Citation: Scott, D. (1997). Exploring Time Patterns in People's Use of a Metropolitan Park District.  Leisure Sciences, 19(3):159-174.

Address all correspondence to: David Scott, Department of Recreation, Park and Tourism Sciences, Texas A&M University, 312 Francis Hall, College Station, TX 77843-2261.

Exploring Time Patterns in People's Use of a Metropolitan Park District

David Scott

Texas A&M University

 

The purpose of this paper is to explore how leisure behavior is organized in terms of temporal frames of reference. We postulate that leisure occurs within the context of predictable timetables that are shaped by the general timetables of the social system, cyclical events, and the synchronization of individual time schedules. Results from a study of visitors to a metropolitan park district indicated that there were significant associations between the characteristics of visitors and when they visited, and what activities people engaged in was associated with the time of day, day of the week, and season of the year when they visited.

Keywords: time, temporality, leisure timetables, urban parks, park visitation

Many sociologists and anthropologists have noted that all societies and groups of people are structured by their conception of time. According to Maines (1987), "Time drives society; it is a basic mechanism through which social acts, organizations, institutions, cultures, and social structures exist and operate" (p. 303). With the exception of time diary studies, there are only a few studies that have examined the extent to which leisure behavior is related to different dimensions of time, including time of day, day of the week, and season of the year. A goal of this paper is to reintroduce the issue of temporality into the leisure literature. Using data from a study of users of a metropolitan park district, we examined the relationship between different time dimensions and the social characteristics of park users, and the relationship between these time dimensions and rates of activity participation.

Literature Review

The noted anthropologist, Edward T. Hall (1983), stated that time is a primary organizer for all activities . . . [and] nothing occurs except in some kind of time frame" (p. 3). The development of common time systems is necessary to synchronize and coordinate activity. This point was made by Sorokin and Merton (1937) nearly sixty years ago: "All social events which are periodical; which demand at a certain time, the presence of a number of individuals . . . necessitate some common means of time designations which will be mutually understood by those concerned" (p. 626). Although time has a key influence in shaping all cultures, its importance may be magnified in the industrial world where it is often treated as a commodity and segmented into distinct patterns (de Grazia, 1962).

As a point of departure, we posit that leisure behavior is organized in terms of temporal frames of reference. Moreover, we argue that there exist general leisure timetables in society that shape the distribution of free time, when people participate in specific leisure activities, and when people seek different kinds of recreational outcomes or benefits. This premise is both a restatement and an elaboration of a proposition made by the sociologist, Max Kaplan (1975). He noted that specific times periods, such as seasons of the year, days of the week, and hours of the day, are often associated with distinctive leisure activities. He provided the following example to illustrate his point: "There is the old adage: he who drinks at 8 p.m. is a gentleman, he who drinks at 8 a.m. is an alcoholic; similarly, there are acceptable times for going to the theater or making love" (p. 53). Unfortunately, Kaplan does not elaborate on this proposition and there are few studies that have systematically investigated these linkages.

In the paragraphs that follow, we assert that the development of leisure timetables are influenced by three factors: (a) the timetables of a society's institutions, (b) cyclical events, and (c) the synchronization of individual time schedules (Bull, 1978). Although there is overlap among these different factors, they provide a useful framework for understanding how leisure timetables develop.

The Impact of Institutions and the Primacy of Industrial Timetables

Institutions in society--including places of work, school, the family, and churches--all have timetables to which people must orient themselves. It follows that leisure behavior tends to be tied to the general timetables of the social system (Kelly & Godbey, 1992; Seeley, Sim, & Looley, 1956). Employment schedules, school calendars, and family responsibilities influence when people are able to engage in leisure activities and take vacations (Lundberg, Komarovsky, & McInerny, 1934; Kelly, 1983; Roberts, 1970).

Work in particular dominates the organization of time in modern society. For most of history, time was viewed from a cyclical, natural, and mostly biological perspective. Only over the last two to three hundred years have personal calendars, alarm clocks, and watches been used to divide time into increasingly precise and smaller amounts (Goodale, 1991). This emphasis on precision in time keeping stems largely from a shift from an agricultural society to an industrial one, as it became necessary to ensure employee regularity, efficiency, and productivity levels (Thompson, 1967).

