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Modeling daily visits to the 2010 Taipei International Flora Exposition
Urban Forestry & Urban Greening 13 (2014) 725–733
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Modeling daily visits to the 2010 Taipei International Flora Exposition Ai-Tsen Su, Chia-Kuen Cheng ∗∗ , Yann-Jou Lin ∗ Department of Horticulture and Landscape Architecture, National Taiwan University, Taiwan
a r t i c l e
i n f o
Keywords: Closing effect Rainfall Sunshine Temperature Urban park Use forecasting Use pattern
a b s t r a c t The 2010 Taipei International Flora Exposition (Flora Expo) was held in several urban parks in the center of the city. Over the course of the Expo, substantial variation was observed in the number of visitors per day. The aim of this study is to investigate the factors associated with the daily number of visitors to the Flora Expo. Our results suggest that there were periodic changes in the number of daily visits over time as well as with differences between the days of the week. In addition, temperature, duration of sunshine and precipitation were significantly correlated with the number of daily visits. Regression models suggest that the day of the week, long holiday, proximity to the closing date, temperature, duration of sunshine and hours of rainfall showed strong correlation with daily visits. Higher visits were recorded on Saturdays, Sundays and long holidays. When the temperature at noon increased by 1 ◦ C, an average of 1435 additional visits was recorded. When the duration of sunshine increased by 1 h, an average of 895 additional visits was recorded. Approximately 1232 fewer visits were recorded when the duration of rainfall increased by 1 h. Our results also show significant closing effects on daily visits over the last four weeks. These findings may provide useful information for the operation and management of similar festivals in urban parks and help managers to accurately assess and predict the number of visitors. © 2014 Elsevier GmbH. All rights reserved.
Introduction Urban parks provide important recreational opportunities for urban residents that help maintain their leisure quality of life. The numbers of visitors are therefore very important information for urban green space planning and management. With the development of society, the role of urban parklands has gradually diversified (Cranz, 1982). In particular, seasonal activities or festivals are often held in parks (Dines and Brown, 2002; Getz, 1991). These festivals offer the public opportunities to participate in social activities and enhance their quality of life. Such festivals typically do not last long, as gathering a large number of visitors for a longer time may damage environmental resources or reduce the quality of recreation services. Understanding the general patterns of recreation use could not only help managers and planners prepare for rapid increases in use on certain seasons or at specific times (Dwyer, 1988); but also foresee potentials for user conflicts (Arnberger, 2006; Arnberger and Eder, 2007). Thus, an accurate prediction of
∗ Corresponding author at: 138, Sec. 4, KeeLung Rd., Taipei, Taiwan. Tel.: +886 233664860. ∗∗ Corresponding author at: 138, Sec. 4, KeeLung Rd., Taipei, Taiwan. Tel.: +886 233669759. E-mail addresses: [email protected] (A.-T. Su), [email protected] (C.-K. Cheng), [email protected] (Y.-J. Lin). http://dx.doi.org/10.1016/j.ufug.2014.07.001 1618-8667/© 2014 Elsevier GmbH. All rights reserved.
the daily visitor attendance during festivals enables administrators to plan and implement management strategies in advance in order to mitigate the impacts on society (Ritchie and Smith, 1991; Deery and Jago, 2010; Reverté and Izard, 2011), the environment (Collins et al., 2009; Ahmed and Pretorius, 2010; Mallen et al., 2010) and the economy (Gelan, 2003; Kim et al., 2003; Lee and Taylor, 2005). A number of studies have therefore been dedicated to estimating the number of visitors to urban forests and parks (Dwyer, 1988; Dwyer et al., 1990; Arnberger and Hinterberger, 2003; Arnberger, 2006; Arnberger and Eder, 2007) as well as festivals (Snowball, 2004; Baumann et al., 2009; Xu et al., 2009; Jiang et al., 2011; Zhang, 2011). Previous studies have demonstrated that the number of visitors to urban parks is associated with numerous factors, including the season (Ritchie and Beliveau, 1974; Getz, 1991; Butler, 2001; Koenig-Lewis and Bischof, 2005), the day of the week (Dwyer, 1988; Dwyer et al., 1990; Arnberger and Hinterberger, 2003; Ploner and Brandenburg, 2003; Arnberger, 2006; Arnberger and Eder, 2007), special holiday (Van Wagtendonk, 1981; Butler, 2001; Lim and McAleer, 2001) and the weather (Dwyer, 1988; Allcock, 1989; Dwyer et al., 1990; Butler, 2001; Arnberger and Hinterberger, 2003; Ploner and Brandenburg, 2003; Arnberger, 2006; Hall and Page, 2006; Hartmann, 1986; Brandenburg et al., 2007; Scott et al., 2007; Xu et al., 2009; Moore, 2010; Becken, 2012; Finger and Lehmann, 2012). Furthermore, studies of marketing and consumer behavior have observed a so-called closing effect, in which the number
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of transactions significantly increases as the end of a transaction period approaches (e.g., Auter and Moore, 1993; Curras-Perez et al., 2011; Kauffman and Wang, 2001). Many park festivals are organized to take place within a limited time frame. However, few, if any, studies have addressed the question of the closing effect, which means that its association with the number of visitors to a festival remains to be clarified. The 2010 Taipei International Flora Exposition (Flora Expo) was a six-month festival based in several existing parks, and provided a good opportunity for understanding visitation patterns for an urban park festival. The purpose of this study is to examine the correlations among day and its location within the week/Expo, weather conditions, the closing effect, and the number of daily visitors by analyzing the fluctuation pattern of daily visitors to the event in urban parks (the Flora Expo). The following research questions were investigated: (1) Are there temporal fluctuations in daily visits to urban parks, including week and the day of week? (2) Are the daily visitor numbers on long holidays different from visitation on normal days? (3) Do the daily visits vary with different weather conditions? (4) Does a closing effect exist in such an event in urban parks? With the increasing tendency of holding festivals and events in urban parks, the results could provide information regarding similar activities being considered for other urban parks. Literature review Estimation of visitor attendance Estimating visitor attendance has been important to the fields of recreation and tourism. An accurate estimate can help managers develop more effective strategies to meet visitor needs. A number of studies have therefore focused on predicting demands for recreation. Such studies typically use explanatory models that are based on the assumption that correlations exist between these variables and the number of visitors. For example, Dwyer (1988), Dwyer et al. (1990), Vries and Goossen (2002), Ploner and Brandenburg (2003), Brandenburg et al. (2007) and Neuvonen et al. (2007) predict visitor use of urban forests, recreation areas and protected areas using explanatory models. Festivals in urban parks only last a short time, during which a large number of visitors appear. To provide better services, an accurate prediction of the number of visitors per day is required. Accordingly, many studies have tried to predict number of visitors for various festivals, such as the Shanghai World Expo (Xu et al., 2009; Jiang et al., 2011; Zhang, 2011), the Korea International Travel Fair (Lee et al., 2008), the South African National Arts Festival (Snowball, 2004), the South Africa Science Fair (Snowball, 2004) and sporting events in Hawaii (Baumann et al., 2009). Xu et al. (2009) investigated the associated variables for visitor numbers in large-scale exhibition activities in order to provide strategic recommendations for visitor management. Lee et al. (2008) combined a quantitative model and a qualitative method to predict the number of visitors to the 2012 Korea International Travel Fair. In all of these aforementioned studies, however, visitor attendance at urban park festivals is rarely studied. Factors associated with seasonal fluctuation Visitor numbers to scenic sites usually vary over time. This seasonal fluctuation or cyclic demand (Ritchie and Beliveau, 1974) has been considered the most important feature of visitor numbers with respect to festivals (Butler, 2001; Getz, 1991). Butler (2001) defined seasonal fluctuation as the “temporal imbalance in the phenomenon of tourism, which may be expressed in terms of dimensions in such elements as numbers of visitors, expenditure
