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Recreation use of urban forests: An inter-area comparison
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Urban Forestry & Urban Greening 4 (2006) 135–144 www.elsevier.de/ufug
Recreation use of urban forests: An inter-area comparison Arne Arnberger Institute of Landscape Development, Recreation and Conservation Planning, BOKU – University of Natural Resources and Applied Life Sciences, Peter Jordan-Straße 82, 1190 Vienna, Austria
Abstract Recreation use in two urban forests in Vienna, Austria was compared. Visitors to an inner-urban forest and to a peri-urban forest were monitored by means of video observation during 1 year, from dawn to dusk. The amount of use and the temporal use pattern of the main user types, identified by video interpreters as walkers, cyclists, dog walkers and joggers, were compared. In the inner-urban forest, surrounding settlements, schools and business areas evoked high-use pressure, commuting activities, high shares of all-day activities, more morning and evening use particularly on workdays and, overall, more workday use. The peri-urban forest was, by far, not so heavily used and the proportion of daily routine activities such as dog walking and jogging was reduced because of the lower population density in the surroundings. While the potential for user conflicts in the inner-urban forest seemed to be quite high at weekends and workday late afternoons and evenings, in the peri-urban forest this potential was only high during weekend afternoons in the warmer season, due to the temporally concentrated appearance of walkers and bicyclists. r 2006 Elsevier GmbH. All rights reserved. Keywords: Activity types; Commuting; Daily use pattern; Long-term video monitoring; User conflicts
Introduction Need for recreational use data across forests Differences in recreation use between urban and backcountry forests are apparent (Ho¨rnsten and Fredman, 2000; Konijnendijk, 2000). Urban forests suffer from more intense and multiple recreation use, day-use oriented activities such as dog walking and jogging and higher use levels on workdays. However, recreation use pressure may also differ among urban forests. The degree of urbanity in terms of the surroundings, such as the number and closeness of settlements, business areas and schools, may influence use levels, user composition, and the temporal distribution of activity types through Tel.: +43 1 47654 7205; fax: +43 1 47654 7209.
E-mail address: [email protected]. 1618-8667/$ - see front matter r 2006 Elsevier GmbH. All rights reserved. doi:10.1016/j.ufug.2006.01.004
commuting and recreation use. Consequently, innerurban and peri-urban forests can perceive various kinds of recreation use (Van Herzele et al., 2005), and urban forest owners and administrations will face completely different challenges in providing for sustainable urban forest management. Due to the intense and multiple use urban foresters need reliable and detailed visitation data on activity types in order to understand recreation use in their forests and to identify management strategies that are ecologically sound while, at the same time, being acceptable to the traditional area users. Comparisons of different kinds of urban forests regarding their recreational use patterns would not only contribute to the further understanding of the recreational use of forests, but also further assist area administrations in designing and managing urban forests as buffer zones from the hectic daily life of the city.
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User composition and daily use levels of activity types are very useful tools for comparative analyses across forest types because they provide essential information about who is using the urban forest – and when. Different activity types have different expectations and needs and, consequently, display different behavioural, temporal, and spatial use patterns. The overlap of the daily use patterns of several activity types can produce peak loads, resulting in crowding perceptions, user conflicts and even in the displacement of some users or activity groups (Arnberger, 2003; Price and Chambers, 2000). Daily use patterns, which also provide information about under-use (Arnberger and Haider, 2005; Baum and Paulus, 1991; Kaplan and Kaplan, 1982), are indicators of the amount and temporal usage of, and demand for, recreational infrastructure and form the basis for many management decisions. A prerequisite for area comparisons are long-term counting data about the amount of use, activity types and their daily use pattern. However, such data about urban forest use are often not available or are extrapolated from visitor counts carried out on a few days only. Long-term monitoring using automatic counters and pressure pads has become quite common in different forests (Arnberger and Brandenburg, 2002; Melville and Ruohonen, 2004; Rauhala et al., 2002; Visschedijk and Henkens, 2002; Watson et al., 2000; Volk et al., 1995), but they offer no indication as to the activities in which visitors are engaged. Precisely this information is essential in multiple-use areas, compared to backcountry areas where hikers are the dominant users.
Use pattern of activity types in urban forests based on long-term counting Recently, image-based long-term observations have been carried out in urban and suburban forests. Video monitoring applications result in a fine resolution of temporal use patterns for different activity types during the year. However, very few studies have used this approach for long-term observations. Arnberger and Brandenburg (2002) monitored the year-long use pattern in an exurban national park in Austria, using time-lapse video recording at two main access points in addition to on-site interviews, counts by human observers, and counts using infrared sensors. They assessed the total amount of use in the national park and identified the main user groups, their temporal distribution as well as user types with specific visiting motives. Based on the daily use pattern on workdays, the authors distinguished gastronomic visitors from recreation visitors. Janowsky and Becker (2003) videoed the number of users and variety of recreational activities users engaged in while visiting the urban forest of
Stuttgart, Germany. One camera provided 1-year data, whereas the other camera was set up at three different spots for short-time observations. A signal from a motion detector activated the recording control station, which triggered the video camera to store images over a 5 s interval. The video information was used in conjunction with in-depth interviews and geographic data in order to derive an optimised forest road network that would most effectively meet the requirements of forest management as well as the needs of outdoor recreationists. The researchers analysed the daily use pattern of activity types, differentiated by the day of the week, based on a 2-h evaluation. In this, urban forest walkers came predominantly in the afternoon across all days of the week, while joggers’ activities resulted in a morning use peak on Sundays and in an evening use peak on workdays. Cyclists appeared between noon and late evening on Sundays while, on workdays, use levels were shifted more into the evening hours. Arnberger and Hinterberger (2003) compared the yearly, weekly, and daily use pattern of dog walkers, keeping their dogs on or off the leash, in a peri-urban forest in Vienna based on a 1-year video monitoring at access points. Dog walkers whose dogs were off the leash tended to make their visits on workdays and during less frequented visiting hours of the day compared to dog walkers whose dogs were leashed. It is surprising that, so far, little research has focused explicitly on long-term use patterns of activity types in urban forests and on comparisons of urban forest use, when the need for such basic information, in a reliable manner, is obvious for several management purposes. In this study, recreation use patterns in an inner-urban and a peri-urban forest were compared. In each forest, recreation use was monitored by means of video observation during 1 year, daily from dawn to dusk. The daily use patterns of the main activity types were compared by weekends and workdays in regard to peak loads, the potential for user conflicts, and management challenges.
Study areas Data were collected in an inner-urban forest, which is situated in the south of Vienna, Austria, and in a periurban forest in the east of the city (Fig. 1). The innerurban forest of 120 ha was opened to the public in the late 1980s and is managed by the municipal forest department. Residential and business areas, an old people’s home, a hospital, schools, and garden allotments encircle this forest. About 53,000 inhabitants live within 15 min walking distance. Several sections of the forest are conservation areas and it provides about 30 km of forest roads, gravel trails and footpaths.
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Fig. 1. Location of the forests in Vienna.
Cycling is permitted only on two trails and dogs are allowed, but must remain on the leash. Access by public transport is easy and several parking lots are provided. The peri-urban forest is part of the Danube Floodplains National Park with a total area of 2400 ha and is also managed by the forest department of Vienna. Suburbs of Vienna, communities, areas of intensive agriculture and the Danube River surround the forest. Close to 15,000 inhabitants live within 15 min walking distance. The forest provides numerous access points and there are no access restrictions for day use. A dense network of 142 km of forest roads and trails and several recreational pathways permeate the forest, especially in close proximity to main residential areas. Several trails are open for bicycling and one international cycle route passes through the forest. Dogs are allowed, but must remain on the leash. Access by public transport is suboptimal and several parking lots are provided.