A major consequence of this movement was a growing segmentation between work and non-work. As noted by Cross (1990), "industrialization drove play from work and eliminated the seasonal ebbs in the flow of work so characteristic of artisan and agricultural life" (p. 73). As work became more regularized, so did free (leisure) time. The movement to regularized free time occurred over many years and reflected a growing consciousness on the part of workers to recapture time which they could call their own. As noted by Thompson (1967), workers adapted themselves to new industrial time keeping systems but also sought to control how much time they would give over to employers. Thus, the segmentation of work from non-work ultimately resulted in what Cross (1990) described as a repackaging of leisure, "including the typically modern notions of free evenings, the weekend, paid summer vacations, as well as a lengthy childhood and retirement (p. 73)."

Results from time diary and other studies confirm that the availability and distribution of free time in people’s lives tends to conform to industrial timetables. These studies have shown that people have less free time on weekdays than they do on weekends (Robinson, 1989); that free time activities on weekdays are generally pursued during the evening hours and at home (Csikszentmihalyi & Larson, 1984); and that the weekend has become the dominant time when leisure activities outside the home are pursued (Rybczynski, 1991).

Peak usage of public parks and recreation areas also tends to be regulated by industrial timetables. Two studies show that peak visitation of city parks and plazas coincides with workers’ lunch breaks. In an observational study of city parks and plazas, Whyte (1980) found that 80% of all visits occurred between noon and 2:00 p.m. Similar results were reported by More (1985), who found that people’s use of parks in Boston and Hartford peaked around lunch and early afternoon, and was greater on weekdays than on Saturdays. In contrast, rates of visitation to suburban parks and dispersed recreation areas has been found to be far greater on weekends than on weekdays (Dwyer, 1988; Gobster, 1988; Manning & Comier, 1980).

Surprisingly, there has been little research that has focused on the relationship between industrial timetables to rates of participation in specific leisure activities. One exception is a time diary study reported by Spring (1993). In that study, participation rates varied markedly by day of the week. Eating out, for instance, tended to occur primarily on Fridays, Saturdays, and Sundays. The other is a study reported by Mannell and Zuzanek (1991) who found that interest and constraints to exercise varied by both time of day and day of the week. Findings from these studies probably reflect a tendency on the part of the individual to adjust his or her time schedule in accordance with the time requirements of the leisure activity. Thus, what leisure activities a person chooses to participate in at a given time is both a function of the expected duration of the activity and the amount free time at his or her disposal. As noted by Bammel and Bammel, "activities which require either a large time block (weekend camping trip), [or] a special time (daylight hours) . . . are participated in less than activities which can be done in short periods at any time of the day or year" (p. 93).

The Cyclical Nature of Leisure

Leisure is also organized in terms of cyclical timetables, including holidays, annual celebrations, and changes in the seasons. Historians have generally noted that cyclical events were of central importance in the lives of people in agricultural societies. Cross (1990), for example, stated that festivals were integral to how people in the premodern world marked time. He stated that they lived "in remembrance of one festival and in expectation of the next" (p. 22). Yet cyclical events survive today and tend to be powerful forces in the organization of leisure. Annual holidays and celebrations (e.g., Thanksgiving, the Super Bowl, and birthdays) all provide contexts for recurrent activity in the lives of celebrants.

Changes in the seasons are likely to influence leisure in a variety of ways. In temperate zones, winter months provide opportunities for skiing, skating and sledding; summer months, in contrast, provide opportunities for boating, swimming outside, and a host of other outdoor recreation activities. Seasonal changes also probably account for the timing of sport seasons. Outside sports, such as baseball and football, have traditionally been played during warm weather months; inside sports, such as basketball and wrestling, are typically played during cold weather months. Wildlife watching tends to be influenced by changes in the seasons. Birdwatching, for example, is often at its best during spring and fall migrations.

