction falls within the 80% conﬁdence interval of the NVUM Local visitor use estimate.

Absolute errors between the Mid-distance segment model predictions and NVUM

estimates range from 2,500 visits to 580,000 visits under the OLS formulation and from

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8,000 to 450,000 visits under the Tobit formulation (Table 35). Most percent errors in the

Region 2 models are less than 30%—all are less than 100%. Percent errors of Region 9

estimates are generally greater, but most are still less than 50%. The percent errors for the

Mark Twain NF and Ottawa NF under the Tobit formulation are substantial.

Table 35. Mid-distance Visitor Model Errors.

 

Absolute Absolute Percentage Percentage

Error (OLS)' Error (Tobit) ' Error (OLS) b Error (Tobit) b

Bighorn NF 56,515 60,917 57% 61%

Black Hills NF 2,559 7,967 9% 29%

Grand Mesa, U.,G.

NF’s 173,809 22,009 56% 7%
Medicine Bow

NF 9,479 31,727 5% 18%

Nebraska NF 7,109 27,122 22% 83%
Pike San Isabel

NF 23,125 19,846 13% 11%

Routt NF 27,1 19 147,025 7% 37%

Shoshone NF 3,745 1 1,332 7% 21%

White River NF 332,261 59,891 47% 9%

Total 551,972 39,827 28% 2%

Allegheny NF 164,758 47,982 48% 14%
Chequamegon-

Nicolet NF 581,380 298,193 70% 36%
Chippewa NF 361,798 138,308 82% 31%
Hoosier NF 2,530 162,021 2% 128%
Huron-Manistee

NF 106,517 60,815 23% 13%

Mark Twain NF 4,960 223,656 9% 406%
Monongahela NF 58,259 148,520 24% 60%
Ottawa NF 52,352 270,488 159% 824%
Shawnee NF 60,533 73,538 45% 55%
Wayne NF 30,549 98,505 23% 75%
Total 1,399,159 447,219 47% 15%

 

‘ abs(Model prediction - NVUM Mid-distance visit estimate)
b (absolute error/NVUM Mid-distance visit estimate)*100

135

The statistical relationships and predictive abilities of the recreation use models
estimated in this study using OLS and Tobit model formulations are very similar.
Lacking a clear difference in the performance of the two models, the single-log OLS
model is more attractive given its common usage and the ease of interpreting variable
coefﬁcients and model summary statistics. Independent variable coefﬁcients estimated

using the Tobit model are more difﬁcult to interpret as they reﬂect both the likelihood of

participation as well as the level of recreation use of those participating.
Evaluation of Combined Model Predictions

To this point, Local and Mid-distance model predictions have been evaluated
individually. The second and third evaluations of model performance are based upon the
combined model predictions. The summed model predictions for Local (permanent
residents only) and Mid-distance segments were compared with the NVUM estimates of
summed Local and Mid-distance recreation use (Table 36). Combined, Local and Mid-
distance model predictions for Region 2 generally fall below the NVUM estimates of use.
Only the summed model predictions for the Black Hills NF fall within the NVUM
estimates. This pattern of underestimation can be traced to the consistent underestimation
Of Local recreation use predictions. In Region 9, summed model predictions for the
Hoosier NF, Huron-Manistee NF, and Shawnee NF fall within the NVUM conﬁdence
intervals. The Local and Mid-distance model predictions (individually) were within the
NVUM conﬁdence intervals for the Hoosier NF while only the model predictions for

Local and IVIid-distance use (individually) were statistically consistent with NVUM data

136

for the Shawnee and Huron-Manistee NF, respectively. No clear pattern of error

for Region 9 combined predictions.

exists

Table 36. Model Predictions and NVUM estimates of Local and Mid-distance

 

 

Recreation Use.
Local+OLS Local+Tobit NVUM
Mid-distance Mid-distance Estimate Lower Upper
Bighorn NF 155,299 150,897 373,687 284,152 463,222
Black Hills NF 608,647 ' 603,239,. 534,476 437,362 631,590
Grand Mesa,
U.,G.
NF ’5 874,663 1,026,463 1,700,796 1,205,014 2,196,578
Medicine Bow
NF 368,333 409,539‘ 466,198 399,951 532,445
Nebraska NF 235,541 201,310 89,352 71,491 107,213
Pike San Isabel
NF 3,161,494 3,204,465 2,224,797 1,913,103 2,536,491
Routt NF 583,502 703,408 979,415 899,887 1,058,943
Shoshone NF 214,664 222,251 312,339 274,515 350,163
White River NF 1,343,546 1,615,916 1,982,126 1,889,759 2,074,493
Region 2 7,545,687 8,137,486 8,791,670 8,278,236 9,305,104
Allegheny NF 421,836 634,576 895,962 646,078 1,145,846
Chequamegon-
N icolet
NF 532,270 815,457 1,428,759 997,702 1,859,816
Chippewa NF 229,526 453,016 1,169,189 1,006,087 1,332,291
Hoosier NF 539,957' 699,448 492,299 415,156 569,442
Huron-Manistee
NF 862,989. 1,030,321 669,331 445,975 892,687
Mark Twain NF 953,858 1,182,474 467,861 407,086 528,636
Monongahela NF 584,121 674,382 508,707 439,116 578,298
Ottawa NF 136,626 354,762 1 1 1,684 95,870 127,498
Shawnee NF 428,149. 562,220 450,078 400,974 499,182
Wayne NF 859,173 988,227 379,630 320,825 438,435
Region 9 5,548,505 7 ,394,883' 7,102,543 6,576,245 7,628,841

 

* Model prediction falls within the 80% conﬁdence interval of the NVUM Local visitor use estimate.

 

Thus far, the performance of recreation use models has been evaluated based

upon ability to predict visit estimates consistent with the NVUM estimates. An

137

alternative evaluation is the ability of the models to predict the percent of recreation use
associated with the Local and Mid—distance visitor segments consistent with the
percentage estimates of Local and Mid-distance recreation use based upon NVUM. Given
the differences in recreation behavior and participation, identifying the percentage of use
by alternative visitor segments can enhance recreation managers’ decision-making—even
if the total number of visits is unknown. For most forests, the percentages of predicted
Local visitor use (as a function of summed Local permanent resident and Mid-distance

use) are very similar to percentages derived from the NVUM estimates (Table 37).

Table 37. Local Percentage of Local and Mid-distance Segment Recreation Use
based gon Model Prediction and NVUM Estimates.