of visitors, traffic on highways and other forms of transportation, employment and admissions to attractions.“The fluctuation phenomenon typically only receives brief discussions in textbooks (e.g., Getz, 1991; Cooper et al., 1993; Burns and Holden, 1995; Hall and Page, 2006), and only a small number of studies have addressed it (Allcock, 1989; Butler, 1994; Highama and Hinch, 2002). Two main reasons are generally believed to cause seasonal fluctuations in daily visitor numbers: natural factors and institutionalized factors (Baron, 1975; Hartmann, 1986). Typical natural factors include climatic factors, such as temperature, rainfall, snowfall, and sunshine (Hartmann, 1986; Dwyer, 1988; Allcock, 1989; Dwyer et al., 1990; Butler, 2001; Arnberger and Hinterberger, 2003; Ploner and Brandenburg, 2003; Hall and Page, 2006; Arnberger, 2006; Brandenburg et al., 2007; Scott et al., 2007; Moore, 2010). It is widely accepted that pleasant climatic factors increase visitor numbers. Dwyer (1988) suggests that deviations of the daily temperature at noon, percentage of the day with sun, and percent of the day with rain from the respective monthly average are associated with the percentage change of daily use of urban forest recreation sites. Arnberger and Hinterberger (2003) suggest that the recreation use patterns of some activities, such as biking, hiking and jogging, are associated with temperature, so higher use levels of urban forests could be observed in warmer weather (Arnberger, 2006). Becken (2012) indicates that higher maximum temperature is associated with the monthly overnight stays in Westland and confirms that the warmer summer is associated with more overnight stays. Furthermore, rainfall also has a strong and significant correlation with visits in outdoor recreation activities (Finger and Lehmann, 2012). Dwyer et al. (1990) suggest that an increase in the percentage of the day when it was raining above the monthly average tends to decrease use of an urban lake. Brandenburg et al. (2007) indicate that cycling is an activity performed during mild weather, which is generally sunny with a temperature greater than 5 ◦ C, few clouds and no precipitation. There were differences in the association between precipitation with the behavior of commuting and recreational cyclists on workdays. The second factor that causes seasonal fluctuations in daily visitor numbers is primarily related to human behavior and regulations, in particular the existence of fixed activities such as major holidays and festivals. Even the economic cycle can affect travel behavior (Cooper et al., 1993). In comparison to natural factors, institutionalized factors are subject to major changes and are less stable (Butler, 2001). The most common institutionalized factor with seasonal fluctuations is holidays, with school holidays (Lim and McAleer, 2001) and factory holidays (Butler, 2001) of particular importance. Van Wagtendonk (1981) conducted a study of visitors to the Yosemite National Park in the United States from 1972 to 1979 and found that peaks in visitor numbers coincided with national holidays; in particular, the number of visitors increased significantly on Memorial Day, July 4th, and Labor Day. Social norms or fashion also cause seasonal fluctuations in recreational use (Baum and Hagen, 1999; Butler, 2001). Social phenomena often lead to the designation of specific times for specific activities, thereby causing seasonal fluctuations in visits. In Taiwan, common examples are popular activities such as the flowering season and the Music Festival, which bring a large number of visitors to attractions. To some degree, fluctuation in visits due to popular societal activities also occurred in the case of the Flora Expo. Sports seasons are also believed to be a common cause of seasonal fluctuations in visitor numbers (Butler, 2001; Van Wagtendonk, 1981). Day to day variations have been identified as a major pattern in the use of urban parks or forests; with the highest daily use on weekends (Dwyer, 1988; Ploner and Brandenburg, 2003; Arnberger and Hinterberger, 2003; Arnberger, 2006; Arnberger and Eder, 2007). For example, there are higher use levels at weekends in the urban forests (Arnberger, 2006), and the use intensities in
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urban forests were significantly different for the day of the week (Arnberger and Hinterberger, 2003; Arnberger and Eder, 2007). Institutionalized factors and natural factors are not completely independent of one another; in fact, there is often an interaction between these two factors (Allcock, 1989) because some holidays or traditional festivals are related to certain seasons and weather conditions, and many activities depend on specific weather conditions. Closing effects In addition to the previously mentioned variables, a time limit may also cause the number of visitors to fluctuate. Verhallen and Robben (1994) noted that product availability is often negatively correlated with product value and preference. Therefore, when consumers perceive that products are limited or difficult to obtain, their demand for these products often increases (Verhallen and Robben, 1994; Brannon and Brock, 2001). Similarly, when the sale of a product nears its end, consumer desires to purchase the product increase (Auter and Moore, 1993; Curras-Perez et al., 2011). In the field of business, the so-called cycle ending effect states that the number of transactions will increase rapidly at the end of a trading period because of time constraint and limited order (Kauffman and Wang, 2001). Additionally, the number of transactions not only increases near the end of the trading period, consumer perception of a product changes. In consumer behavior research, this change is known as the service-ending effect theory, which holds that consumers view the final products or services to be the best and therefore determine to purchase them (Oh et al., 2009). When the period open to visitors is limited, more visitors are observed closer to the end of the visiting period. This phenomenon indicates the occurrence of the closing effect. In the existing literature on visitor numbers and festivals, however, only a small number of empirical studies have addressed the association with the closing effect. Methods Study site The theme of the Flora Expo was “Flower, River, New Horizon.” The purpose of the Expo was to attract visitors to enjoy the unique charm of Taipei City and inspire them to implement green practices in their daily lives. Unlike other exhibitions, the Flora Expo was located in the Yuanshan area, the center of Taipei City (Fig. 1). It was held in sites among many of the city’s existing artistic and cultural locations, including the Zhongshan Soccer Stadium, Taipei Children’s Recreation Center, Taipei Fine Arts Museum, Taipei Story House, and the Lin-An-tai Historical Home (Fig. 2). The Flora Expo occupied a site measuring 91.8 ha and consisted of four major areas: Yuanshan Park, Fine Arts Park, Xinsheng Park and Dajia Riverside Park. Each area was covered with many outdoor gardens and pavilions. The Yuanshan Park was the biggest outdoor flora exhibition area of the Flora Expo, and the gardens were decorated with various types of flowers and plants for different events and themes. Most of the events at the Expo were recurrent, with different exhibitions and shows changing every few days. To keep the visitors’ interests, the flowers in the gardens were kept in bloom by replanting overnight for continuity between themes. A number of indoor floral design competitions and exhibitions took place in the pavilions within this area. In the Fine Arts Park, various types of global gardens from a number of countries were shown outdoors. The Xinsheng Park was the second largest in Taipei City, and included the Fujian Style Garden, Serenity Garden, Garden Maze and Flower Base under Trees. The exhibition featured Taiwan’s cutting-edge technology, bountiful natural environment and precious bonsais presented in pavilions and green houses. In the Dajia Riverside Park, Taiwan Flower Area contained a view with a wide
Fig. 1. Map of Taipei City, routes of Mass Rapid Transit (MRT) and location of the Flora Expo and the weather station.
display of colorful flowers arranged in different patterns. A giant tent provided space for artistic groups to perform. Furthermore, floral tunnels connected the different areas. There were various ways to get to the Flora Expo. The main entrances were located near Taipei’s Mass Rapid Transit (MRT) Stations. Once visitors reached Taipei City, they could take MRTs, city buses, free Expo shuttle buses or taxis to the Expo within half an hour. Visitors could also take sightseeing boats on the Keelung River from the Dajia Riverside, Meiti and Xikou Piers to the Flora Expo. There were several types of tickets to the Flora Expo, including day-tickets, afternoon tickets, evening tickets, group tickets, 3-day passes and Flora Expo Passes. Day tickets, which cost 300 NTDs (approximately 7.22 Euros), included general admission for one day. Afternoon and evening tickets were for admission after 1 p.m. and 5 p.m., respectively. Group tickets were for parties of 10 or more. Three-day passes were limited to a period of three consecutive days, starting from the date of first entry. The holders of Flora Expo Passes were granted unlimited entry to the Expo over the course of the event. Some packages could be purchased before the opening of the Flora Expo, such as day-tickets for early birds or Flora Expo Mascot Memorial Tickets (a set containing 12 day tickets and 1–2 additional day tickets for free). Daily visits to the flora expo The Flora Expo was open between 9 a.m. and 10 p.m. for a total of 171 days from November 6, 2010 to April 25, 2011. During this time, the Expo attracted a considerable number of visitors. A total of 8,963,666 visitors were recorded. The average daily number of visitors on weekdays (not including national holidays) and holidays
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Fig. 2. Map of the Flora Expo.
(including weekends and national holidays) was 46,067 and 66,440, respectively. In terms of the long-term pattern, the number of visitors increased slightly week by week; the exception was during the long holidays in which there were peaks in the number of visitors. The approach of the Expo’s closing date also prompted more visitors to attend. The number of visitors increased significantly and reached the highest peak on the closing week. A record number of daily visitors (183,774 visitors) occurred on the last weekend before the Expo was closed (Fig. 3). These patterns suggest long holiday and closing effects. In terms of short-term fluctuations, there was a significant and regular weekly cycle of change in the number of visitors. The number of visitors peaked on Saturdays and Sundays and hit a low point almost every week from Tuesday to Thursday.
Data collection and measurement of variables Visitor data reflected the number of visitors on a given day. There are several methods for collecting visitor data in the existing literature, such as official records (Dwyer et al., 1990), on-site counters (Dwyer, 1988; Arnberger and Eder, 2007), actual field observations (Dwyer, 1988; Arnberger and Hinterberger, 2003) and video recording (Ploner and Brandenburg, 2003; Arnberger, 2006; Arnberger and Eder, 2007; Brandenburg et al., 2007). The visitor numbers used in this study were published daily on the Flora Expo official website. Considering the availability and feasibility of data collection, two types of data were collected in this study: day and its location within the week/Expo and meteorological information (Table 1).
Fig. 3. Daily number of visitors during the Flora Expo.