Again, there are only a few studies that have systematically investigated the linkages between season and recreation behavior. A few studies have documented that peak usage of recreation areas in temperate zones occurs during warm weather months (Dwyer, 1988; Manning & Cormier, 1980). More recently, researchers have used season as a variable in the segmenting of tourism markets. Spotts and Daniel (1993) examined differences between fall and summer visitors to upper Michigan parks and found that fall visitors tended to be older, spend less money, use fewer information sources, stay for a shorter duration, and have a smaller party size than summer visitors. In a study of visitors to the Texas coast, Backman (1994) found that summer visitors were older, had higher incomes, and were more family focused than spring visitors. Seasonal differences in benefits sought were also found among tourists to Massachusetts (Calantone and Johar, 1984), and Hilton Head Island in South Carolina (Bonn, Furr, and Uysal, 1992). These studies provide some evidence that recreation behavior is influenced by seasonal changes.

The Synchronization of Time Schedules

Leisure timetables are also likely to develop as people seek to synchronize individual timetables (Bull, 1978). Any leisure activity involving two or more people requires some coordination of individual time schedules. This can be a demanding task given the multiple demands on people's time. Synchronizing individual time schedules is made less problematic by developing a "regular" calendar of activity. Most serious and social bridge clubs, for example, play at the same time each week or month. Regularly meeting clubs allow "members to know ahead of time when and where a group meeting is to be held, and adjustments in schedules can be made accordingly" (Scott, 1991, p. 332).

Individuals may also adjust the timing of their recreation involvement to avoid other people. Becker, Niemann, and Gates (1979) reported that river users and riparian landowners purposefully avoided using and visiting rivers during periods of high use. In a study of users of Chicago parks, Hutchison (1994) found that elderly people visited parks in the morning, and then left when other age groups appeared. According to Hutchison, this succession of users was partly based on racial considerations as the older adults were often of a different racial or ethnic background from other population groups in the area.

Purpose and Rational for the Study

Thus far it has been argued that there exists predictable leisure timetables in society and that these are shaped by employment schedules and other institutions, cyclical events, and the synchronization of individual timetables. A handful of studies were cited that supported these assertions. Time diary studies, in particular, have demonstrated that the distribution and allocation of free time tends to conform to industrial timetables. A few studies have also documented that peak usage of recreation areas occurs at predictable times. To date, however, there is very little research that has examined whether time dimensions (including time of day, day of the week, and season of the year) are associated with rates of participation in specific activities. There is also little research that has focused on whether distinct population groups tend to visit recreation areas at particular times.

Therefore, the purpose of this study is to explore the relationship between time and actual patterns of use in a metropolitan park district. Most existing studies of urban parks have focused almost exclusively on the relationship between time to peak usage (e.g., Dwyer, 1988; Whyte, 1980). One exception is More's (1985) study of park users in Boston and Hartford. More reported that rates of activity participation varied greatly across different times of day. He also found that females’ use of the parks was lowest in the morning and highest around lunch time. Another exception is Hutchison's (1994) study of Chicago parks. Here, findings pertaining to time were limited to a reporting of what time of day men and women and people of different age groups visited. As noted, a disproportionate fraction of morning visitors were older.

Given the general findings reported above, it is likely that people's use of a metropolitan park district is organized in terms of time. Guiding this study were the following hypotheses:

(1) Population groups will vary in terms of when they visit metropolitan parks.

(2) There will be differences in activity rates among park visitors by time of day, day of the week, and season of the year.

(3) The relationships between time of day and rates of activity participation will be different for weekday visitors and weekend visitors.

The first two of these hypothesis are straightforward and need no elaboration. The third hypothesis deserves special comment. Here we expect there to be less variation in participation rates on weekends than on weekdays. The main reason for this is that people generally have more leisure time on weekends than they do on weekdays. The slackening of time and role constraints on weekends should allow people a greater degree of freedom to participate in leisure activities any time they want.

This study will make at least three contributions. First, and foremost, this study will further our understanding of how leisure behavior is organized in terms of temporal frames of reference. A related contribution is that this study will help us understand the temporal dynamics of people's use of metropolitan parks. Thus, this study will extend findings reported from studies of urban park visitors (Dwyer, 1988; Hutchison, 1994; More, 1985). Finally, this study may provide information that will be useful in the marketing and planning of park services. The segmentation of users of recreation areas may be facilitated by understanding the temporal dimensions of recreation behavior (Backman, 1994).

