Outdoor Recreation on Galiano Island

OUTDOOR RECREATION ON GALIANO ISLAND:

FACTORS WHICH INFLUENCE PARTICIPATION

by

DOUGALD GEORGE MacDONALD

B.Sc. University of Western Ontario, 1965

A THESIS SUBMITTED IN PARTIAL FULFILMENT OF

THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF SCIENCE

in the School

of

Community and Regional Planning

We accept this thesis as conforming to the

required standard

THE UNIVERSITY OF BRITISH COLUMBIA

April, 1970 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study.

I further agree tha permission for extensive copying of this thesis for scholarly purposes may be granted by the Head of my Department or by his representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission.

Dougald George MacDonald

Department of School of Community and Regional Planning

The University of British Columbia Vancouver 8, Canada

20, 1970 i

Abstract

The purpose of this thesis is to examine the factors which explain the patterns of outdoor recreation that a given population pursues on a given land surface. Patterns of use were understood as (1) the types of activities the population pursued and the frequency with which they pursued them, and (2) the distribution of these activities over a differentiated land surface. Emphasis was placed on designing a methodological framework within which the explanatory value of postulated sets of factors can be examined.

The data used to illustrate the methodology was taken from a study of the outdoor recreational use of Galiano Island done by The

University of British Columbia School of Community and Regional

Planning during the summer of 1969.

(1) I made the operational assumption that the observed variation in the types and frequency of outdoor recreation activities pursued by groups of visitors to Galiano Island could be explained by differences in the "internal" characteristics of the groups. Internal characteristics were defined as all factors contributed by the recreationists themselves such as age, sex^ experience, etc., which could influence the way they respond to the landscape. In effect, internal characteristics represent the translative mental processes

that mediate between the stimulii the recreationist receives from the landscape and his perceived use of it. I hypothesized that variations in the frequency and types of outdoor recreation activities ii which visitors to Galiano Island pursued could be explained by a selected set of socioeconomic and demographic characteristics of the visitor group. The hypothesis was tested by canonical correla• tion analysis. The results obtained were not significant at the

0.05 level of probability . I argued that the negative results were attributable to the characteristics of the data and that the hypothesis, in a conceptual sense, was not incorrect.

(2) The second assumption made was that the areal variation in the recreational use of the land surface, generally and for specific activities, could be explained by variations in the characteristics of the landscape itself. I hypothesized that the number of visits

(irrespective of activity) that recreationists paid to lot areas on

Galiano Island could be predicted from measures of the accessibility, development, and proximity to the shoreline of the lot. Following this, I hypothesized that the number of visits paid to lot areas for each of three specific activities (going to the beach, camping, hiking) could be predicted from the same set of measurements. These hypotheses were tested by multiple regression analysis. Two of the four analyses produced equations which were significant at the 0.001 level of probability. The first showed that the combined total of visits for all activities could be predicted from measurements of the accessi• bility, development, and proximity to the shoreline of the lot area.

The second significant relationship showed that visits paid to lot areas for the purpose of going to the beach could be predicted from the same set of measurements. Both equations, although significant, iii had questionable explanatory value owing to the nature of the data.

The sample size was too small to permit adequate testing of the hypotheses.

The recent literature and the direction pointed to by the results obtained in this study support the conceptual framework which I have presented. However, the data used were largely in• adequate to test the conceptual basis of the methodologies proposed.

I have'suggested throughout the study where improvements for the collection of data can be made. Acknowledgment

I wish to thank Dr. H. P. Oberlander for the supervision he offered in data collection during the summer of 1969 and for the permission he granted me to use this data in my thesis. For her patience and help in the conceptual formula• tion of this study, I should like to thank

Dr. Nirmala devi Cherukupalle, my supervisor. TABLE OF CONTENTS

CHAPTER PAGE

I INTRODUCTION TO THE STUDY: A CONCEPTUAL FRAMEWORK . . 1

II GALIANO ISLAND: THE CASE STUDY 13

2.1 Geography and Access 13

2.2 Climate, Landform, Biota 14

2.3 Land Use and Development 18

2.4 The Case Study: Methods 20

III OUTDOOR RECREATION: FACTORS INFLUENCING PARTICIPATION 24

3.1 Participation: Some General Considerations . 24

3.2 Participation: Recent Literature ...... 25

3.3 Factors Which Affect the Participation Rates

of Visitors to Galiano Island 33

3.4 Canonical Correlation Analysis; 41

3.5 Discussion of the Results ..... 42

IV OUTDOOR RECREATION: FACTORS WHICH AFFECT THE AREAL

DISTRIBUTION OF USERS 47

4.1 Overview ...... 47

4.2 Recent Literature ...... 47

4.3 Factors Which Influence the Areal Distribu•

tion of Visitors to Galiano Island ..... 54

4.4 Regression Analysis ...... 62

4.5 Discussion of the Results ...... 80 CHAPTER PAGE

V SUMMARY: DIRECTIONS FOR RESEARCH 85

5.1 Summary ..... 85

5.2 Directions for Research 87

SELECTED LIST OF REFERENCES 96

APPENDIX I Definitions . 101

II Questionnaire 103 LIST OF TABLES

TABLE PAGE

I Questionnaire Distribution Schedule 22

II Values Assigned to Variable 6 According to Income

Category . . 40

III Canonical Correlation Analysis: Significance Tests

for 6 Linear Combinations of Predictor and

Dependent Variables 43

IV Correlation Matrix: Variables used in the Regression

Analysis of Visits (Dependent Variable) 65

V Multiple Regression Analysis Results for all Visits

(Dependent Variable) . 69

VI Correlation Matrix: Variables used in the Regression

Analysis of Visits to the Beach (Dependent

Variable) 72

VII Multiple Regression Analysis Results for Visits to

the Beach (Dependent Variable) 74

VIII Correlation Matrix: Variables used in the Regression

Analysis of Visits for Camping (Dependent Variable) 76

IX Multiple Regression Analysis Results for Visits for

Camping (Dependent Variable) 77

X Correlation Matrix: Variables used in the Regression

Analysis of Visits for Hiking (Dependent Variable) 78

XI Multiple Regression Analysis Results for Visits for

Hiking (Dependent Variable). 79 LIST OF FIGURES

FIGURE PAGE

1 Frequency Distribution of Group Size 35

2 Frequency Distribution of Children less than

1 Year Old ... • 35

3 Frequency Distribution of Children 2-5 Years Old . . 36

4 Frequency Distribution of Children 8-10 Years Old . . 36

5 Frequency Distribution of Age Groups 37

6 Frequency Distribution of Income Categories 37

7 Frequency Distribution of Activities Pursued by

Visitor Groups . 38 LIST OF MAPS

MAP PAGE

1 Present Land Use . 18

2 Combined Total of Visits for all Activities ..... 56

3 Visits for Going to the Beach 57

4 Visits for Camping 58

5 Visits for Hiking ...... 59

6 Residuals for Regression of all Visits 81

7 Residuals for Regression of Visits to the Beach ... 82 CHAPTER I

INTRODUCTION TO THE STUDY : A CONCEPTUAL FRAMEWORK

The objective of this thesis is to provide a methodology for

an examination of the factors which influence the patterns of outdoor

recreational use on the Gulf Islands of British Columbia. Specifically,

I have examined the patterns of recreational use of Galiano Island. The material collected in the case study was used only to illustrate the methods I have proposed and to define the research necessary to satisfy

these methods.

Outdoor recreation involves an interaction between people and

the environment. I have defined outdoor recreation as leisure-time

activities which are voluntarily pursued on areas of the earth's surface

that have not been appreciably altered by human activity. I have termed

these "natural areas".

More and more space is needed for man's activities. Nearly one million acres of land in the United States alone are paved or otherwise

denuded of vegetation each year. Irreversible damage to the quality

and productive capacity of biotic communities has been done, more damage

is in progress and, unfortunately, more seems likely. Land areas which

remain in their natural state are being threatened by "progress". Yet

preservation and conservation is necessary for the continuing welfare

of man. Unfortunately, the preservationists too infrequently have a

strong "economic" case to present. It is difficult to match dollar

estimates of land value under industrial or commercial use with weakly 2 defined concepts of recreational benefits and yet emerge the winner. We have no way of quantifying the social value of outdoor recreation in dollar terms.

It is extremely difficult to arrive at an operational definition of the recreational requirements of a population. Do people "need" to engage in outdoor recreation? Can the satisfaction of these needs be measured in terms of a reduction of crime rates or a decreased incidence of mental and physical illness? Unfortunately, these questions remain largely unanswered. But we can infer from the behavior of people, both past and present, that outdoor recreation is an important social insti• tution. In the absence of a better index, an examination of the way populations use outdoor environments may provide insight into their requirements.

Patterns of outdoor recreational use in various settings have often been documented but only infrequently explained. The problem is to explain why observed patterns of recreational use occur. If we understood the causal parameters which underlie recreational behavior patterns it would be possible to satisfy the current needs of recreation- ists as well as having a rational basis for predicting future requirements.

As stated earlier, outdoor recreation involves the interaction between people and the environment. Given the same outdoor environment, not all people behave in the same way towards it. It is evident that the same environment can have different meanings for different groups of people. These differences in meaning can be resolved into two categories, utility and quality. Environments which have utility differences are 3 seen as differing in function; environments with quality differences are seen as having the same function but have differential values for this function. The point is, why are these differences observed?

Our sense experiences are not simply related to the physical context of the stimuli we receive. A pattern of assumptions, needs and expectations mediates between stimulus and perceptual experience. The perceptual experience is individual; it involves our values, needs, purposes, and experiences as much as it involves the stimuli received from the landscape. We can assume that this intermediary process, the process of translating visual stimuli into a perceptual experience, is culturally determined within a broad context. Every culture has a set of mutually recognized cues and expectancies; within every culture finer divisions exist. There are social, psychological, financial, geographic and physiological considerations which influence patterns of recreational use. Recreationists can be divided into subpopulations, each characterized by unique interest profiles.

At any given time, the translative mental processes of subpopula• tions may be characterized by socioeconomic and demographic characteris• tics.''" The assumption made is that such characteristics condition the response of an individual to the environment and therefore are causes of the behavior of that individual in the environment. On the basis of

Outdoor Recreation Resources Review Commission, Participation in Outdoor Recreation : Factors Affecting Demand Among American Adults, (Washington, D.C. : U.S. Government Printing Office, 1962). 4 this assumption, I hypothesized that the kind and frequency of recreation• al activities that visitors to Galiano Island pursued could be predicted from a selected set of their socioeconomic and demographic characteris• tics. By using canonical correlation analysis, I tried to establish a relationship between sets of recreation activities and sets of socio• economic and demographic characteristics. The hypothesis (Hypothesis I) and the analysis designed to test it are discussed in Chapter III.

Hypothesis I was formulated to show how the internal character• istics of the observer affect his use of the landscape. Internal factors include all characteristics of the individual which determine the types of outdoor recreation he will pursue on a given land surface. The internal characteristics of the individual mediate between the stimuli received directly from the landscape and the final image of that land• scape upon which the individual acts. I do not pretend to have made an exhaustive examination of these characteristics. Nor could I impute

that the factors which I have chosen are causal of recreational be• havior patterns , were I able to confirm Hypothesis I. The causes which underlie recreational behavior patterns are a complex amalgam of many

internal factors which is best described by such inoperative phrases

as "value system". At best, I may confirm that the set of measurements which I have chosen are an "index" of the value system of the recreation-

ist in that the behavior of the recreationist can be predicted from

them. The hypothesis was designed to answer the research question,

"Given a particular land area, is it possible to predict the kind and

frequency of outdoor recreation activities that populations would pursue

if they visited that land area?" 5

The population which is treated in the case study consisted of a number of people measurably different on various socioeconomic and demographic characteristics. All visited the same small island and pursued various outdoor recreation activities on the land surface of that island. In simple terms, I have examined why patterns of outdoor recreation activities varied; and whether the observed variance could be explained by differences in socioeconomic and demographic character• istics.

I have measured how different recreationists respond to the same landscape. This is a measurement of the way in which different groups of people satisfy their recreational objectives on the same land surface, where socioeconomic and demographic characteristics are an

"index" of the difference in recreational objectives. The way in which recreationists fulfil their objectives is a function of the characteris• tics of the land surface itself. For instance the recreationist who prefers mountain climbing to hiking may be limited to his second prefer• ence by the gentle topography of the area he visits. Thus the recreation• al behavior exhibited by visitors to an area is specific to that area.

Put in another area, the same population could easily exhibit different patterns of behavior.

Each natural area exhibits a range of landscape characteristics which determine the recreational uses for which it is suited. Within a natural area, the specific values that measurable elements in the landscape assume determine the utility and quality of that sector of the landscape for outdoor recreational use. This is confirmed by the 6

observation that recreationists do not visit the same areas for the

same purposes, and that some areas are visited more frequently than

others.

The second set of factors which I have considered are the

attributes of the land area to which a population of recreationists

does respond by pursuing specific activities in specific areas. If

these can be successfully defined, it should be possible to make an

inventory of the attributes of a land area which are highly correlated with user perception and from the information gained from the confirma•

tion of the first hypothesis, predict the patterns of recreational use

that various populations would exhibit on that land area. However,

this .method of prediction could only be applied to land areas which

contained the same general range of attributes. The solution of the

first hypothesis only provides an answer to the way in which a given

population responds to a given environment. The same population could

respond in an entirely different fashion to a different environment and vice versa. However, one can assume that populations with similar

socioeconomic and demographic characteristics taken from the same general

area would react in a predictable fashion if confronted with environments

of the same nature. I would suggest that this condition applies to the

southern group of Gulf Islands.