Methods The recreational use in the study areas was investigated during a 1-year time period using a combination of long- and short-term counting methods, as well as onsite interviews. The data for the study presented here were collected by means of video observation, using this monitoring method, in a comparable manner, for observing access points. Video monitoring was undertaken at five main access points in the peri-urban forest and at three main access points in the inner-urban forest over a period of 1 entire year, daily from dawn to dusk. Monitoring took place in the peri-urban forest between 1998 and 1999 (Arnberger and Brandenburg, 2001; Arnberger and Hinterberger, 2003,) and in the innerurban forest between 2002 and 2003 (Arnberger, 2003). Each monitoring unit consisted of a weatherproof black-and-white video camera with integrated heating and one or two time-lapse video recorders. In order to avoid vandalism and to allow for unobstructed observa-
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tions, the cameras were hidden in nesting boxes, the cables were concealed, and the recording units themselves were placed in buildings or construction-site containers inaccessible to visitors. The cameras were installed on wooden poles about 4 m above ground, or on the walls and roofs of buildings. The time-lapse video recorders captured single images at fixed intervals of 1.6 s over the entire day. With this frequency, and about 14 h of operation per day, a 240-min video tape can store 3 weeks worth of data. With the type of video camera installed, and its specific setting, it was impossible to identify individuals in the video images, ensuring their anonymity. This was achieved by the low image resolution of the black-andwhite cameras and a minimum distance between camera and visitor. As a drawback, it was impossible to distinguish between people entering or leaving the forest several times per day at the same or another camera location. Therefore, only people entering the forests at the access points were taken into account for the use comparison; visits – and not visitors – to the forests were counted. For the analyses, only 15 or 20 min of observations per hour were taken into account, but this had no negative impact upon the significance of the results because the data based on a 15- or 20-min evaluation were statistically verified by the data of a complete 2week survey. The examination using linear regression resulted in the R2 values of 0.9 or higher (Arnberger, 2003; Brandenburg, 2001). Thereby, analysing costs could be reduced drastically. The tapes were viewed on a television monitor by trained students and counts were classified and recorded on a MS-Excel spreadsheet. The following data were captured from the video tapes: date and day of the week, time of visit, location of station, direction of movement, number of persons in a group, activity type, and number of leashed and unleashed dogs. Video recordings are an excellent source of information about recreation use and therefore a very useful management tool. Beside the identification of activity types and their temporal distribution, the size of the group and visitor behaviour can be recorded. In addition, at high use levels, video monitoring provides more accurate data about the amount of use compared to human observers. Some of the limiting factors currently associated with the use of video monitoring include: video tape analysing costs and miscounts of user types by video interpreters. The main cost factors are labour costs, for the installation and maintenance of devices, and in particular, for the interpretation and analysis of the video tapes. For short-term observations, the total expenses of video observation are relatively high, but considering the proportional costs per registration day and station over the period of 1 year, long-term monitoring costs are low, particularly when
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compared to long-term surveys by human observers. Another drawback associated with the video monitoring method is the inaccurate assessment of visitor numbers at low-use levels, in particular of faster moving activities such as bicycling and jogging. This inaccuracy is introduced during the interpretation process, when video tape analysts check the tapes as fast as possible and consequently might overlook some users at low-use levels. Heavy rain and dense fog can also lead to miscounts (for a detailed description of the video monitoring approach see Arnberger et al. 2005). In addition, on-site interviews and counts by human observers were conducted. In the inner-urban forest, visitors were intercepted at access points along the main trail section between April and October 2002. The interviews took place on five randomly selected work days and five randomly selected Sundays between 8 a.m. and 7 p.m. In the peri-urban forest, interviews were conducted at 12 access points on two randomly selected work days and two Sundays between 8 a.m. and 7(5) p.m. in spring and summer. Sampling sizes were 952 in the inner-urban forest and 780 in the peri-urban forest. Human observers counted visitors from 8 a.m. to 7(5) p.m. on the days of the interviews in both forests. These personal observations were undertaken at side entrances to the forests, where no video cameras were installed. For the estimation of the total annual number of visitors and user types to the forests, the results of long-term permanent video monitoring at selected sites and temporally selective counting by human observers were combined. From the ratio between video counts and personal observation counts, observing all entrances on the sampling days, visitor numbers for entrance points without video observation could be extrapolated for all days of the year, taking into account external factors such as weather conditions and the day of the week. For that extrapolation process, only entering visits were taken into account. For both forests, use levels varied only slightly between workdays and were, therefore, aggregated for the following analyses. Saturdays were combined with Sundays because of a similar use pattern whereby, in the inner-urban forest, use patterns on Saturdays were more different to Sundays than in the peri-urban forest. As the use patterns for Sundays and public holidays showed no significant differences, Saturdays, Sundays and public holidays will be referred to as ‘weekends’.
Results Amount of use Visitor counting resulted in an annual use estimate of around 1.24 million visits to the inner-urban forest and
0.60 million visits to the peri-urban forest. Depending on the day of week, remarkable differences were observed. At weekends, an average of 4300 daily visits to the inner-urban forest were recorded, resulting in a density of 36 visits per hectare per day, whereas in the peri-urban forest an average of 3000 daily visits and a density of 1.2 visits was videoed. On workdays, the differences between the forests were even more pronounced. The inner-urban forest showed an average of about 3000 daily visits with a density of 25 visits per day per hectare, whereas 1000 visits per day per hectare and a density of only 0.4 visits was recorded for the peri-urban forest. Thus, in the peri-urban forest, weekend use was about 2.9-times higher than workday use, whereas in the inner-urban forest this factor was about 1.4.
User composition Similarities and differences in user composition were observed between the forests. Video interpreters identified four main activity types in both forests: walkers, bicyclists, dog walkers, and joggers (Table 1). In the inner-urban forest, the majority of users enjoyed walking, followed by cycling, dog walking, and jogging activities with fairly equal shares. In the peri-urban forest, however, walking and cycling activities dominated. Only a minority went jogging. Area differences in user composition were observed according to weekdays (Table 2). Compared to the
Table 1. Shares of activity types across forests based on 1-year video monitoring and counts by human observers Activity types
Inner-urban forest (%) Peri-urban forest (%)
Walkers 49 Bicyclists 18 Dog walkers 17 Joggers 16 Others o1
40 47 10 3 o1
Table 2. Shares of activity types by weekends and workdays based on 1-year video monitoring Activity types Walkers Bicyclists Dog walkers Joggers
Inner-urban forest (%) Peri-urban forest (%) Weekends
Workdays
Weekends Workdays
43 36 39 35
57 64 61 65
62 55 50 51
38 45 50 49
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inner-urban forest, peri-urban forest walkers were predominantly weekend users. Equal numbers of dog walkers and joggers were videoed in the peri-urban forest both at weekends and on workdays, whereas the inner-urban forest perceived more workday use by all activity types.
20%
Daily use patterns of activity types Similarities and differences in daily use patterns of activity types between the forests were recorded by the 1-year video monitoring (Figs. 2 and 3). Use patterns of bicyclists differed by the day of the week and by area.
Walkers
Bicyclists 20%
15%
Shares
Shares
15%
10%
5%
10%
5%
0%
0% 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Time of Day
6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Time of Day
Joggers
Dog Walkers 20%
20%
15%
Shares
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10%
10%
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0% 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Time of Day
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6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Time of Day
Inner urban forest-Weekend Peri-urban forest-Weekend Inner urban forest-Work day Peri-urban forest-Work day
Fig. 2. Daily use pattern by activity types and weekdays across forests (6 means period between 6:00 and 6:59).
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Inner urban forest- Workdays 20%
15%
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Inner urban forest- Weekends 20%
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0% 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Time of Day
6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Time of Day
Peri-urban forest- Weekends
Peri-urban forest- Workdays 20%
20%
15%
Shares
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0%
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0% 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Time of Day
6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Time of Day
Bicyclists Dog Walkers
Walkers Joggers
Fig. 3. Daily use pattern of activity types at weekends.