Study Area and Procedures

Data for this paper came from an in-park survey conducted of visitors to Cleveland Metroparks, a metropolitan park district in and around Cleveland, Ohio. The park district consists of over 19,000 acres of land in 12 different reservations (parks). The parks range markedly in size. The smallest park is Huntington Reservation which is 103 acres in size. The largest park is Brecksville Reservation which is 3,090 acres. Central to the mission of Cleveland Metroparks is conservation of open spaces. Thus, compared to many city development throughout the park district is limited. Facilities and features operated by the park district include hiking, bridle, and paved bicycling trails; nature centers; golf courses; swimming beaches; picnic areas and facilities; play fields; wildlife sanctuaries; and boating and fishing areas. Tennis courts are absent in all the parks and only a few parks have basketball courts. Day use characterizes people’s use of park district facilities and areas.

The parks are located almost entirely within the suburbs of Cleveland. In fact, only one park has boundaries that are located within the city of Cleveland. All but three of the parks are located in cities and/or neighborhoods in which Anglos predominate. The three other parks are located in residential areas that are racially diverse. None of the parks are located in communities in which minorities predominate.

The survey was conducted in all 12 parks during one week periods during the spring, summer, and fall of 1991. Visitors at each park were surveyed on-site on both weekdays and weekend days during each season, and during the morning (7:00 A.M.- 11:00 A.M.), afternoon (11:00 A.M. - 3:00 P.M.), and evening (3:00 P.M.-7:00 P.M.). The University of Akron’s Survey Research Center conducted the study in behalf of Cleveland Metroparks. Trained interviewers made contact with visitors at different locations in each park. For purposes of sampling, interviewers were instructed to randomly select among groups (an individual visiting alone was treated as a group) and among individuals within groups. Interviewers were given the following instructions to ensure that a random selection of visitors were selected for the study:

You will find a YELLOW sheet taped to the front of your interviewing session envelope. This sheet will contain instructions for selecting groups to sample. These instructions are in the form of two numbers, where the first number is the starting group and the second number is the "skip" interval. For example, if the sampling numbers were 2-1, you would start interviewing with the second group to arrive and then interview every group thereafter. For sampling numbers of 4-3, you would interview starting with the fourth group to arrive and conduct an interview with every third group thereafter. If there are two or more people in the group, go to the random number table, locate the column with the correct number for the group, read down the column until you reach a number to be used and interview that person. Each time you use the random number table, scratch it off the table. For counting purposes within groups, consider the person on the far left of the group as 1, work to the right, the next person is 2, and so on, for all individuals in the group who appear to satisfy our age criterion of being at least 14 years old. If people in a group arrive on foot or on bicycles, consider the closest person as number 1, the next closest number 2 and so on.

Interviews were conducted in parks in approximate proportion to the rate of visitation. Altogether, 4960 people were interviewed on-site about their use of park district facilities, and their satisfaction with different features of the park district. Demographic data were also collected from each respondent.

The three time dimensions in this study included time of day (morning, afternoon, and evening), day of the week (weekdays and weekends), and season of the year (spring, summer, and fall). Visitor characteristics used to answer the first hypothesis included sex, age (measured in years), race (African-Americans versus Anglos)^1, and employment status (individuals employed outside the home either full-time or part-time versus individuals not employed outside the home). Age was recoded into the following four categories: 1=24 years or less, 2=25 to 44 years, 3=45 to 65 years, and 4=65 years and older.

Ten park activities were used to answer the second research question. These included relaxation, walking or hiking, picnicking, walking a dog, observing nature, fishing, swimming, running or jogging, visiting a nature center, and bicycling. Respondents were shown a card with 24 activities and asked simply to name those they planned to participate in while visiting. Hence, response categories for each activity was dichotomous (did not participate or did participate). The ten activities chosen for analysis were among the most frequently mentioned activities.