I have limited my discussion of the attributes of the land surface

which affect the distribution of recreationists to the most general

considerations. I hypothesized that the number of times recreationists

visited any surveyed lot area can be predicted from the accessibility of ' 7 the lot, the extent of development of the lot, and the proximity of areas within that lot to the shoreline (Hypothesis II). By using regression analysis, I explored the validity of this hypothesis for all reported visits, irrespective of activity, and then for all reported visits for each of three specific activities. The results are presented in Chapter IV.

Having stated the specific nature of the problem which I have examined, I wish now to place the study I have discussed into the broader context of the problems faced by agencies which are responsible for the allocation of land for outdoor recreation.

The responsibility for providing most outdoor recreational opportunities in North America has been assumed by the public sector.

The problem can be simplified into three components: users, facilities, and resources. The users are the people who engage in outdoor recreation activities; the resources include all aspects of the landscape, both natural and man-made, which are suited for outdoor recreational use; and the facilities are the land areas, including development, which have been allocated for outdoor recreational use. The task is to locate facilities within the resource base which will satisfy the recreational requirements of a population over a long term. 2

Anderson described a four-part methodology to relate the recreational requirements of users to the recreational potential of the landscape:

K.A. Anderson, A User Resource Planning Method, (California: National Advisory Council on Regional Recreation Planning, 1959). 8

(1) Estimate the recreational requirements of the population in

a specified area by categorizing recreational experiences

into user groups and measuring the environmental character•

istics required by each user group.

(2) Estimate the potential of recreational resources as they

relate to user groups.

(3) Relate the recreational requirements of the population to

the potential of the resource base.

(4) Estimate the costs and benefits of providing recreational

services in various sectors of the resource base.

Anderson suggested that a measure of the recreational requirements

of a population was the corner stone of the four part method. As I

discussed earlier in this chapter, it is only possible to use proximate measures such as behavior as indicative of recreational requirements.

The kinds of activities people pursue, and the amount of time and money

they spend on each activity are behavioral measures that have been used

to judge recreational need. But these are simply measures of consumer behavior which is influenced by the rapidly changing social, economic

and technological conditions in North America. Measurements of the

recreational behavior patterns of a population may be proximate measures

of the recreational requirements of that population for a very short

time only. An understanding of the factors which underlie patterns of

consumption in outdoor recreation is mandatory. Only if these factors

are understood can the recreational requirements of a population be

projected. 9

A second consideration in measuring the recreational requirements of a population is the evaluation of the environmental characteristics required by each user group.

The quality of resources which are allocated for outdoor recrea• tion is an important determinant of the optimum enjohment and use that can be derived from them. But the information about what constitutes quality is inadequate. Within any geographic region the land surface may display marked differences in soil type, vegetation, relationship to bodies of water, etc. These factors strongly influence the adaptibility of the land surface to various types of outdoor recreation. In fact, these characteristics define the type and amount of recreational services the land area can produce over a given period of time.

On the one hand they prescribe the carrying capacity of an area for each of the various kinds of recreational services it might possibly accommodate. On the other hand, they influence strongly the real costs of producing such services, whether these costs are incurred in the form of expenditures to maintain resource characteristics in the face of constant use, or in the form of resource depletion (i.e., a change in resource character• istics) .3

Research on the quality of the land surface potentially usable for outdoor recreation must identify the specific attributes of the resource base which are required for individual types of resource-based recreation.

This can be achieved by selecting measurable attributes of the environment

National Academy of Sciences, A Program for Outdoor Recreation Research, (Washington, D.C.: National Academy of Sciences, 1969), p. 51. 10

and correlating these with the user response to them. Once significant

correlations are established, a site quality classification of the

resource base can be made in terms of individual types of resource-

based recreation. Thus the relative attractiveness of various land

areas for different types of outdoor recreation activities can be

defined.

In assessing the quality of a particular resource for outdoor

recreation, the effect of various levels and types of use on the resource

must be considered. The costs of ignoring the ecological characteristics

of the resource are low when it is infrequently used but they become

increasingly important at higher levels of use. A report by the National 4

Academy of Sciences suggested that research on the quality of resources

should take into account specific attributes of the resource type which

not only are related to human perception of that resource but which also

have a high correlation with user impacts on resource ecology. This

step is essential for determining the number of individuals the resource

could serve while remaining in.a long-term productive capacity. And it

can define the cost of managing the resource to maintain its attractive•

ness. Information on the susceptibility of the resource to damage by

recreationists in combination with a site quality classification would

permit agencies to design systems of recreational land from the stand•

point of the capability of resources to both attract and accommodate

4 •••,..»- National Academy of Sciences, A Program for Outdoor Recreation •Research, (Washington, D.C.: National Academy of Sciences, 1969). recreationists. As yet, no operational classification scheme of this sort exists.

Most current schemes for the classification of the recreational quality of the land surface assume uniform conditions of access. The recreational land classification scheme"* produced by ARDA (Agricultural and Rural Development Act, 1965) is a case in point. Land areas were ranked according to the relative capability of each land unit to attract recreationists—assuming a common level of demand and accessibility in all areas. But, in a practical sense, the value that the land surface holds for the recreationist also depends on how easily it is accessible to him. In most situations it is not possible to classify the land surface in terms of its accessibility to the recreationist because the land surface would have to be classified in as many ways as there were points of access to it. However, the case study I have considered in this thesis does permit the inclusion of accessibility factors into a system of land classification. Most of the visitors to Galiano Island arrive by ferry at Sturdies Bay; few arrive from the other ferry terminal at Montague Harbor. All of the visitors included in the sample chosen for the analysis disembarked at Sturdies Bay. Thus, the accessibility of various land units to these recreationists can be defined in terms of some measurement of distance, time, or cost from Sturdies Bay.

Department of Forestry and Rural Development of Canada, Field Manual : Land Capability Classification for Outdoor Recreation (Ottawa: Queen's Printer, 1967). 12

This study is an attempt to measure the potential of recreational resources on Galiano Island by measuring the response of a specific population to the land surface. Of the four steps proposed by Anderson,

I have only.dealt with the first. However, the first step is the cornerstone of the entire method. My analysis has been exploratory rather than definitive. I hope to show that the methodology I have used, subject to the refinements I have suggested throughout this thesis, can be applied to obtain accurate information on the response of recreationists to the land surface of the Gulf Islands of British Columbia.

Definitions of the operational terms used in this study have been placed in Appendix I. CHAPTER II

THE CASE STUDY

2.1 Geography and Access

The Canadian Gulf Islands lie in the Strait of Georgia between

Vancouver Island and the mainland of British Columbia. They fall between the 48th and 49th Parallel North Latitude and between the

123rd and 124th Meridian West Longitude. They extend from Comox

Harbor in the north to the International boundary in the south, covering an area of approximately 100,000 acres. The Islands are divided into two major groups, northern and southern. The northern group, which includes Hornby and Denman Islands, is separated from the southern group by a distance of approximately 40 miles. The southern group is comprised of some 25 larger islands and many smaller islets. Galiano Island is one of the larger, more accessible islands of the southern subgroup.

Galiano Island is approximately 18 miles long and one to three miles wide. The long axis of Galiano runs from northeast to south• west. The southwestern end of Galiano Island, the widest point of the island, borders on Active Pass, the narrowest passage of a busy shipping route between Vancouver and Victoria. The main passenger and shipping terminal of Galiano Island is Sturdies Bay, a small harbor in Active Pass. The British Columbia Ferries supply a regular service to Galiano Island throughout the year. Arrivals and 14 departures each day are more frequent in summer than in winter. Daily ferry service to Montague Harbour on the southern shore of Galiano

Island is also provided from Swartz Bay, on Vancouver Island. There is relatively little traffic at this point. Privately owned marinas, which cater to pleasure boat traffic are located in Sturdies Bay, Montague

Harbour and Lighthouse Bay on the northeasterly tip of Galiano Island.

2.2 Climate, Landform, Biota

Temperatures are mild and pleasant year round on the Gulf

Islands. The frost-free period averages between 200 and 250 days per year. Precipitation averages from 30 to 40 inches per annum, falling mostly in winter months. Average temperatures are 38 degrees in

January and 62 degrees in July.

Galiano Island is one of the more rugged and least fertile of the Gulf Islands. The northern shoreline of Galiano Island is composed of gradually sloping rock shelves. From the northern shoreline the land slopes upward, ending in precipitous bluffs along much of the southern shoreline. The southwestern portion of Galiano has a gently rolling topography broken by Mount Sutil and Mount

Galiano which approach 1,000 feet in elevation.

The vegetation of Galiano Island is characteristic of the

"Coastal Douglas Fir Biogeoclimatic Region" described by Krajina.

Within this general biogeoclimatic region, the forest trees of

Galiano Island are further described as being characteristic of the

V. J. Kratina, Ecology of Western North America, Volume II (Vancouver: The University of British Columbia, 1969). 15

"Drier (Garry Oak—Douglas Fir) Subzone". This vegetational sub zone occurs in parts of southern British Columbia and Vancouver Island which have a low total annual rainfall. It is one of the least productive forest zones of coastal British Columbia. The following trees are characteristic of this subzone:

1. Douglas Fir (Pseudotsuga menziesii). Douglas fir stands

are common but limited to wetter habitats.

2. Grand Fir (Abies graridis). The Grand Fir is not a common

tree in the dry subzone; its occurrence is limited to

areas of rich soils.

3. Western Red Cedar (Thuja plicata). Stands of Western

Red Cedar are limited to low wet areas. Scattered speci•

mens occur in drier habitats but exhibit.poor growth.

4. Garry Oak (Quercus garyana). The Garry Oak occurs as

isolated specimens on hills and bluffs. The Gulf Islands

_of British Columbia and parts of southern Vancouver

Island mark the northern limit of its distribution.

5. Arbutus (Arbutus menziesii). The Arbutus is the only

broadleafed evergreen found in British Columbia. As in

the case of the Garry Oak, the Arbutus is also at the

northern limits of its distribution in the Gulf Islands.

Its habitat is much the same as the Garry Oak. Both

species are often seen growing side-by-side, forming a

very open forest. 16

6. Lodgepole Pine (Pinus contorta). The Lodgepole Pine

occurs in almost all of the open forests of the Gulf

Islands. It has very general requirements and may be

observed in most habitats with the exception of very

shaded areas.

The trees listed below may also occur on the Gulf Islands but comprise a smaller proportion of the natural forest cover.

7. Bitter Cherry (Priirius emarginata) .

8. Red Alder (Alnus rubra).

9. Broadleaf Maple (Acer macrophyllum).

10. Vine Maple (Acer circinatum).

The above list is not exhaustive; but. it does contain those tree species most characteristic of the Gulf Islands. Logging and grazing activities in certain areas have changed the natural vegeta• tion of the Gulf Islands. For instance, recently logged areas may contain large populations of Alder, a species not abundant in virgin forest stands.

The principal mammals of the southern Gulf Islands are deer, raccoons, and various rodents. The abundance of mammals varies from island to island. The avifauna of the Gulf Islands is also varied, consisting of land birds, shore birds, and sea birds. The latter two groups are most abundant.

The intertidal zone of the shorelines of the Gulf Islands support an abundant invertebrate fauna as well as many species of seaweed and algae. Oysters, clams, various crustaceans, starfish, etc., are visible on all but the most rocky shores which, are favored by barnacles and mussels.

2.3 Land Use and Development

White settlement on Galiano Island began in the 1880's^ and has continued to expand at an increasingly rapid rate into the pre• sent. Most of the present cottage development is limited to the southwestern end, below a line running from Montague Harbour to Sala• manca Point. Approximately 300 people now make Galiano Island their permanent home; as many more have summer cottages on the island.

Most of the northeastern two-thirds of the land area of

Galiano Island is in Certified Tree Farm Number 19, owned by MacMillan

Bloedel Limited (see Map No. 1). Most of the valuable timber has been removed; logging activity occurred only in isolated pockets during the summer of 1969. In an interview with a MacMillan Bloedel executive,

I was told that the company had spent considerable money in managing and improving their property on Galiano Island and that they expected a crop of merchantable timber in 50 to 70 years.

Public access to the Tree Farm was not limited during the summer of 1969. In fact there was little evidence from the land

surface where the boundaries of the Tree Farm exist.

B. C. Historical Association, Gulf Islands Branch, A Gulf Islands Patchwork (Sidney, B.C.: Peninsula Printing Co. Ltd., 1961).

19

Galiano Island now has only three full-time farms on the

southeastern end. These occupy a relatively small proportion of

the total land area. Agricultural activity on Galiano Island has

become less important as the value of the land for cottage develop•

ment has increased.

The rapid rise in land prices on the Gulf Islands attests

to their growing popularity, both to residents of British Columbia

and to Americans seeking vacation homes. The fast moving real

estate market on the Gulf Islands has recently attracted land

speculators and developers who have advertised their saleable hold•

ings to a widespread public. The Sunday, August 24, 1969 issue of

the New York Times featured an article on the Canadian Gulf Islands which described

... the thickly forested hills, the promise—but no iron• clad guarantee-—of year-round golf, the sailing, the fishing, the privacy and the quiet.