While the main cycling patterns in the inner-urban forest were observed in the late afternoon and evening hours both at weekends and on workdays, daily use patterns in the peri-urban forest were constantly high between 10 a.m. and 4 p.m. at weekends and between 10 a.m. and 6 p.m. on workdays. On workdays, an additional early morning peak of bicyclists was videoed in the inner-
urban forest. Across the forests, higher bicyclists’ use intensities could be recorded in the evening hours of the workdays compared to weekends. In contrast to bicyclists, the walkers’ use pattern resulted in a distinct use peak in the afternoon, in particular at weekends. In the peri-urban forest this peak was extreme between 2 p.m. and 3 p.m. at
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weekends. More than 21% of all walkers’ use was counted in this hour. In the inner-urban forest this peak was less pronounced and, on workdays, shifted more towards the late afternoon. Similar to bicyclists, walkers showed more intense use in the evening hours on workdays compared to weekends in both forests. On workdays, more early morning and evening use of walkers was observed in the inner-urban forest, while in the peri-urban forest a small peak between 9 a.m. and 11 a.m. was videoed. Dog walkers had a use pattern similar to walkers, visiting mainly in the afternoon of weekends. This use peak was more pronounced in the peri-urban forest, while the inner-urban forest was subjected to more evening use. On workdays, different daily use patterns were observed. The peri-urban dog walkers’ use pattern resulted in two peaks, one in the morning and one in the afternoon. The use pattern of inner-urban forest dog walkers, however, remained on a rather constant use level over the day with more early morning and late evening use. As with walkers and bicyclists, more evening use by dog walkers was recorded in both forests on workdays compared to weekends. The use patterns of joggers were very distinct from other activity groups and differed remarkably based on the day of the week, but not across forests. At weekends, joggers appeared mainly between 9 a.m. and 11 a.m. while, on workdays, most use was observed in the evening. The evening use by inner-urban joggers was more intense compared to peri-urban joggers.
Discussion Similarities in recreation use pattern In both forests, the highest use levels were observed at weekends and here in the afternoons, representing the typical weekend afternoon walk. Use patterns on workdays were influenced by school and working times, resulting in increasing use levels after midday and more evening use (Figs. 2 and 3). Across forests, use levels were stable or dropped slightly at noon, an indicator that some visitors from the surroundings went home to eat. Janowsky and Becker (2003) also videoed higher use levels in the afternoon, on workdays and at weekends, but no midday influence, whereas Arnberger and Brandenburg (2002), in contrast, videoed a noon peak on workdays because of the existence of a local restaurant. Across forests, the main user groups were walkers, bicyclists, dog walkers and joggers (Table 1). In both forests, the use patterns of walkers and dog walkers resulted in use peaks in the afternoon both on workdays and at weekends, while jogging is an activity with
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completely different daily progressions of use on workdays and weekends. On workdays, jogging is carried out before and after work time with a higher preference to jog after work. At weekends, however, jogging was done in the morning hours. Janowsky and Becker (2003) received a similar result for their study in the urban forest of Stuttgart. Obviously, joggers preferred to sleep a little bit longer at the weekend, but avoided the times of heaviest use and hottest conditions during the afternoon.
Differences in recreation use pattern Several factors distinguish the recreation use of the inner-urban forest from the peri-urban forest: first, the inner-urban forest is subjected to about 40 times higher use density. This forest is encircled by residential areas and close to four times more people live within a 15-min walking distance causing the high use pressure. In addition, this forest is easily accessible by public transport from other parts of the city. Second, the inner-urban forest is characterised by more workday use, especially in the morning and evening, predominantly produced by bicyclists and walkers (Figs. 2 and 3). In the immediate neighbourhood of the forest, there are business areas and schools. According to interview results, close to 2% of visitors to this forest (Arnberger, 2003) indicated that they use the area as part of their way to or from work or school. In the surveys of the peri-urban forest (Arnberger and Brandenburg, 2001), however, no respondent stated trail use as a part for his/her travel to or from work. Therefore, due to the lack of surrounding business areas and schools, only recreational bicycling and walking took place in the peri-urban forest whereas both recreation and commuting activities occurred in the inner-urban forest, resulting in higher workday use levels and more workday morning and evening use. Third, in the inner-urban forest, dog walkers and joggers appeared in much higher shares (Table 1) because of the high number of surrounding residential areas and the lack of alternative green spaces in the south of Vienna where dogs are allowed. Dog walking and jogging are everyday activities caused by the need to walk the dog and the habit of jogging daily (Table 2), increasing the overall use pressure and resulting in relatively more workday use. These users also contribute to higher morning and evening use on workdays because of walking the dog and jogging before and after work. Fourth, despite the use pattern produced by working times and commuting activities, use levels in the innerurban forests were higher in the evening hours, both on workdays and at weekends. Apparently, visitors used the inner-urban forest simply for a short outing in their immediate neighbourhood before nightfall while, due to
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its distance from residential areas, many visitors to the peri-urban forest had to travel much longer and, consequently, avoided a short-term visit in the late evening on workdays and at weekends. In contrast, in the peri-urban forest more use occurred between late morning and afternoon both at weekends and especially on workdays. There are several reasons for the use concentration of the peri-urban forest (Figs. 2 and 3). First, the workday use in this forest was not influenced by commuting activities, resulting in relatively more use concentration between the late morning and afternoon hours. Second, fewer daily routine activities were carried out, normally increasing the use intensities of the morning and evening hours. Third, higher shares of bicyclists were observed because the forest size is more suitable for recreation bicycling. This user group came mainly between 10 a.m. and 4 p.m., while in the inner-urban forest, bicycling was done more in the late afternoon or evening. Fourth, due to the higher attractiveness of the peri-urban forest, in terms of size and natural elements, more visitors came to experience nature and the landscape (Arnberger and Brandenburg, 2001). Nature-interested visitors stay longer in the forest and they would not arrive in the late afternoon and evening hours (Arnberger and Brandenburg, 2002). In addition, visitors coming from more distant settlements need some time to travel to the forest, contributing to the late morning use peak.
Potential for user conflicts depending on the day of the week In urban forests, peak use levels are a result of an overlap of several activity types, making use of the resource at the same time. In both forests, most use occurred in the afternoons of the weekends due to the overlap of walkers’, bicyclists’ and dog walkers’ use patterns (Fig. 3). During this period, user conflicts and crowding perceptions were most likely, whereby this potential was drastically higher in the inner-urban forest due the 28 times higher use density at weekends. Consequently, more than 60% of the on-site visitors to the inner-urban forest perceived the area as overcrowded or crowded at weekends and on public holidays (Arnberger, 2003). The, almost complete, absence of joggers at these high-use times, as revealed by long-term video counting, would lead to reduced user conflicts with this group. That joggers were seen as a less disruptive user group was also confirmed by Arnberger and Haider (2005) who used a visual choice model displaying various trail use scenarios in the inner-urban forest. This conflict reducing behaviour had more affect on the inner-urban forest, as about 16% of the entering users were joggers, whereas they only accounted for 3% of the users in the peri-urban forest (Table 1).
In regard to user conflicts, the situation is completely different in the forests on workdays. In the inner-urban forest, both recreation and commuting bicycling and walking took place in the late afternoon and evening hours. Their use patterns coincided with the use of recreational walkers and bicyclists as well as daily routine activities such as jogging and dog walking. This was not the case in the peri-urban forest because of the absence of commuting activities, lower shares of jogging and dog walking activities and much lower overall use levels. Compared to the peri-urban forest, use density in the inner-urban forest was 63 times higher, resulting in a high chance for user conflicts. In the inner-urban forest, another period with a higher conflict potential would be the morning hours. This early morning use peak was caused by the temporal overlap of commuting and daily routine activities. The overlap of jogging and dog walking activities and fastmoving bicyclists may result in conflict perceptions. Compared to the peri-urban forest, this conflict potential was much greater because of higher use levels and the larger shares of dog walkers and joggers. Even if the share of joggers in the visitors entering was not high, one should consider that joggers made several laps of the inner-urban forest increasing the use pressure drastically, compared to the 20 times larger peri-urban forest providing more trails. Especially joggers reported conflict perceptions with dog walkers because, precisely in the low-use hours of the day, dog walkers tended to release their dogs from the leash (Arnberger, 2003; Arnberger and Hinterberger, 2003), whereas dog walkers reported some conflict perceptions with fast moving bicyclists (Arnberger, 2003).