The sample was reduced to 4437 individuals by deleting all cases in which data were not available for one or more of the study variables. Although this reduced the sample, it also meant that all analyses were based on the same cases, so that variation from one stage of the analysis did not reflect changing sample composition.

For each of the three hypotheses, the time variables were cross tabulated with the other variables of interests. Chi-square tests were used to determine the significance of the relationships. It is important to note that multivariate statistics (including discriminant analysis and logistic regression) were also used to examine these relationships. The findings from these tests were almost identical to those observed using the more simple bivariate tests. For purposes of summary, only results using the bivariate tests are reported in the next section. Readers may write the author for a summary of the results from the multivariate analyses.

Results

Descriptive Statistics

Table 1 provides a summary of descriptive statistics for all study variables. Less than one-third (32.4%) of respondents visited in the morning, 39.2% visited in the afternoon, and 28.5% visited in the morning. Six out of ten respondents visited on weekdays. More than one quarter (26.0%) of all respondents visited during the spring, 50.7% visited during the summer, and 23.3% visited during the fall.

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TABLE 1 ABOUT HERE

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The majority of park visitors were male (54.8%) and Anglo (90.9%). About half (48.5%) of all visitors were between the ages of 25 and 44. One-quarter of visitors were 65 years of age and older. Two-thirds of all visitors were employed either full-time or part-time.

The four most frequently pursued activities were relaxation (50.8%), walking or hiking (44.2%), picnicking (19.7%), and observing nature (12.7%). Less than one out of ten visitors said they engaged in activities such as walking a dog, fishing, swimming, running or jogging, visiting a nature center, or bicycling.

Predicting When People Visit Parks

Sex was the weakest and least significant of the four population variables in terms of differentiating when people visited the parks (Table 2). In contrast, age, race, and employment status were each significantly related to all three time dimension variables.

There was little difference between men and women in terms of what time of day and season of the year they visited the parks. However, men and women did differ slightly in terms of the day of the week they visited (p . 01). Here, females made up 43.5% of weekday visitors and 47.8% of weekend visitors.

The age-groups differed quite significantly in terms of the time of day (p . 001), day of the week (p . 001) and season of the year (p . 001) when they visited. In general, older adults comprised a disproportionate fraction of morning visitors while younger and middle age adults tended to visit more often in the afternoon and evening. For example, 17.4% of morning visitors were 65 years of age and older, compared with 11.6% of afternoon visitors, and 9.3% of evening visitors. In contrast, adults between the ages of 25 to 44 comprised 41.0% of morning visitors, 52.0% of afternoon visitors, and 52.1% of evening visitors. Age differences by day of the week showed that older adults tended to visit more on weekdays than weekends. Fifteen percent of weekday visitors were 65 years of age and older; only 8.9% of weekend visitors were 65 years of age and older. Finally, summer visitors were disproportionately younger, while fall visitors were disproportionately older. For example, individuals under the age of 25 comprised 13.5% of summer visitors but only 7.8% of fall visitors. In contrast, adults 65 years of age and older made up only 10.6% of summer visitors but 17% of fall visitors.

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TABLE 2 ABOUT HERE

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There were significant differences between African-Americans and Anglos in terms of each of the three time variables. African-Americans comprised only 7.7% of afternoon visitors, but 9.3% of morning visitors and 10.8% of evening visitors (p .01). African-Americans made up a greater fraction of weekend visitors (11.1%) than weekday visitors (7.8%) (p .001). The differences by season were even sharper (p .001). African-Americans comprised 9.5% of spring visitors and 10.9% of summer visitors, but only 4.7% of fall visitors.

Employment status was significantly related to all three time variables at the .001 level of confidence. Visitors who were not employed made up 37.5% of morning visitors but only 27.6% of evening visitors. Visitors who were not employed also made up 37.9% of weekday visitors but only 25.8% of weekend visitors. Finally, visitors who were not employed made up a disproportionate fraction of spring (35.4%) and fall (37.1%) visitors.

Relationship of Time to Participation in Activities

Table 3 provides a summary of participation rates for the ten activities broken down by time of day, day of the week, and season of the year. For purposes of simplicity, we have omitted corresponding rates of non-participation. All of the time dimension variables were significantly related to participation rates in at least half of the activities. These patterns remained basically the same even when the effects of gender, age, race, employment status, and other time variables were statistically controlled.