Yet this popularity is cause for concern; cottagers find privacy a

highly priced commodity on many of the more settled islands. Cottage

developments are assuming the atmosphere of typical modern sub•

divisions. The fear that the Gulf Islands may evolve into a suburban

dormitory for the fast growing cities of southern British Columbia

has led to the commission of several studies. The study described

in this thesis was funded by the Donner Canadian Foundation and

supervised by Dr. H. P. Oberlander, Director of the School of Community

and.Regional Planning at The University of British Columbia. 20

2.4 The Case Study : Methods

During the summer of 1969, I was employed as a research assistant to Dr. Oberlander to study recreational land use patterns on the Gulf Islands of British Columbia. Following a decision to gather data by using a questionnaire, several different question• naires were constructed and tested both in the laboratory and in the field. An example of the final questionnaire design is presented in Appendix 2.

The "population" from which we wished to gather information consisted of all groups of people who visited Galiano Island between

August 8 and September 1, 1969. The sampling "unit" was a visitor

"group'.' which consisted of at least one person and occasionally included as many as ten people.

The questionnaires were distributed to groups of people leav• ing Galiano Island between August 8 and September 1. The distribution points were the two terminals of the British Columbia Ferries on

Galiano Island, Sturdies Bay and Montague Harbor. Two girls in their early twenties were employed by the School of Community and Regional

Planning to assist me in the questionnaire distribution. The method of distribution was to approach persons waiting for outbound ferries

(both car and foot passengers), explain the purpose of the question• naire in brief , and request the prospective respondents to return the completed questionnaire at their earliest convenience. The question• naires were all pre-addressed and stamped. 21

The original intention of the questionnaire distribution schedule was to contact every group of recreationists who visited

Galiano Island during the study period. In a rough sense, this objective was accomplished. However, we did not take a representa• tive sample from weekday traffic. The British Columbia Ferries employees on Galiano Island assured us that weekday traffic was small in proportion to weekend traffic. As a result we decided to sample on weekends only, to avoid the additional expense of week• day distribution. Consequently the sample is weighted toward weekend visitors with little representation from weekday visitors. Table 1 contains the number of questionnaires which were distributed by day within the sampling period. A smaller number of questionnaires were distributed at campsites, marinas, etc., on Galiano Island.

However, these represented a small proportion of the total and were omitted from the analysis because we did not have control over who received the questionnaires and when they were received. We gave questionnaires to campsite and marina owners after they had volun• teered their co-operation to distribute them.

Our method of distribution did not separate people who owned property on Galiano Island from those who did not. Consequently, both groups received and completed the questionnaire. The respondents were later separated into two groups on the basis of their answer to question 5 (see Appendix II). Only the visitor group was examined in this thesis. TABLE I

QUESTIONNAIRE DISTRIBUTION SCHEDULE

Date Distribution Point Number Distributed

Aug. 8 Galiano Lodge 25 10 Sturdies Bay 51 15 Montague Harbour 4 15 Sturdies Bay 9 16 Sturdies Bay 16 17 Montague Harbour 15 17 Sturdies Bay 34 17 Private Campsites (2) 25 19 Sturdies Bay 4 22 Sturdies Bay 8 22 Montague Harbour 8 23 Sturdies Bay 4 23 Montague Harbour 6 24 Sturdies Bay 86 24. Montague Harbour 21 26 Sturdies Bay 6 27 Sturdies Bay 16 29 Sturdies Bay 40 29 Other (Marinas & Store) 21 31 Montague Harbour 9 Sept . 1 Montague Harbour 9 1 Sturdies Bay 30

Total 442 '•23

By October 15, 1969, 140 of the total of 422 distributed questionnaires had been returned. This represented a 35.3% response, a surprisingly good response rate for a mail-back questionnaire.

Of the 140 questionnaires, approximately two-thirds had been com• pleted by visitors. The number of usable questionnaires varied according to the particular analysis... This limitation has been discussed in the introduction to each analysis.

The questionnaires were coded and the information was trans• ferred to punch-cards. The facilities at The University of British

Columbia Computing Centre were used to aid in the data analysis. CHAPTER III

OUTDOOR RECREATION: FACTORS INFLUENCING PARTICIPATION

3.1 Participation: Some General Considerations

The increase in leisure time, discretionary income, and the number of accessible areas have broadened the range of recreational

opportunities available to North Americans. But opportunity has not been equal to all. There are social, psychological, financial, geographic and physiological considerations which act as barriers to participation. Some activities may be exclusive to certain social classes; others are not. Physiological.considerations limit the pursuit of strenous activities to the young and healthy. Yet persons of all ages may enjoy a drive in the country.

There is every indication that the influence these barriers to participation currently exert on the recreation patterns of people will change. And as the Influence of these factors changes the number and type of participants in various activities will change. Thus participation data is specific to the time and place at which it is gathered and is related to the prevailing social> economic, and political conditions. Therefore the projection of participation in outdoor recreation activities first requires that those factors which underlie demand be isolated. The following section summarizes the most significant discoveries in the recent literature on the estimation and explanation of participation rates. 3.2 Participation: Recent Literature

The earliest and best known analysis of participation rates 8 was done by the Outdoor Recreation Resources Review Commission

(1962) , hereafter referred to as ORRRC. The authors of ORRRC Report 9

No. 20, (1962) hypothesized that the amount and kinds of outdoor activities pursued by recreationists were related to various socio• economic and demographic characteristics of the participants. Home interviews were conducted across the United States of America to find the rates of participation in 22 selected activities.

Participation rates were combined to form a single "activity index". This was a quantitative combination of the estimated time spent in various activities over a 12 month period. Two analyses were made. First, the authors examined the relationship between the activity index and single socioeconomic characteristics, i.e., income; second, a form of multivariate analysis was used to identify the relationship between participation rates and selected socio• economic characteristics—-while holding all other factors constant.

They called the technique a "Multiple Classification Analysis" which provided.the answer to the following question: "Given a (numerical) dependent variable and knowledge about the classification of each individual on a number of attributes, how can we predict each

ORRRC, loc. cjt.

Ibid. individual's position with respect to the dependent variable in such a way as to minimize the sum of squares of the errors (the error variance)?""*"^ The ORRRC report treated the outdoor recreation activity scale as the dependent variable and each of the following characteristics as independent variables; income, education, occu• pation, length of paid vacation, race, age, life cycle stage, region, and place of residence. The analysis was made separately for men and women to eliminate the interaction effect between sex and other independent variables. The result of their findings for each in• dependent variable are presented below.

Income

In general, participation rates rose from the lowest to the highest income groups.

Education

The higher educated show a higher participation rate.

Occupation

Activity increases as the occupational status hierarchy is ascended, i.e., professionals generally rank higher on the activity scale than unskilled labourers.

Paid Vacation

Participation rates rise in proportion to the length of

-paid vacation.

100RRRC, loc. cit. Place of Residence

Four categories in the rural-Durban continuum were.identified the cities themselves, the surrounding suburbs, the areas adjacent to the suburbs and extending to a distance of 50 miles from the population centre, outlying areas at least 50 miles from a city of

50,000 people. No significant difference between participation rates and location of residence were noted.

Sex

Men were found.to be slightly more active than women. The difference in some activities, i.e., hunting, was more pronounced than in others.

Age

An increase in age (beginning at age 12) corresponded with a decrease on the activity scale. In qualitative terms, there was a general shift from active to more sedentary activities in older people.

Life Cycle

The variable describing life cycle was a combination of age, marital status, and children's age. The relationship between life cycle and position on the activity scale was weak and probably reflective only of age differences.

Race

Negroes were much less active participants than whites. 28

Robert Daiute^ presented several objections to both the methods and applicability of the ORRRC report. His first criticism was that the report was too general to be applicable to the planning process at local levels. Conditions vary from locality to locality.

Therefore, it is not reasonable to assume that forecasts of activity

rates based on national averages will be accurate at the local level.

Regional differences in participation rates, • both;:current and pro-

12

jected, are found in the California State Planning Monographs.

The authors of this series, in conjunction with the Stanford Research

Institute, projected demand for outdoor recreation activities within

specified distances of several of the larger metropolitan centres in

California. They first established a relationship between recreation•

al activity patterns and socioeconomic characteristics. Then they

forecasted recreational demand for selected activities from the

expected frequency change in socioeconomic characteristics. Fore•

casts differed for each of the metropolitan areas.

Daiute also raised the objection that the ORRRC report assumed

that children under 12 years of age did not exert an influence on the

choice of recreation activities away from home. He estimated that

R. J. Daiute, "Methods for Determination of Demand for Outdoor Recreation," Land Economics, 42 (1966), 327-338. 12 California Department of Parks and Recreation, Outdoor Recreation Outlook to 1980 (California: Department of Parks and Recreation, June, 1966). 29

"the error could be on the order of underestimating demand by

50 per cent in the case of families with two adults and two children

under twelve." In addition to this consideration, I would guess

that the number and ages of children under twelve greatly influence

the location and type of recreational activities pursued by a

recreating group. Families with very young children are more

dependent on such facilities as sanitary and food services than

families with older children. Also, one would not expect families

to take very young children on arduous trips i.e., hiking, wilder•

ness camping, etc. However, after children have reached 8 or 10

years of age they may not be a burden to the parents, even on

strenuous outings.

The precedent established by the ORRRC report stimulated

other researchers to pursue similar lines of research on a local,

rather than national, level. All of the research has followed the

same general pattern; surveys are administered to uncover the factors which govern present demand on the assumption that future demand

can be predicted from an understanding of the forces underlying

present demand. Demographic and socioeconomic variables have been

correlated with participation rates—on a general scale and for

specific outdoor recreation activities. The ORRRC study reported 2

an R (multiple correlation coefficient) of 0.30 between the com•

posite effect of all of the socioeconomic characteristics listed

earlier in this chapter and the activity scale. This means that

30% of the observed variation in the activity scale was explained by socioeconomic and demographic characteristics. However, one might question the applicability of participation rates as an activity

scale to the planning process. A mathematical relationship, regard• less of its statistical significance, is of little value if it

cannot be translated in workable terms. And an activity scale,

calculated as an aggregate of the participation rates in various activities, is of little use for specific design purposes. Planners must design facilities for specific activities. There is no assurance that participation in different outdoor activities will

exhibit similar rates of increase. In fact, there is evidence to

the contrary. Thus research on activity scales has little applied value.

A more useful branch of the research on recreational demand has explored the value of selected demographic and socioeconomic

characteristics.as predictive of participation rates in single 13

activities. Green and Wadsworth examined the factors affecting participation in camping in Indiana. They concluded that

the variables that tend to have a significant effect on camping are: Cl) occupation, (2) age, (3) vacation, (4) education, and (5) type of camping outfit. Generally speaking, persons classified as managers and laborers do the most camping, participation decreases with age, par• ticipation increases with days of paid vacation and education, and campers with the most expensive types of camping outfits do the most camping.

. B. L. Green and H. A. Wadsworth, Campers: What Affects Participation and What Do They Want? (Indiana: Purdue University Agriculture Experiment Station, 1966), p. 2. Green and Wadsworth used a multiple regression type of analysis with number of nights camped during the calendar year 1963 as dependent variable and independent variables including five of the above 2 listed, The analysis yielded R of approximately 0.20, explaining only 1/5 of the variation.

Other camping demand studies include work done by McCurdy

14 and Mischon who tested 10 socioeconomic factors individually and in all combinations with the number of nights people camped in

1964 in private campgrounds along the Ohio River. Using regression 2 analysis, they reported an R of 0.0897. Thus only 9% of the variation between the number of nights camped and the factors tested 15 2 (10) was explained. King obtained an R of 0.26 between the square.root of the days camped and 28 socioeconomic characteristics.

Evidently a majority of the variation was associated with some other set of variables than those he tested.

The evidence from the literature suggests that socioeconomic and demographic characteristics are related to the types of outdoor recreation activities a person chooses, and to the frequency with which he engaged in these activities. However, all reported statis• tical relationships are weak. The large, unexplained variance may be attributable to two factors: either the statistical model is

D. R. McCurdy and R. M. Mischon, A Look at the Private Camp• ground User (Columbus, Ohio: U.S. Forest Service, 1965).

"^D. A. King, Office Report (St. Paul, Minnesota: U.S. Forest Service Lake States Forest Experimental Station, 1964).

j 32

incorrect, or the unexplained variation is associated with variables not included in the analysis.

Most researchers have used linear models, i.e., regression

and classification analysis to explore the relationship between

socioeconomic variables and participation rates. But the relation•

ship may not be linear. This possibility exists and should be

explored rather than making the unexamined assumption that the

relationships are linear.

A second criticism of the types of mathematical techniques used by researchers is that participation rates in activities have been either independently related to socioeconomic and demographic

characteristics or they have been combined into an index which

obscures the independent contribution of each activity. Neither method is satisfactory. I would guess that recreationists make

few single activity trips. Rather, people probably have a spectrum

of interests in outdoor activities; the choice of any one or several

activities could depend on the supply available, the weather, how

crowded desired facilities are, etc. To an extent, one activity

can be substituted for another if conditions are favourable. There•

fore, the measurement of participation rates should be on patterns

of related activities while retaining an independent measurement participation in each activity.

The third reason for the weak statistical relationships

reported in the literature could be that the majority of the variation

is associated with another set of variables. A report on a recent 33 16 recreational conference at the National Academy of Sciences Summer

Study Centre suggested that

... sociological variables are being introduced into the models less because of logical considerations than because of what is currently amenable to measurement. The cooperation of economists and behavioral scientists is needed if more specific prediction models are to be developed to show the demand for outdoor recreation activities and facilities in terms of quantity, quality, and location as a function of users' location numbers, social characteristics, and pychological needs.