Potential for user conflicts depending on the season The potential for user conflicts also depends on the season. The highest use levels in both forests were observed during the warmer season, in particular in May. Arnberger and Brandenburg (2002) and Janowsky and Becker (2003) obtained similar results for recreation use of forests. In particular, bicyclists are highly influenced by weather conditions, appearing mainly in the warmer season or during good weather conditions (Ploner and Brandenburg, 2003). Thus, conflicts with this group in the wintertime, or in rainy and cloudy conditions, were less likely. The influence of weather, however, differed across forests. Compared to the peri-urban forest, the innerurban forest use was less influenced by weather because of commuting bicyclists and daily routine activities, appearing also in the colder season, when it is cloudy or even slightly rainy (Brandenburg et al., 2004; Ploner and Brandenburg, 2003). Consequently, when an overlap
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between these activities occurred, as in the inner-urban forest, this kind of conflict might not only be restricted to the warmer seasons. In the peri-urban forest, bicyclists were the biggest user group and the potential for user conflicts depended much more on the weather. The potential seemed to be high between 2 p.m. and 3 p.m. at weekends, during the warmer season only, because of the overlap of walkers’, dog walkers’ and bicyclists’ use patterns. Due to the much higher use levels, the overlap of commuting and recreation activities, and the high share of everyday users such as dog walkers and joggers, the potential for user conflicts in the inner-urban forest is not only given for the weekend afternoons, but also for workday late afternoons and evenings. Thus, user conflicts are quite likely throughout the week and year, also caused by the low influence of weather conditions on commuting and everyday activities. The peri-urban forest is, by far, not so heavily used and the proportion of daily routine activities is reduced because of the lower population density in the surroundings. Recreation use is more concentrated between late morning and late afternoon with a distinct peak hour at weekends, while, in the evenings, the peri-urban forest, also part of a national park, is low visited, providing rest periods for wildlife. Due to the high share of bicyclists, the potential for user conflicts is quite likely during weekend afternoons in the warmer season only.
Conclusion In urban forests, the monitoring of recreation activities has to consider the multiple use by activity types. Long-term video monitoring can detect daily use levels by activity types to answer the question ‘‘who comes when’’ and provides the basis for comparative analyses. Such comparisons contribute to a better understanding of urban forest use by recreationists in general. In addition, data on the amount and temporal distribution of public use and activity types are the basis for numerous planning decisions and management measures. Recreation use data gained by this study can be used for the planning for woodlands, providing information about the potential amount and kind of use a forest can perceive per year, at weekends and on workdays, depending on the surroundings. These use data are also indicators for the amount and kind of recreational infrastructure needed to avoid user conflicts in advance. Ultimately, one of the central issues in urban forest planning and management is the problem of predicting the responses and reactions of different activity types to recreational infrastructure, plans and management actions (Vuorio et al., 2003).
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The degree of urbanity results in completely different recreational use levels, use pattern and user composition in these two urban forests in Vienna. Based on the observed use patterns, the heavily used inner-urban forest plays an important role as an everyday environment for the recreation and commuting activities of the local population, whereas the peri-urban forest attracts not only everyday users from the surroundings but also high shares of recreation visitors, especially bicyclists, serving more as a daily recreation area for the region. As such, the management of the urban forests differs considerably. In the peri-urban forest, management efforts should target the potential conflict between bicyclists, walkers and dog walkers during weekend afternoons in the warmer season, whereas in the innerurban forest the potential for conflicts is given throughout the week and even the year with a focus on weekend and workday late afternoons and evenings. Based on use data, forest management can tailor, even temporarily, direct or indirect measures to address specific user groups should there be a need to reduce user conflicts or crowding perceptions, illegitimate behaviour or for maintenance operations. Information about shared tracks for forest users at access points, measures to reduce bicycling speed and the presence of forest rangers during peak periods could indirectly reduce user conflicts. Additional designated and attractive trails for commuting bicyclists outside the forests, separating them from other users or establishing a oneway system for bicyclists, would be some possible further management options for reducing the potential conflict between bicyclists and other users. Overall, more attractive green spaces in the surroundings would deflect some use pressure from the forests. Some limitations of this study have to be mentioned. This study provides information about the potential for user conflicts, however, not about the amount of occurring conflicts. Such information would require either a long-term monitoring of all trails within the forests using video observation or visitor surveys about the amount of perceived conflicts. In addition, the video observation recorded recreation use between dawn and dusk. Thus, information about night use, which might play a more important role for the inner-urban forest, cannot be provided. Ideally, the observations should have been made simultaneously, as the trend in outdoor recreation could have changed within 5 years and weather conditions perceived in the different years of observation might also have had an influence on daily use pattern. However, one can assume that, given the huge sample size, variations in daily use patterns due to weather conditions play only a minor role. This comparison included only two urban forests. Future research should test if the findings about the differences in recreation use between these would also hold for other urban forests.
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Acknowledgements The Forest Department of the City of Vienna commissioned the Institute of Landscape Development, Recreation and Conservation Planning to collect data on public use.
References Arnberger, A., 2003. Modellierung sozialer Tragfa¨higkeitsgrenzen von Erholungsgebieten. Dargestellt am Erholungsgebiet Wienerberg. Unpublished Dissertation, BOKU – University of Natural Resources and Applied Life Sciences, Vienna. Arnberger, A., Brandenburg, C., 2001. Der Nationalpark als Wohnumfeld und Naherholungsgebiet – Ergebnisse der Besucherstromanalyse im Wiener Anteil des Nationalpark Donau-Auen. Naturschutz und Landschaftsplanung 33 (5), 157–161. Arnberger, A., Brandenburg, C., 2002. Visitor structure of a heavily used conservation area: the Danube Floodplains National Park, Lower Austria. In: Arnberger, A., Brandenburg, C., Muhar, A. (Eds.), Monitoring and Management of Visitor Flows in Recreational and Protected Areas; Conference Proceedings. Institute for Landscape Architecture and Landscape Management, Vienna, pp. 7–13. Arnberger, A., Haider, W., 2005. Social effects on crowding preferences of urban forest visitors. Urban Forestry & Urban Greening 3 (3–4), 125–136. Arnberger, A., Hinterberger, B., 2003. Visitor monitoring methods for managing public use pressures in the Danube Floodplains National Park, Austria. Journal for Nature Conservation 11 (4), 260–267. Arnberger, A., Haider, W., Brandenburg, C., 2005. Evaluating visitor monitoring techniques: a comparison of counting and video observation data. Environmental Management 36 (2), 317–327. Baum, A., Paulus, P.B., 1991. Crowding. In: Stokols, D., Altman, I. (Eds.), Handbook of Environmental Psychology, vol. 1, second ed. Krieger Publishing Company, Florida, pp. 533–570. Brandenburg, C., 2001. Erfassung und Modellierung von Besuchsfrequenzen in Erholungs- und Schutzgebieten – Anwendungsbeispiel Nationalpark Donau-Auen, Teilgebiet Lobau. Unpublished Dissertation, BOKU – University of Natural Resources and Applied Life Sciences, Vienna. Brandenburg, C., Matzarakis, A., Arnberger, A., 2004. Visitor motivation and dependence on the weather of recreationists in Viennese recreation areas. Berichte des Meteorologischen Institutes der Universita¨t Freiburg 12, 189–197. Ho¨rnsten, L., Fredman, P., 2000. On the distance to recreational forests in Sweden. Landscape and Urban Planning 51, 1–10.