Local Percentage, Local Percentage,
Local+OLS Mid- Local+Tobit Mid-

 

distance“ distanceb NVUM
Bighorn NF 72% 74% 73%
Black Hills NF 96% 97% 95%
Grand Mesa, U.,G. NF 84% 72% 82%
Medicine Bow NF 54% 49% 62%
Nebraska NF 83% 97% 63%
Pike San Isabel NF 95% 94% 92%
Routt NF 28% 23% 60%
Shoshone NF 73% 71% 83%
White River NF 72% 60% 65%
Region 2 81 % 75 % 78 %
Allegheny NF 58% 39% 62%
Chequamegon-Nicolet NF 53% 34% 42%
Chippewa NF 65% 33% 62%
Hoosier NF 76% 59% 74%
Huron-Manistee NF 59% 50% 32%
Mark Twain NF 95% 76% 88%
Monongahela NF 48% 41 % 52%
Ottawa NF 38% 14% 71%
Shawnee NF 83% 63% 70%
Wayne NF 88% 77% 66%
Region 9 71 % 53 % 58%

 

' (Local model prediction/ (Local prediction + OLS Mid-distance prediction))* 100
b (Local model prediction/ (Local prediction + Tobit Mid-distance prediction))*100
Note: alternatively, the percent of primary purpose Mid-distance visitor use is (1- Local percent)

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Model Application to Out-of-Sample National Forests

The constructed Local and Mid-distance segment models presented in the
previous sections were applied to Year 1 NVUM forests to assess their out-of—sample
predictive ability. Predictions from the Local permanent resident model fall below the
NVUM estimates for all Year 1 forests (Table 38). This pattern of disparity is consistent

with the results from model veriﬁcation.

Table 38. Model and NVUM Estimates of Permanent Resident Local
Visitor Recreation Use for Out-of-Sample Forests.

 

 

Model NVUM
Estimate Estimate Lower Upper

Region 2

Arapaho-

Roosevelt NF 2,325,266 4,003,679 3,180,636 4,858,144
Rio Grande NF 183,341 753,544 463,998 1,070,810
San Juan NF 553,017 951,972 703,868 1,222,135
Region 9

Green

Mountain NF 568,255 1,459,691 1,181,227 1,756,025
Hiawatha NF 92,644 279,962 230,528 333,743
Superior NF 168,510 2,006,922 1,538,942 2,506,133
White

Mountain NF 191,044 471,622 296,058 671,081

 

Mid-distance model predictions fall within the NVUM estimates for two of three
of the Year 1 Region 2 national forests (Table 39). Mid-distance model estimates for
Region 9 national forests are all below the NVUM estimates. The Region 9 national
forests used in model validation include some of the premier recreation forests in that

Rﬁgion. In particular, the Green Mountain NF and the White Mountain NF located in the

139

New England portion of the Region offer excellent recreation opportunities. Assuming
NVUM estimates are correct, the constructed Mid-distance models likely do not capture
the “attractiveness” of these premier Region 9 national forests. Better measures of forest
attractiveness may improve the ability of the Mid-distance models to predict recreation
use at national forests such as the White Mountain NF.

Table 39. Model Predictions and NVUM Estimates of Mid-distance Segment
Recreation Use for Out-of-Sangrle Forests.‘I

OLS Tobit NVUM
Models Models Estimate Lower Upper

 

Region 2

Arapaho-Roosevelt NF 378,554" 302,062“ 309,323 295,844 415,587
Rio Grande NF 277,749 461,068 131,098 90,865 271,829
San Juan NF 173,253 245,964* 273,200 234,152 462,819
Region 9

Green Mountain NF 178,750 358,706 690,772 537,698 860,306
Hiawatha NF 47,410 199,278 135,298 106,615 167,362
Superior NF 259,214 619,555 1,047,040 780,706 1,340,882
White Mountain NF 130,156 398,000 1,321,450 872,923 1,796,634
' Combined recreation use of Day and Overnight trips. NVUM estimates exclude recreation use where the
NF is not the primary trip purpose.

* Model prediction falls within the 80% conﬁdence interval of the NVUM Local visitor use estimate.

The Percentages of forest-level Local visitor use, as estimated from the
constructed Local and Mid-distance models, are consistent with the NVUM estimates for
four of the seven out-of—sample national forests (Table 40). Model-based Local
percentages are below NVUM estimates for the Rio Grande NF and the Superior NF and
higher than NVUM estimates for the White Mountain NF. These validation results are

Similar to those found under veriﬁcation.

140

Table 40. Local Percentage of Local and Mid-distance Segment Recreation Use
based upon Model Prediction and NVUM Estimates, Out-of-Sample Forests.

Local Percentage, Local Percentage,
Local+OLS Mid- Local+Tobit Mid-

 

distance”I distanceb NVUM

Region 2

Arapaho-Roosevelt NF 86% 89% 93%
Rio Grande NF 40% 28% 85%
San Juan NF 76% 69% 78%
Region 9

Green Mountain NF 76% 61% 68%
Hiawatha NF 66% 32% 67%
Superior NF 39% 21% 66%
White Mountain NF 59% 32% 26%

 

' Local model prediction/ (Local prediction + OLS Mid-distance prediction)
b Local model prediction/ (Local prediction + Tobit Mid-distance prediction)
Note: alternatively, the percent of primary purpose Mid-distance visitor use is (1- Local percentage)

Discussion of Model Results

A Clear difﬁculty in evaluating the performance of the Local and Mid-distance
recreation use models is that both the model predictions and the comparison values (i.e.
the NVUM ﬁgures) are estimates of the actual recreation use. Given disparity in the
estimates, it is impossible to deﬁnitively determine which (the model predictions, the
NVUM ﬁgures, or both) is “incorrect”. Even consistency between the two estimates does
not deﬁnitively conﬁrm that recreation use has been correctly quantiﬁed—though it lends

support to both estimates.

At the ﬁnest level of evaluation (i.e. number of visits), model predictions were

frequently inconsistent with the NVUM estimates. The promising exception to this was

141

the performance of the Mid-distance segment models estimated for Region 2 national
forests. Summed output from Local and Mid-distance models (the second level of
evaluation) also was generally inconsistent with the NVUM estimates. Use was typically

underestimated in Region 2 while there was no discemable pattern in Region 9.

At the most aggregate evaluation level (percentage of Local recreation use) model
outputs actually compare well with NVUM estimates. In particular, Region 2 predictions
are almost entirely consistent with the NVUM estimates. In Region 9, considering only
Local percentages from the “Local + OLS Mid-distance” column, model predictions are
largely “in the ballpark” with the NVUM estimates. Assurrring NVUM is “correct”, the
models appear to predict reliable estimates of the relative percentages of visitors from the
two distance segments considered. The ability to estimate relative percentages of use by
distance segments may be beneﬁcial for resource planners when identifying national

forest “recreation markets”.