In summary, participation rates have been used to measure patterns of outdoor recreation use. Participation rates have been related to selected socioeconomic and demographic characteristics of recreationists on the assumption that future patterns of recreational use can be predicted from the expected frequency changes in socioeconomic and demographic characteristics. However, all reported statistical relationships between participation rates and socioeconomic characteristics have been weak.

3.3 Factors Which Affect the Participation Rates of Visitors to Galiano Island

I have used data from the case study to relate participation rates:in 6 selected outdoor recreation activities to a set of selected socioeconomic and demographic characteristics of the visitors

National Academy of Sciences, A Program for Outdoor Recreation Research (Washington, D.C.: National Academy of Sciences, 1969), p. 36. to Galiano Island. I hypothesized that the socioeconomic and demographic characteristics of visitors to Galiano Island are related to the types of outdoor recreation activities they engage in while on Galiano Island. In a general sense, the sixteen socioeconomic variables can be considered as predictors; similarly the outdoor recreation activities can be regarded as criteria variables. The sample chosen for analysis consisted of 49 questionnaires. Although

139 questionnaires were returned, only 49 had complete and accurate information for each of the 22 variables for the visitor group.

Rather than attempt to interpret erroneous responses, I chose to exclude them from the sample. The following list briefly describes each variable; Figures 1 to 7 illustrate the data in graphic form.

Socioeconomic and Demographic Characteristics (Predictors)

1. The size of the visitor group. This variable simply

measures the number of people in the group.

2. The number of persons less than 1 year old in the group.

3. The number of persons between 2 and 5 years old in the

group.

4. The number of persons between 6 and 10 years old in the

group.

5. The average age of the group. The mean for each age

group of the 7 age groups in the questionnaire was

calculated, multiplied by the number of persons in that

age category, and divided by the total number of persons

in the group. Frequency

1 2 3 4 5 6 7 Number of Persons in visitor group

Figure 1. Frequency distribution of group size (variable 1)

Frequency

0 1 Number of children less than 1 year old in the visitor group. Figure 2. Frequency distribution of children less than 1 year 0 1 2 Number of children 2-5 years old in the visitor group.

Figure 3, Frequency distribution of children 2-5 years old.

Frequency

0 1 2 3 Number of children 6-10 years old in the visitor group.

Figure 4. Frequency distribution of children 6-10 years old Frequency

10-15 16-20 21-25 26-30 31-35 36-40 41-45 46-50 51"55 56"60

Average age of the group

Figure 5. Frequency distribution of age groups.

Frequency

1 23 4 5 67 8 9 10 Income category

Figure 6. Frequency distribution of income categories (See Table II, p. 40). Frequency

1 2 3 4 5 6 Activities

Legend: Activity -No. Activity 1 Going to the beach 2 Camping 3 Boating and fishing 4 Horseback riding 5 Golf 6 Hiking

Figure 7. Frequency distribution of activities pursued by visitor groups. 6. Total family yearly income before taxes. The incomes

reported in the questionnaire were assigned a value from

1 to 9 according to Table II.

7. Managerial.

8. Professional and technical.

9. Clerical.

10. Sales.

11. Service and recreation.

12. Transportation and communication.

13. Loggers and related workers.

14. Craftsmen, production process, and related workers.

15. Laborers.

16. Students, housewives, retired persons (draft dodgers).

Outdoor Recreation Activities (Criteria)

17. Going to the beach to walk, swim, explore, gather shells,

18 picnic• • , etc, .

Variables 7 to 16 are occupational categories, classified according to Dominion Bureau of Statistics (D.B.S.) criteria. The D.B.S. scale lists 3 more categories: (1) farmers and farm workers, (2) fishermen, trappers and hunters, (3) miners, quarrymen and related workers. These three occupational categories were not represented in the sample. Each of the 10 occupational variables can assume a value of 1 or 0, i.e., either the respondent is employed in a professional or technical capacity (variable 8) or he is not.

18 Variables 17 to 22 inclusive were also treated as binary variables which could assume a value of either 0 or 1. The value of 1 was assigned to an activity variable if the respondent partici• pated in the activity, 0 if he did not. TABLE II

VALUES ASSIGNED TO VARIABLE 6 ACCORDING TO INCOME CATEGORY

Value for Total Family Yearly Variable 6 Income before Taxes

•1. 0 - 3,000

2. 3,001 - 4,999

3. 5,000 - 7,499

4. 7,500 - 9,999

5. 10,000 - 12,500

6. 12,501 - 15,000

7. 15,001 - 20,000

8. 20,001 - 25,000

9. 25,001 +

Note: Income categories are not equal. Therefore, in theory, each, income'category should be considered as a separate variable which can hold the values of 0 or 1; a 0 value simply indicates that the respondent does not fall into that income class while a value of 1 signifies that the respondent does fall into that income class. However, in view of the small sample size, I have treated income as a single variable with the property of an interval scale, i.e., income category 9 is 9 times larger than income category 1. Since this is not strictly true, I will take this source of error into account when interpreting the results of the analysis. 18. Camp ing overnight.

19. Boating and fishing.

20. Horseback riding.

21. Golf.

22. Walking or hiking (not on the beach).

3.4 Canonical Correlation Analysis

The hypothesis was tested by a canonical correlation analysis.

The following description of canonical correlation analysis is extremely brief. For a more detailed treatment the reader should

19 20 consult Cooley and Lohnes and Anderson.

Canonical correlation analysis requires two sets of measure• ments of different parameters on the same experimental subjects.

Canonical analysis yields linear combinations of predictor variables which are maximally correlated with linear combinations of dependent variables. Several different linear combinations of the two sets are possible. Each linear combination of predictor and dependent variables is determined so that the set of predictor variables is maximally correlated with the set of criterion variables, subject to the restriction that the new combination is independent of (not correlated with) previous linear combinations.

W. W. Cooley and P. R. Lohnes, Multivariate Procedures for the Behavioral Sciences (U.S.A.: John Wiley & Sons Inc., 1966). 20 T. W. Anderson, An Introduction to Multivariate Statistical Analysis (New York: John Wiley & Sons Inc., 1958). 42

Canonical correlation analysis has been used to relate two sets of variables. The analysis answered the following two research questions: (a) "Is the pattern of participation in outdoor recreation 21 activities significantly related to the chosen set of socioeconomic and demographic variables?" (b) "In what ways can weights be assigned to each variable within each set to make the correlation between the sets a maximum?"

Table III contains the results pertinent to question (a). The maximum canonical correlation Rc obtained in the analysis was 0.705 which is not significant at the 0.05 level of probability. There• fore the set of socioeconomic and demographic variables is not significantly related to the choice of outdoor recreation activities.

3.5 Discussion of the Results

The results of the analysis must lead to the rejection of,the hypothesis that socioeconomic and demographic variables are related to the choice of outdoor recreation activities. I have offered several explanations which could account for the observed results.

Cooley and Lohnes describe a method for testing the signi• ficance of each canonical correlation (R ). First the statistic _/\_ (lambda) is calculated. Then "the X2 approximation for the distri• bution of provides a test for the null hypothesis that the p variates are unrelated to the q variates." (Cooley and Lohnes, p. 37). The significance of each of the m different linear combinations can be tested by using the X2 statistic. If the null hypothesis can be rejected for one or more of these linear combinations of predictor and criterion variables, the results will allow statistical inter• pretation at a specified level of significance. TABLE III

CANONICAL CORRELATION ANALYSIS: SIGNIFICANCE TEST FOR 6 LINEAR COMBINATIONS OF PREDICTOR AND DEPENDENT VARIABLES

% Tests of Successive Latent Roots

Number of Roots Corresponding Removed Canonical R P

0 0.705 0.107 83.89 >0.05

1 0.664 0.212 58 .15 >0.05

2 0.613 0.380 36.30 >0.05

3 0.477 0.609 18.60 >0.05

4 0.386 0.788 8.93 >0.05

5 0.272 0.926 2.88 >0.05 44

(1) The most obvious reason for rejecting the hypothesis is

that there simply is no overall relationship between

socioeconomic and demographic characteristics and the

type of resource based activities that recreationists

pursue on Galiano Island. However, this conclusion

may only apply to the specific predictor and criterion

variables which I have considered.

(2) A second possible reason for the negative results

observed is that the sample was too small. The low

level of accuracy achieved in such a small sample could

have obscured most interrelationships between predictor

and criterion variables.

(3) A.third reason for the observed negative results could

be the coarse scale of measurement achieved for some

variables, i.e., age. Particularly, the measurement of

participation rates could have been improved. In the

analysis the measure of participation rates only re•

flected that the visitor had engaged in specific activities

while on Galiano Island. No measure of the amount of

time spent in each activity was obtained in the case

. study. Since the length of a visit to Galiano Island

varied between respondents, this factor should have been

taken into account in the measurement of participation

rates. However, we did not foresee, this difficulty in

designing the questionnaire. 45

The general range of literature on the relationship of socioeconomic and demographic characteristics to participation in outdoor recreation activities indicates that researchers have succeeded in defining only the very broadest relationships. The conclusions typically include statements such as "Younger people tend to pursue more active pursuits than older people." These facts are often self-evident and require little research to support them. However, researchers have not been successful in defining how more subtle differences in socioeconomic characteristics deter• mine choices of recreation activities. The case study presented in this thesis is a case in point. All of the activities tested were resource-based activities and all could be considered active rather than passive activities. In fact it would be difficult to describe broad criteria by which these activities could be differentiated into two or more generally distinct categories. The activities were too similar in type to expect that the people who pursued them would exhibit broad differences in socioeconomic or demographic character• istics. One could anticipate that subtle differences would exist, but this expectation was not confirmed by the analysis.

I am reluctant to discard the hypothesis that I have tested solely on the basis of the results obtained in this analysis. The study was exploratory and could be refined considerably along the lines that I have suggested in this discussion. Measurements should be more accurate and the sample size should be greatly increased. In addition, I feel that the application of canonical correlation analysis is particularly suited to the exploration of this type of

problem. This statistical technique takes into account the inter•

action between sets of variables rather than considering their

separate effects. The advantage of canonical correlation analysis

is that it shows how two sets of variables are related to each other

and how the variables within each set contribute to this relation•

ship. It can be used to identify clusters of predictor and criteria

variables and.to identify the relative importance of each. For

example one might find that participation rates in particular outdoor

recreation activities could be predicted from a combination of

several different socioeconomic characteristics of differential

importance. This kind of result would be particularly useful in

explaining the relationship between recreational behavior and the

prevailing social and economic conditions. Government agencies

could take account of these results to forecast the probable shifts

in patterns of consumption of outdoor recreation activities accord-

:ing to expected frequency changes in socioeconomic characteristics. CHAPTER IV

OUTDOOR RECREATION: FACTORS WHICH AFFECT THE

AREAL DISTRIBUTION OF USERS

4.1 Overview

In this chapter I have discussed factors which affect the areal distribution of recreationists. I have examined (1) the kinds of activities which occur at various locations in the land• scape, and (2) the relative frequencies with which specific activities are pursued in the landscape. From information taken from the case study I have attempted to relate observed variations in the areal distribution of recreationists on Galiano Island with characteristics of the landscape. To this end I have constructed the hypothesis that the number of times any lot area on Galiano

Island was visited can be predicted from measurements of three characteristics of the lot area: accessibility, degree of develop• ment, and proximity to shoreline. Within this general hypothesis,

I have examined how effectively these three general characteristics can be used to predict the number of times various lot areas were visited for all activities and for each of three specific activities.

4.2 Recent Literature

Outdoor recreation involves the interaction between man and the environment. The nature of this interaction varies according 48 to the activity pursued. Deer hunters seek different environments than picknickers; camping enthusiasts may prefer landscapes which are unattractive to hikers. The range of landscape types which is suitable for various activities may be broad or narrow. However, it is apparent that recreationists locate in areas which are both attractive and amenable to the types of activities they wish to pursue.

The broad attributes of the land surface which are suited for specific activities are self-evident. For instance it is intuitively obvious that campers would not choose to locate in a swampy area whereas nature study groups could find a swampy area satisfying to their particular objectives. But the subtle differences in land• scape quality, as they relate to a particular recr,eation activity, are more difficult to define. The way in which we perceive these differences may provide insight into the actual nature of the differ• ences.

The reaction of recreationists to the environment has played a large part in defining recreational areas. Many areas considered to be "beautiful" have been set aside for public parks. However, little is known about the kinds of stimuli which elicit this response in the observer. The human perceptual environment consists of all external stimuli to which a person can respond. Then our five senses can all act as receptors.for stimuli produced by the environment.

Most researchers have considered vision to be the dominant.sense. The encounter or confrontation [with the environment] almost by definition involves perception, a perception that is essentially visual. Having as primates subordinated our other sensory modes, we must now admit that we move in a visual world and that our relationship to it--our ecology-^ is in part shaped by the nature and limitations of vision.

Sight is of crucial importance and probably influences our response to environments more directly and with greater salience than do our other senses.^3

Each landscape possesses a visual quality which evokes a

response in the observer. Current research on the perceptual charac•

teristics of landscapes assumes that human behavior can be explained

in terms of causes and conditions that are beyond the individual.

Then it should be possible to produce planned behavior patterns by manipulating the nature of the perceived object. But the "nature"

of the perceived object has undergone many interpretations.