A.-T. Su et al. / Urban Forestry & Urban Greening 13 (2014) 725–733 Table 1 Variables used in modeling daily visits to the Expo. Day and Its Position within the Week/Expo Name Values Week
The number of weeks into the Expo
Day of the week
Weekday: Monday to Friday Saturday Sunday
Long holiday
Yes: Chinese New Year or Peace Memorial Day no: normal day
Proximity to the closing date
Last week before closing: week 26 of the Expo Second to the last week before closing: week 25 of the Expo Third to the last week before closing: week 24 of the Expo Fourth to the last week before closing: week 23 of the Expo Other weeks from the opening: week 1 to week 22
Meteorological information Variable Temperature at 8 a.m. Temperature at noon Maximum temperature Minimum temperature Average temperature Sunshine duration Precipitation Rainfall
Details Temperature in ◦ C at 8 a.m. Temperature in ◦ C at 12 noon Maximum temperature from 9 a.m. to 10 p.m. Minimum temperature from 9 a.m. to 10 p.m. Average temperature from 9 a.m. to 10 p.m. Sunshine hours between 8 a.m. and 7 p.m. Precipitation in mm between 8 a.m. and 10 p.m. Rainfall in hours between 8 a.m. and 10 p.m.
729
collected. The temperature data included the temperature in ◦ C at 8 a.m. and at 12 noon, as well as the maximum, minimum and average temperature during the opening hours of the Flora Expo. The correlation matrix among all of the variables is shown in Table 2. The visitor numbers were presented with both actual visitor numbers and the natural logarithm of visitor numbers. The natural logarithm of visitor numbers was its logarithm to the base e. All temperature variables showed significant correlations with visitor numbers and the natural logarithm of visitor numbers. The temperature at 12 noon was used in this study due to its highest coefficients of correlation on both variables of visitor attendance, with a Pearson correlation coefficient of 0.61 and 0.66 (p ≤ 0.01). Besides, it is more feasible for park managers to gather the information about temperature at noon from weather forecasting. Considering the opening hours of the Flora Expo, the hourly data of sunshine duration and precipitation used in this study were measured between 8 a.m. and 10 p.m. The sunshine duration showed significant correlations with the visitor numbers and the natural logarithm of the visitor numbers, with a Pearson correlation coefficient of 0.48 and 0.50 (p ≤ 0.01). The precipitation data included precipitation in mm and hours of rainfall and were obtained by aggregating the hourly data between 8 a.m. and 10 p.m. The correlation matrix shows there was no correlation between precipitation in mm and the visitor attendance. The hours of rainfall had significantly negative correlations with both variables in visitor attendance, with a Pearson correlation coefficient of −0.23 and −0.30 (p ≤ 0.01). As the result, hours of rainfall were selected for the following models.
Results The number of weeks into the Flora Expo refers to the number of weeks that had passed since the opening of Expo. The day of the week was category data that treated “Saturday” and “Sunday” as two dummy variables, with “weekday” used as the reference category. The long holidays in this study were February 3–5 (the 1st through 3rd days of the first lunar month) and February 26–28 (of the long holiday for Peace Memorial Day). The value of long holidays was treated as a dummy variable, with normal days used as the reference category. The normal days represented those days which were not included in the long holidays. In terms of the effect of proximity to the closing date (closing effect), the last four weeks before the closing of the Flora Expo were treated as four dummy variables, with the weeks from the opening to the 22nd week used as the reference category. Meteorological data were downloaded from the Atmospheric Research Database of the Taiwan Typhoon and Flood Research Center, National Applied Research Laboratories. Hourly data recorded by the nearby Dazhi automatic measuring station were also used. Data on temperature, precipitation and sunshine duration were
Temporal fluctuations Week and daily visit Visitor attendance exhibited a week to week variation pattern. As the Expo’s closing date drew closer, more visitors attended. A simple regression analysis was performed to analyze the association between weeks and daily visitor attendance to the Flora Expo. The results showed that the number of weeks into the Expo was significantly associated with the number of daily visits to the Flora Expo, with the number of daily visitors increasing by 1293 with each passing week (Table 3). The day of week and daily visit The variation of visitor numbers revealed that there was a cyclical change in the daily number of visitors. A one-way analysis of variance was performed to analyze the daily number of visitors in a week, and the results showed that there was a significant difference in the number of visitors on different days of the week (F = 12.69,
Table 2 The correlation matrix among all of the variables.
Visitor Number LN (visitor number) Temp. at 8 a.m. Temp. at 12 noon Max. temp. Min. temp. Avg. temp. Sunshine duration Precipitation Hours of rainfall * **
p ≤ 0.05. p ≤ 0.01.
Hours of rainfall
Precipitation
Sunshine duration
Avg. temp.
Min. temp.
Max. temp.
−.23** −.30** −.17* −.27** −.27** −.20* −.26** −.26** .87** –
−.14 −.18* −.09 −.19* −.19* −.12 −.18* −.22** –
.48** .50** .27** .53** .54** .32** .49** –
.59** .64** .92** .99** .98** .96** –
.50** .56** .96** .92** .90** –
.60** .65** .88** .99** –
Temp. at 12 noon .61** .66** .89** –
Temp. at 8 a.m.
LN (visitor number)
Visitor number
.46** .53** –
.95** –
–
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Table 3 Association between 7-day week and daily visits to the Flora Expo. Estimate B
t 8.60* 4.87*
(Constant) 34,875.44 Week 1292.53 R2 = 0.12; adjusted R2 = 0.12; F = 23.75* *
Regression models for predicting visitor attendance to the Flora Expo
p ≤ 0.001.
p ≤ 0.001). The number of visitors were the highest on Sundays and Saturdays, with an average 70,206 and 63,354. Long holiday and daily visit The Flora Expo spanned long holidays, including the Chinese New Year and the long holiday for Peace Memorial Day. The average daily number of visitors on normal days was approximately 50,674, and the average daily number of visitors on long holidays was approximately 100,434. The daily visitor attendances on long holidays were higher than daily visitor attendance on normal days. Proximity to the closing date and daily visit As the closing date of the Expo approached, the number of visitors increased significantly. Visitor attendance dramatically increased week-by-week at the end of the Expo. The results of ANOVA indicate significant differences among 7-day weeks before the closing. Average daily attendance at 22 weeks from the opening was 46,724 visitors. Average daily attendance in the 23rd week, also known as the fourth to the last week before closing, was 71,101 visitors. Average daily attendance in the 24th week, also known as the third to the last week before closing, was approximately 79,023 visitors. Average daily attendance in the 25th week, also known as the second to the last week before closing, was approximately 98,620 visitors. Finally, average daily attendance in the 26th week, also known as the last week before closing, was up to 153,648 visitors (Table 4). Weather conditions During the Expo, the average daily temperature was approximately 17.35 ◦ C (standard deviation [SD] = 3.68); the lowest daily temperature was 8.61 ◦ C, and the highest daily temperature was 24.73 ◦ C. The average daily sunshine duration was approximately 3.06 h (standard deviation [SD] = 3.82); the average number of sunshine hours seems low given that the days with sun in winter or spring are usually partly cloudy in Taipei. The lowest daily sunshine duration was 0 h, and the highest daily sunshine duration was 11 h. The total number of days with sunshine during the Expo was 106. The average daily rainfall duration was approximately 0.81 h (standard deviation [SD] = 2.37); the lowest daily rainfall duration was 0 h, and the highest daily rainfall duration was 14 h. The total of rainy days during the Expo was 52. Table 4 Analysis of daily visitor attendance on proximity to the closing date.
Proximity to the closing date
*
p ≤ 0.001.