One confounding issue (that remains unresolved) in comparing model predictions
with NVUM use estimates is seasonal homeowner recreation use. Seasonal homeowner
recreation use is clearly identiﬁed in the model predictions but is not clearly identiﬁed in
the NV UM use estimates. There is no single question in the NVUM survey that can be
used to identify national forest users recreating on the national forest coincident with use
of their seasonal home. In NVUM Year 4, seasonal homeowners can be partially
identiﬁed via their responses to two questions; however, correct identiﬁcation depends

largely on how respondents interpret the questions. Regardless, if a seasonal homeowner

142

were surveyed it is not clear what ZIP code (permanent or seasonal residence) the
respondent would provide as the ZIP code question stated only “what is your home ZIP
code?” rather than “what is the ZIP code of your permanent home?”. Therefore, it is
unclear in what distance segment (i.e. Local, Mid-distance, and Long-distance) that
seasonal homeowner recreation use should be counted. Given this uncertainty, predicted
seasonal homeowner use cannot be Simply added into one distance segment or another
and thus remains largely excluded from comparisons with NVUM estimates. This
imprecision will more greatly inﬂuence the comparisons between model predictions and
NVUM estimates for forests with extensive seasonal homeownership and corresponding
use. In future years, it would be beneficial to include distinct questions to identify

seasonal homeowners and the locations of their seasonal and permanent residences.

Irrespective of the prediction comparisons, the parameters and coefﬁcients of the
constructed models are largely consistent with theory. Participation rates and annual visit
frequencies estimated for the Local models are downward sloping (for the most part) and
appear reasonable. For the Mid-distance models, the coefﬁcients on travel distance are
negative and signiﬁcantly different from zero, as expected. Signiﬁcant coefﬁcients on
other variables in the Mid-distance models appear reasonable and are nearly always

consistent with expectations.

The approach to modeling recreation use undertaken in this research differed from

Other studies in that the recreation use of those living at different proximities to the

national forest and completing different trip types (in the case of the Mid-distance

143

segment) were modeled separately rather than in one single model. The aim of this
approach was to capitalize on differences in how visitors respond to the factors
inﬂuencing recreation use (e. g. travel distance, resource characteristics, socioeconomic

conditions, etc).

Two common Challenges to travel cost modeling are 1) limited variability in
travel distance (or cost) and 2) disparities in on-site time between user groups. Separation
of models for Local and Mid-distance recreation use likely controlled for both of these
problems. Travel distance is relatively similar for those living in the Local area (due
largely to the small range in distance) and travel distance appears to have limited impact
on marginal visitation rates of those living in this area (Chapter 3, Figure 3). Conversely,
the Mid-distance segment can be characterized by highly variable travel distance and
decreasing marginal visitation corresponding to increases in travel distance. Concerning
on-site time, comparisons of visit durations for Local and Mid-distance visitors revealed
statistically signiﬁcant differences within trip type between the two segments. Use of
separate models for these two groups controlled fOr this statistical difference in visit

length.

The development of separate zonal travel cost models for day and overnight trips
highlighted the differences in relationships between recreation use and factors inﬂuencing
recreation between the two trip types that might not be captured in a single zonal travel
cost model. In three of four day trip models, travel distance was the sole independent

variable with coefﬁcients statistically different from zero. Comparatively, at least two

144

independent variables (more frequently three or more) were statistically significant
predictors of overnight recreation use. Separation of Mid-distance visitors into day and
overnight trips also controlled for problems related to differences in on-Site time that may

have otherwise inﬂuenced the performance of the travel cost models.

145

CHAPTER 5

SUMMARY, CONCLUSIONS, AND POLICY IMPLICATIONS

Introduction

This closing chapter serves as a final discussion of the research. In the first
section, the research objectives, methods, and results of the research are brieﬂy restated.
Based upon the results, the study conclusions and implications for policy are identiﬁed in
the next two sections. The Chapter closes with a discussion of the study limitations and

an outline of recommendations for future research.

Summary

The general objective of this research was to characterize and model the
recreation use of USDA FS visitors classiﬁed into three distance-based visitor segments.
The four Speciﬁc objectives of the study were to:

1) identify the recreation behavior, activity participation, and consumption

patterns of Local, Mid-distance, and Long-distance USDA FS visitors in
USDA FS regions 2 and 9,

2) statistically compare the recreation behavior, activity participation, and

consumption patterns of visitors to USDA FS Regions 2 and 9 within the

distance-based segments,

146

3) statistically compare the recreation behavior, activity participation, and
consumption patterns of Local, Mid-distance, and Lon g-distance visitors
within USDA FS regions 2 and 9, and

4) model forest-level recreation use of Local and Mid-distance recreation visitors
for national forests located in USDA FS regions 2 and 9.

Much of the data used in this research were obtained from the USDA FS NVUM project.
NVUM visitor survey data were used to identify and complete statistical analyses of the
distance segment visitor characteristics, and the NVUM recreation use estimates were

employed to estimate and evaluate the predictive ability of the recreation use models.

Prior to addressing the speciﬁc objectives of the study, the boundaries separating
the distance-based segments from one another were identiﬁed. Based upon previous
research and an examination of the regional patterns of recreation use, Local visitors were
deﬁned to originate from within 30-miles of the national forest, Mid-distance visitors
from between 30 and 200-miles of the national forest, and Long-distance visitors from

the “rest of the world”.

Local visitors typically complete a large number of visits annually, with each
visit generally being of short duration. On any one visit, these visitors typically
participate in a limited number of activities, visit a limited number of sites, and recreate
with few other individuals. In contrast, Long-distance visitors complete very few visits
annually, recreate in large parties, visit multiple recreation sites, and participate in a

number of recreation activities. For the most part, the characteristics of Mid-distance

147

visitors generally fall between the extremes exhibited by the other two segments. One
characteristic of note, however, is that Mid-distance visitors exhibit the longest visit

durations of the three distance-based segments.

Within trip-type and distance segment, few statistically signiﬁcant differences in
recreation behavior exist between visitors to national forests located in the two study
regions. Worth special mention are the ﬁndings of no statistical differences in the
recreation characteristics of Mid-distance visitors between USDA FS regions 2 and 9.
Statistical differences that were found include differences in annual visits, the duration of

visits, and the party sizes of Local and Long-distance visitors on some trip-types.

Across all distance segments, a greater percent of recreation use in Region 9
relative to Region 2 is associated with hunting and ﬁshing. Conversely, downhill skiing
and mountain biking are more common for all distance segments in Region 2. In addition
to the above differences, Mid-distance visitors in Region 9 are more frequently engaged
in cross-country skiing and hiking than comparable visitors in Region 2. Similarly, hiking

is much more common among Long-distance visitors in Region 9 than in Region 2.