One school of thought has concerned itself with spaces defined

by objects in the landscape as they are seen by, or affect, the 24

observer. Litton suggested that the landscape could.be described

by the following six factors: distance, observer position, form,

spatial definition, light, and sequence. Within each of these

factors, he established subcategories representative of specific

ranges in the total variation of each of the factors. The scheme

was applied to several landscapes, using different methods of 99 P. Shepard and D. McKinley, Editors * Preface to A. Port- mann, "The Seeing Eye" in The Subversive Science (New York: Hougton Mifflin Co., 1969), p. 115. 23 E. L. Shafer, "Perception of Natural Environments," Environment and Behavior, (June, 1969), pp. 71-82, p. 71. 24 R. B. Litton, Jr., Forest Landscape Descriptions and Inventories (California: U.S.D.A. Forest Service Research Paper PSW-49, 1968). notation. However, Litton did not test his classification scheme against the behavioral response of people to the landscape. Con• sequently, the validity of his ideas remains unknown.

25

Thiel explained the nature of the visual experience in terms similar to those used by Litton. Thiel stated that the

"experience of space results from the visual perception of light defined relationships between positions and qualities of surfaces, 26 screens and objects." Thiel described surfaces as two dimensional forms, objects as three dimensional forms, and screens as perforated surfaces or closely spaced objects. Unfortunately Thiel did not try to quantify these conceived relationships. Nor did he relate his notational scheme to behaviorial patterns and preferences.

Litton and Thiel are representative of many researchers in the field of aesthetic appreciation. They tried to describe the nature of the perceived object. But their analyses are mere opinion, erudite as they may appear, until actually tested on other human beings. Also, the terms which they have used to describe the land• scape are only marginally operational for empirical research. How does one measure the "light defined relationships between positions and qualities of surfaces, screens and objects?" Evidently research• ers must resort to more prosaic terminology before their opinions can be applied to empirical research on the perceived environment.

P. Thiel, "A Sequence-Experience Notation," Town Planning Review, XXXII (April, 1961), pp. 33-^52.

2^Ibid. , p. 53. Shafer correlated the number of visitor days spent in various campgrounds in the Adirondacks and four characteristics of each campground,

(1) square feet of developed beach

(2) square feet of water at the beach

(3) total number of campsites

(4) number of islands accessible by motorboat. . 2

The correlation analysis yielded an R of 0.96, an exceptionally high figure. The equation Shafer presented explains practically all of the variation in campground use. Shafer states that "the visual aspects of a few environmental variables can significantly explain how campers are attracted to Adirondack campgrounds." But it could be argued that Shafer's choice of variables reflect campground

capacity more than visual quality. In a crowded camping area like

the Adirondacks, campgrounds are usually filled to capacity. Shafer's

results may simply mean that the intensity of campground usage in

the Adirondacks is highly correlated with campground capacity. 28

A study done by Ungar contains the same conceptual error.

Ungar tried to devise an index of attraction for each Indiana State

park by using multiple regression analysis to relate visits to the

27 E .L. Shafer and R.;C. Thompson, "Models that Describe Use of Adirondack Campgrounds," Forest Science, 14 (1968), pp. 383-391. 28 A. Ungar, Traffic Attraction of Rural Outdoor Recreational Areas (U.S.A.: National Cooperative Highway Research Program Report 44, 1967). 52 facilities and natural characteristics of the parks. He found that the best single'predictor of the number of visitors to a park was the number of picnic tables present. It is hard to believe that picnic tables are the prime attraction of Indiana State Parks. In areas where the demand for camping exceeds or matches the supply of campsites, campgrounds will always be filled to capacity. Variables which reflect campsite capacity will correlate highly with the number of campers. It is a mistake to construe these variables as measurements of visual quality.

The number of stimuli a recreationist receives from the land• scape is large indeed. It is difficult to measure the influence that one isolated stimulus has on the individual. According to scientific procedure the influence of one variable can be tested only by changing its value while keeping all other variables constant.

Consequently, it is virtually impossible to test the behavior of experimental subjects by using the natural environment as a laboratory.

Means of laboratory simulation have been devised to overcome this difficulty. 29

Shafer etal. used 8 x 10-inch black and white photographs to test the reaction of people to various landscapes. Respondents were asked to rate landscapes in order of preference. Each photograph was given a preference score and related to measurements of different

29 E. L. Shafer et al., "Natural Landscape Preferences: A Predictive Model,"'J. of Leisure Research, 1 (1969), pp. 1-19. items in the photographed landscape. These included measurements of the areas in the photographs where detail of the vegetation could be seen, areas of non-vegetation, areas of water, etc. A regression analysis using preference scores as the dependent variable and measurements on the photograph as independent variables yielded an 2

R of 0.55. Shafer tested the predictive quality of his equation by exposing people to another series of landscape photos. He found that the equation did predict "quite accurately" how people ranked other photos. But people may have responded to the medium of presentation as well as the information content of the photographs. It is con• ceivable that a photograph of a particular landscape could elicit an aesthetic response that people would not experience if actually placed in the landscape. While difficult, the results obtained by landscape simulation must be correlated with landscape preferences exhibited in the field before the results can be fairly applied to the recreational planning process.

This brief review of recent research on the visual quality of landscapes as it affects outdoor recreationists indicates that the exploration of environmental perception is in its infancy. It also shows that a large part of the research is not relevant to the decisions recreation planners have to make. The results are either not operational or their meaning is ambiguous. 54

4.3 Factors Which Influence the Areal Distribution of Visitors to Galiano Island

Postulated relationships between the characteristics of any landscape and the response of recreationists to these characteristics are difficult to prove from behavioral data. The fact that recrea• tionists locate their activities in a particular section of the landscape does not mean that they are responding only to the visual characteristics of the site they have chosen. Other factors such as accessibility to the area evidently have some influence on the location of recreationists. The problem is to determine how much influence factors such.as accessibility have in explaining the areal distribution of recreationists. If this can be determined, the effect of factors such as accessibility can be held constant and direct measurements of the visual quality of the landscape can be tested against behavioral data. At this point-, the results obtained in field investigations using behavioral data could be compared with laboratory simulation techniques such as photographs. However, the initial step in using behavioral data to measure preferences for different landscape types must isolate the effects of factors.other than the direct visual quality of the landscape before more refined analyses can be made.

The questionnaire administered to visitors to Galiano Island was designed to show what lot areas people visited and what activities they pursued in each lot area. Information on 6 outdoor recreational activities was collected. However, I have selected only 3 for consideration. These were (1) camping, (2) going to the beach, and

(3) hiking (not on the beach). Horseback riding was excluded from analysis because only two respondents reported that they had partici• pated in this activity. Golfing was excluded because it depended on facilities rather than natural resources. Boating and fishing were excluded because these activities are not conducted on the land surface.

Each area could have been visited for more than one recreation activity. The following 4 maps show the distribution and intensity of recreational use of surveyed lot areas on Galiano Island for

(1) all visitors, irrespective of activity, (2) persons who went to the beach, and (3) persons who went hiking (not on the beach) and

(4) persons who went camping. I remind the reader that" one respondent could have engaged in any or all of these activities.

Therefore the total number of visits paid to all lot areas is larger than the number of respondents.

I have postulated a general set of factors which could in• fluence the distribution of people in the landscape regardless of the activity patterns chosen. They are (1) measures of accessibility,

(2) measures of development, and (3) measures of the perceived prox• imity to the shoreline.

(1) Accessibility:

Accessibility can be defined by a diverse number of measure• ments. Time, cost, convenience, and safety have all been used in

60 various forms. All four factors are interrelated; the relative importance of each Is difficult to assess. For the purpose of this analysis I have assumed that distance is the single best measure of all these factors. All of the visitors included in the subsample which I have used on this thesis arrived at Sturdies Bay, the main ferry terminal on Galiano Island. I have assumed that the time, cost, convenience, and safety of travel to any lot area on Galiano

Island is proportional to its distance from Sturdies Bay. However, there Is one discontinuity which may influence the accuracy , of this assumption. Only the eastern end of Galiano Island is served by a paved highway. The paved road gives way to a gravel road approximately

5 miles east of Sturdies Bay. The remaining 13 miles to the eastern end of the island were easily passible to ordinary vehicles in the summer of 1969, but the ride was not comfortable. However, owing to the short distances involved, I assumed that this discontinuity could be overlooked.

Under the general heading of accessibility, I have also in• cluded a consideration of the number of miles of road surface, both gravel and hard top, contained within each lot. The greater the length of road surface per lot area the more "exposure" the lot has to the visitor. Since most visitors travel along roads, there is a higher probability, all other factors being constant, that lot areas with more road surface will be visited than lot areas with little or no road surface. 61

2. Development

A measurement of the extent to which a lot is developed is important for two considerations: (1) there is a greater probability that visitors will be attracted to developed areas for reasons which, initially, are external to their interests in outdoor recreation activities, and (2) the extent of development could act as a deterrent to the pursuit of recreational activities in any lot area because the proportion of private property or land inaccessible to the recrea- tionist will increase as development increases.

The questionnaire indicated (see question 12) some people came to Galiano Island to visit friends who were property owners.

If so, I have assumed that these visitors would have a greater probability of pursuing recreational activities in the lot area that contained the property of a friend. Also, from personal conversations with visitors to Galiano Island, there was evidence that a certain number of people came with the intention of looking for property.

Most of the saleable property was located in the developed lot areas.

Then this particular group of people, because of an external interest, may also have been more prone to pursue recreational activities in developed areas. However, a conflicting possibility does exist; recreationists who have no external interest in lot areas may have found that development was in direct conflict with their recreational objectives. Then the more developed lot areas may have received proportionally fewer visits. I have not tried to separate the in• fluence of these two variables in the analysis. I mention these two conflicting possibilities only to alert the reader that the extent

of development of a lot area could influence the distribution of recreationists in opposite ways, depending on the initial purpose of their visit.

(3) Perceived Relationship to the Shoreline:

It seems intuitively apparent that the single most attractive feature of any small island is its shoreline. One might argue that

this particular measurement is relevant only to those specific activities which are conducted on the shoreline. However, the data suggests that most recreationists, regardless of the activity

they pursued tended to locate in areas which are near the shoreline,

or at least offer a view of the shoreline.

Specific measurements of accessibility, development and proximity to shoreline were made and related to the number of visits

paid to each lot area for the combined total of three recreation

activities. The statistical model used was regression analysis.

4.4 Regression Analysis

In discussing the results, I have assumed that the reader has

a basic knowledge of regression analysis. The technique.is common

and has been documented in most intermediate level statistics texts.

The sources which I found most useful were Blalock,"^ Cherukupalle'^

'H. M. Blalock, Social Statistics (New York: McGraw-Hill, 1960).

31 Nirmala devi Cherukupalle, Regression Analysis : Interpreta• tion of Computer Output, etc. (University of British Columbia: mimeographed handout, 1969). 32 and Bjerring. Regression analysis tests the hypothesis that variations in a dependent (criterion) variable are a linear function of variations in one or more independent (predictor) variables. The regression equation takes the general form,

y = a + b, x, + b. x„ + . . . . + b x . * • J 112 2 n n

where y is the dependent variable, x^, x2, . . • ., xn are the indepen• dent variables, b., b„, . . . ., b are the coefficients of the 1 2 n regression equation, and a is the point at which the hyperplane .... intercepts the y axis. The coefficients of the regression equation

(b's) are computed to minimize the sums of squares of the differ• ences between predicted value of y and the observed values of y.

«

Analysis

The objective of the analysis is to determine (1) whether accessibility, development and proximity to shoreline, as a set, can be used to predict the number of visits paid to each lot area for all recreation activities , and (2) the relative importance of each measurement for prediction. The variables used in the first multiple regression analysis are listed below.

32 J. H. Bjerring et al., UBC Trip (Triangular Regression Package) (University of British Columbia: Computing Centre, 1969). *Lower case letters, i.e., x, y, have been used throughout this chapter to describe the general case. Upper case letters are frequently used in texts. Dependent Variable:

1. The total number of visits paid to a surveyed lot area

over the duration of the study period. A total of 47

lots were visited (see Map No. 2, p. 56). However, lot

35 was excluded from the analysis because it is a public

park and received many more visits, proportionally, than

any private lots. Thus the number of observations (lots)

for the first analysis was 46.

Independent Variables:

2. Length of road surface (hard top and gravel) per lot

area in.hundreds of feet.

3. Distance of lot area from Sturdies Bay in thousands of

feet.

•;4. Number of buildings per lot area.

5. Length of road surface per lot area that is within 33 150 feet of the shoreline.

The correlation coefficients between the variables used in the analysis are presented in Table IV. These can be divided into

These measurements were taken from topographic maps of Galiano Island prepared by the Surveys and Mapping Branch of the Department of Lands, Forests and Water Resources of British Columbia. The scale of the maps was 1" = 1,320'. Cultural features were up• dated to June, 1969, on the M234 series of maps. I point out to the reader that not all lots contain pre• cisely the same area, nor is the configuration of their perimeters similar. However, I have ignored this factor in the analysis because it is not related to user perception. 65

TABLE IV

CORRELATION MATRIX: VARIABLES* USED IN THE REGRESSION ANALYSIS OF ALL VISITS (DEPENDENT VARIABLE)

Variable Number

1 2 3 4 5

1 1.0

2 0.4990 1.0

3 -0.0286 -0.2228 1.0 Variable 4 0.4409 0.7141 -0.3262 1.0 Number 5 0.5378 0.5206 0.0801 0.4146 1.0

* Variable Number Name

1 Visits (combined total).

2 Length of road surface per lot;

3 Distance of lot from Sturdies Bay.

4 Number of buildings per lot.

5 Length of road surface within 150 feet of

the shoreline. 66

two categories: the correlation between the dependent variable and

each Independent variable; the correlations among the independent

variables. . A brief discussion of correlation analysis may.help the

reader interpret the results.