Last week before closing Second to the last week before closing Third to the last week before closing Four to the last week before closing Other weeks from the opening
Mean
F
153,647.50
29.14*
98,619.86 79,023.29 71,101.14 46,724.06
To enable us to better understand the association with each predictor and the number of visitors to the Flora Expo, a regression analysis was conducted with the number of weeks into the Expo, the day of the week, long holiday, proximity to the closing date, temperature, rainfall and sunshine duration used as predictors in the model. Two different functional forms, additive or multiplicative, were suggested by Cooper et al. (1993) to measure the associations of various factors with daily use. For additive functions, changes in use are estimated in the absolute number of visitors. In the case of multiplicative models, changes in use are estimated by multiplying the coefficients (multiplier) in visitor numbers at a specific time (Dwyer, 1988). Both a linear regression and a semilogarithmic multiplicative model were considered. The linear regression analysis shows a significant association between the day of the week and daily visitor attendance to the Flora Expo. On Saturdays and Sundays, the average numbers of visitors were 16,336 and 18,113 higher than on weekdays, respectively. The effects of long holidays and proximity to the closing date were both significantly increased visitor attendance. During the long holidays, the number of visitors was 40,754 higher than on normal days. In the weeks before the Expo closed, the number of visitors on the last week before closing was 86,277 higher than at 22 weeks from the opening. The numbers of visitors on the second, third and fourth to the last weeks before closing were approximately 46,412, 21,712 and 18,122 higher than at 22 weeks from the opening, respectively. The number of visitors increased by 1435 with each 1 ◦ C increase in temperature at 12 noon, and the number of visitors also increased by 895 with each 1 h increase in sunshine duration. The number of visitors decreased by 1232 with each 1 h increase in rainfall duration. The total variance explained by the model was 79% (Table 5). Comparing the predicted values with the actual visits, the mean prediction error of the model was 93 visitors, and these two values were highly correlated (R = 0.89, p ≤ 0.01). In the semi-logarithm model, the factor exp(B) value for the constant was shown as 19,661.64, which meant that, on average, 19,662 visitors could be expected on a weekday with the temperature at 12 noon at the freezing point, no rainfall, no sunshine, not during long holidays, and not in the four weeks before the closing. The coefficients for the independent variables could be converted to multipliers with this multiplicative form. If it was a Saturday or a Sunday, visitor numbers would increase by 35% or 33% (factor 1.35/factor 1.33). Visitor attendance would increase by 69% (factor 1.69) on long holidays. The number of visitors would increase by 120% (factor 2.20) on the last week before closing; the visitor numbers would increase by 94% (factor 1.94), 35% (factor 1.35) and 36% (factor 1.36) on the second, third and fourth to the last weeks before the closing, respectively. Each additional degree Celsius of the temperature at 12 noon would increase the number of visitors by 4% (factor 1.04), each additional hour of sunshine by 2% (factor 1.02). The visitor numbers would decrease by 3% (factor 0.97) with each additional hour of rainfall. The total variance explained by the model was 71% (Table 5). The mean prediction error between the predicted visits and the actual visits was 1327 visitors, and these two values were also highly correlated (R = 0.90, p ≤ 0.01). Although there were significant correlations among some variables as shown in Table 2, the coefficients were relatively low, which suggesting confound effect might not be a serious problem for the model. Besides, the values of variance inflation factors (VIF) shown in Table 5 are all under 5, suggesting no multicollinearity among variables. The explanatory variables for the final models were selected by examining the correlations, t-values, and the values of VIF among variables. Both models accurately reflect overall
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Table 5 Regression for daily visits to the Flora Expo. Linear model Estimate B (Constant) Saturday Sunday Long holiday Last week before closing Second to the last week before closing Third to the last week before closing Fourth to the last week before closing Temperature at noon Hours of rainfall Sunshine duration R2 Adjusted R2 F * ** ***
12,173.16 16,335.62 18,113.47 40,754.42 86,276.95 46,411.74 21,711.72 18,122.19 1435.38 −1232.39 894.74
Semi-logarithmic model t 2.63** 5.89*** 6.63*** 7.67*** 9.56*** 9.56*** 4.32*** 3.75*** 5.55*** −2.92** 2.87** 0.79 0.78 60.88***
VIF
Estimate B
t
1.06 1.06 1.09 1.07 1.05 1.13 1.04 1.69 1.13 1.52
9.89 0.30 0.29 0.52 0.79 0.66 0.30 0.31 0.04 −0.03 0.02
103.02*** 5.22*** 5.05*** 4.74*** 4.22*** 6.60*** 2.89** 3.11** 6.69*** −3.64*** 2.43* 0.71 0.70 39.64***
exp(B) 19,661.64 1.35 1.33 1.69 2.20 1.94 1.35 1.36 1.04 0.97 1.02
VIF 1.06 1.06 1.09 1.07 1.05 1.13 1.04 1.69 1.13 1.52
p ≤ 0.05. p ≤ 0.01. p ≤ 0.001.
fluctuation patterns, sudden increases in the number of visitors during long holidays, and the dramatically increasing number of visitors in the weeks preceding the Expo’s closing date (Fig. 4). Discussion and conclusions Recreation use patterns of urban green spaces This study verifies several factors associated with variation in number of daily visitors to the Flora Expo. Snowball (2004) demonstrates that the location of festivals affects the type of visitor and the length of stay. A festival near a large city is more likely to attract day and short-stay visitors than a festival in an isolated area. Arnberger (2006) also suggested that use density would be higher for innerurban forests than peri-urban ones. The Flora Expo was located in Taipei’s city center, so similar high use density was observed. The daily visits to the Flora Expo on Saturdays were different from Sundays, and the ratio of weekend use to weekday use was about 1.4, which accords with Arnberger’s (2006) result in the inner-urban forest. In comparison with the use on normal days, the daily visits on long holidays were significantly higher, which accords with Van
Wagtendonk’s (1981) and Lim and McAleer’s (2001) results in visitor numbers on national or school holidays. Although people tend to travel during holidays (Hamal, 1996), empirical studies that examine the effect of long holidays on the number of daily visitors are lacking. The results of current study fill the gap of this research niche, which demonstrated that during holidays that are longer than three days, such as Chinese New Year and Peace Memorial Day, the number of visitors at the exhibition increased significantly. Moreover, empirical studies have proven that the closing effects were highly significant, not only in marketing and consumer behavior (Auter and Moore, 1993; Verhallen and Robben, 1994; Brannon and Brock, 2001; Kauffman and Wang, 2001; Oh et al., 2009; Curras-Perez et al., 2011), but also in festivals held in urban parks. The average daily visitor numbers in the third and the fourth weeks before closing were approximate 20,000 more than the average daily numbers during the preceding 22 weeks. The increased amount of average daily visit in the second to the last week before closing doubled the average daily numbers of visits in the last two weeks, which was approximate 40,000 more than the average daily numbers in the preceding 22 weeks. The increased average amount of visitors in the last week before closing also doubled the increased average daily visits in the second
Fig. 4. Comparison of the model forecasting results vs. Flora Expo visitor data.
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to last week before closing, which was approximately 80,000 more than the average daily numbers during the preceding 22 weeks. A geometric increase in visits occurred during the last four weeks of the 26-week exhibition. However, the closing effects in shorter or longer duration of exhibitions might not be as long as four weeks, and more comparisons among different festivals could be made in future studies. Among Flora Expo visitors, 53% came from Taipei (Lin et al., 2011). The study site is in the center of a major city and there is no major attraction nearby; 87% of respondents indicated that the Flora Expo was their main destination (Lin et al., 2011). Additionally, transportation to and from the Flora Expo was so convenient that local visitors did not have to pre-plan their trips. Variation in visitor attendance was thus more sensitive to weather conditions, such as temperature, sunlight and rainfall. The temperature at noon was found to be a significant predictor for visitor numbers in this study. Allcock (1989), Dwyer (1988) and Dwyer et al. (1990) have discussed the association of season and weather with visitor numbers. Dwyer (1988) noted that visitor attendance increased when there is a temperature increase above the monthly average during cold months. However, during hot months, the number of visitors may decrease as the temperature rises above the monthly average. This study demonstrates that during the Flora Expo, the temperature positively correlated with the number of daily visitors, which accords with Dwyer’s (1988) results for cold seasons. The reason might be that the Taipei Flora Expo ran from November to April, which coincided with the winter and spring transition in Taipei. The daily temperature during the exhibition ranged from 8.61 ◦ C to 24.73 ◦ C, which is a comfortable temperature transition from cold to cool. Since the seasonal use pattern and the weather are correlated, Dwyer (1988) and Dwyer et al. (1990) measured daily weather variables as differences from monthly averages in their models to separate out the associations with daily weather and season (month). In the current study, the association between temperature and daily visits were not varied with month, as there are not major seasonal effects. The current result indicates when the temperature at noon rose by 1 ◦ C, the number of visitors increased. Interpretation of model selection In terms of model selection, Dwyer (1988) and Dwyer et al. (1990) used semi-logarithmic models to model daily recreational use. The results indicated that multiplicative models provide better fits than additive models. However, the present study compared an additive model using actual visitor numbers with a multiplicative model using the natural logarithm of the number of visitors as dependent variable. The linear regression model shows that with changing weather or day and its location within the week/Expo, visitor attendance on a particular day of the Flora Expo would be altered by an absolute amount. The semi-logarithm model revealed that visitor numbers would be expected to be altered by a multiplier. The explanatory power of our linear regression model (79%) was relatively better than that of the semi-logarithmic model (71%), and there was also a lower mean prediction error in the linear regression model. Consequently the additive model was recommended for our estimation. Examining the observed and predicted attendance shown in Fig. 4, both models performed well in predicting daily visits to the Flora Expo. However, divergences seemed to exist between the observed and the predicted attendance over a number of consecutive days in mid-January and mid to late February. While no special event is held during this period, it is suspected that it might be associated with the school holidays. MidJanuary was the end of semester in Taiwan, and fewer field trips were arranged in this period. Mid- to late February was the winter vacations period for school. Further studies of these factors would be useful.