Comparing the distance segments to one another, statistically signiﬁcant
differences between two or more segments were found for all the visitor characteristics
under consideration. The majority of the differences are consistent with the postulated
characteristics of distance segment visitors identiﬁed in Chapter 3. There were a greater

number of statistical differences between segments among Region 2 visitors than among

148

Region 9 visitors. In Region 2, the recreation Characteristics of all three distance
segments were frequently statistically different from one another. In Region 9, the
recreation characteristics of Local visitors were typically unique from those exhibited by

Mid-distance and Long-distance visitors.

Many of the statistical differences between the distance segments can be traced to
differences in trip-type. Local visitors are most likely to complete day trips, Midsdistance
visitors are most likely to complete overnight visits, and Long-distance visitors take more
“not primary” trips than any of the other two segments. When accounting for trip-type,
the recreation behavior of visitors in the three distance segments is more similar.
However, statistical differences do remain for visit duration and party size variables.

Differences in the number of visits annually cannot be tested within trip-types.

Considering recreation activities, Local visitors are more likely to ﬁsh than
visitors in the other two segments. In Region 2, Local visitors more frequently engage in
hiking than visitors in the other two segments, while in Region 9 Local visitors more
frequently hunt and drive for pleasure than other visitors. Long-distance visitors more
frequently visit the national forest to complete nature-related activities (i.e. viewing
scenery, viewing wildlife, nature study, or visiting a nature center) than visitors in the
other two segments. Long-distance visitor are also more frequently engaged in hiking
than Mid-distance visitors. The most frequently reported primary activities for Mid-

distance visitors are downhill skiing and hiking. Additionally, these visitors are more

149

likely than the other two visitor groups to be using OHV’s or to be camping in developed

portions of the forest.

Separate models were developed for the Local and Mid-distance visitor segments.
The recreation use of Long-distance visitors was not modeled as the factors inﬂuencing
the recreation are not clearly understood. Local recreation use was modeled using
population ﬁgures obtained from the 2000 Census and participation rates and annual visit
frequencies estimated from NVUM survey data and recreation use ﬁgures. Mid-distance
recreation use was modeled via multi-site zonal travel cost models incorporating
variables related to travel distance, income, the degree of rurality, the availability of
substitutes, forest acreage, and number of forest units within an administrative forest. For
the most part, the parameters and coefficients of the models are consistent with
theoretical expectations. In the case of the Local models, the estimated participation rates
generally decrease with increasing distance to the national forest. The estimated annual
visit frequencies of Local visitors are reasonable and consistent with expectations.
Considering Mid-distance models, the coefﬁcients on travel distance are negative and
signiﬁcant in every case. When Signiﬁcant, the coefficients on other variables in the Mid-

distance models are reasonable and largely consistent with expectations.

Forest-level predictions of recreation use were generally not consistent with the
NVUM estimates of recreation use. In Region 2, only one forest-level prediction of Local
Visitor use was within the conﬁdence interval of the NVUM estimate. In Region 9, three

predictions were within the NVUM confidence intervals. Region 2 model predictions of

150

Local use typically fell below the NVUM estimates while there was no particular pattern
of discrepancy for the Region 9 Local model predictions. Predictions of Mid-distance
recreation use were more frequently consistent with the NVUM estimates. Consistency
with the NVUM estimates was more frequent in Region 2 than in Region 9. Percent
errors for all models were generally below 50%, though percent errors for some forest-
level predictions were quite high. At the most aggregate comparison level (percent of
Local visitor use), model predictions and the NVUM estimates were largely consistent.

This was particularly true in Region 2.

In the modeling approach adopted here, Local visitors were modeled separately
from Mid-distance visitors and the recreation use of Mid-distance day trip visitors was
modeled separately from recreation use of Mid-distance overnight visitors. In a
conventional modeling approach, the recreation use of all these groups would likely have
been modeled in aggregate. Considering just Mid-distance visitors, the variables found to
be statistically signiﬁcant predictors of recreation use were quite different. In all but one
case, the only statistically signiﬁcant coefﬁcient in models of day trip recreation use was
travel distance. In contrast, in models of overnight Mid-distance recreation use, at least
two variable coefﬁcients (and more commonly four) were Statistically different from
zero. Forest acreage, number of forest units, RUC code of the origin, and travel distance

commonly inﬂuenced overnight Mid-distance visitor recreation use.

151

Conclusions

Segrnenting recreation users based upon their proximity to the recreation resource
yields visitors groups with distinct recreation consumption patterns, recreation behavior,
and primary activity preferences. Notable and deﬁning differences occur in the number of
visits completed annually, the duration of those visits, party size, and trip-type. Worthy of
particular note are the marked differences in trip type frequency. Nearly 75% of Local
visitors complete day trips, more than 60% of Mid-distance visitors complete overnight
trips, and Long-distance visitors can be typiﬁed by the percent of “not primary” visits
undertaken (33%). Even when accounting for trip type differences, many of the
distinctions and patterns in recreation characteristics remain evident—though not

necessarily statistically signiﬁcant.

Within distance segments and trip-types, the recreation characteristics of national
forest visitors in Regions 2 and 9 are generally analogous. Statistical differences do exist
in some cases for Local and Long-distance visitors. The most notable difference between
the two regions, within segments, is a dramatic difference in the percentages of visitors
engaged in downhill skiing. In particular, Mid-distance and Long-distance visitors in
Region 2 are much more likely to be downhill skiing than their Region 9 counterparts.
Even with a clear difference in recreation activity, there are no statistically signiﬁcant
differences in the recreation characteristics of Mid-distance visitors between the two

study regions.

152

Using NVUM recreation use estimates, N VUM visitor survey data, and Census
2000 population estimates, the participation rates of Local segment visitors were
determined as the initial step in development of the Local recreation use model. In
general, as expected, the participation rates of Local populations decrease with increasing
distance to the national forest. An estimated 87 % and 39% of those living within the
proclamation boundaries of Region 2 and Region 9 national forests participate in national
forest recreation, respectively. At the farthest distances, the participation rates of Local
visitors in Regions 2 and 9 fall to approximately 10% and 3%, respectively (excluding
the 30-mile band in Region 2). The greatest numbers of visits annually are completed by
those living in the two nearest distance bands. The number of visits completed annually is

relatively constant for those living beyond 15 miles from the forest boundary.

Multi-site zonal travel cost models of day and overnight Mid-distance recreation
use were constructed for USDA FS regions 2 and 9. Mid-distance segment day trip
recreation use is related primarily to the travel distance between visitor origin and the
national forest. Conversely, the recreation use of overnight Mid-distance visitors is
inﬂuenced by a number of factors, including travel distance, substitutes, and forest
characteristics. Both model formulations adopted in this study appear appropriate for use

in estimating the recreation use of USDA FS visitors at this scale of analysis.