Simple correlation analysis is always used in conjunction with regression analysis as an aid to interpreting the results of

the regression analysis. Simple correlation analysis describes

the strength of a relationship between 2 variables. The statistic

descriptive of the relationship is r, the Pearson product-moment',

correlation coefficient. The correlation coefficient can assume

any value between +1 and-l. A correlation coefficient of +1

indicates a perfect linear relationship with a positive slope,

i.e., for every unit increase in variable x there is a corresponding

unit increase in variable;y. Similarly, a correlation coefficient

of -1 describes a perfect linear relationship with a negative slope,

i.e., for every unit increase in x there is a corresponding unit

decrease in variable ;y. A value of 0 indicates that no linear

relationship between variables x and y exists. Values of r which

fall between +1 and -1 explain a successively smaller proportion of

the variation as they approach 0.

Table IV shows a relatively high positive correlation between

the dependent variable and the length of road surface per lot, the

number of cottages per lot and the length of road surface within

150 feet of the shoreline per lot. However, the dependent variable

shows only a weak negative correlation with the distance of the lot 67 from Sturdies Bay. These results suggest that 3 of the 4 independent variables are related to the number of visits paid to each lot area and they may, as a set, be used to account for the observed variations in the dependent variable. However, the independent variables themselves, may be interrelated. They may, instead of being measures of 3 independent parameters, be interrelated. This introduces the problem of collinearity.

If two or more independent variables are highly correlated, they are said to be collinear, i.e., they do not have independent and additive explanatory value and may in fact be different measure• ments of the same phenomenon. If the simple correlation coefficients between independent variables exceed the absolute value.of 0.7, collinearity does exist. In general, the problem of collinearity is not important if the regression equation is.to.be used for pre• dictive purposes. But if the researcher wishes to make causal inferences, he may not be able to separate out the explanatory effect of each independent variable in a collinear series.

The correlation coefficients between the independent variables in Table IV show that collinearity is not a serious problem in this set of data. With the exception of variables 2 and 4 (length of road surface per.lot area and the number of buildings per lot area) the correlation coefficients between the independent variables do not exceed ±0.7. Variables 2 and 4 both appear to be measurements of the extent of development of lot areas. 68

A multiple regression analysis using the same set of variables 2 2 yielded an R of 0.3628. The R value Is called the coefficient of multiple determination of the regression equation. This coefficient states how much of the variation in the dependent variable was accounted for by the independent variables, as a set. In this case

36% of the observed variation in the number of visits paid to lot areas was accounted for by variations in the 4 independent variables. 2

The significance of the value for R Is determined by the F- probability for the equation. This is merely a measurement of the probability that the observed results could have happened by chance.

In this analysis the F- probability for the entire equation was

0.0006, indicating that the observed results could have happened by sheer chance-less than 1 time in 1000. Thus the regression equation is significant at the 0.001 level of probability.

Table V contains the coefficients (b's) for the regression equation along with the F- ratio and associated probability for each. The coefficients are the relative weights given to each variable in the equation. The data I used are not standardized and the relative importance of the weights given to each variable is not readily apparent because of differences in units. However, an examination of the equation will clarify the explanatory value of each variable. I have substituted the observed values of each independent variable for lot 33 in the equation below to obtain the predicted value of y(y). 69

TABLE V

MULTIPLE REGRESSION ANALYSIS RESULTS FOR ALL VISITS (DEPENDENT VARIABLE)

Variable* Coefficient Standard Error F-Ratio F-Probability

2 0.0198 0.0161 1.5079 0.2241

3 0.0036 0.0135 0.0720 0.7795

4 0.0215 0.0299 0.5184 0.4820

5 0.1815 0.0756 5.7569 0.0198

Constant 1.8832 0.7311

* Variable Number Name

1 Visits (combined total).

2 Length of road surface per lot.

3 Distance of lot from Sturdies Bay.

• 4 Number of buildings per lot.

5 Length of road surface within 150 feet

of the shoreline. y = 1.8832 +0.0198 X2 +0.0036 X3 +0.0215 X4 +0.1815 X^

y = 1.8832 + 0.0198 (87) +0.0036 (216) +0.0215 (17) + 0.1815 (20)

y =1.8832 + 1.72 +0.78 +0.36+3.62

y =8.31

The observed value for y (lot area 33) was 13 visits. Variable 5

(the length of road surface per lot area that is within 150 feet of the shoreline) contributes most to the observed value of y, followed by variable 2 (the length of road surface, hard top and gravel, per lot area in hundreds of feet). The other two independent variables (3 and 4) appear to have little influence on the predicted value of y. The F- probabilities for the regression coefficients in this equation (Table V, p. 69) show that only variable 5 is significantly different from 0 at the 0.05 level of probability and that variable 2, although not significant, is closer to being sig• nificant than variables 3 and 4. Thus the predicted value of y is determined largely by the constant term in the equation (1.8832) and variable 5.

The hypothesis that the number of visits paid to any lot area for all activities can be predicted from measures of accessi• bility, development and proximity to shoreline is acceptable at the

0.001 level of significance. But the analysis has contributed little to the understanding of the factors which generally influence the distribution of recreationists on Galiano Island. The dependent 71 variable (the number of visits paid to lot areas) had a small range due to the small number of respondents. The mean value of the dependent variable was only 3.125 with a standard deviation ± 1.803.

Thus the value which y assumed fell between 0.322 and 5.928 in approximately 66% of the observed cases. The value of the constant alone (1.8832) would place the predicted value of y in this range.

The small sample size and the small variation in the dependent variable produced results which have little interpretive value.

The preceding analysis tested the hypothesis that visits to lot areas for all activities in combination can be predicted from measurements of accessibility, development and proximity to shoreline.

In the following section I have narrowed this hypothesis to test whether visits to lot areas for each of 3 specific activities, hiking, camping and going to the beach, can be predicted from the same independent variables. Three multiple regression analyses were performed using the same four independent variables and the follow• ing 3 dependent variables: (1) the number of times lot areas were visited for "going to the beach", (2) the number of times lot areas were visited for "camping", and (3) the number of times lot areas were visited for "hiking".

(1) "Going to the Beach"

The correlation coefficients (Table VI) between the dependent variable (visits paid to lot areas for the purpose of "going to the beach" (see Map No. 3, p. 57)) and the 4 independent variables are similar to those obtained in the previous analysis. The dependent 72

TABLE VI

CORRELATION MATRIX: VARIABLES* USED IN THE REGRESSION ANALYSIS OF'VISITS' TO THE BEACH (DEPENDENT VARIABLE)

1 2 3 4 5

1 1.0

2 0.5963 1.0

3 -0.0839 -0.1740 1.0

4 0.4984 0.7450 -0.3458 1.0

5 0.6244 0.5380 0.1824 0.3322 1.0

* Variable Number Name

1 Visits ("Going to the Beach").

2 Length of road surface per lot.

3 Distance of lot from Sturdies Bay.

4 Number of buildings per lot.

5 Length of road surface within 150 feet

of the shoreline. 73

variable is positively correlated with the length of road surface

per lot area, the number of buildings per lot area and the length

of road surface within 150 feet of the shoreline per lot area. The

distance of the lot from Sturdies Bay shows a weak negative corre•

lation with the dependent variable.

2

The R of the regression equation was 0.50. Therefore the

independent variables explained 50% of the observed variation in the number of visits paid to lot areas for "going to the beach." The overall F- probability of the regression equation was 0.0007; the results could have occurred by chance less than 1 time in 1,000.

The regression equation is significant at the 0.001 level of pro• bability even though only 26 observations were considered.

As in the previous analysis the regression equation has predictive value but little explanatory value. Variable 5 is the only regression coefficient (Table VII) that differs significantly from 0 at the 0.05 level of probability. Variable 5 (the length of road surface within 150 feet of the shoreline per lot area) in combination with the constant in the regression equation (2.1458) account for most of the observed variation in the number of visits paid to lot areas. The mean observed value of the dependent variable was 3.000 with a standard deviation of 1.918. Therefore, 66% of the observed values of y fell between 1.082 and 4.918. The constant value in the regression equation alone (2.1458) put the predicted value of y within this range. It is significant that of the 26 lot areas included in the analysis, 15 did not have any road surface 74

TABLE VII

MULTIPLE REGRESSION ANALYSIS RESULTS FOR VISITS TO THE BEACH (DEPENDENT VARIABLE)

Variable* Coefficient Standard Error F-Ratio F-Probability

2 0.0119 0.0146 0.6669 0.4283

3 -0.0054 0.0119 0.2096 0.6553

4 0.0145 0.0227 0.4103 0.5355

5 0.1319 0.0538 6.0155 0.0220

Constant 2.1458 0.6716

* Variable Number Name

1 Visits ("Going.to the Beach").

2 Length of road surface per lot.

3 Distance of lot from Sturdies Bay.

4 Number of buildings per lot.

5 Length of road surface within 150 feet

of the shoreline. 75 within 150 feet of the shoreline. For these 15 lot areas, the pre• dicted value of y was largely determined by the value of the constant and minor values added by variables 3 and 4.

This analysis shows only a slight divergence from the results obtained in the previous analysis where the combined number of visits for hiking, camping and going to the beach was treated as the depen• dent variable. One of the reasons for the similarity between the two results is that 42 of the total of 84 visits paid to 46 lot areas were for the purpose of "going to the beach". Thus 50% of the combined sample of visits paid to all lot areas was made for the single purpose of going to the beach.

(2) Camping

Only 14 areas (see Map No. 4, p. 58) with the exclusion of area 35, were visited for camping. The results of the analysis are presented in Tables VIII and IX. The dependent variable shows low correlation coefficients with all of the independent variables.

The F- probability for the regression equation is 0.59, indicating that the results are insignificant. None of the coefficients of determination is significant (Table IX).

(3) Hiking

The number of lot areas that were visited for hiking is 30

(see Map No. 5, p. 59). The results of the regression analysis are presented in Tables X and XI. Again, the dependent variable shows no significant correlations with any of the independent variables. TABLE VIII

CORRELATION MATRIX: VARIABLES* USED IN THE REGRESSION ANALYSIS OF VISITS FOR CAMPING (DEPENDENT VARIABLE)

Variable Number

1 1.0

2 -0.2645 •1.0 Variable 3 0.4840 -0.5574 1.0 Number 4 -0.2893 0.8802 -0.6865 1.0

5 -0.2537 0.8218 -0.4246 0.5657 1.0

* Variable Number Name

1 Visits (camping).

2 Length of road surface per lot.

3 Distance of lot from Sturdie's Bay.

4 Number of buildings per lot.

5 Length of road surface within 150 feet

of the shoreline. >

77

TABLE IX

MULTIPLE REGRESSION ANALYSIS RESULTS FOR VISITS FOR CAMPING (DEPENDENT VARIABLE)

Variable* Coefficient Standard Error F-Ratio F-Probability

2 -0.0002 0.0175 0.0002 0.9360

3 0.0176 0.0139 1.5989 0.2367

4 0.0036 0.0225 0.0263 0.8483

5 -0.0101 0.0696 0.0211 0.8586

Constant 0.7808 0.5529

* Variable Number Name

1 Visits (camping).

2 Length of road surface per lot.

3 Distance of lot from Sturdies Bay.

4 Number of buildings per lot.

5 Length of road surface within 150 feet

of the shoreline. 78

TABLE X

CORRELATION MATRIX: VARIABLES* USED IN THE REGRESSION ANALYSIS OF VISITS FOR HIKING (DEPENDENT VARIABLE)

1 2 3 4 5

1 1.0

2 -0.1393 1.0

3 -0.2506 -0.3161 1.0

4 0.1565 0.7045 -0.3573 1.0

5 0.0858 0.4974 0.0514 0.4262 1.0

* Variable Number Name

1 Visits (hiking).

2 Length of road surface per lot.

3 Distance of lot from Sturdies Bay.

4 Number of buildings per lot.

5 Length of road surface within 150 feet

of the shoreline. TABLE XI

MULTIPLE REGRESSION ANALYSIS RESULTS FOR VISITS FOR HIKING (DEPENDENT VARIABLE)

Variable* Coefficient Standard Error F-Ratio F-Probability

2 -0.0002 0.0091 0.0005 0.9298

3 -0.0085 0.0076 1.2709 0.2699

4 0.0022 0.0167 0.0180 0.8637

5 0.0144 0.0393 0.1348 0.7153

Constant 2.0748 0.4856

* Variable Number Name

1 Visits (hiking).

2 Length of road surface per lot.

3 Distance of lot from Sturdies Bay.

4 Number of buildings per lot.

5 Length of road surface within 150 feet

of the shoreline. 80

Only 7% of the observed variance in y was accounted for by the

independent variables. The F probability for the overall equation was 0.74, indicating that the results are highly insignificant.

4.5 Discussion of the Results

The measurements of accessibility, development and proximity

to shoreline which I proposed do not satisfactorily explain the distribution of recreationists on Galiano Island, either generally, or for specific activities. In two cases, (1) all visitors combined, and (2) beach goers only, these four general measurements did sig• nificantly account for the variation in the number of visits paid

to lot areas. The predictive quality of both significant equations was primarily attributable to variable 5, the length of road surface within 150 feet of the shoreline.