Janowsky, J. von, Becker, G., 2003. Characteristics and needs of different user groups in the urban forest of Stuttgart. Journal for Nature Conservation 11 (4), 251–259. Kaplan, S., Kaplan, R., 1982. Humanscape: Environments for People. Ulrich’s Books, Ann Arbor. Konijnendijk, C., 2000. Adapting forestry to urban demands – role of communication in urban forestry in Europe. Landscape and Urban Planning 52, 89–100. Melville, S., Ruohonen, J., 2004. The development of a remote-download system for visitor counting. In: Sieva¨nen, T., Erkkonen, J., Jokima¨ki, J., Saarinen, J., Tuulentie, S., Virtanen, E. (Eds.), Policies, Methods and Tools for Visitor Management. Proceedings of the Second Conference on Monitoring and Management of Visitor Flows in Recreational and Protected Areas, Rovaniemi, Finland, pp. 37–43. Ploner, A., Brandenburg, C., 2003. Modelling visitor attendance levels subject to day of the week and weather: a comparison between linear regression models and regression trees. Journal for Nature Conservation 11 (4), 297–308. Price, C., Chambers, T.W.M., 2000. Hypotheses about recreational congestion: tests in the Forest of Dean (England) and wider management implications. In: Font, X., Tribe, J. (Eds.), Forest Tourism and Recreation. CABI, Wallingford, pp. 55–74. Rauhala, J., Erkkonen, J., Iisalo, H., 2002. Standardisation of visitor counting: experiences from Finland. In: Arnberger, A., Brandenburg, C., Muhar, A. (Eds.), Monitoring and Management of Visitor Flows in Recreational and Protected Areas; Conference Proceedings, Institute for Landscape Architecture and Landscape Management, Vienna, pp. 258–263. Van Herzele, A., De Clercq, E.M., Wiedemann, T., 2005. Strategic planning for new woodlands in the urban periphery: through the lens of social inclusiveness. Urban Forestry & Urban Greening 3, 177–188. Visschedijk, P.A.M., Henkens, R.J.H.G., 2002. Recreation monitoring at the Dutch Forest Service. In: Arnberger, A., Brandenburg, C., Muhar, A. (Eds.), Monitoring and Management of Visitor Flows in Recreational and Protected Areas; Conference Proceedings, Institute for Landscape Architecture and Landscape Management, Vienna, pp. 65–67. Volk, H., Suchant, R., Roth, R., Wossidlo, R., 1995. Die Integration von Wintersport, Erholung und Naturschutz im Wald. Mitteilungen der Forstlichen Versuchs- und Forschungsanstalt Baden-Wu¨rttemberg, Heft 187. Vuorio, T., Emmelin, L., Sandell, K., 2003. Methods for monitoring outdoor recreation and tourism in large nature areas: the case of So¨dra Ja¨mtlandsfja¨llen. Working Paper 3, European Tourism Research Institute, O¨stersund, Sweden. Watson, A.E., Cole, D.N., Turner, D.L., Reynolds, P.S., 2000. Wilderness recreation use estimation: a handbook of methods and systems. General Technical Report RMRSGTR-56, USDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO.
Urban Forestry & Urban Greening 4 (2006) 135–144 www.elsevier.de/ufug
Recreation use of urban forests: An inter-area comparison Arne Arnberger Institute of Landscape Development, Recreation and Conservation Planning, BOKU – University of Natural Resources and Applied Life Sciences, Peter Jordan-Straße 82, 1190 Vienna, Austria
Abstract Recreation use in two urban forests in Vienna, Austria was compared. Visitors to an inner-urban forest and to a peri-urban forest were monitored by means of video observation during 1 year, from dawn to dusk. The amount of use and the temporal use pattern of the main user types, identified by video interpreters as walkers, cyclists, dog walkers and joggers, were compared. In the inner-urban forest, surrounding settlements, schools and business areas evoked high-use pressure, commuting activities, high shares of all-day activities, more morning and evening use particularly on workdays and, overall, more workday use. The peri-urban forest was, by far, not so heavily used and the proportion of daily routine activities such as dog walking and jogging was reduced because of the lower population density in the surroundings. While the potential for user conflicts in the inner-urban forest seemed to be quite high at weekends and workday late afternoons and evenings, in the peri-urban forest this potential was only high during weekend afternoons in the warmer season, due to the temporally concentrated appearance of walkers and bicyclists. r 2006 Elsevier GmbH. All rights reserved. Keywords: Activity types; Commuting; Daily use pattern; Long-term video monitoring; User conflicts
Introduction Need for recreational use data across forests Differences in recreation use between urban and backcountry forests are apparent (Ho¨rnsten and Fredman, 2000; Konijnendijk, 2000). Urban forests suffer from more intense and multiple recreation use, day-use oriented activities such as dog walking and jogging and higher use levels on workdays. However, recreation use pressure may also differ among urban forests. The degree of urbanity in terms of the surroundings, such as the number and closeness of settlements, business areas and schools, may influence use levels, user composition, and the temporal distribution of activity types through Tel.: +43 1 47654 7205; fax: +43 1 47654 7209.
E-mail address: [email protected]. 1618-8667/$ - see front matter r 2006 Elsevier GmbH. All rights reserved. doi:10.1016/j.ufug.2006.01.004
commuting and recreation use. Consequently, innerurban and peri-urban forests can perceive various kinds of recreation use (Van Herzele et al., 2005), and urban forest owners and administrations will face completely different challenges in providing for sustainable urban forest management. Due to the intense and multiple use urban foresters need reliable and detailed visitation data on activity types in order to understand recreation use in their forests and to identify management strategies that are ecologically sound while, at the same time, being acceptable to the traditional area users. Comparisons of different kinds of urban forests regarding their recreational use patterns would not only contribute to the further understanding of the recreational use of forests, but also further assist area administrations in designing and managing urban forests as buffer zones from the hectic daily life of the city.
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User composition and daily use levels of activity types are very useful tools for comparative analyses across forest types because they provide essential information about who is using the urban forest – and when. Different activity types have different expectations and needs and, consequently, display different behavioural, temporal, and spatial use patterns. The overlap of the daily use patterns of several activity types can produce peak loads, resulting in crowding perceptions, user conflicts and even in the displacement of some users or activity groups (Arnberger, 2003; Price and Chambers, 2000). Daily use patterns, which also provide information about under-use (Arnberger and Haider, 2005; Baum and Paulus, 1991; Kaplan and Kaplan, 1982), are indicators of the amount and temporal usage of, and demand for, recreational infrastructure and form the basis for many management decisions. A prerequisite for area comparisons are long-term counting data about the amount of use, activity types and their daily use pattern. However, such data about urban forest use are often not available or are extrapolated from visitor counts carried out on a few days only. Long-term monitoring using automatic counters and pressure pads has become quite common in different forests (Arnberger and Brandenburg, 2002; Melville and Ruohonen, 2004; Rauhala et al., 2002; Visschedijk and Henkens, 2002; Watson et al., 2000; Volk et al., 1995), but they offer no indication as to the activities in which visitors are engaged. Precisely this information is essential in multiple-use areas, compared to backcountry areas where hikers are the dominant users.
Use pattern of activity types in urban forests based on long-term counting Recently, image-based long-term observations have been carried out in urban and suburban forests. Video monitoring applications result in a fine resolution of temporal use patterns for different activity types during the year. However, very few studies have used this approach for long-term observations. Arnberger and Brandenburg (2002) monitored the year-long use pattern in an exurban national park in Austria, using time-lapse video recording at two main access points in addition to on-site interviews, counts by human observers, and counts using infrared sensors. They assessed the total amount of use in the national park and identified the main user groups, their temporal distribution as well as user types with specific visiting motives. Based on the daily use pattern on workdays, the authors distinguished gastronomic visitors from recreation visitors. Janowsky and Becker (2003) videoed the number of users and variety of recreational activities users engaged in while visiting the urban forest of
Stuttgart, Germany. One camera provided 1-year data, whereas the other camera was set up at three different spots for short-time observations. A signal from a motion detector activated the recording control station, which triggered the video camera to store images over a 5 s interval. The video information was used in conjunction with in-depth interviews and geographic data in order to derive an optimised forest road network that would most effectively meet the requirements of forest management as well as the needs of outdoor recreationists. The researchers analysed the daily use pattern of activity types, differentiated by the day of the week, based on a 2-h evaluation. In this, urban forest walkers came predominantly in the afternoon across all days of the week, while joggers’ activities resulted in a morning use peak on Sundays and in an evening use peak on workdays. Cyclists appeared between noon and late evening on Sundays while, on workdays, use levels were shifted more into the evening hours. Arnberger and Hinterberger (2003) compared the yearly, weekly, and daily use pattern of dog walkers, keeping their dogs on or off the leash, in a peri-urban forest in Vienna based on a 1-year video monitoring at access points. Dog walkers whose dogs were off the leash tended to make their visits on workdays and during less frequented visiting hours of the day compared to dog walkers whose dogs were leashed. It is surprising that, so far, little research has focused explicitly on long-term use patterns of activity types in urban forests and on comparisons of urban forest use, when the need for such basic information, in a reliable manner, is obvious for several management purposes. In this study, recreation use patterns in an inner-urban and a peri-urban forest were compared. In each forest, recreation use was monitored by means of video observation during 1 year, daily from dawn to dusk. The daily use patterns of the main activity types were compared by weekends and workdays in regard to peak loads, the potential for user conflicts, and management challenges.