It is quite difﬁcult to evaluate the predictive ability of the constructed models. The

central limiting factor in an evaluation of the models is that the “true” forest-level

recreation use of Local and Mid-distance visitor segments is not known. Consistency

153

between the model predictions and the N VUM estimates (when it occurred) only implies
that the two are consistent. Some additional complicating factors in using the NVUM
estimates to evaluate model predictions is the inability to quantify recreation use
associated with seasonal homes in the NVUM estimates and the signiﬁcant number of

NVUM respondents (and associated recreation use) with an unknown origin.

While there is no evidence to suggest that the current models are sufficient, the
forest-level recreation use of Local and Mid-distance visitor segments can be modeled
using the basic approach adopted in this study. That is, predicting forest-level recreation
use based upon estimates of the populations living in proximity to national forests and
knowledge of the recreation consumption patterns of those populations is possible given
some model reﬁnements and a more definitive evaluation. A clear beneﬁt of modeling
recreation use via the approach adopted here is the ability to predict future recreation use
levels given estimates of future conditions under the assumption that all relationships

between recreation use and population characteristics remain the same.

Policy Implications

The distance-based approach to segmenting recreation visitors is grounded,
somewhat, in the classification of outdoor recreational uses and resources developed by
Clawson and Knetsch (1967). The approach presented here classiﬁes visitors based upon
their proximity to the resource while Clawson and Knetsch (1967) Classify recreation

resources based, largely, upon proximity to visitor populations. The observed recreation

154

behavior of Local, Mid-distance, and Long-distance visitors largely correspond to the
postulated characteristics of recreation visitors to Clawson and Knetsch’s user-oriented,

intermediate, and resource-based recreation resources, respectively.

Given their distinct recreation Characteristics, distance-based segmentation can
provide a framework for classifying USDA FS visitors. Ultimately, distance-based
segmentation represents another way for USDA FS planners and managers to consider
recreation use. Incorporating the distance-based segmentation adopted in this study with
the traditional activity-based segmentation may increase the information provided by

both.

Currently, many national forests are undertaking “recreation niche” or “recreation
market” analysis. The goal of these analyses is to identify the “role” or “Special
opportunities” that the individual forests play in providing recreation opportunity to the
public. Distance-based segmentation seems to offer an excellent framework for
individual national forests to identify, at least in part, their role in the provision of
recreation opportunities both now and under future conditions (using predictions of future
visitor segment use). For example, a national forest that determines its recreation use is
predominantly associated with Local visitors could identify their recreation market as
serving frequent visitors who are primarily on day trips, recreating in small visitor
parties, and spending only a short period of time on the forest during any one visit. Given
the observed pattern of recreation activities presented here, popular recreation activities

on this national forest would likely include hunting, fishing, and hiking. Similarly, a

155

national forest attracting a signiﬁcant number of Long-distance visitors could expect
visitors that visit very infrequently, perhaps for the ﬁrst time, recreate in large parties,
and visit a number of recreation sites. A forest attracting a number of Long-distance
visitors may do well to offer a variety of interpretive recreation activities since Long-
distance visitors frequently engage in passive nature-related pursuits. Further
understanding of the motivations of distance-segmented visitors would add the

understanding of a forest’s market.

Comparisons between visitors in Region 2 and Region 9 revealed few statistical
differences after accounting for distance segment and trip-type. Similarly, Stynes and
White (2003) have found that USDA FS regions explain very little of the variation in
visitor spending. Both of these ﬁndings are counter to the notion that USDA FS regions
can be used to explain or delineate visitor characteristics. Based upon the results of this
study, visitor proximity to the resource and trip-type probably capture more variation in
recreation behavior than USDA FS region. Region probably does explain some variation
in recreation activity propensities given that some USDA FS regions do have unique

natural features conducive to some speciﬁc activities.

In this study, zonal travel cost models were employed only for a speciﬁc subset of
recreation use. In a conventional application, a single model may have been estimated for
all recreation use or for all recreation use within a certain trip-type (e. g. day trips). At a
minimum, it appears that separate travel cost models should be estimated for visitors

engaged in different types of trips. Based upon the results of this study, the factors

156

commonly accepted as inﬂuencing recreation use (e.g. distance, substitutes, amenities)
likely inﬂuence the recreation use of day and overnight visitors differently. Given
variation in the recreation characteristics of Local, Mid-distance, and Long-distance
visitors, it also seems likely that distance, substitutes, and Site amenities inﬂuence the
recreation use of these groups in different fashions. Models that combine visitors
originating from these different distance segments likely fail to capture these different
functional relationships. However, a comprehensive evaluation of alternative model

formulations is required to determine this for certain.

Limitations and Recommendations for Future Research

This study is based upon the revealed behavior of recreation visitors (as quantified
via the NVUM survey data). The recreation characteristics identiﬁed in this study do not
provide information related to the desires of the distance-segment populations, but rather
their consumptive patterns. Additional surveys are required to identify the desires of the
distance-based segment visitors and those of the general population. Similarly, the
motivations for recreation of national forest visitors were not quantiﬁed in this study. It is
likely that the motivations of visitors within the three distance segments differ, but
additional study is needed to quantify these differences. A greater understanding of
visitor motivation in the context of proximity to the recreation resource would likely be

very informative.

157

The models in this study were constructed using data from 19 administrative
national forests located in two USDA FS regions. The other 100 administrative national
forests located in seven other regions offer a variety of recreation Opportunities and serve
diverse recreation visitors. The models presented here apply only to visitors to national
forests located in USDA FS Regions 2 and 9. There is no evidence to suggest that the
parameters and coefﬁcients of the estimated models are transferable to national forests
located in other areas. Likewise, there is no support for applying the parameters and
coefﬁcients estimated in this study for recreation use on lands managed by other

government agencies.

Two measurement problems frequently arise in estimating zonal travel cost
models: quantifying the site characteristics that inﬂuence the behavior of recreation
visitors and quantifying the availability of substitute recreation Sites. Coarse measures of
both were employed in this study. Different approaches to quantifying the attractiveness
of national forests and national forest substitutes may identify relationships between these
factors and recreation use not found in this Study. Additionally, the incorporation of
substitute recreation opportunities managed by state and/or local agencies in the models

of recreation use may result in different relationships.

In the course of this study, many opportunities for future research have been
identiﬁed.
0 Seasonal homeowners likely represent a Signiﬁcant component of national forest

recreation use, particularly in some portions of the country. Currently it is difﬁcult to

158

identify their recreation use, and little is known about seasonal homeowner visitors.
Future studies should be directed at identifying the national forest participation rates
and recreation characteristics of these users. With the recent expansion in the second-
home market, the extent of seasonal homeowner recreation use will likely increase.
Local visitor recreation use constitutes more than 50% of the recreation use in regions
2 and 9. Given the importance of their recreation use, a household survey directed
speciﬁcally at populations living around national forests would be beneﬁcial. Of
particular interest is greater information concerning the consumption patterns,
motivations, and desires of these individuals.