The analytic value of regression analysis can be extended by plotting residual values. These values are the differences between the observed values of y and the predicted values of y (y) obtained in the analysis. The plot of the residual values shows where the independent variables, as a set, either overestimated the observed value of y. The areal distribution of these residual values often provides insight into the nature of other variables which could account for some of the unexplained variance in the dependent variable.

Two plots of residual values are shown on Maps 6 and 7. Map number 6 shows the difference between the observed number of visits paid to lot areas for the combined total of 3 activities and the

83 number of visits predicted by the regression equation. Map number 7

shows the difference between the observed number of visits paid to

lot areas for the purpose of going to the beach and the number of visits predicted by the regression equation.

The distribution of residual values observed on both maps provides little additional information. In general, calculated values are lower than observed values for heavily visited areas and vice versa. This trend may merely reflect the fact that the regression equation is more accurate around the mean value of the dependent variable than it is for extreme values of y, i.e., the error assoc•

iated with the predicted values of y increases as the distance from

the mean observed value of y increases.

Several conclusions can be drawn from the preceding analyses.

First, the analyses suggest that the distance of the lot area from

Sturdies Bay does not affect the intensity of recreational use on various parts of Galiano Island. This is probably due to the fact

that the overall length of Galiano Island is only 18 miles. Distance

is not a deterrent to recreational use on such a small land area.

Secondly, the length of road surface contained within a lot

area also does not have an effect on the distribution of recreational use. I had postulated that the length of road surface per lot area determines how much of the lot area is exposed to the recreationist

and that this would affect the probability of visitation. I still believe that this postulate is valid in general sense; but the specific measurement that I used is not adequate. Instead, measurements of the "scenic quality" of the land surface visible from the road should be taken into account.

Thirdly, the measurement which I used to describe development

(the number of buildings per lot area) also seemed to have little influence on the distribution of recreationists. Earlier in this chapter I suggested that development could either act as an attractant or repellant to recreationists depending on their initial purpose in visiting a lot area. It is possible that both of these factors were operative and that, in combination, no net effect was observed. Also, a measure of the number of buildings per lot area does not account for their distribution in a lot area. In cases where all development was concentrated in one section of the lot area, it is quite possible that recreationists actually used the undeveloped section. Yet this possibility was not accounted for in the analyses. Measurements of the distribution and intensity of development in a lot area must be considered.

In summary, the four measurements which I used in the pre•

ceding analyses did not satisfactorily explain the observed variation in the number of visits paid to lot areas. The observed variation must be associated with another set of variables. I would expect

that these variables include the actual characteristics of the land•

scape itself. I have made some suggestions in the next chapter as

to how these could be measured. CHAPTER V

SUMMARY: DIRECTIONS FOR RESEARCH

5.1 Summary o

In this thesis I have made an examination of the factors which

influence the recreationists' response to the resource base. These

factors were divided into two categories, internal factors and ex•

ternal factors. Internal factors include all characteristics of the

individual including age, sex, previous experience, occupation,

income, etc. , which influence his response to outdoor environments.

External factors include.all stimuli from the landscape.to which

the perceiver can respond. Both definitions encompass a large possible number of measurements. Within each of the broad possible ranges of

internal and external factors, I selected a few for examination. The

selection was guided by the work of other researchers wherever

possible, but a large part of the choice had to be intuitive.

I assumed that the internal characteristics of an individual

determine his response to a given outdoor environment. Operationally,

internal characteristics were defined as a selected set of socio-eco• nomic and demographic characteristics; response was defined by the types

of activities that were pursued during the visit. I hypothesized that

the types and frequencies with which 6 specified outdoor recreation

activities were pursued by visitors to Galiano Island could be pre•

dicted from their socioeconomic and demographic characteristics.

Canonical correlation analysis was used to test the hypothesis. The analysis provided sets of linear combinations of predictor (socio• economic and demographic) variables which were maximally correlated with sets of criterion (activity) variables. None of the reported linear combinations were significant at the 0.05 level of probability

Therefore the hypothesis was rejected.

Secondly, I argued that the external characteristics of the landscape could be used to explain the number of times land areas were visited by recreationists for specified types of use. Usage was operationally defined as the frequency of visits to lot areas; external characteristics of the landscape were defined as 4 measure• ments representing the accessibility, development, and proximity to water of visited lot areas. I proposed that this set of external factors would explain.the distribution of all recreationists, regard• less of the particular activities which they engaged in. To this end I hypothesized that the number of visits paid to a lot area on

Galiano Island could be predicted from the accessibility, develop• ment, and proximity to water of that lot area. The statistical model used was multiple regression analysis. The predictive equation obtained was statistically significant at the 0.001 level of probabil ity but had little analytic value. Next, I examined by multiple regression analysis whether the number of visits paid to a lot area for each of 3 specific outdoor recreation activities (going to the beach, hiking, camping) could be predicted from the same set of external factors. Significant results were obtained for the number of visits paid to lots for "going to the beach". The predictive 87

equations for hiking and camping were not significant. Again, the

significant relationship had little analytic value.

Perhaps one of the most important phases of an exploratory

research program such as this is to glean, from the results, infor• mation which can be used either to rework hypotheses or improve data

collection. I am inclined to feel that the hypotheses are valid

regardless of the results obtained. But considerable refinement in

the program for data.collection is possible. I have outlined below

the improvements which should be incorporated into continuing research

on the Gulf Islands study.

5.2 Directions for Research

(a) - Hypothesis I

1. The sample size should be increased.

.2. Measurements of activity patterns should include not

only the types of activity pursued but also the amount

of time spent in each activity per visit. The question•

naire was not designed to reflect the time respondents

had spent in particular activities during their visit

to Galiano Island. The activities were similar enough

in type that most visitors, regardless of age, sex,

income, etc., could have been expected to pursue each.

Thus socioeconomic and demographic characteristics did

not differentiate between this qualitative measure of

activity patterns. But a quantitative measurement of the amount of time spent in each activity could well be related to the socioeconomic and demographic characteris tics which I have suggested.

Wherever possible, all measurements should be made on an interval scale, i.e., values can be ranked and the exact distance between them can be determined.

Because of the qualitative nature of the measurements obtained in the questionnaire, I was forced to include too many binary variables in the canonical correlation analysis. As a result, simple correlation analysis would have been meaningless. Regression and canonical correlation analysis can include binary variables if two conditions are satisfied: (1) observations are divisible into two mutually exclusive classes, and

(2) the effect of the class difference is to change the

Y intercept level ,of the equation without changing the slope coefficients. The first condition is easily met.

However, the second condition is much more difficult to fulfil; there Is no assurance that the slopes for two or more classes of data are the same. This assumption should be tested. However, in equations with a large number of variables, the computational difficulties in plotting and solving separate equations are large.

Consequently most researchers merely assume equal slopes 89

This assumption may be frequently incorrect and thereby

violate the mathematical assumptions inherent in

regression and canonical correlation analyses.

4. It may be useful to make measurements of some socio•

economic and demographic characteristics on a finer

scale. Age, particularly, is an example.

The questionnaire asked respondents to include members

of their group in one of 5 age classes up to 25 years

old but provided only two choices over 25 years old.

The coarse scale of measurement used may have obscured

subtle relationships.

(B) Hypothesis II.

1. The number of "observations must be increased.

The low number of respondents was the primary cause of

the artificially significant results observed in the

regression analysis. The number of visits per lot area

(dependent variable) had a very small range and variations

could be significantly explained by a constant term in

the regression equation.

2. The map on the questionnaire should contain more reference

points.

It is doubtful that respondents could always pinpoint

the lot areas they had visited. Some contour intervals, 90

minor roads and buildings should be drawn on the map.

3. The set of 4 general measurements which I used to describe

landscape attributes is only a first step toward the

research that is required.

Given a larger number of respondents, regression analysis

and particularly the examination of residual values

obtained could lead to a more accurate determination of

the landscape attributes.to which recreationists respond.

My efforts were limited because of the low number of

observations available for analysis.

The major task of research on environmental perception is to identify and quantify the elements in a landscape that are significantly related to public preferences for that landscape.

It is evident that all outdoor recreation activities do not bear the same relationship t° the landscape. Hunting, for instance, involves the direct harvest of some of the products of the environ• ment; the abundance of that "product" will play a large part in determining where hunters will locate. On the other hand, hiking has less defined requirements. Perhaps hikers appreciate differences in slope, respond to open vegetational cover, and prefer to avoid low wet areas. Or perhaps all three elements combine to form a pleasantly diverse environment attractive for hiking. Whichever is the case, the researcher, in the absence of supporting literature, will be forced to adopt an intuitive approach in identifying the landscape elements to which particular groups of recreationists respond.

The second problem is how to measure landscape elements.

Measurements must coincide with the way users perceive the resource.

It would be pointless to describe the exact area of a designated land surface If recreationists did not perceive this value. This, point brings up the important problem of deciding what particular measurements of landscape elements will produce the best correlations with user perception.

34 35 Litton and Shafer used measurements of the relative position and area occupied by elements in a visual field. However, this approach presents one important operational difficulty if applied in the field. Any land area contains an infinite number of vantage points; depending on the position of the observer, the distribution of elements in the visual field will vary from point to point. Also, land areas are more frequently viewed from some vantage points than others. It would be difficult to consider these facts and arrive at any single index descriptive of the visual quality of the entire area.

34 R. B. Litton, Jr., Forest Landscape Descriptions and Inventories (California: U.S.D.A. Forest Service Research Paper PSW-49, 1968. 35 E. L. Shafer et al., "Natural Landscape Preferences: A Predictive Model," J. of Leisure Research, 1 (1969), pp. 1-19. A possible alternative would be to measure the distribution of various landscape elements in an area. At least 3 measures of distribution can be made from topographic maps and.aerial photographs.

These are: (1) the area occupied by any landscape element (2). the range of values it can assume, and (3) a measure of the dispersion of that landscape element in the area. Data could be collected by a point sampling method. The most efficient sample design would be

36 the "systematic stratified unaligned" design proposed by Berry.

Theoretical and empirical analyses have shown that systematic stratified samples provide.more precise estimates of the mean than either random, stratified random, or systematic samples. Systematic stratified samples can be further subdivided into aligned or un• aligned samples. Systematic stratified aligned samples have a perfectly even set of points, i.e. , after locating one point at random, the remaining points are placed at regular intervals from it. In.contrast, the systematic stratified unaligned sample does not have an.even set of points; the points are located at random within strata according to a method developed by Berry. Thus, the possibility of bias due to periodicities in the data are elimin• ated. The theory and method of applying this type of sample design are outlined by Berry in the U.S. Department of Agriculture Hand• book Number 237.

B. J. L. Berry, Sampling, Coding and Storing Flood Plain Data (U.S.A.: U.S.D.A. Agricultural Handbook No. 237, 1962). An alternate suggestion for the measurement of types and diversity of landscape characteristics is that certain groups of animals could be used as indicators of the visual diversity of an area. Animal species are adapted to specific habitats; the numbers and distribution of animal species reflect the number and kind of habitats within any land area. Bird species in particular occupy habitats which are differentiable to the untrained observer. There• fore an inventory of the number of bird species in an area may reflect the visual diversity of the area. However, this method has limita• tions. Certain bird species are less tolerant to human traffic than others; in some well-used areas the habitat may exist but bird species occupancy may not reflect this fact.

The research which I have described in this thesis is only one component of the four point method proposed by Anderson (see

Chapter I). However, an understanding of the factors which define the recreational potential of the land surface is the basis for the entire planning method. Improved methods of data collection could help to define the internal characteristics of a population which determine its response to a recreational resource. From this information the patterns of participation in outdoor recreation activities of any population should be predictable.

Forecasting .the future socioeconomic and demographic character• istics of visitors to Galiano Island is a separate and detailed problem. It should be possible to simulate future patterns of visitation by taking into account expected changes in development, transportation, costs of travel, leisure time, etc. The most important consideration is that forecasts of future visitation to the Gulf Islands must be considered as a particular component in a system of recreational land which may involve all of British Columbia and parts of the northwestern United States. A change in one part of the system will affect all other components, i.e., major recreational development in the Howe Sound region could attract recreationists who would ordinarily have visited the Gulf Islands.

A simulation model may well be the only practical way in which to consider the problem of forecasting recreational traffic.

The second phase of the research contained in this thesis described a method of examining the landscape characteristics to which recreationists respond. By improved data collection, it should be possible to determine a continuum of landscape charac• teristics which have differential attractiveness from any particular recreation activity. With this information the landscape could be classified in terms of user response.

The particular research methodology which I have proposed in this thesis can be used to isolate the factors which determine recreational use at any particular point in time. It is applicable

to the planning process in a qualitive sense. To be useful in a quantitative sense, the information gained must be incorporated

into a dynamic model. More cottage development on Galiano Island

is inevitable. This development will change patterns of recreational use. The methodology which I have proposed will show how the attractiveness of any one lot area would be changed by development of that lot. But the problem is that the changes in desirability of any one lot may change patterns of visitation to all other lots.

Previously unattractive lot areas may suddenly become the most attractive recreational land on the island. Thus the determination of the factors which underlie patterns of recreational use is only the first step in designing a systems model which is useful, in a predictive sense, to the planning process. 96

Selected List of References

Anderon, K.:R. A User Resource Planning Method. National Advisory- Council on Regional Recreation Planning. Hidden Valley, Loomis, California, 1959.