Study areas Data were collected in an inner-urban forest, which is situated in the south of Vienna, Austria, and in a periurban forest in the east of the city (Fig. 1). The innerurban forest of 120 ha was opened to the public in the late 1980s and is managed by the municipal forest department. Residential and business areas, an old people’s home, a hospital, schools, and garden allotments encircle this forest. About 53,000 inhabitants live within 15 min walking distance. Several sections of the forest are conservation areas and it provides about 30 km of forest roads, gravel trails and footpaths.
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Fig. 1. Location of the forests in Vienna.
Cycling is permitted only on two trails and dogs are allowed, but must remain on the leash. Access by public transport is easy and several parking lots are provided. The peri-urban forest is part of the Danube Floodplains National Park with a total area of 2400 ha and is also managed by the forest department of Vienna. Suburbs of Vienna, communities, areas of intensive agriculture and the Danube River surround the forest. Close to 15,000 inhabitants live within 15 min walking distance. The forest provides numerous access points and there are no access restrictions for day use. A dense network of 142 km of forest roads and trails and several recreational pathways permeate the forest, especially in close proximity to main residential areas. Several trails are open for bicycling and one international cycle route passes through the forest. Dogs are allowed, but must remain on the leash. Access by public transport is suboptimal and several parking lots are provided.
Methods The recreational use in the study areas was investigated during a 1-year time period using a combination of long- and short-term counting methods, as well as onsite interviews. The data for the study presented here were collected by means of video observation, using this monitoring method, in a comparable manner, for observing access points. Video monitoring was undertaken at five main access points in the peri-urban forest and at three main access points in the inner-urban forest over a period of 1 entire year, daily from dawn to dusk. Monitoring took place in the peri-urban forest between 1998 and 1999 (Arnberger and Brandenburg, 2001; Arnberger and Hinterberger, 2003,) and in the innerurban forest between 2002 and 2003 (Arnberger, 2003). Each monitoring unit consisted of a weatherproof black-and-white video camera with integrated heating and one or two time-lapse video recorders. In order to avoid vandalism and to allow for unobstructed observa-
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tions, the cameras were hidden in nesting boxes, the cables were concealed, and the recording units themselves were placed in buildings or construction-site containers inaccessible to visitors. The cameras were installed on wooden poles about 4 m above ground, or on the walls and roofs of buildings. The time-lapse video recorders captured single images at fixed intervals of 1.6 s over the entire day. With this frequency, and about 14 h of operation per day, a 240-min video tape can store 3 weeks worth of data. With the type of video camera installed, and its specific setting, it was impossible to identify individuals in the video images, ensuring their anonymity. This was achieved by the low image resolution of the black-andwhite cameras and a minimum distance between camera and visitor. As a drawback, it was impossible to distinguish between people entering or leaving the forest several times per day at the same or another camera location. Therefore, only people entering the forests at the access points were taken into account for the use comparison; visits – and not visitors – to the forests were counted. For the analyses, only 15 or 20 min of observations per hour were taken into account, but this had no negative impact upon the significance of the results because the data based on a 15- or 20-min evaluation were statistically verified by the data of a complete 2week survey. The examination using linear regression resulted in the R2 values of 0.9 or higher (Arnberger, 2003; Brandenburg, 2001). Thereby, analysing costs could be reduced drastically. The tapes were viewed on a television monitor by trained students and counts were classified and recorded on a MS-Excel spreadsheet. The following data were captured from the video tapes: date and day of the week, time of visit, location of station, direction of movement, number of persons in a group, activity type, and number of leashed and unleashed dogs. Video recordings are an excellent source of information about recreation use and therefore a very useful management tool. Beside the identification of activity types and their temporal distribution, the size of the group and visitor behaviour can be recorded. In addition, at high use levels, video monitoring provides more accurate data about the amount of use compared to human observers. Some of the limiting factors currently associated with the use of video monitoring include: video tape analysing costs and miscounts of user types by video interpreters. The main cost factors are labour costs, for the installation and maintenance of devices, and in particular, for the interpretation and analysis of the video tapes. For short-term observations, the total expenses of video observation are relatively high, but considering the proportional costs per registration day and station over the period of 1 year, long-term monitoring costs are low, particularly when
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compared to long-term surveys by human observers. Another drawback associated with the video monitoring method is the inaccurate assessment of visitor numbers at low-use levels, in particular of faster moving activities such as bicycling and jogging. This inaccuracy is introduced during the interpretation process, when video tape analysts check the tapes as fast as possible and consequently might overlook some users at low-use levels. Heavy rain and dense fog can also lead to miscounts (for a detailed description of the video monitoring approach see Arnberger et al. 2005). In addition, on-site interviews and counts by human observers were conducted. In the inner-urban forest, visitors were intercepted at access points along the main trail section between April and October 2002. The interviews took place on five randomly selected work days and five randomly selected Sundays between 8 a.m. and 7 p.m. In the peri-urban forest, interviews were conducted at 12 access points on two randomly selected work days and two Sundays between 8 a.m. and 7(5) p.m. in spring and summer. Sampling sizes were 952 in the inner-urban forest and 780 in the peri-urban forest. Human observers counted visitors from 8 a.m. to 7(5) p.m. on the days of the interviews in both forests. These personal observations were undertaken at side entrances to the forests, where no video cameras were installed. For the estimation of the total annual number of visitors and user types to the forests, the results of long-term permanent video monitoring at selected sites and temporally selective counting by human observers were combined. From the ratio between video counts and personal observation counts, observing all entrances on the sampling days, visitor numbers for entrance points without video observation could be extrapolated for all days of the year, taking into account external factors such as weather conditions and the day of the week. For that extrapolation process, only entering visits were taken into account. For both forests, use levels varied only slightly between workdays and were, therefore, aggregated for the following analyses. Saturdays were combined with Sundays because of a similar use pattern whereby, in the inner-urban forest, use patterns on Saturdays were more different to Sundays than in the peri-urban forest. As the use patterns for Sundays and public holidays showed no significant differences, Saturdays, Sundays and public holidays will be referred to as ‘weekends’.
Results Amount of use Visitor counting resulted in an annual use estimate of around 1.24 million visits to the inner-urban forest and
0.60 million visits to the peri-urban forest. Depending on the day of week, remarkable differences were observed. At weekends, an average of 4300 daily visits to the inner-urban forest were recorded, resulting in a density of 36 visits per hectare per day, whereas in the peri-urban forest an average of 3000 daily visits and a density of 1.2 visits was videoed. On workdays, the differences between the forests were even more pronounced. The inner-urban forest showed an average of about 3000 daily visits with a density of 25 visits per day per hectare, whereas 1000 visits per day per hectare and a density of only 0.4 visits was recorded for the peri-urban forest. Thus, in the peri-urban forest, weekend use was about 2.9-times higher than workday use, whereas in the inner-urban forest this factor was about 1.4.