The results in Table 3 indicate a weak linear association between daily visits and the number of the week. However, when all of the other explanatory variables were added, its association with the numbers of weeks became insignificant (Table 5), which indicates that the correlation with all of the other explanatory variables replaced its association with the number of weeks. However, there was no correlation between the number of weeks and any of the weather variables, which meant that the results were not confounded by the correlation between the weeks and weather conditions. Moreover, the closing effects were highly significant, which empirically verified the existence of a closing effect in festivals held in urban parks such as the Flora Expo. In addition, the association between long holiday and daily visit is confirmed in the study. Applications of urban park management The study used a linear regression model to predict the number of daily visitors to the six-month Flora Expo. One of the strengths of this study is the detailed attendance data of all 171 days event period. The results could be applied to other sites which also have similar conditions. The results indicate that the number of daily visitors increased on Saturdays, Sundays and long holidays, when the temperature was relatively high and when sunshine duration was long. The visitor number decreased when hours of rainfall increased. Additionally, the study confirmed the presence of a closing effect during festivals in urban parks: when the end of the period open to visitors approaches, the number of visitors significantly increases. These findings may provide useful information for the operation and management of similar festivals and help managers to accurately assess and predict the number of visitors and estimate potential revenues from entrance fees. When large numbers of visitors are expected, managers can arrange manpower and other resources in advance to provide better services for visitors, such as assigning additional staff and/or volunteers on weekends and long holidays, arranging additional shuttle bus service near the closing time, or arranging facility maintenance schedules. Moreover, a large number of visitors may increase traffic congestion and conflicts over recreational resources or activities (Arnberger, 2006; Arnberger and Eder, 2007), which could threaten visitors’ experience. Therefore, administrators can better prepare for large visitor numbers by route planning or visitor capacity control to maintain the recreational quality of urban parks. In addition, different marketing, advertising or promotional strategies can be implemented to attract more visitors when fewer visitors are expected. When festivals are held in urban parks, other complementary management may be needed. For example, local residents could enjoy free admission before 7 a.m., which can minimize the impact of the Expo on their regular recreational use. The park use pattern and attitudes of the local residents before and during the Expo were tracked. Some users kept using these parks for leisure activities as usual, and some users used other neighborhood parks temporarily (Lin et al., 2011). Although most of the local residents believed that the Flora Expo is beneficial in environmental, economic, and social ways, and the inconvenience during the Expo was temporary and acceptable, it is suggested that the space competition between festivals and public open green spaces should be considered and monitored when using a free accessible urban park for similar activities. Acknowledgements The authors want to thank two anonymous reviewers for their useful comments and editing which have greatly improved this paper.
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References Ahmed, F., Pretorius, L., 2010. Mega-events and environmental impacts: the 2010 FIFA World Cup in South Africa. Alternation 17 (2), 274–296. Allcock, J.B., 1989. Seasonality. In: Witt, S.F., Moutinho, L. (Eds.), Tourism Marketing and Management Handbook. Prentice Hall, New York, pp. 387–392. Arnberger, A., 2006. Recreation use of urban forests: an inter-area comparison. Urban For. Urban Green. 4 (3–4), 135–144. Arnberger, A., Eder, R., 2007. Monitoring recreational activities in urban forests using long-term video observation. Forestry 80 (1), 1–15. Arnberger, A., Hinterberger, B., 2003. Visitor monitoring methods for managing public use pressures in the Danube Floodplains National Park, Austria. J. Nat. Conserv. 11 (4), 260–267. Auter, P.J., Moore, R.L., 1993. Buying from a friend: a content analysis of two teleshopping programs. Journal. Quart. 70 (2), 425–436. Baron, R.R.V., 1975. Seasonality in Tourism: A Guide to the Analysis of Seasonality and Trends for Policy Making. The Economist Intelligence Unit, London. Baum, T., Hagen, L., 1999. Responses to seasonality: the experiences of peripheral destinations. Int. J. Tour. Res. 1 (5), 299–312. Baumann, R., Matheson, V.A., Muroi, C., 2009. Bowling in Hawaii: examining the effectiveness of sports-based tourism strategies. J. Sports Econ. 10 (1), 107–123. Becken, S., 2012. Measuring the effect of weather on tourism: a destination and activity-based analysis. J. Travel Res. 52 (2), 156–167. Brandenburg, C., Matzarakis, A., Arnberger, A., 2007. Weather and cycling – a first approach to the effects of weather conditions on cycling. Meteorol. Appl. 14 (1), 61–67. Brannon, L.A., Brock, T.C., 2001. Limiting time for responding enhances behavior corresponding to the merits of compliance appeals: refutations of heuristic-cue theory in service and consumer settings. J. Consum. Psychol. 10 (3), 135–146. Burns, P., Holden, A., 1995. Tourism: A New Perspective. Prentice Hall, London/New York. Butler, R.W., 1994. Seasonality in tourism: issues and problems. In: Seaton, A.V. (Ed.), Tourism: The State of the Art. John Wiley & Sons, Chichester, pp. 332–339. Butler, R.W., 2001. Seasonality in tourism: issues and implications. In: Baum, T., Lundtorp, S. (Eds.), Seasonality in Tourism. Pergamon, Amsterdam, pp. 5–21. Collins, A., Jones, C., Munday, M., 2009. Assessing the environmental impacts of mega sporting events: two options? Tour. Manage. 30 (6), 828–837. Cooper, C., Fletcher, J., Gilbert, D., Wanhill, S., 1993. Tourism: Principles and Practice. Pitman, London. Cranz, G., 1982. Politics of Park Design: A History of Urban Parks in America. MIT Press, MA. Curras-Perez, R., Ruiz-Mafe, C., Sanz-Blas, S., 2011. What motivates consumers to teleshopping? The impact of TV personality and audience interaction. Market. Intell. Plann. 29 (5), 534–555. Deery, M., Jago, L., 2010. Social impacts of events and the role of anti-social behaviour. Int. J. Event Festiv. Manage. 1 (1), 8–28. Dines, N.T., Brown, K.D., 2002. Time-saver Standards for Landscape Architecture (CD-ROM), 1st ed. McGraw-Hill, New York. Dwyer, J.F., 1988. Predicting daily use of urban forest recreation sites. Landsc. Urban Plann. 15 (1–2), 127–138. Dwyer, J.F., Schroeder, H.W., Buck, R.L., McGinty, D., 1990. Modeling daily boating use at a new urban lake. In: More, T.A., Donnelly, M.P., Graefe, A.R., Vaske, J.J. (Eds.), Proceedings of the 1990 Northeastern Recreation Research Symposium. Northeastern Forest Experiment Station, Radnor, PA, pp. 175–180. Finger, R., Lehmann, N., 2012. Modeling the sensitivity of outdoor recreation activities to climate change. Climate Res. 51, 229–236. Gelan, A., 2003. Local economic impacts: The British Open. Ann. Tourism Res. 30 (2), 406–425. Getz, D., 1991. Festivals, Special Events, and Tourism. Van Nostrand Reinhold, New York. Hall, C.M., Page, S.J., 2006. The Geography of Tourism and Recreation: Environment, Place and Space, 3rd ed. Routledge, London. Hamal, K., 1996. Modeling domestic holiday tourism demand in Australia: problems and solutions. Asia Pac. J. Tour. Res. 1 (2), 35–46.
733
Hartmann, R., 1986. Tourism, seasonality and social change. Leisure Stud. 5, 25–33. Highama, J., Hinch, T., 2002. Tourism, sport and seasons: the challenges and potential of overcoming seasonality in the sport and tourism sectors. Tour. Manage. 23 (2), 175–185. Jiang, S., Du, Y., Sun, L., 2011. Analysis of mode choice performance among heterogeneous tourists to Expo Shanghai 2010. Commun. Comp. Inform. Sci. 135, 154–164. Kauffman, R.J., Wang, B., 2001. New buyers’ arrival under dynamic pricing market microstructure: the case of group-buying discounts on the internet. J. Manage. Inform. Syst. 18 (2), 157–188. Kim, S.S., Chon, K., Chung, K.Y., 2003. Convention industry in South Korea: an economic impact analysis. Tour. Manage. 24 (5), 533–541. Koenig-Lewis, N., Bischof, E.E., 2005. Seasonality research: the state of the art. Int. J. Tour. Res. 7 (4–5), 201–219. Lee, C.-K., Taylor, T., 2005. Critical reflections on the economic impact assessment of a mega-event: the case of 2002 FIFA World Cup. Tour. Manage. 26 (4), 595–603. Lee, C.-K., Song, H.-J., Mjelde, J.W., 2008. The forecasting of International Expo tourism using quantitative and qualitative techniques. Tour. Manage. 29 (6), 1084–1098. Lim, C., McAleer, M., 2001. Monthly seasonal variations: Asian tourism to Australia. Ann. Tour. Res. 28 (1), 68–82. Lin, Y.-J., Chang, C.-Y., Chen, H.-M., Cheng, C.-K., Lin, B.-S., 2011. 2010 Taipei International Flora Exposition – Landscape Benefit Evaluation and International Academic Conference Report. National Taiwan University, Taipei. Mallen, C., Stevens, J., Adams, L., McRoberts, S., 2010. The assessment of the environmental performance of an international multi-sport event. Eur. Sport Manage. Quart. 10 (1), 97–122. Moore, W.R., 2010. The impact of climate change on Caribbean tourism demand. Curr. Issues Tour. 13 (5), 495–505. Neuvonen, M., Sievänen, T., Tönnes, S., Koskela, T., 2007. Access to green areas and the frequency of visits – a case study in Helsinki. Urban For. Urban Green. 6 (4), 235–247. Oh, H., Kim, H.-C., Hong, K.-W., 2009. A dynamic perspective of meeting planners’ satisfaction: toward conceptualization of critical relevancy. Tour. Manage. 30, 471–482. 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. J. Nat. Conserv. 11 (4), 297–308. Reverté, F.G., Izard, O.M., 2011. The role of social and intangible factors in cultural event planning in Catalonia. Int. J. Event Fest. Manage. 2 (1), 37–53. Ritchie, J.R.B., Beliveau, D., 1974. Hallmark events: an evaluation of a strategic response to seasonality in the travel market. J. Travel Res. 13 (2), 14–20. Ritchie, J.R.B., Smith, B.H., 1991. The impact of a mega-event on host region awareness: a longitudinal study. J. Travel Res. 30 (1), 3–10. Scott, D., Jones, B., Konopek, J., 2007. Implications of climate and environmental change for nature-based tourism in the Canadian Rocky Mountains: a case study of Waterton Lakes National Park. Tour. Manage. 28 (2), 570–579. Snowball, J.D., 2004. Interpreting economic impact study results: spending patterns, visitor numbers and festival aims. S. Afr. J. Econ. 72 (5), 1076–1084. Van Wagtendonk, J.W., 1981. The effect of use limits on backcountry visitation trends in Yosemite National Park. Leisure Sci. 4 (3), 311–323. Verhallen, T.M.M., Robben, H.S.J., 1994. Scarcity and preference: an experiment on unavailability and product evaluation. J. Econ. Psychol. 15, 315–331. Vries, S.de, Goossen, M, 2002. Modelling recreational visits to forests and nature areas. Urban For. Urban Green. 1 (1), 5–14. Xu, M., Yang, L., Cao, M., 2009. Strategies for managing complex visitor flow issues at Shanghai World Expo 2010. In: 2009 IEEE International Conference on Service Operations, Logistics and Informatics, the IEEE Intelligent Transportation Systems Society, Informs Service Science Section, Chicago, Illinois, pp. 592–597. Zhang, Y., 2011. Daily visitor volume forecasts for Expo 2010 Shanghai, China. In: Wang, F.-Y., Berté, S., Chen, Y. (Eds.), International IEEE Conference on Intelligent Transportation Systems. The IEEE Intelligent Transportation Systems Society, Washington, DC, pp. 496–500.