This research assumes that Local segment visitors recreate at the nearest national
forest when more than one forest is located within 30 miles of the visitor’s origin. The
validity of this assumption can be examined using the NVUM survey respondents and
spatial databases of visitor origins and national forest boundaries. Such an analysis
would yield a greater understanding of local visitor consumption patterns.

The boundaries between the three distance segments were identiﬁed via an aggregate
approach. Additional research aimed at better identifying the boundaries between
Local, Mid-distance, and Long-distance visitors would be beneﬁcial. Further reﬁned
boundaries could be identiﬁed via analysis of changes in recreation behaviors or
based upon the motivations of users. An examination of patterns of day trip use and
overnight trip use may be particularly useful in future delineations of the boundaries
between distance segments.

The Local visitor models show promise in the ability to estimate forest—level

recreation use of local populations. Future development of these models is

159

appropriate. Speciﬁc future tasks may be to develop participation rates applicable to
populations within certain age classes, better understanding of how local users choose
between federal, state, and locally—managed recreation opportunities within the local
area, and identiﬁcation of ways to incorporate more local variation into the models.
Distance is a key variable inﬂuencing recreation use. In this study, a Euclidean-based
approach incorporating an adjustment for barrier crossings was adopted for
calculating distances from populations to forest resources. Alternate approaches to
estimating distance post-hoe include using simple Euclidean distance, network
distance, or the development of a cost surface using a GIS. Further research is needed
to compare these alternate post-hoc approaches to distance calculation and the
potential impacts of their use on recreation research.

In a recent revision of the NVUM survey instrument, survey respondents are asked to
report their travel distance. This offers an excellent opportunity to identify the
patterns in how visitors report travel distance (e. g. rounding) and to evaluate the
consistency of post-hoe travel distance calculations with reported travel distances.
While the Mid-distance models perform fairly well, some further reﬁnements may
lead to more informative models. In particular, development of an alternate approach
to aggregating visitor origins into zones may lead to better identiﬁcation of the factors
inﬂuencing recreation use by these visitors. Additionally, as stated, reﬁnements in
quantifying both the national forest attractiveness and substitute availability may be
beneﬁcial. Currently only federal lands are considered as substitutes. Future studies
may choose to include recreation areas managed by state and local governments as

potential substitutes. Additional information from recreation users regarding the site

160

Characteristics inﬂuencing their recreation decision and the other sites they considered
would be very useful.

The recreation use of Long-distance visitors was not modeled—largely due to
uncertainty into what factors inﬂuence the recreation use of these individuals.
Development of models of Long-distance recreation use, coincident with reﬁnement
of the other two models, would create a package of models that could estimate the
expected recreation use from each distance segment; thereby providing estimates of

total recreation use.

161

APPENDIX A

162

Introduction

This Appendix includes additional detail on the NVUM process. Included are
detailed descriptions of how proxy sites are incorporated in the NVUM process, the
process for identifying sample days within national forests, Changes to the visitor
questionnaire that have occurred during the ﬁrst NVUM cycle, and of the mathematical
procedures used to estimate forest-level recreation use based upon NVUM trafﬁc counts

and visitor surveys.

Proxy Sites

At some sites within national forests visitors are required to pay a user fee or
obtain a recreation permitl. The types of sites that frequently require permits or fees
include ski areas, developed campgrounds, and some Wilderness areas. These sites differ
from others because reliable estimates of visitation, the permits or fees (the proxy),
already exist. As such, visitation at “proxy Sites” can be estimated by identifying a
conversion factor between the proxy and the number of site visits. The appropriate
conversion is identified via additional questions asked of survey respondents on proxy
site sample days. On proxy sites the strata is based upon the proxy site type and the type
of proxy used (e. g. OUDS with fee envelopes) rather than expected level of exiting
recreation trafﬁc. Because visitation estimates could be developed based primarily on the

proxy, fewer visitor surveys are administered on sample days at proxy Sites.

 

’ Some sites require the user to purchase a Recreation Fee Demo sticker or another broadly applicable use
permit. Broadly applicable fees and permits are not considered here.

163

Sample Day Selection

Approximately 200 sample days are allocated to each administrative forest. These
200 sample days represent, on average, 64,000 Site days identiﬁed for each administrative
forests (English et al., 2002). On an individual sample day a trafﬁc counter is installed at
the site for a 24-hour period and visitors interviews are conducted during a six-hour
interview period (from either 8 am to 2 pm or 2 pm to 8 pm, altered as needed to
constrain visitor surveying to daylight hours only). The number of sample days to occur
in each Stratum within each administrative forest is determined at the USDA FS regional
level. The three-step allocation process is described in detail in English and others (2002)
and outlined here. Each region is allocated 200 sample days per adrrrinistrative forest
sampled in a given NVUM year. First, each administrative forest is allocated eight
sample days for viewing corridor sites. Next, each administrative forest is allocated, via a
stratiﬁed approach, up to 50 sample days for the proxy strata. Finally, eight sample days
are allocated to each non-proxy strata within each administrative forest (each
administrative forest may have up to 12 non-proxy strata). Any remaining unallocated
Sample days (at the regional level) are allocated across strata across administrative forests
based upon the product of 1) the standard error of the strata estimated from previous
NVUM years2 and 2) a weight of 20 for high use sites, 10 for medium use sites, and one
for low use sites. The effect of this ﬁnal step is to allocate the remaining sample days to
those strata with perceived high levels of recreation use and large variation in observed

recreation use.

 

2 No standard errors for strata existed in the ﬁrst two years so this allocation was based solely upon the
strata weights.

164

The selection of individual sample days (dates and locations for sampling) within
strata within individual administrative forests is completed by NVUM national-level staff
using a simple random sample with the following minor adjustment. Logistical
limitations constrain sampling to three or fewer Sites on a single calendar day on a single
administrative forest. For those calendar days on individual forests where more than three
sample days are selected, NVUM personnel retain two of those sample days, place the
other(s) back in the population of site days and then resarnple the necessary number of
sample days from the population. In the ﬁrst cycle of NVUM no attempts were made to
insure that a temporally or spatially representative group of sample days within strata

within an administrative forest was obtained.

Survey Revisions

During the ﬁrst NVUM cycle the survey form has undergone several revisions.
Copies of the survey forms (including the economic addition) used in the ﬁrst cycle are
available from USDA FS NVUM personnel. Most of the revisions during the ﬁrst
NVUM cycle were made to correct spelling and/or grammatical errors and to change the
survey form layout. However, in the ﬁnal year of the cycle a signiﬁcantly revised
economic supplement was introduced. Changes introduced in this ﬁnal form (Revision 5)
came about, in large part, due to the analyses of survey results collected in the initial
years of NVUM. AS result of these changes there are some differences between the year 4

data and the data collected in the previous three years.