Anderson, T. W. Ah Introduction to Multivariate Statistical Analysis. New York: John Wiley & Sons, 1958.

Berry, B. J. L. Sampling Coding and Storing Flood Plain Data. U.S!A.: United States Department of Agriculture, Farm Economics Division, Agricultural Handbook No. 237, 1962.

—, and D. F. Marble. Spatial Analysis. A Reader in Statistical Geography. New Jersey: Prentice-Hall, 1958.

Bishop,:D. W. "Some Multivariate Analysis Techniques and their Approaches to Recreation Research." National Conference on Recreation-Research. University Park, Pa., U.S.A., 1966, pp. 177-193.

Bjerring, J. H., et al. U.B.C. Trip (Triangular Regression Package). University of British Columbia: Computing Centre, 1969.

Bpyet, Wayne E., and George S. Tolley. "Recreation Project Based on Demand Analysis." J. of Farm Econ., 48 (1961), pp. 984-1001.

Burch, William R. Jr. "Two Concepts for Guiding Recreation Manage• ment Decisions." Journal of Forestry, 62 (1964), pp. 707-712.

Cherukupalle, Nirmala devi. "Multiple Regression Analysis—Inter• pretation of Computer Output, etc." Mimeographed handout, U.B.C, 1968.

Clark, S.B.K. "Landscape Survey and Analysis on a National Basis." Planning Outlook, New:Series, Vol. 4, (Spring 1968), pp. 15- 29.

Clawson, Marion. Methodsl of Measuring the Demand for and Value of Outdoor Recreation. U.S.A.: R.F.F. Reprint No. 10, Feb., 1959.

—. "Measuring Outcomes in Terms of Economic Implications for Society." National Conference oh Recreation Research, University Park, Pa., U.S!A., 1966, pp. 54-61.

-, and J. Knetsch. "Outdoor Recreation Research: Some Con• cepts and Suggested Areas of Study." Natural Resources Journal, Vol. 3, (Oct. 1963), pp. 250-275. 97

Cooley, W. and P. R. Lohnes. Multivariate Procedures for the :Behavioral Sciences. New York: John Wiley, 1962.

Cowan, Ian McTaggart. "Conservation and Man's Environment." Park Practice:Trends, Vol. 3 (1966), pp. 23-30.

Crampon, L.J. "The Gravitation Model: A Tool for Travel Market Analysis." : The Tourist Review, Vol. 20, (1965), pp. 110-116.

Currie, L. "Landscape-as System." Geog. R., LIV, 1 (1964), pp. 121-124.

Daiute, R. J. "Methods for Determination of Demand'. for Outdoor Recreation." Land Econ., 42 (1966), pp. 327-338.

Davis, Robert K. "The Value of Big Game Hunting in a Private Forest." Transactions of the Twenty-Ninth North American Wildlife and Natural Resources Conference, Washington, D.C., Wildlife Management Institute, 1964, pp. 393^403.

Dotzehko, A. D., N. T. Papanichos, and D. S. Romine. "Effect of Recreational Use on Soil and Moisture Conditions in Rocky Mountain National Park." J_. Soil Wat. "Conserv. , 22 (1967), pp. 196-197.

Ellis, J. B. "A Systems Model of Recreational Travel in Ontario: A Progress Report." Report RR 126, Department of Highways, Ontario, 1967.

. "A Systems Model for Recreational Travel in Ontario: Further Results." Report RR 148, Department of Highways, Ontario, 1969.

Ferriss, A. L. "Types of Recreation Surveys." National Conference oh Recreation Research. University Park, Pa., U.S.A., 1966, pp. 160-176.

Green, B.L., and H.A. Wadsworth. "Campers: What Affects Participa• tion and What do they Want?" Researcli Bull. 1^.823_, Purdue University Agriculture Experiment Station, Indiana, 1966.

Hamill, Louis. "Process of Making Good Decisions about the use of the Environment of Man." Natural Resources Journal, Vol. 8 (April 1968), pp. 279-301.

Helliwell, D. R. "Valuation of Wildlife Resources." Regional Studies, Vol. 3 (1969), pp. 41-47.

Hopkins, W. S. "Research and User Preferences." National Conference oh Recreation Research, University Park, Pa., U.S.A., 1966, pp. 81-85. 98

King, L. J. Statistical Analysis in Geography. New Jersey: Prentice-Hall, 1969.

Knetsch, Jack L. "Forest Recreation: A Case of Nonmarket Resource Use." J. of Forestry, Vol. 65 (Feb. 1967), pp. 102-105.

Krajina, V. J., ed. Ecology of Western North America. Vol. 1. University of British Columbia : Department of Biology and Botany, 1965.

-. Ecology of Western North America. Vol..2. University of British Columbia ^Department of Biology and Botany, 1969.

Litton, R. B. Jr. Forest Landscape Description and Inventories—A Basis for Land Planning and Design. Berkeley, Calif: Pacific SW Forest and Range Exp. Sta., 1968.

Lucas, Robert;C. "Wilderness Perception and Use : The Example of the Boundary Waters Canoe Area." Natural Resources Journal, Vol. 3 (Jan. 1964), pp. 394-411.

McCurdy, Dwight R. "A.Second Look at Camping Demand Predictions." J. of Forestry, Vol. 64 (Sept. 1966), p. 631.

: , and R. M. Mischon. "A Look at the Private Campground User." U.S. Forest Service Research Paper CS-18. Forest Expt. Sta., Columbus, Ohio, 1965.

Pearse, Peter H. "Water Based Recreation Demands." Forecasting the Demand for Water. Ottawa: Dept. of Energy, Mines and Resources, 1968, pp. 161-193.

Piatt, Robert B., and John F. Griffiths. Environmental Measurement and Interpretation. New York: Rheinhold, 1964.

Outdoor Recreation Resources Review Commission. Wilderness and Recreation—A Report oh Resources, Values, and Problems. Washington, D.C.: O.R.R.R.C. Study Report No. 3, 1962.

-. The Quality of Outdoor Recreation as Evidenced by User Satisfaction. Washington, D.C. : O.R.R.R.C. Report No. 5, 1962.

-. Participation in Outdoor Recreation: Factors Affecting Demand Among American Adults. Washington, D.C. O.R.R.R.C. Study Report No. 20., 1962.

-. Urban Growth and the Planning of Outdoor Recreation. Washington, D.C. V O.R.R.R.C. Study Report No. 22, 1962. 99

Reid, L.M. "Utilizing User Preferences in Predicting Outdoor Recreation Demand." National Conference on Recreation Research, University Park, Pa., U.S.A.,:(1966), pp. 86-93.

Richards, J. Howard. "Gross Aspects of Planning and Outdoor Recreation with Particular Reference to Saskatchewan." The Can; Geog., Vol. XI, 2 (1967), pp. 117-123.

Sessoms, H. Douglas. "New Bases for Recreation Planning." .J.A. LP. Vol. XXX, No. 1 (Feb. 1964), pp. 26-33.

Shafer, E.L. "Perception of Natural Environments." Environment Behavior, Vol. 1 (June, 1969), pp. 71-82.

-, et al. "Natural Landscape Preferences : A Predictive Model." J. of Leisure Research, Vol. 1 (1969), pp. 1-19.

, and R.C. Thompson. "Models that Describe Use of Adirondack Campgrounds." Forest Science,14 (1968), pp. 383-391.

Shepard, Paul. Man in the Landscape. New York: Alfred A. Knopf, 1967.

, and Daniel McKinley, etd. The Subversive Science. New York: Hougton Mifflin Co., 1969.

Sonnenfeld, J. "Equivalence and Distortion of the Perceptual Environ• ment." Env^ Vol. 1, (June 1969), pp. 83-99/

Taylor, G.D., and;W. T. Clark. "Proposed Methodology for an Inven• tory and Classification of Land for Recreational Use." For. Chron., Vol. 42 (1966), pp. 153-159.

Thiel, Philip. "A.Sequence-Experience Notation." Town Planning Review, Vol. XXXII (1961) , pp. 33-52.

Trice, A.H., and S.E. Wood. "Measurement of Recreation Benefits." Land Economics, Vol. 34, No. 3 (Aug. 1958), pp. 195-207.

Ullman,;E. L. and D. J. Volk. "An Operational Model for Predicting Reservoir Attendance and Benefits: Implication of a Location Approach to Water Recreation." Papers of the Michigan Academy of Science Arts and Letters, Vol. XLVII (1962), pp. 473-484.

Ungar, Andrew. Traffic Attraction of Rural Outdoor Recreational Areas. U.S.A.: National Cooperative Highway Research Program Report 44, 1967. 100

U.S. Department of the Interior, Bureau of Outdoor Recreation. A Program for Outdoor Recreation Research. Washington, D.C.: National'. Academy of Sciences, 1969.

Wolfe, Roy I. "Perspectives in Outdoor Recreation: A Bibliographical Survey," Geog. R., Vol. 54 (1964), pp. 203-38.

:—. "Parameters of Recreation Travel in Ontario." Proceedings of the 1965 Convention Canadian Good Roads Association Saskatoon,;Saskatchewan, Sept. 27-30, 1965. pp. 235-261. APPENDIX I

DEFINITIONS 102

Definitions

1. Landscape - an area of the earth's surface which supports

the growth of terrestrial organisms, both

plant and animal.

2. Natural Area - a landscape which has not been appreciably

altered by human activity.

3. Outdoor Recreation -r leisure time activities which people

pursue in natural areas.

4. Environment - the environment consists of all of the types

of stimuli to which an organism can respond.

5. Lot Area - an area defined by survey lines registered

in the Surveyor General's office in Victoria.

These are identified by either numbers or

letters as in map no. 1.

6. Variable - any attribute, trait or characteristic which

takes on two or more quantities, or qualities. APPENDIX II

QUESTIONNAIRE THE UNIVERSITY OF WILL YOU VISIT GALIANO ISLAND FIVE BRITISH COLUMBIA. Note-. If you have YEARS FROM TODAY ? "V-I^p .already received and completed this or the , COON BAY resident questionnaire, The Gulf Islands are a unique doorway .'-to, the please return this copy Paoi fie - recreation area; they -attract- more visitors each year. We hope that their The ques t i o.nna i re natural attractiveness will-be preserved - is ro be f i 1i ed cu t by and we think you do too. the head of the family or the head of the group. The School of Community and Regional planning of the tin iv ers i ty of ' 8r j t i sh Columb i a lias If you are travelling in chosen GaTTano Island to stinTy" how the a group choose one increasing pressure for outdoor recreation person who is repre• affects you - and how it is changing the sentative of the group natural character of the island. to complete the quest i onnaire. Your answers wi 1.1 be'confidential;.do not your name-. Please return the completed Date coup 1 eted : i gn SALISVA^ ques t i onna i r c to us as soon as possible. Day \__ ' HELP US TO HELP YOU! Month

1. Where is your perman• 4.. Looking at the list j Look ing at the map of ent home? of activities below, put jGaliano, you wi11 see Ci ty a check mark beside that it is divided into Prov. State those activities that lots, each of which has Country you and your group took ; an identifying nuriber. 2.(a) When did you land part in during this trip Beside the activities on Ga1i ano Is1 and ? to Ga1i ano 1 s1 and. you checked, write in the number(s) of the Day ___ ;squares that you did Month Going to the beach to each act.i vi ty in. (b) When did you 1esve walk, swim, explore, GaIi ano I s1 and ? gather shells- picnic etc Day Camping overnight Boating and fishing Horseback'rtdina 3. How did you travel Golf.. . ' ; to Galiano Island? Walking or hiking (not i ' Pr i vate B.oat_ on beach) " '4 ' Ferry Other (wr i te in) Plane

5. What was your major 6. Please draw a line . neans of travel on on the map along those ia1iano is 1 and ? >-<3ads that you travel ie Car • on to show which roads you took and how . far 8icycie vou went. Wa 1 k i nc, 7. In the table be low. wr i te i n'those act i vi.ti es your group wanted, to do, but didn't, and write in the reason why your group- didn't do them. Outdoor Activities Reason..

GEORGIA HiUS

SALAMANCA GALIANO ISLAND POINT

8. How many times have - .11.-'What kind of a trip .you visited Galiano i s th i s ? .STURDIES BAY Is land ? Major annual vacation.. ?. if you stayed one or Weekend or overnight _____ too re nights on Galiano. Day outing Island .where did 'you .-• Combined bus-iness stay? _. trip & -v&e&t i-on ___ Own cottage : . Comb i ned v i s i t to . f r i ends S- r e I a t i ves Mote I, Lodge . , ' and vacat ion „•-" 14 .' P) eas e- wr {'te in t he .13.. jf you own..property Campground". number of people .in your on Ga 1 i ano 1 s 1'and Fri end '"s • • . 12., Heve you • comp 1 eted A party who are' Check here .cottage- . ' • '.. (a). High school » v Ace Group Ha leg Female and locate It on trie"' Other.- ' ; (b) Unirvers i ty • , .; 0- 1 '. year map wi th an X. '• ••' (Wr i te in) What, is- your 'tota,J:. 2-7 5 . years 13-'. 16. Any comments you •f am i 1 y y e a r ly i n c. ©m e/ 6-10 years - have wouId be we 1 come; 10 . P 1 ease wr i te- i n your 11-15 . years before taxes'- ' / '/ please use the back of pecupation. • ' ; 16-25 years \ the 26-50 years Questionnaire..- Over SO.years THANK YOU