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Preparing and simulating recreational activity: Two approaches
Preparing and Simulating Recreational Activity: Two Approaches Paul R. Saunders Washington State University, USA Herman F. Senter Clemson University, USA
The issue of how best to predict or simulate recreation use and recreation demand has by no means been laid to rest. Two recent articles in Annals have dealt with this issue in different ways and at regional and national levels. This commentary deals first with the comments by Smith (below) on the gravity or network model of Saunders et al. (1981) and second with comments on the directional bias model of Smith and Brown (1981). Projecting recreation use of a particular area or participation in a particular activity can be challenging at best. Numerous agency efforts to address anticipated recreation demand (amount of participation that would occur if necessary recreation facilities were available) such as the National Park Service Mission 66 or the US. Forest Service Operation Outdoors are now history. Predictors of demand and participation rates recognize that while they may be called upon to make projections into the next century, the inputs to their models yield results which are inherently more reliable over a span of five to ten years or less. A Gravity Model The gravity or network model developed by Saunders et al. ( 198 1) was not intended solely to be a tool to forecast the future. It was a tool whose utility would be the simulation of the effects on participation of a proposed set of future circumstances, including projected shifts in population, participation levels, and recreation supply. The figures given in the Savannah River
This Department ts Intended to accommodate short rejoinders. commentaries, and rebuttals on Annals. especially feature articles. book nzviews. research notes and reports. Attempts should be such contributions immediately after each issue of Annals appears so that they are published In Issue. The Editor reserves the tight to evaluate materials for their applicablllty and usefulness whether to publish each lin ortelnal or edlted form1 in this iournal.
Annals Printed
of Tourism Research. In the USA All rtghts
Vol. 10. pp. 265.276. reserved.
1963 @ 1983 J. Jafa,?
the contents of made to submit the subsequent and till decide
0160.7363/83/ and Pergamon
3.00 + .oO Press Ltd
REJOINDERS
AND COMMENTARY
Basin application discussed in the article represent one possible set of future circumstances whose underlying assumptions are stated (p. 249). The discussion of demand on page 241 points out that, for a given activity, demand exists whether or not supply is available. A precise, mathematical definition of demand is given by the equation on page 246. This formula explicitly expresses demand for a particular activity arising from a population center as a function of population size, a per capita participation rate, and certain population characteristics expressed as-the degree of urbanization. The inclusion of time in the equation indicates that population size, participation, and urbanization may vary over time. Total demand is the sum of demands from all population centers. Thus, demand is not “all or nothing,” nor is it used ambiguously, it is in fact precisely defined. The concept of willingness to pay for participation in an activity is embodied in the model as willingness to travel, measured in travel time zones, as illustrated on page 248. Demand for an activity is divided or reduced according to willingness to travel various distances (times) to participate, with willingness to travel expressed as a percentage of the population that will travel the specified time in order to participate. Since for most activities these percentages decline with increasing travel times, the name “gravity model” is used, in this case a discrete (as opposed to continuous) model. For example, if primitive camping areas are abundant but located far from a given population center, some or all demand for primitive camping arising from that population center may be unmet (unsatisfied) because individuals are unwilling to travel (to pay the cost) sufficiently far to participate. Both participation rates and willingness to travel factors should, in practice, be established by sample surveys of residents throughout the study region. Careful interpretation of survey results can mitigate the potential for bias that may arise from a local abundance or absence of supply for a particular activity. Farticipation rates as well as population sizes and the inventory of facilities may vary over time, as the equation on page 246 suggests. Thus, in applications of the model to forecast future behavior, the user may, on the basis of past trends, vary participation rates and/or willingness to travel with time (e.g., project a 5% annual increase in tennis participation]. Or, to simulate the effects of increased gasoline prices, radii of travel time zones could be reduced over time. A Directional Bias Model Directional bias has been defined (Smith and Brown 1981) as “some force that facilitates or encourages travel in one direction more than in any other direction.” That argument assumes that for a given population or region there is a tendency for people to travel in a particular direction to seek their recreation activities. The travel flow from this point of origin has been reduced to a single vector which represents the overall or majority flow of a population from that center. While this concept has validity for describing tourist travel, it ignores several important factors essential to travel models.