User composition Similarities and differences in user composition were observed between the forests. Video interpreters identified four main activity types in both forests: walkers, bicyclists, dog walkers, and joggers (Table 1). In the inner-urban forest, the majority of users enjoyed walking, followed by cycling, dog walking, and jogging activities with fairly equal shares. In the peri-urban forest, however, walking and cycling activities dominated. Only a minority went jogging. Area differences in user composition were observed according to weekdays (Table 2). Compared to the
Table 1. Shares of activity types across forests based on 1-year video monitoring and counts by human observers Activity types
Inner-urban forest (%) Peri-urban forest (%)
Walkers 49 Bicyclists 18 Dog walkers 17 Joggers 16 Others o1
40 47 10 3 o1
Table 2. Shares of activity types by weekends and workdays based on 1-year video monitoring Activity types Walkers Bicyclists Dog walkers Joggers
Inner-urban forest (%) Peri-urban forest (%) Weekends
Workdays
Weekends Workdays
43 36 39 35
57 64 61 65
62 55 50 51
38 45 50 49
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inner-urban forest, peri-urban forest walkers were predominantly weekend users. Equal numbers of dog walkers and joggers were videoed in the peri-urban forest both at weekends and on workdays, whereas the inner-urban forest perceived more workday use by all activity types.
20%
Daily use patterns of activity types Similarities and differences in daily use patterns of activity types between the forests were recorded by the 1-year video monitoring (Figs. 2 and 3). Use patterns of bicyclists differed by the day of the week and by area.
Walkers
Bicyclists 20%
15%
Shares
Shares
15%
10%
5%
10%
5%
0%
0% 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Time of Day
6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Time of Day
Joggers
Dog Walkers 20%
20%
15%
Shares
Shares
15%
10%
10%
5%
5%
0%
0% 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Time of Day
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6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Time of Day
Inner urban forest-Weekend Peri-urban forest-Weekend Inner urban forest-Work day Peri-urban forest-Work day
Fig. 2. Daily use pattern by activity types and weekdays across forests (6 means period between 6:00 and 6:59).
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Inner urban forest- Workdays 20%
15%
15%
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Shares
Inner urban forest- Weekends 20%
10%
5%
10%
5%
0%
0% 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Time of Day
6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Time of Day
Peri-urban forest- Weekends
Peri-urban forest- Workdays 20%
20%
15%
Shares
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15%
10%
5%
5%
0%
10%
0% 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Time of Day
6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Time of Day
Bicyclists Dog Walkers
Walkers Joggers
Fig. 3. Daily use pattern of activity types at weekends.
While the main cycling patterns in the inner-urban forest were observed in the late afternoon and evening hours both at weekends and on workdays, daily use patterns in the peri-urban forest were constantly high between 10 a.m. and 4 p.m. at weekends and between 10 a.m. and 6 p.m. on workdays. On workdays, an additional early morning peak of bicyclists was videoed in the inner-
urban forest. Across the forests, higher bicyclists’ use intensities could be recorded in the evening hours of the workdays compared to weekends. In contrast to bicyclists, the walkers’ use pattern resulted in a distinct use peak in the afternoon, in particular at weekends. In the peri-urban forest this peak was extreme between 2 p.m. and 3 p.m. at
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weekends. More than 21% of all walkers’ use was counted in this hour. In the inner-urban forest this peak was less pronounced and, on workdays, shifted more towards the late afternoon. Similar to bicyclists, walkers showed more intense use in the evening hours on workdays compared to weekends in both forests. On workdays, more early morning and evening use of walkers was observed in the inner-urban forest, while in the peri-urban forest a small peak between 9 a.m. and 11 a.m. was videoed. Dog walkers had a use pattern similar to walkers, visiting mainly in the afternoon of weekends. This use peak was more pronounced in the peri-urban forest, while the inner-urban forest was subjected to more evening use. On workdays, different daily use patterns were observed. The peri-urban dog walkers’ use pattern resulted in two peaks, one in the morning and one in the afternoon. The use pattern of inner-urban forest dog walkers, however, remained on a rather constant use level over the day with more early morning and late evening use. As with walkers and bicyclists, more evening use by dog walkers was recorded in both forests on workdays compared to weekends. The use patterns of joggers were very distinct from other activity groups and differed remarkably based on the day of the week, but not across forests. At weekends, joggers appeared mainly between 9 a.m. and 11 a.m. while, on workdays, most use was observed in the evening. The evening use by inner-urban joggers was more intense compared to peri-urban joggers.
Discussion Similarities in recreation use pattern In both forests, the highest use levels were observed at weekends and here in the afternoons, representing the typical weekend afternoon walk. Use patterns on workdays were influenced by school and working times, resulting in increasing use levels after midday and more evening use (Figs. 2 and 3). Across forests, use levels were stable or dropped slightly at noon, an indicator that some visitors from the surroundings went home to eat. Janowsky and Becker (2003) also videoed higher use levels in the afternoon, on workdays and at weekends, but no midday influence, whereas Arnberger and Brandenburg (2002), in contrast, videoed a noon peak on workdays because of the existence of a local restaurant. Across forests, the main user groups were walkers, bicyclists, dog walkers and joggers (Table 1). In both forests, the use patterns of walkers and dog walkers resulted in use peaks in the afternoon both on workdays and at weekends, while jogging is an activity with
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completely different daily progressions of use on workdays and weekends. On workdays, jogging is carried out before and after work time with a higher preference to jog after work. At weekends, however, jogging was done in the morning hours. Janowsky and Becker (2003) received a similar result for their study in the urban forest of Stuttgart. Obviously, joggers preferred to sleep a little bit longer at the weekend, but avoided the times of heaviest use and hottest conditions during the afternoon.
Differences in recreation use pattern Several factors distinguish the recreation use of the inner-urban forest from the peri-urban forest: first, the inner-urban forest is subjected to about 40 times higher use density. This forest is encircled by residential areas and close to four times more people live within a 15-min walking distance causing the high use pressure. In addition, this forest is easily accessible by public transport from other parts of the city. Second, the inner-urban forest is characterised by more workday use, especially in the morning and evening, predominantly produced by bicyclists and walkers (Figs. 2 and 3). In the immediate neighbourhood of the forest, there are business areas and schools. According to interview results, close to 2% of visitors to this forest (Arnberger, 2003) indicated that they use the area as part of their way to or from work or school. In the surveys of the peri-urban forest (Arnberger and Brandenburg, 2001), however, no respondent stated trail use as a part for his/her travel to or from work. Therefore, due to the lack of surrounding business areas and schools, only recreational bicycling and walking took place in the peri-urban forest whereas both recreation and commuting activities occurred in the inner-urban forest, resulting in higher workday use levels and more workday morning and evening use. Third, in the inner-urban forest, dog walkers and joggers appeared in much higher shares (Table 1) because of the high number of surrounding residential areas and the lack of alternative green spaces in the south of Vienna where dogs are allowed. Dog walking and jogging are everyday activities caused by the need to walk the dog and the habit of jogging daily (Table 2), increasing the overall use pressure and resulting in relatively more workday use. These users also contribute to higher morning and evening use on workdays because of walking the dog and jogging before and after work. Fourth, despite the use pattern produced by working times and commuting activities, use levels in the innerurban forests were higher in the evening hours, both on workdays and at weekends. Apparently, visitors used the inner-urban forest simply for a short outing in their immediate neighbourhood before nightfall while, due to
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its distance from residential areas, many visitors to the peri-urban forest had to travel much longer and, consequently, avoided a short-term visit in the late evening on workdays and at weekends. In contrast, in the peri-urban forest more use occurred between late morning and afternoon both at weekends and especially on workdays. There are several reasons for the use concentration of the peri-urban forest (Figs. 2 and 3). First, the workday use in this forest was not influenced by commuting activities, resulting in relatively more use concentration between the late morning and afternoon hours. Second, fewer daily routine activities were carried out, normally increasing the use intensities of the morning and evening hours. Third, higher shares of bicyclists were observed because the forest size is more suitable for recreation bicycling. This user group came mainly between 10 a.m. and 4 p.m., while in the inner-urban forest, bicycling was done more in the late afternoon or evening. Fourth, due to the higher attractiveness of the peri-urban forest, in terms of size and natural elements, more visitors came to experience nature and the landscape (Arnberger and Brandenburg, 2001). Nature-interested visitors stay longer in the forest and they would not arrive in the late afternoon and evening hours (Arnberger and Brandenburg, 2002). In addition, visitors coming from more distant settlements need some time to travel to the forest, contributing to the late morning use peak.