Time of day was significantly related to rates of participation in seven activities. These relationships were all significant at the .001 level of confidence. Morning was associated with higher than average rates of participation in physical activities such as walking or hiking and running or jogging. Half of all morning visitors said they walked or hiked, compared with 39.4% of afternoon and 44.3% of evening visitors. More than twice as many morning visitors than afternoon visitors reported running or jogging (8.6% versus 4.1%). Walking a dog tended to occur during the morning (10.5%) and evening (9.2%) rather than the afternoon (6.6%).

Other activities, in contrast, were pursued more often at other times of day. Afternoon was associated with higher rates of picnicking and swimming. Nearly a quarter of all afternoon visitors said they picnicked, compared with 14.6% of morning and 19.4% of evening visitors. Over one-tenth of afternoon visitors said they went swimming, compared with 4.5% of morning and 7.3% of evening visitors. Finally, both afternoons and evenings were associated with higher than average rates of relaxation and observing nature. Over half of all afternoon and evening visitors said they relaxed, compared with 41.3% of morning visitors. Similarly, 13.7% of afternoon and 14.3% of evening visitors said they observed nature, compared with 10.0% of morning visitors. There were no differences in participation rates by time of day for activities such as fishing, visiting a nature center, and bicycling.

Weekday and weekend visitors differed significantly across five activities. Weekend visitors were significantly more likely than weekday visitors to engage in relaxing (54.3% versus 45.8%), picnicking (26.9% versus 15.1%), visiting a nature center (7.2% versus 4.8%), and bicycling (7.4% versus 3.3%). Each of these relationships was significant at the .001 level of confidence. In contrast, 7.7% of weekday visitors said they went swimming, compared with 6.2% of weekend visitors (p .05). Rates of participation did not differ between weekday and weekend visitors for activities such as walking or hiking, observing nature, walking a dog, fishing, and running or jogging.

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TABLE 3 ABOUT HERE

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There were significant seasonal differences across seven of the activities. Summer was associated with higher than average rates of participation in relaxation (p .001), picnicking (p .001), and swimming (p .001). Fifty-five percent of summer visitors said they relaxed, compared with 45.8% of spring and 39.4% of fall visitors. Twenty-six percent of summer visitors said they picnicked, which was nearly twice the rate of spring (14.3%) and fall visitors (12.4%). Fourteen percent of summer visitors said they swam, compared with only .3% of spring and none of the fall visitors. Fishing tended to be pursued more often by summer (9.3%) and spring visitors (7.8%) than fall visitors (3.0%) (p .001).

Walking and hiking, walking a dog, and visiting a nature center were pursued more often by visitors interviewed during the spring and/or fall. About half of all fall and spring visitors said they walked or hiked, compared with 37.8% of summer visitors (p. .001). Fall visitors reported the highest rate of dog walking at 12.4%, followed by 9.3% of spring visitors, and only 6.6% of summer visitors (p. .001). Slightly more spring visitors said they visited a nature center (7.4%) than summer (5.1%) and fall visitors (5.3%) (p. .05). There were no significant differences among spring, summer, and fall visitors for activities such as observing nature, running or jogging, and bicycling.

The Interactive Effects of Day of the Week and Time of Day

To what extent are the relationships between time of day and rates of activity participation the same for weekday visitors and weekend visitors? To answer this question, we created a three-variable table that cross tabulates rates of activity participates by time of day and day of the week (Table 4). We have once again left out rates of non-participation to ease interpretation. As before, the observed patterns remained essentially the same when the effects of gender, age, race, employment status, and season of the year were statistically controlled.

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TABLE 4 ABOUT HERE

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There is some evidence that the impact of time of day on when people participated in different park activities is stronger on weekdays that it is on weekends. Time of day was significantly related to participation in seven activities on weekdays but only five activities on weekends. On weekdays, time of day was significantly related to participation in relaxation, walking or hiking, picnicking, walking a dog, fishing, swimming, and running or jogging. On weekends, in contrast, time of day was significantly related to participation in relaxation, picnicking, observing nature, swimming, and running or jogging. Although there appears to be greater variation in activity rates on weekdays than on weekends, this pattern requires both qualification and amplification.