165

Three primary changes occurred in Revision 5: 1) substantive changes to the
“primary purpose” question, 2) changes in questions relating to the visitor’s trip length
and stay in the local area, and 3) removal of the “Sharing question” and explicitly
requesting trip expenditures per party rather than per person. The “primary purpose”
question is used to identify visitors whose trip to the forest is secondary to some other trip
purpose. Identifying “non-primary” visitors is integral to correctly estimating economic
impact and estimating use values, and also provides some information relating to the
motivation of the visitor. In the ﬁrst three years, trip purpose was determined via a two
part question (questions 3 and 4 economic addition, forms 1 — 4). In the course of
analyzing the economic survey data it was determined that these questions did not
deﬁnitively determine whether the forest was the primary reason the visitor was away
from home and that the answers provided were frequently inconsistent. In Revision 5
these questions were removed and replaced with a single question on the general survey
(Question 11 general survey, Revision 5) that speciﬁcally detemrines whether recreation
on the national forest is the primary reason the visitor is away from home. Use of the
revised question yields a greater percentage of non-primary visitors (Stynes and White,

2005b).

Visitor trip length is primarily used to determine whether visitors are on day or
overnight recreation trips. It is also used to place Spending on a per-night basis and can be
used in analysis of visitor recreation behavior. Revisions l — 4 determined trip length, in
terms of days and hours away from home, via a single question (Question 2 economics

addition, revisions 1 — 4). While the original question determines total trip length what is

166

more informative for economic impact analysis and planning applications is the length of
Stay in the local area (preferably in terms of nights rather than days). In Revision 5
respondents report both the number of nights away from home (if any) as well as the
number of nights in the local area (questions 1 — 3 economic addition, Revision 5). In
addition, the respondent is asked to identify the types of overnight lodging used in the
local area (Question 4 economic addition, Revision 5). Using the previous surveys,
visitors passing through the local forest area and staying overnight away from home were
classiﬁed as overnight visitors when they Should have, in fact, been classiﬁed as day trip
visitors (since they were not staying overnight locally). The changes to the trip length
question alleviates this problem and results in fewer overnight visitors in the Year 4

sample.

NVUM Visit Estimation

The NVUM traffic counters and visitor surveys are used together to develop
estimates of total visitation on individual sample days for non-proxy sites. Estimates of
sample day visitation for non-proxy sites are computed as follows (all formulae in this
section are adapted from English et al., 2004):

SVhi = Chi * Ph *Vh
where SVh, is the estimate of site visits for given stratum h on sample day i, Chi is the
number of cars obtained on the trafﬁc counter adjusted for number of axles and two way
trafﬁc, Pb is the proportion of exiting vehicles that are last exiting recreationists

(estimated from the visitor survey) averaged across all sample days in the strata, and V1, is

the number of persons in last exiting recreation vehicles (estimated from the survey)

167

 

 

averaged across all sample days in the strata. The average number of site visits across all
sample days in given stratum h are computed as:
i=1 "h
where m, is the number of sample days in stratum h. To estimate the number of total Site
visits for stratum h, the average site visit estimate for stratum h is multiplied times the
number of site days (Nb) in the population of stratum h:
SVh = N, * S—Vh,

Site visit estimates for proxy sites are developed from the following: 1) the annual
total proxy count for a given site k within a given proxy stratum h (PM), 2) the proxy
compliance rate for a given site k within a given proxy stratum h (Cth), and 3) a
conversion factor (21.) to facilitate transfer from the proxy count for stratum h to a visit
count for stratum h. The ﬁrst two components are combined to develop the effective
“compliance based proxy count” for each site day (PChk):

P
pchk =__hk_

Cth .

The site day proxy count is combined to form a mean daily proxy count for the stratum h:

K
ZPChk
Z
k=1

9

N hk

where Nhk is the number of Site days for site k stratum h.

168

The conversion factor Z1. is constructed from visitor survey responses completed
on the proxy sample day, combining 1) the reported number of proxies completed per
visitor or group (e. g. number of fee envelopes used to cover an individual or group while
camping) and 2) the reported number of people each proxy covers (e. g. number of people
covered by a single fee envelope). Combined they form an aggregate conversion factor

for stratum h:

"h
250,,-
2h : i=1

"h
25R,"-
i=1

9

where SGh, is the sum of group size for stratum h on given sample day i and SRM is the

sum of number of proxies for stratum h on a given sample day i.

From the compliance based proxy count and the proxy conversion factor the

average site visit estimate for proxy stratum h can be computed:
"517;. = Z}. *F'C'h .
The total number of Site visits for proxy stratum h is computed by multiplying the

average site visits for stratum h by the number of site days in stratum h:
S Vh = S—Vh * N h
The average number of Site visits for all sample days, combining proxy and non-proxy

sites, on a given national forest is computed as

_. H _
SV = 2W}, *SVh ,
h=1

169

where

u@— Nh

_ _H .
:ZNh
h=1

The total number of site visits for a national forest is

SV=SV*N,

where N is the total number of site days on the national forest.

While the number of site visits is of interest for some applications, the number of
visits to the national forest as a whole is generally more useful. Since many visitors will
recreate at multiple locations within an individual forest the number of site visits

overestimates the total number of national forest visits. To convert site visits to national
forest visits survey respondents are asked to report the number of Sites visited and the
number of days spent in the GFA during the current recreation visit (NShjj). With this
information the number of national forest visits on a given sample day (NFV,) can be

computed as:
NFVh, = Chi * P], * CHARM.

CBAR, is computed as

 

 

n V ..
CBARhi = 1 2 hi}

LEvh, FINShij ’

where LEV, is the number of last exiting vehicles in Stratum h on sample day i, thj is the

number of visitors in vehicle j in stratum h on sample day i, and NShjj is the number of

170

 

sites visited by vehicle j in stratum h on sample day i. The average number of forest visits

across all sample days in given stratum h are computed as

—— "h NFV .
NFVh = Z——’"—.
i=1 "h

The number of national forest visits for stratum h is then computed as

~th = Nth *Nh.

 

To estimate the number of national forest visits from the proxy sites the

conversion factor ANFa is developed:

 

nh
ZSCh,
ANFh =t_=l_
nh 9
25R,“-
i=1
where SC” is deﬁned
SC - it ’"7 )
hr - .
j=l NShij

The mean number of national forest visits to proxy Sites is then computed as

NFVh = ANFh *P—Ch.

From this the total number of national forest visits for stratum h is
~th = NFVh * Nh .