266
1983 ANNALS
OF TOURISM
RESEARCH
REXOINDERS AND COMMENTARY
Philosophically it is unwise to argue that “reasons for this bias are not important to the definition or use of the index.” Knowing the causes of this bias allows one to determine if the desired bias is a mathematical anomaly or a logical result. Use of the directional index by the uninitiated could result in serious errors of measurement and application, particularly amongst government tourism planners. What are the causal factors of directional bias? Askham (1982) has developed a travel, recreation, and tourism model that identifies causal factors, three of which are discussed below. First, directional bias is a function of the ability to travel to recreation sites. An essential portion of this travel ability is travel time from the point of origin to the recreation site. Gravity or travel allocation models which use travel time (Saunders et al. 1981) have this factor built in. Travel time is not only affected by road. rail, and air networks, but also by the cost of travel (Cole and LaPage 1980: Cron 1980; Kaiser and Moeller 1980). Second, directional bias is a function of site attractiveness. Attractiveness is influenced by what one can do at the site, the synergistic complement of two or more activities (Landis 19791, facility capacity, and facility quality, to name a few. One would expect a directional bias toward more attractive sites. The directional bias index of tourism travel into or out of a province may also be affected by levels of attractiveness, e.g., city us. provincial us. national parks. Third, directional bias is a function of the activity. Backpacking in Rocky Mountain national and provincial parks and alpine skiing could result in the observed westward bias. Hunting and fishing for selected species or travel to cultural resource areas might produce a bias in other directions. The activity is also a function of the season, the socioeconomic status of the recreationists. and regional recreation patterns. In addition, certain activities have higher participation rates than others. These three factors of directional bias, travel time, site attractiveness, and the activity can interact in numerous ways to effect the outcome of a directional bias index. The Smith and Brown (1981) index uses only number of trips, population center values, and distance between population centers to estimate bias. Their iterative model may reflect travel time through distance between population centers, but site attractiveness and recreation activity are ignored. All of these factors were present in the Saunders et al. ( 1981) network model. Smith and Brown (1981) have developed an idea which warrants further research. However. the lack of important determinants of recreation travel permits only speculation on the causes of direction travel bias, thus, their inability to explain the travel patterns of Quebec and the Maritimes. An index or model is only as good as its ability to incorporate causal factors to explain the phenomenon it seeks to describe. 0 •I REFERENCES Askharn, L.R 1982 Assessing
the Impact of Policy Decisions
1983 ANNALS OF TOURISM RESEARCH
and Economic Changes on Travel,
267
REJOINDERS AND COMMENTARY
Recreation and Tourism: A Systems Theory. Northwest Scientific Association. 55th Annual Meeting. Walla Walla College. College Place,Washington. Cole, G.L. and W.F. IaPage 1980 Camping and RV Travel Trends. In Proceedings 1980 National Outdoor Recreation Trends Symposium. General Technical Report NE-57. Washington. DC: U.S. Forest Service. Cron, R.M. 1980 Analysis of 1979 Visitor Use at Oconee State Park. Masters Paper. Clemson University, South Carolina: Department of Recreation and Park Administration. Kaiser, H.R and G.H. Moeller 1980 Trend Indicators Needs for Effective Recreation Planning-A Statistical Blueprint for the 80s. In Proceedings 1980 National Outdoor Recreation Trends Symposium. General Technical Report NE-57. Washington, DC: U.S. Forest Service. Landis. J.D. 1979 Forecasting the Demand for Skifng in the Western U.S. In First Annual National Conference on Recreation Planning and Development. New York: American Society of Civil Engineers. Saunders, P.R, H.F. Senter, and J.P. Jarvts 1981 Forecasting Recreation Demands in the Upper Savannah River Basin. Annals of Tourism Research 8(2):236-256. Smith, S.L.J.and B. Brown 1981 DirectIonal Bias in Vacation Travel. Annals of Tourism Research 812): 257-270. Submitted 20 May 1982 Accepted 3 June 1982
Cycles and Capacity: A Contradiction in Terms? Geoftky wall Department of Geography University of Waterloo. Canada
Students of tourism have yet to develop a strong theoretical base for their studies. Such a base may be generated indigenously, from within the subject. or exogenously. by borrowing concepts from outside of the discipline and applying them in a new context. Although the notion of cycles has attracted attention in many disciplines, the tourist cycle can be regarded as an indigenous development. However, since there are only a limited number of empirical studies which employ tourist cycles and test the extent to which such cycles can be recognized, the concept and the ideas which it embodies are best regarded as a hypothesis until further testing is undertaken. Hovinen’s f 1982) paper, which bears great resemblance to a similar publication in the Cunczdian Geographer-( 198 1I, is one of the few tests of the applicability of the resort cycle concept and therein lies its major contribution. The concept of carrying capacity, which is also employed by Hovinen, has been developed in other disciplines and applied to tourism. Carrying 268