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Urban Forestry & Urban Greening journal homepage: www.elsevier.com/locate/ufug
Modeling daily visits to the 2010 Taipei International Flora Exposition Ai-Tsen Su, Chia-Kuen Cheng ∗∗ , Yann-Jou Lin ∗ Department of Horticulture and Landscape Architecture, National Taiwan University, Taiwan
a r t i c l e
i n f o
Keywords: Closing effect Rainfall Sunshine Temperature Urban park Use forecasting Use pattern
a b s t r a c t The 2010 Taipei International Flora Exposition (Flora Expo) was held in several urban parks in the center of the city. Over the course of the Expo, substantial variation was observed in the number of visitors per day. The aim of this study is to investigate the factors associated with the daily number of visitors to the Flora Expo. Our results suggest that there were periodic changes in the number of daily visits over time as well as with differences between the days of the week. In addition, temperature, duration of sunshine and precipitation were significantly correlated with the number of daily visits. Regression models suggest that the day of the week, long holiday, proximity to the closing date, temperature, duration of sunshine and hours of rainfall showed strong correlation with daily visits. Higher visits were recorded on Saturdays, Sundays and long holidays. When the temperature at noon increased by 1 ◦ C, an average of 1435 additional visits was recorded. When the duration of sunshine increased by 1 h, an average of 895 additional visits was recorded. Approximately 1232 fewer visits were recorded when the duration of rainfall increased by 1 h. Our results also show significant closing effects on daily visits over the last four weeks. These findings may provide useful information for the operation and management of similar festivals in urban parks and help managers to accurately assess and predict the number of visitors. © 2014 Elsevier GmbH. All rights reserved.
Introduction Urban parks provide important recreational opportunities for urban residents that help maintain their leisure quality of life. The numbers of visitors are therefore very important information for urban green space planning and management. With the development of society, the role of urban parklands has gradually diversified (Cranz, 1982). In particular, seasonal activities or festivals are often held in parks (Dines and Brown, 2002; Getz, 1991). These festivals offer the public opportunities to participate in social activities and enhance their quality of life. Such festivals typically do not last long, as gathering a large number of visitors for a longer time may damage environmental resources or reduce the quality of recreation services. Understanding the general patterns of recreation use could not only help managers and planners prepare for rapid increases in use on certain seasons or at specific times (Dwyer, 1988); but also foresee potentials for user conflicts (Arnberger, 2006; Arnberger and Eder, 2007). Thus, an accurate prediction of
∗ Corresponding author at: 138, Sec. 4, KeeLung Rd., Taipei, Taiwan. Tel.: +886 233664860. ∗∗ Corresponding author at: 138, Sec. 4, KeeLung Rd., Taipei, Taiwan. Tel.: +886 233669759. E-mail addresses: [email protected] (A.-T. Su), [email protected] (C.-K. Cheng), [email protected] (Y.-J. Lin). http://dx.doi.org/10.1016/j.ufug.2014.07.001 1618-8667/© 2014 Elsevier GmbH. All rights reserved.
the daily visitor attendance during festivals enables administrators to plan and implement management strategies in advance in order to mitigate the impacts on society (Ritchie and Smith, 1991; Deery and Jago, 2010; Reverté and Izard, 2011), the environment (Collins et al., 2009; Ahmed and Pretorius, 2010; Mallen et al., 2010) and the economy (Gelan, 2003; Kim et al., 2003; Lee and Taylor, 2005). A number of studies have therefore been dedicated to estimating the number of visitors to urban forests and parks (Dwyer, 1988; Dwyer et al., 1990; Arnberger and Hinterberger, 2003; Arnberger, 2006; Arnberger and Eder, 2007) as well as festivals (Snowball, 2004; Baumann et al., 2009; Xu et al., 2009; Jiang et al., 2011; Zhang, 2011). Previous studies have demonstrated that the number of visitors to urban parks is associated with numerous factors, including the season (Ritchie and Beliveau, 1974; Getz, 1991; Butler, 2001; Koenig-Lewis and Bischof, 2005), the day of the week (Dwyer, 1988; Dwyer et al., 1990; Arnberger and Hinterberger, 2003; Ploner and Brandenburg, 2003; Arnberger, 2006; Arnberger and Eder, 2007), special holiday (Van Wagtendonk, 1981; Butler, 2001; Lim and McAleer, 2001) and the weather (Dwyer, 1988; Allcock, 1989; Dwyer et al., 1990; Butler, 2001; Arnberger and Hinterberger, 2003; Ploner and Brandenburg, 2003; Arnberger, 2006; Hall and Page, 2006; Hartmann, 1986; Brandenburg et al., 2007; Scott et al., 2007; Xu et al., 2009; Moore, 2010; Becken, 2012; Finger and Lehmann, 2012). Furthermore, studies of marketing and consumer behavior have observed a so-called closing effect, in which the number
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of transactions significantly increases as the end of a transaction period approaches (e.g., Auter and Moore, 1993; Curras-Perez et al., 2011; Kauffman and Wang, 2001). Many park festivals are organized to take place within a limited time frame. However, few, if any, studies have addressed the question of the closing effect, which means that its association with the number of visitors to a festival remains to be clarified. The 2010 Taipei International Flora Exposition (Flora Expo) was a six-month festival based in several existing parks, and provided a good opportunity for understanding visitation patterns for an urban park festival. The purpose of this study is to examine the correlations among day and its location within the week/Expo, weather conditions, the closing effect, and the number of daily visitors by analyzing the fluctuation pattern of daily visitors to the event in urban parks (the Flora Expo). The following research questions were investigated: (1) Are there temporal fluctuations in daily visits to urban parks, including week and the day of week? (2) Are the daily visitor numbers on long holidays different from visitation on normal days? (3) Do the daily visits vary with different weather conditions? (4) Does a closing effect exist in such an event in urban parks? With the increasing tendency of holding festivals and events in urban parks, the results could provide information regarding similar activities being considered for other urban parks. Literature review Estimation of visitor attendance Estimating visitor attendance has been important to the fields of recreation and tourism. An accurate estimate can help managers develop more effective strategies to meet visitor needs. A number of studies have therefore focused on predicting demands for recreation. Such studies typically use explanatory models that are based on the assumption that correlations exist between these variables and the number of visitors. For example, Dwyer (1988), Dwyer et al. (1990), Vries and Goossen (2002), Ploner and Brandenburg (2003), Brandenburg et al. (2007) and Neuvonen et al. (2007) predict visitor use of urban forests, recreation areas and protected areas using explanatory models. Festivals in urban parks only last a short time, during which a large number of visitors appear. To provide better services, an accurate prediction of the number of visitors per day is required. Accordingly, many studies have tried to predict number of visitors for various festivals, such as the Shanghai World Expo (Xu et al., 2009; Jiang et al., 2011; Zhang, 2011), the Korea International Travel Fair (Lee et al., 2008), the South African National Arts Festival (Snowball, 2004), the South Africa Science Fair (Snowball, 2004) and sporting events in Hawaii (Baumann et al., 2009). Xu et al. (2009) investigated the associated variables for visitor numbers in large-scale exhibition activities in order to provide strategic recommendations for visitor management. Lee et al. (2008) combined a quantitative model and a qualitative method to predict the number of visitors to the 2012 Korea International Travel Fair. In all of these aforementioned studies, however, visitor attendance at urban park festivals is rarely studied. Factors associated with seasonal fluctuation Visitor numbers to scenic sites usually vary over time. This seasonal fluctuation or cyclic demand (Ritchie and Beliveau, 1974) has been considered the most important feature of visitor numbers with respect to festivals (Butler, 2001; Getz, 1991). Butler (2001) defined seasonal fluctuation as the “temporal imbalance in the phenomenon of tourism, which may be expressed in terms of dimensions in such elements as numbers of visitors, expenditure