There were three activities--walking or hiking, walking a dog, and fishing--where there was clearly more variation in rates of participation on weekdays than on weekends. On weekdays, time of day was significantly related to participation rates in all three of these activities. On weekends, there was no significant relationship, however, between time of day and participation in these activities. Two other activities--picnicking and swimming--also followed this general pattern. Although the relationship between time of day and participation in these two activities was significant on both weekdays and weekends, the strength of these relationships were somewhat stronger on weekdays.

There was one activity--observing nature--in which there was actually greater variation in participation rates on weekends. Among those who visited on weekends, afternoon and evening visitors were twice as likely as morning visitors to engage in observing nature (p .001). On weekdays, there was virtually no difference in rates of observing nature among morning, afternoon, and evening visitors. The relationship between time of day and participation rates in the remaining activities--relaxation, running or jogging, visiting a nature center, and bicycling--were basically consistent.

Discussion

The purpose of this study was to explore the relationship between time and patterns of use in a metropolitan park district. We hypothesized that population groups would vary in terms of when they visited the parks, that there would be differences in activity rates among park visitors by time of day, day of the week, and season of the year, and that the relationships between time of day and rates of activity participation would be different for weekday visitors and weekend visitors. In general, each of these hypotheses were supported. These findings need elaboration.

Age was more closely related to when people visited parks than the other population characteristics included in these analyses. Consistent with findings reported elsewhere (Hutchison, 1994), older adults comprised a disproportionate fraction of morning visitors. Older adults also tended to visit more often than their younger counterparts on weekdays and during the fall. It should not be surprising that older adults visit parks during non-peaks times. Unlike their younger counterparts, they tend to have fewer family and work roles that constrain when they are able to visit parks and other recreation areas. The tendency of older adults to visit on weekdays and during the morning may also reflect a conscious decision to make park visitation a part of a routine rather than a break from it (Godbey & Blazey, 1983).

These findings are paralleled by those pertaining to employment status: individuals who were not employed outside the home tended to visit in the morning, on weekdays, and during the fall. In contrast, individuals who were employed outside the home tended to visit parks during time periods when constraints related to work are relaxed--during the evening and on weekends. These patterns may change in the future should there be a corresponding change in the organization of employment schedules (Martin & Mason, 1994). For example, a movement toward greater flexibility in work schedules may result in a blurring of conventional temporal frames of reference.

Men outnumbered women as park visitors by a small margin: 54.8% to 45.2%. This pattern is consistent with findings of urban parks reported by both More (1985) and Hutchison (1994). More and Hutchison also reported that males and females tended to visit parks at different times of day. We found no evidence of this pattern in this study. However, we did find that that a greater fraction of women visited on weekends than on weekdays. The data provide no specific information about why this occurred. However, women are more likely than men to report family commitments as a constraint to park visitation (Scott & Jackson, 1996), and women tend to feel less entitled to leisure compared to men (Deem, 1986). It could be that family commitments and lack of entitlement may be more acutely experienced by women on weekdays than on weekends.

African-Americans made up only one-tenth of all park district visitors (they comprised one-quarter of the study population). African-Americans also displayed distinctive temporal patterns of visitation. In general, African-Americans disproportionately visited parks during the evening, on weekends, and during the summer months. One explanation for these distinct temporal patters of visitation requires greater understanding of the local situation. Over half of all African-American respondents were surveyed in two of the twelve parks. Moreover, one-third of all African-American respondents were surveyed in just one park. Each of these parks is also used heavily by Anglos. Additional analysis of the data revealed that African-American and Anglos tended to use these two parks at different times. In the one park, for example, African-Americans comprised 33% of all spring visitors, 57% of all summer visitors, and only 13% of all fall visitors. Moreover, during the summer, African-Americans comprised less than half of all weekday visitors but over two-thirds of all weekend visitors. Hence, it seems plausible that differences in temporal visitation in these parks may reflect a process of succession whereby both African-Americans and Anglos synchronize the scheduling of their use of the parks so as to avoid members of the other race.