1983 ANNALS OF MURISM RESEARCH
The issue of how best to predict or simulate recreation use and recreation demand has by no means been laid to rest. Two recent articles in Annals have dealt with this issue in different ways and at regional and national levels. This commentary deals first with the comments by Smith (below) on the gravity or network model of Saunders et al. (1981) and second with comments on the directional bias model of Smith and Brown (1981). Projecting recreation use of a particular area or participation in a particular activity can be challenging at best. Numerous agency efforts to address anticipated recreation demand (amount of participation that would occur if necessary recreation facilities were available) such as the National Park Service Mission 66 or the US. Forest Service Operation Outdoors are now history. Predictors of demand and participation rates recognize that while they may be called upon to make projections into the next century, the inputs to their models yield results which are inherently more reliable over a span of five to ten years or less. A Gravity Model The gravity or network model developed by Saunders et al. ( 198 1) was not intended solely to be a tool to forecast the future. It was a tool whose utility would be the simulation of the effects on participation of a proposed set of future circumstances, including projected shifts in population, participation levels, and recreation supply. The figures given in the Savannah River
This Department ts Intended to accommodate short rejoinders. commentaries, and rebuttals on Annals. especially feature articles. book nzviews. research notes and reports. Attempts should be such contributions immediately after each issue of Annals appears so that they are published In Issue. The Editor reserves the tight to evaluate materials for their applicablllty and usefulness whether to publish each lin ortelnal or edlted form1 in this iournal.
Annals Printed
of Tourism Research. In the USA All rtghts
Vol. 10. pp. 265.276. reserved.
1963 @ 1983 J. Jafa,?
the contents of made to submit the subsequent and till decide
0160.7363/83/ and Pergamon
3.00 + .oO Press Ltd
REJOINDERS
AND COMMENTARY
Basin application discussed in the article represent one possible set of future circumstances whose underlying assumptions are stated (p. 249). The discussion of demand on page 241 points out that, for a given activity, demand exists whether or not supply is available. A precise, mathematical definition of demand is given by the equation on page 246. This formula explicitly expresses demand for a particular activity arising from a population center as a function of population size, a per capita participation rate, and certain population characteristics expressed as-the degree of urbanization. The inclusion of time in the equation indicates that population size, participation, and urbanization may vary over time. Total demand is the sum of demands from all population centers. Thus, demand is not “all or nothing,” nor is it used ambiguously, it is in fact precisely defined. The concept of willingness to pay for participation in an activity is embodied in the model as willingness to travel, measured in travel time zones, as illustrated on page 248. Demand for an activity is divided or reduced according to willingness to travel various distances (times) to participate, with willingness to travel expressed as a percentage of the population that will travel the specified time in order to participate. Since for most activities these percentages decline with increasing travel times, the name “gravity model” is used, in this case a discrete (as opposed to continuous) model. For example, if primitive camping areas are abundant but located far from a given population center, some or all demand for primitive camping arising from that population center may be unmet (unsatisfied) because individuals are unwilling to travel (to pay the cost) sufficiently far to participate. Both participation rates and willingness to travel factors should, in practice, be established by sample surveys of residents throughout the study region. Careful interpretation of survey results can mitigate the potential for bias that may arise from a local abundance or absence of supply for a particular activity. Farticipation rates as well as population sizes and the inventory of facilities may vary over time, as the equation on page 246 suggests. Thus, in applications of the model to forecast future behavior, the user may, on the basis of past trends, vary participation rates and/or willingness to travel with time (e.g., project a 5% annual increase in tennis participation]. Or, to simulate the effects of increased gasoline prices, radii of travel time zones could be reduced over time. A Directional Bias Model Directional bias has been defined (Smith and Brown 1981) as “some force that facilitates or encourages travel in one direction more than in any other direction.” That argument assumes that for a given population or region there is a tendency for people to travel in a particular direction to seek their recreation activities. The travel flow from this point of origin has been reduced to a single vector which represents the overall or majority flow of a population from that center. While this concept has validity for describing tourist travel, it ignores several important factors essential to travel models.