The average number of national forest visits for all sample days on a given national forest

is computed, combining proxy and non-proxy sites, as

171

 

__ H _
NFV= zwhi‘NFVh,

 

h=1
where
N h
W}, — H
2”}:
h=1

The total number of national forest visits for a given national forest is then

NFV=NFV*N,

where N is the number of site days for the national forest.

172

 

 

APPENDIX B

173 J
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178

Table B-6. Modeled Recreation Use Estimates and Confidence Intervals for
National Forests in the Study Area Sampled in FY2001, FY2002, FY2003.

 

Modeled Recreation CI Lower Use Upper Use
80% Estimate Estimate

Region 2
Bighorn NF 529,242 24% 402,436 656,048
Black Hills NF 734,802 18% 601,289 868,316
Grand Mesa, U.,G.
NFs 2,067,412 29% 1,464,761 2,670,063
Medicine Bow NF 562,138 14% 482,258 642,018
Nebraska NF 126,775 20% 101,433 152,117
Pike San Isabel NF 2,811,811 14% 2,417,876 3,205,746
Routt NF 1,494,708 8% 1,373,338 1,616,079
Shoshone NF 492,390 12% 432,762 552,019
White River NF 2,894,585 5% 2,759,697 3,029,473
Region 9
Allegheny NF 970,968 28% 700,165 1,241,771
Chequamegon-Nicolet
NF 1,550,322 30% 1,082,590 2,018,054
Chippewa NF 1,319,701 14% 1,135,603 1,503,799
Hoosier NF 515,539 16% 434,754 596,324
Huron-Manistee NF 700,099 33% 466,476 933,722
Mark Twain NF 491,409 13% 427,575 555,243
Monongahela NF 694,512 14% 599,503 789,521
Ottawa NF 230,575 14% 197,925 263,224
Shawnee NF 509,560 1 1% 453,967 565,153
Wayne NF 387,176 15% 327,203 447,150

 

179

Table B-7. Local Use Percentages of Modeled Recreation Use for National

 

 

Forests (NF) in the Study Area.
Lower Percentage Upper Percentage

Percentage Estimate
Region 2
Bighorn NF 51.8% 49.3% 54.2%
Black Hills NF 69.0% 66.2% 71.7%
Grand Mesa, U.,G.
NFs 67.2% 65.1% 69.2%
Medicine Bow NF 51.4% 49.0% 53.8%
Nebraska NF 44.7% 40.2% 49.2%
Pike San Isabel NF 72.7% 70.9% 74.5%
Routt NF 39.3% 36.5% 42.0%
Shoshone NF 52.5% 49.5% 55.6%
White River NF 44.2% 42.4% 46.0%
Region 9
Allegheny NF 57.2% 54.5% 60.0%
Chequamegon-
Nicolet NF 38.4% 36.0% 40.8%
Chippewa NF 55.1% 52.2% 58.0%
Hoosier NF 70.9% 67.9% 73.9%
Huron-Manistee NF 30.3% 27.7% 32.9%
Mark Twain NP 84.0% 82.0% 86.0%
Monongahela NF 37.8% 35.8% 39.8%
Ottawa NF 34.2% 31.3% 37.1%
Shawnee NF 62.1% 59.5% 64.6%
Wayne NF 64.3% 61.2% 67.4%

 

180

Table B-8. Mid-distance Use Percentages and Primary Purpose Percentages of
Modeled Recreation Use for National Forests (NF) in the Study Area.

 

 

Lower Upper Primary
Mean Percentage Percentage Purpose
Percentage Estimate Estimate Percentagg_

Region 2

Bighorn NF 19.6% 17.6% 21.5% 96.3%
Black Hills NF 3.8% 2.6% 4.9% 100.0%
Grand Mesa, U.,G. NP 16.4% 14.8% 18.0% 92.1%
Medicine Bow NF 34.9% 32.6% 37.2% 90.3%
Nebraska NF 29.8% 25.7% 33.9% 86.5%
Pike San Isabel NF 6.6% 5.6% 7.7% 96.2%
Routt NF 28.9% 26.3% 31.4% 90.8%
Shoshone NF 12.5% 10.5% 14.5% 87.1%
White River NF 27.7% 26.1% 29.3% 87.6%
Region 9

Allegheny NF 36.0% 33.4% 38.6% 97.3%
Chequamegon-Nicolet NF 56.2% 53.7% 58.6% 95.7%
Chippewa NF 34.1% 31.3% 36.9% 98.2%
Hoosier NF 25.5% 22.6% 28.3% 96.4%
Huron-Manistee NF 66.2% 63.5% 68.9% 98.6%
Mark Twain NF 13.4% 11.6% 15.3% 83.4%
Monongahela NF 51.7% 49.6% 53.8% 68.6%
Ottawa NF 30.7% 27.9% 33.5% 46.4%
Shawnee NF 27.5% 25.2% 29.9% 95.3%
Wayne NF 34.7% 31.6% 37.8% 97.3%

 

181

APPENDIX C

182

Table C-l. Standard Errors of Annual Visit Frequency Estimates by Region

and National Forest Aggeggtion Group.”I

 

 

 

Distance Band
Aggregation
Group” 0 0.10 10-15 15.20 20-25 25-30
Region 2 1 12.2 5.4 2.3
2 6.3 3.7 5.7
3 15.1 1.2 1.6 0.8 1.1 0.9
4 7.7 4.6 3.4 2.4 2.9
Regional Avg. 5.3 1.3 1.5 1.0 1.2 0.9
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2 6.8 1.7 1.7 1.7 1.3
3 4.9 3.2 2.8 2.8
4 5.4 2.5 1.1
5 7.7 3.8 4.6 3.9 1.3 4.6
Regional Avg. 2.3 1.1 1.0 1.0 1.3 0.7

 

‘ Estimated from the NVUM survey and weighted by VisExpwt.

b Aggregation Groups deﬁned below.

Region 2: 1 Bighorn NF, Shoshone NF; 2 Black Hills NF, Nebraska NF; 3 Arapaho and

Roosevelt National Forests, Medicine Bow NF, Pike and San Isabel National Forests; 4 Grand
Mesa, Uncompahgre, and Gunnison National Forests, Rio Grande NF, Routt NF, San Juan NF,

White River NF.

Region 9: 1 Hoosier NF, Mark Twain NF, Shawnee NF; 2 Alleghany NF, Monongahela NF,
Wayne NF; 3 Green Mountain and Finger Lakes National Forests, White Mountain NF; 4
Hiawatha NF, Huron-Manistee NF, Ottawa NF; 5 Chippewa NF, Chequamegon/Nicolet NF,

Superior NF.

c Number of cases is less than 30. Region-level average substituted.

 

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