of visitors, traffic on highways and other forms of transportation, employment and admissions to attractions.“The fluctuation phenomenon typically only receives brief discussions in textbooks (e.g., Getz, 1991; Cooper et al., 1993; Burns and Holden, 1995; Hall and Page, 2006), and only a small number of studies have addressed it (Allcock, 1989; Butler, 1994; Highama and Hinch, 2002). Two main reasons are generally believed to cause seasonal fluctuations in daily visitor numbers: natural factors and institutionalized factors (Baron, 1975; Hartmann, 1986). Typical natural factors include climatic factors, such as temperature, rainfall, snowfall, and sunshine (Hartmann, 1986; Dwyer, 1988; Allcock, 1989; Dwyer et al., 1990; Butler, 2001; Arnberger and Hinterberger, 2003; Ploner and Brandenburg, 2003; Hall and Page, 2006; Arnberger, 2006; Brandenburg et al., 2007; Scott et al., 2007; Moore, 2010). It is widely accepted that pleasant climatic factors increase visitor numbers. Dwyer (1988) suggests that deviations of the daily temperature at noon, percentage of the day with sun, and percent of the day with rain from the respective monthly average are associated with the percentage change of daily use of urban forest recreation sites. Arnberger and Hinterberger (2003) suggest that the recreation use patterns of some activities, such as biking, hiking and jogging, are associated with temperature, so higher use levels of urban forests could be observed in warmer weather (Arnberger, 2006). Becken (2012) indicates that higher maximum temperature is associated with the monthly overnight stays in Westland and confirms that the warmer summer is associated with more overnight stays. Furthermore, rainfall also has a strong and significant correlation with visits in outdoor recreation activities (Finger and Lehmann, 2012). Dwyer et al. (1990) suggest that an increase in the percentage of the day when it was raining above the monthly average tends to decrease use of an urban lake. Brandenburg et al. (2007) indicate that cycling is an activity performed during mild weather, which is generally sunny with a temperature greater than 5 ◦ C, few clouds and no precipitation. There were differences in the association between precipitation with the behavior of commuting and recreational cyclists on workdays. The second factor that causes seasonal fluctuations in daily visitor numbers is primarily related to human behavior and regulations, in particular the existence of fixed activities such as major holidays and festivals. Even the economic cycle can affect travel behavior (Cooper et al., 1993). In comparison to natural factors, institutionalized factors are subject to major changes and are less stable (Butler, 2001). The most common institutionalized factor with seasonal fluctuations is holidays, with school holidays (Lim and McAleer, 2001) and factory holidays (Butler, 2001) of particular importance. Van Wagtendonk (1981) conducted a study of visitors to the Yosemite National Park in the United States from 1972 to 1979 and found that peaks in visitor numbers coincided with national holidays; in particular, the number of visitors increased significantly on Memorial Day, July 4th, and Labor Day. Social norms or fashion also cause seasonal fluctuations in recreational use (Baum and Hagen, 1999; Butler, 2001). Social phenomena often lead to the designation of specific times for specific activities, thereby causing seasonal fluctuations in visits. In Taiwan, common examples are popular activities such as the flowering season and the Music Festival, which bring a large number of visitors to attractions. To some degree, fluctuation in visits due to popular societal activities also occurred in the case of the Flora Expo. Sports seasons are also believed to be a common cause of seasonal fluctuations in visitor numbers (Butler, 2001; Van Wagtendonk, 1981). Day to day variations have been identified as a major pattern in the use of urban parks or forests; with the highest daily use on weekends (Dwyer, 1988; Ploner and Brandenburg, 2003; Arnberger and Hinterberger, 2003; Arnberger, 2006; Arnberger and Eder, 2007). For example, there are higher use levels at weekends in the urban forests (Arnberger, 2006), and the use intensities in
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urban forests were significantly different for the day of the week (Arnberger and Hinterberger, 2003; Arnberger and Eder, 2007). Institutionalized factors and natural factors are not completely independent of one another; in fact, there is often an interaction between these two factors (Allcock, 1989) because some holidays or traditional festivals are related to certain seasons and weather conditions, and many activities depend on specific weather conditions. Closing effects In addition to the previously mentioned variables, a time limit may also cause the number of visitors to fluctuate. Verhallen and Robben (1994) noted that product availability is often negatively correlated with product value and preference. Therefore, when consumers perceive that products are limited or difficult to obtain, their demand for these products often increases (Verhallen and Robben, 1994; Brannon and Brock, 2001). Similarly, when the sale of a product nears its end, consumer desires to purchase the product increase (Auter and Moore, 1993; Curras-Perez et al., 2011). In the field of business, the so-called cycle ending effect states that the number of transactions will increase rapidly at the end of a trading period because of time constraint and limited order (Kauffman and Wang, 2001). Additionally, the number of transactions not only increases near the end of the trading period, consumer perception of a product changes. In consumer behavior research, this change is known as the service-ending effect theory, which holds that consumers view the final products or services to be the best and therefore determine to purchase them (Oh et al., 2009). When the period open to visitors is limited, more visitors are observed closer to the end of the visiting period. This phenomenon indicates the occurrence of the closing effect. In the existing literature on visitor numbers and festivals, however, only a small number of empirical studies have addressed the association with the closing effect. Methods Study site The theme of the Flora Expo was “Flower, River, New Horizon.” The purpose of the Expo was to attract visitors to enjoy the unique charm of Taipei City and inspire them to implement green practices in their daily lives. Unlike other exhibitions, the Flora Expo was located in the Yuanshan area, the center of Taipei City (Fig. 1). It was held in sites among many of the city’s existing artistic and cultural locations, including the Zhongshan Soccer Stadium, Taipei Children’s Recreation Center, Taipei Fine Arts Museum, Taipei Story House, and the Lin-An-tai Historical Home (Fig. 2). The Flora Expo occupied a site measuring 91.8 ha and consisted of four major areas: Yuanshan Park, Fine Arts Park, Xinsheng Park and Dajia Riverside Park. Each area was covered with many outdoor gardens and pavilions. The Yuanshan Park was the biggest outdoor flora exhibition area of the Flora Expo, and the gardens were decorated with various types of flowers and plants for different events and themes. Most of the events at the Expo were recurrent, with different exhibitions and shows changing every few days. To keep the visitors’ interests, the flowers in the gardens were kept in bloom by replanting overnight for continuity between themes. A number of indoor floral design competitions and exhibitions took place in the pavilions within this area. In the Fine Arts Park, various types of global gardens from a number of countries were shown outdoors. The Xinsheng Park was the second largest in Taipei City, and included the Fujian Style Garden, Serenity Garden, Garden Maze and Flower Base under Trees. The exhibition featured Taiwan’s cutting-edge technology, bountiful natural environment and precious bonsais presented in pavilions and green houses. In the Dajia Riverside Park, Taiwan Flower Area contained a view with a wide
Fig. 1. Map of Taipei City, routes of Mass Rapid Transit (MRT) and location of the Flora Expo and the weather station.
display of colorful flowers arranged in different patterns. A giant tent provided space for artistic groups to perform. Furthermore, floral tunnels connected the different areas. There were various ways to get to the Flora Expo. The main entrances were located near Taipei’s Mass Rapid Transit (MRT) Stations. Once visitors reached Taipei City, they could take MRTs, city buses, free Expo shuttle buses or taxis to the Expo within half an hour. Visitors could also take sightseeing boats on the Keelung River from the Dajia Riverside, Meiti and Xikou Piers to the Flora Expo. There were several types of tickets to the Flora Expo, including day-tickets, afternoon tickets, evening tickets, group tickets, 3-day passes and Flora Expo Passes. Day tickets, which cost 300 NTDs (approximately 7.22 Euros), included general admission for one day. Afternoon and evening tickets were for admission after 1 p.m. and 5 p.m., respectively. Group tickets were for parties of 10 or more. Three-day passes were limited to a period of three consecutive days, starting from the date of first entry. The holders of Flora Expo Passes were granted unlimited entry to the Expo over the course of the event. Some packages could be purchased before the opening of the Flora Expo, such as day-tickets for early birds or Flora Expo Mascot Memorial Tickets (a set containing 12 day tickets and 1–2 additional day tickets for free). Daily visits to the flora expo The Flora Expo was open between 9 a.m. and 10 p.m. for a total of 171 days from November 6, 2010 to April 25, 2011. During this time, the Expo attracted a considerable number of visitors. A total of 8,963,666 visitors were recorded. The average daily number of visitors on weekdays (not including national holidays) and holidays
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Fig. 2. Map of the Flora Expo.