Findings from this study also revealed that park activities were associated with different times of day, days of the week, and seasons of the year. These patterns were true even when the effects of other variables were controlled. Morning was associated with walking or hiking, walking a dog, and running or jogging; afternoon and evening, in contrast, were associated with relaxation, picnicking, observing nature, and swimming. Weekends were highly associated with relaxation, picnicking, visiting a nature center, and bicycling. Summers tended to be associated with activities such as relaxation, picnicking, and swimming. These results lend support to the idea that leisure activities tend to have specific time referents associated with them, and that these times referents are part of a society’s (or group’s) cultural fabric.

There was also some evidence that the association between time of day and activity participation was stronger on weekdays that it was on weekends. For example, there was a significant relationship between time of day and participation in walking or hiking, walking a dog, and fishing among weekday visitors. On weekends, in contrast, participation rates did not differ significantly among morning, afternoon, and evening visitors. These patterns support the idea that there tends to be a lessening of work and role constraints on weekends whereby people are afforded a greater degree of freedom to participate in leisure activities at any time they want.

In sum, results from this study show that park behavior is organized within the context of temporal frames of reference. The presence of temporal rhythms or leisure timetables in parks can be explained largely in terms of the pervasiveness of industrial timetables, cyclical timetables, and the synchronization of individual timetables. Some activities (e.g., walking, walking a dog, and jogging) require only a short period of time. Consequently, these activities can be readily pursued in the morning, before work and the initiation of other responsibilities. An activity like picnicking, however, generally takes more time and is a group event. Picnicking, thus, requires more advanced scheduling and tends to occur more on the weekends than on weekdays. It is important to note that 20.7% of afternoon visitors on weekdays said they picnicked. Most of this activity occurred among workers taking lunch breaks in the park. Seasonal rhythms existed as well. Swimming, not surprisingly, was at its peak during the warm weather months of summer.

These findings are not anomalies. A great deal of leisure probably occurs within predictable timetables. For many people in the United States, for example, associations probably exists between autumn and football; Saturday nights and dancing; Sunday mornings and going to church; and getting off work and drinking alcohol. More research is needed that examines the structural ramifications of time on leisure behavior.

A replication of this study is needed to determine the extent to which findings can be generalized to other settings, cultural groups (e.g., Hispanics and Asian), regions, seasons (e.g., winter), and other activities. Along these lines, research is necessary to determine whether different population variables (age and gender) interact in their effect on when leisure activities are pursued or when park or other recreation areas are visited. Additional research is also necessary to determine the degree to which members of different population groups share time referents as it relates to different leisure activities. It would also be of interest to know whether leisure services providers and their constituents differ in their perceptions of these time referents. One other research area may be suggested: the impact that time has on the perception of leisure constraints. Following the work of Mannell and Zuzanek (1991), additional research would reveal the extent to which the perception of leisure constraints vary by time of day, day of the week, and season of the year.

Notes

1. Hispanics and other minorities comprised a very small fraction of population in Greater Cleveland (two percent). These minorities also comprised less than three percent of the sample. Because of insufficient numbers, Hispanics and other minorities were excluded from these analyses.

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Author Note

Acknowledgments: Thanks go to Bill Antholine for his assistance on a previous draft of this paper. Thanks also to Jim Gramann, Sarah Nicholls, Scott Shafer, Bill Stewart Peter Witt, and the reviewers for their helpful comments.

Table 1

Frequencies of All Study Variables

Table 2

Time of Day, Day of the Week, and Season of the Year When Population Groups Visited Park District

 

*p<.050, **p<.010, ***p<.001

Table 3

Activity Participation Rates by Time of Day, Day of the Week, and Season of the Year^a

^aPercentages for non-participants are not reported.

*p<.050, **p<.010, ***p<.001

Table 4

Interactive Effects of Time of Day and Day of the Week on Activity Participation Rates^a

 

^aPercentages for non-participants are not reported.

*p<.050, **p<.010, ***p<.001