266
1983 ANNALS
OF TOURISM
RESEARCH
REXOINDERS AND COMMENTARY
Philosophically it is unwise to argue that “reasons for this bias are not important to the definition or use of the index.” Knowing the causes of this bias allows one to determine if the desired bias is a mathematical anomaly or a logical result. Use of the directional index by the uninitiated could result in serious errors of measurement and application, particularly amongst government tourism planners. What are the causal factors of directional bias? Askham (1982) has developed a travel, recreation, and tourism model that identifies causal factors, three of which are discussed below. First, directional bias is a function of the ability to travel to recreation sites. An essential portion of this travel ability is travel time from the point of origin to the recreation site. Gravity or travel allocation models which use travel time (Saunders et al. 1981) have this factor built in. Travel time is not only affected by road. rail, and air networks, but also by the cost of travel (Cole and LaPage 1980: Cron 1980; Kaiser and Moeller 1980). Second, directional bias is a function of site attractiveness. Attractiveness is influenced by what one can do at the site, the synergistic complement of two or more activities (Landis 19791, facility capacity, and facility quality, to name a few. One would expect a directional bias toward more attractive sites. The directional bias index of tourism travel into or out of a province may also be affected by levels of attractiveness, e.g., city us. provincial us. national parks. Third, directional bias is a function of the activity. Backpacking in Rocky Mountain national and provincial parks and alpine skiing could result in the observed westward bias. Hunting and fishing for selected species or travel to cultural resource areas might produce a bias in other directions. The activity is also a function of the season, the socioeconomic status of the recreationists. and regional recreation patterns. In addition, certain activities have higher participation rates than others. These three factors of directional bias, travel time, site attractiveness, and the activity can interact in numerous ways to effect the outcome of a directional bias index. The Smith and Brown (1981) index uses only number of trips, population center values, and distance between population centers to estimate bias. Their iterative model may reflect travel time through distance between population centers, but site attractiveness and recreation activity are ignored. All of these factors were present in the Saunders et al. ( 1981) network model. Smith and Brown (1981) have developed an idea which warrants further research. However. the lack of important determinants of recreation travel permits only speculation on the causes of direction travel bias, thus, their inability to explain the travel patterns of Quebec and the Maritimes. An index or model is only as good as its ability to incorporate causal factors to explain the phenomenon it seeks to describe. 0 •I REFERENCES Askharn, L.R 1982 Assessing
the Impact of Policy Decisions
1983 ANNALS OF TOURISM RESEARCH
and Economic Changes on Travel,
267
REJOINDERS AND COMMENTARY
Recreation and Tourism: A Systems Theory. Northwest Scientific Association. 55th Annual Meeting. Walla Walla College. College Place,Washington. Cole, G.L. and W.F. IaPage 1980 Camping and RV Travel Trends. In Proceedings 1980 National Outdoor Recreation Trends Symposium. General Technical Report NE-57. Washington. DC: U.S. Forest Service. Cron, R.M. 1980 Analysis of 1979 Visitor Use at Oconee State Park. Masters Paper. Clemson University, South Carolina: Department of Recreation and Park Administration. Kaiser, H.R and G.H. Moeller 1980 Trend Indicators Needs for Effective Recreation Planning-A Statistical Blueprint for the 80s. In Proceedings 1980 National Outdoor Recreation Trends Symposium. General Technical Report NE-57. Washington, DC: U.S. Forest Service. Landis. J.D. 1979 Forecasting the Demand for Skifng in the Western U.S. In First Annual National Conference on Recreation Planning and Development. New York: American Society of Civil Engineers. Saunders, P.R, H.F. Senter, and J.P. Jarvts 1981 Forecasting Recreation Demands in the Upper Savannah River Basin. Annals of Tourism Research 8(2):236-256. Smith, S.L.J.and B. Brown 1981 DirectIonal Bias in Vacation Travel. Annals of Tourism Research 812): 257-270. Submitted 20 May 1982 Accepted 3 June 1982
Cycles and Capacity: A Contradiction in Terms? Geoftky wall Department of Geography University of Waterloo. Canada
Students of tourism have yet to develop a strong theoretical base for their studies. Such a base may be generated indigenously, from within the subject. or exogenously. by borrowing concepts from outside of the discipline and applying them in a new context. Although the notion of cycles has attracted attention in many disciplines, the tourist cycle can be regarded as an indigenous development. However, since there are only a limited number of empirical studies which employ tourist cycles and test the extent to which such cycles can be recognized, the concept and the ideas which it embodies are best regarded as a hypothesis until further testing is undertaken. Hovinen’s f 1982) paper, which bears great resemblance to a similar publication in the Cunczdian Geographer-( 198 1I, is one of the few tests of the applicability of the resort cycle concept and therein lies its major contribution. The concept of carrying capacity, which is also employed by Hovinen, has been developed in other disciplines and applied to tourism. Carrying 268
1983 ANNALS OF MURISM RESEARCH