Potential for user conflicts depending on the day of the week In urban forests, peak use levels are a result of an overlap of several activity types, making use of the resource at the same time. In both forests, most use occurred in the afternoons of the weekends due to the overlap of walkers’, bicyclists’ and dog walkers’ use patterns (Fig. 3). During this period, user conflicts and crowding perceptions were most likely, whereby this potential was drastically higher in the inner-urban forest due the 28 times higher use density at weekends. Consequently, more than 60% of the on-site visitors to the inner-urban forest perceived the area as overcrowded or crowded at weekends and on public holidays (Arnberger, 2003). The, almost complete, absence of joggers at these high-use times, as revealed by long-term video counting, would lead to reduced user conflicts with this group. That joggers were seen as a less disruptive user group was also confirmed by Arnberger and Haider (2005) who used a visual choice model displaying various trail use scenarios in the inner-urban forest. This conflict reducing behaviour had more affect on the inner-urban forest, as about 16% of the entering users were joggers, whereas they only accounted for 3% of the users in the peri-urban forest (Table 1).
In regard to user conflicts, the situation is completely different in the forests on workdays. In the inner-urban forest, both recreation and commuting bicycling and walking took place in the late afternoon and evening hours. Their use patterns coincided with the use of recreational walkers and bicyclists as well as daily routine activities such as jogging and dog walking. This was not the case in the peri-urban forest because of the absence of commuting activities, lower shares of jogging and dog walking activities and much lower overall use levels. Compared to the peri-urban forest, use density in the inner-urban forest was 63 times higher, resulting in a high chance for user conflicts. In the inner-urban forest, another period with a higher conflict potential would be the morning hours. This early morning use peak was caused by the temporal overlap of commuting and daily routine activities. The overlap of jogging and dog walking activities and fastmoving bicyclists may result in conflict perceptions. Compared to the peri-urban forest, this conflict potential was much greater because of higher use levels and the larger shares of dog walkers and joggers. Even if the share of joggers in the visitors entering was not high, one should consider that joggers made several laps of the inner-urban forest increasing the use pressure drastically, compared to the 20 times larger peri-urban forest providing more trails. Especially joggers reported conflict perceptions with dog walkers because, precisely in the low-use hours of the day, dog walkers tended to release their dogs from the leash (Arnberger, 2003; Arnberger and Hinterberger, 2003), whereas dog walkers reported some conflict perceptions with fast moving bicyclists (Arnberger, 2003).
Potential for user conflicts depending on the season The potential for user conflicts also depends on the season. The highest use levels in both forests were observed during the warmer season, in particular in May. Arnberger and Brandenburg (2002) and Janowsky and Becker (2003) obtained similar results for recreation use of forests. In particular, bicyclists are highly influenced by weather conditions, appearing mainly in the warmer season or during good weather conditions (Ploner and Brandenburg, 2003). Thus, conflicts with this group in the wintertime, or in rainy and cloudy conditions, were less likely. The influence of weather, however, differed across forests. Compared to the peri-urban forest, the innerurban forest use was less influenced by weather because of commuting bicyclists and daily routine activities, appearing also in the colder season, when it is cloudy or even slightly rainy (Brandenburg et al., 2004; Ploner and Brandenburg, 2003). Consequently, when an overlap
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between these activities occurred, as in the inner-urban forest, this kind of conflict might not only be restricted to the warmer seasons. In the peri-urban forest, bicyclists were the biggest user group and the potential for user conflicts depended much more on the weather. The potential seemed to be high between 2 p.m. and 3 p.m. at weekends, during the warmer season only, because of the overlap of walkers’, dog walkers’ and bicyclists’ use patterns. Due to the much higher use levels, the overlap of commuting and recreation activities, and the high share of everyday users such as dog walkers and joggers, the potential for user conflicts in the inner-urban forest is not only given for the weekend afternoons, but also for workday late afternoons and evenings. Thus, user conflicts are quite likely throughout the week and year, also caused by the low influence of weather conditions on commuting and everyday activities. The peri-urban forest is, by far, not so heavily used and the proportion of daily routine activities is reduced because of the lower population density in the surroundings. Recreation use is more concentrated between late morning and late afternoon with a distinct peak hour at weekends, while, in the evenings, the peri-urban forest, also part of a national park, is low visited, providing rest periods for wildlife. Due to the high share of bicyclists, the potential for user conflicts is quite likely during weekend afternoons in the warmer season only.
Conclusion In urban forests, the monitoring of recreation activities has to consider the multiple use by activity types. Long-term video monitoring can detect daily use levels by activity types to answer the question ‘‘who comes when’’ and provides the basis for comparative analyses. Such comparisons contribute to a better understanding of urban forest use by recreationists in general. In addition, data on the amount and temporal distribution of public use and activity types are the basis for numerous planning decisions and management measures. Recreation use data gained by this study can be used for the planning for woodlands, providing information about the potential amount and kind of use a forest can perceive per year, at weekends and on workdays, depending on the surroundings. These use data are also indicators for the amount and kind of recreational infrastructure needed to avoid user conflicts in advance. Ultimately, one of the central issues in urban forest planning and management is the problem of predicting the responses and reactions of different activity types to recreational infrastructure, plans and management actions (Vuorio et al., 2003).
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The degree of urbanity results in completely different recreational use levels, use pattern and user composition in these two urban forests in Vienna. Based on the observed use patterns, the heavily used inner-urban forest plays an important role as an everyday environment for the recreation and commuting activities of the local population, whereas the peri-urban forest attracts not only everyday users from the surroundings but also high shares of recreation visitors, especially bicyclists, serving more as a daily recreation area for the region. As such, the management of the urban forests differs considerably. In the peri-urban forest, management efforts should target the potential conflict between bicyclists, walkers and dog walkers during weekend afternoons in the warmer season, whereas in the innerurban forest the potential for conflicts is given throughout the week and even the year with a focus on weekend and workday late afternoons and evenings. Based on use data, forest management can tailor, even temporarily, direct or indirect measures to address specific user groups should there be a need to reduce user conflicts or crowding perceptions, illegitimate behaviour or for maintenance operations. Information about shared tracks for forest users at access points, measures to reduce bicycling speed and the presence of forest rangers during peak periods could indirectly reduce user conflicts. Additional designated and attractive trails for commuting bicyclists outside the forests, separating them from other users or establishing a oneway system for bicyclists, would be some possible further management options for reducing the potential conflict between bicyclists and other users. Overall, more attractive green spaces in the surroundings would deflect some use pressure from the forests. Some limitations of this study have to be mentioned. This study provides information about the potential for user conflicts, however, not about the amount of occurring conflicts. Such information would require either a long-term monitoring of all trails within the forests using video observation or visitor surveys about the amount of perceived conflicts. In addition, the video observation recorded recreation use between dawn and dusk. Thus, information about night use, which might play a more important role for the inner-urban forest, cannot be provided. Ideally, the observations should have been made simultaneously, as the trend in outdoor recreation could have changed within 5 years and weather conditions perceived in the different years of observation might also have had an influence on daily use pattern. However, one can assume that, given the huge sample size, variations in daily use patterns due to weather conditions play only a minor role. This comparison included only two urban forests. Future research should test if the findings about the differences in recreation use between these would also hold for other urban forests.
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Acknowledgements The Forest Department of the City of Vienna commissioned the Institute of Landscape Development, Recreation and Conservation Planning to collect data on public use.
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