(including weekends and national holidays) was 46,067 and 66,440, respectively. In terms of the long-term pattern, the number of visitors increased slightly week by week; the exception was during the long holidays in which there were peaks in the number of visitors. The approach of the Expo’s closing date also prompted more visitors to attend. The number of visitors increased significantly and reached the highest peak on the closing week. A record number of daily visitors (183,774 visitors) occurred on the last weekend before the Expo was closed (Fig. 3). These patterns suggest long holiday and closing effects. In terms of short-term fluctuations, there was a significant and regular weekly cycle of change in the number of visitors. The number of visitors peaked on Saturdays and Sundays and hit a low point almost every week from Tuesday to Thursday.
Data collection and measurement of variables Visitor data reflected the number of visitors on a given day. There are several methods for collecting visitor data in the existing literature, such as official records (Dwyer et al., 1990), on-site counters (Dwyer, 1988; Arnberger and Eder, 2007), actual field observations (Dwyer, 1988; Arnberger and Hinterberger, 2003) and video recording (Ploner and Brandenburg, 2003; Arnberger, 2006; Arnberger and Eder, 2007; Brandenburg et al., 2007). The visitor numbers used in this study were published daily on the Flora Expo official website. Considering the availability and feasibility of data collection, two types of data were collected in this study: day and its location within the week/Expo and meteorological information (Table 1).
Fig. 3. Daily number of visitors during the Flora Expo.
A.-T. Su et al. / Urban Forestry & Urban Greening 13 (2014) 725–733 Table 1 Variables used in modeling daily visits to the Expo. Day and Its Position within the Week/Expo Name Values Week
The number of weeks into the Expo
Day of the week
Weekday: Monday to Friday Saturday Sunday
Long holiday
Yes: Chinese New Year or Peace Memorial Day no: normal day
Proximity to the closing date
Last week before closing: week 26 of the Expo Second to the last week before closing: week 25 of the Expo Third to the last week before closing: week 24 of the Expo Fourth to the last week before closing: week 23 of the Expo Other weeks from the opening: week 1 to week 22
Meteorological information Variable Temperature at 8 a.m. Temperature at noon Maximum temperature Minimum temperature Average temperature Sunshine duration Precipitation Rainfall
Details Temperature in ◦ C at 8 a.m. Temperature in ◦ C at 12 noon Maximum temperature from 9 a.m. to 10 p.m. Minimum temperature from 9 a.m. to 10 p.m. Average temperature from 9 a.m. to 10 p.m. Sunshine hours between 8 a.m. and 7 p.m. Precipitation in mm between 8 a.m. and 10 p.m. Rainfall in hours between 8 a.m. and 10 p.m.
729
collected. The temperature data included the temperature in ◦ C at 8 a.m. and at 12 noon, as well as the maximum, minimum and average temperature during the opening hours of the Flora Expo. The correlation matrix among all of the variables is shown in Table 2. The visitor numbers were presented with both actual visitor numbers and the natural logarithm of visitor numbers. The natural logarithm of visitor numbers was its logarithm to the base e. All temperature variables showed significant correlations with visitor numbers and the natural logarithm of visitor numbers. The temperature at 12 noon was used in this study due to its highest coefficients of correlation on both variables of visitor attendance, with a Pearson correlation coefficient of 0.61 and 0.66 (p ≤ 0.01). Besides, it is more feasible for park managers to gather the information about temperature at noon from weather forecasting. Considering the opening hours of the Flora Expo, the hourly data of sunshine duration and precipitation used in this study were measured between 8 a.m. and 10 p.m. The sunshine duration showed significant correlations with the visitor numbers and the natural logarithm of the visitor numbers, with a Pearson correlation coefficient of 0.48 and 0.50 (p ≤ 0.01). The precipitation data included precipitation in mm and hours of rainfall and were obtained by aggregating the hourly data between 8 a.m. and 10 p.m. The correlation matrix shows there was no correlation between precipitation in mm and the visitor attendance. The hours of rainfall had significantly negative correlations with both variables in visitor attendance, with a Pearson correlation coefficient of −0.23 and −0.30 (p ≤ 0.01). As the result, hours of rainfall were selected for the following models.
Results The number of weeks into the Flora Expo refers to the number of weeks that had passed since the opening of Expo. The day of the week was category data that treated “Saturday” and “Sunday” as two dummy variables, with “weekday” used as the reference category. The long holidays in this study were February 3–5 (the 1st through 3rd days of the first lunar month) and February 26–28 (of the long holiday for Peace Memorial Day). The value of long holidays was treated as a dummy variable, with normal days used as the reference category. The normal days represented those days which were not included in the long holidays. In terms of the effect of proximity to the closing date (closing effect), the last four weeks before the closing of the Flora Expo were treated as four dummy variables, with the weeks from the opening to the 22nd week used as the reference category. Meteorological data were downloaded from the Atmospheric Research Database of the Taiwan Typhoon and Flood Research Center, National Applied Research Laboratories. Hourly data recorded by the nearby Dazhi automatic measuring station were also used. Data on temperature, precipitation and sunshine duration were
Temporal fluctuations Week and daily visit Visitor attendance exhibited a week to week variation pattern. As the Expo’s closing date drew closer, more visitors attended. A simple regression analysis was performed to analyze the association between weeks and daily visitor attendance to the Flora Expo. The results showed that the number of weeks into the Expo was significantly associated with the number of daily visits to the Flora Expo, with the number of daily visitors increasing by 1293 with each passing week (Table 3). The day of week and daily visit The variation of visitor numbers revealed that there was a cyclical change in the daily number of visitors. A one-way analysis of variance was performed to analyze the daily number of visitors in a week, and the results showed that there was a significant difference in the number of visitors on different days of the week (F = 12.69,
Table 2 The correlation matrix among all of the variables.
Visitor Number LN (visitor number) Temp. at 8 a.m. Temp. at 12 noon Max. temp. Min. temp. Avg. temp. Sunshine duration Precipitation Hours of rainfall * **
p ≤ 0.05. p ≤ 0.01.
Hours of rainfall
Precipitation
Sunshine duration
Avg. temp.
Min. temp.
Max. temp.
−.23** −.30** −.17* −.27** −.27** −.20* −.26** −.26** .87** –
−.14 −.18* −.09 −.19* −.19* −.12 −.18* −.22** –
.48** .50** .27** .53** .54** .32** .49** –
.59** .64** .92** .99** .98** .96** –
.50** .56** .96** .92** .90** –
.60** .65** .88** .99** –
Temp. at 12 noon .61** .66** .89** –
Temp. at 8 a.m.
LN (visitor number)
Visitor number
.46** .53** –
.95** –
–
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Table 3 Association between 7-day week and daily visits to the Flora Expo. Estimate B
t 8.60* 4.87*
(Constant) 34,875.44 Week 1292.53 R2 = 0.12; adjusted R2 = 0.12; F = 23.75* *
Regression models for predicting visitor attendance to the Flora Expo
p ≤ 0.001.
p ≤ 0.001). The number of visitors were the highest on Sundays and Saturdays, with an average 70,206 and 63,354. Long holiday and daily visit The Flora Expo spanned long holidays, including the Chinese New Year and the long holiday for Peace Memorial Day. The average daily number of visitors on normal days was approximately 50,674, and the average daily number of visitors on long holidays was approximately 100,434. The daily visitor attendances on long holidays were higher than daily visitor attendance on normal days. Proximity to the closing date and daily visit As the closing date of the Expo approached, the number of visitors increased significantly. Visitor attendance dramatically increased week-by-week at the end of the Expo. The results of ANOVA indicate significant differences among 7-day weeks before the closing. Average daily attendance at 22 weeks from the opening was 46,724 visitors. Average daily attendance in the 23rd week, also known as the fourth to the last week before closing, was 71,101 visitors. Average daily attendance in the 24th week, also known as the third to the last week before closing, was approximately 79,023 visitors. Average daily attendance in the 25th week, also known as the second to the last week before closing, was approximately 98,620 visitors. Finally, average daily attendance in the 26th week, also known as the last week before closing, was up to 153,648 visitors (Table 4). Weather conditions During the Expo, the average daily temperature was approximately 17.35 ◦ C (standard deviation [SD] = 3.68); the lowest daily temperature was 8.61 ◦ C, and the highest daily temperature was 24.73 ◦ C. The average daily sunshine duration was approximately 3.06 h (standard deviation [SD] = 3.82); the average number of sunshine hours seems low given that the days with sun in winter or spring are usually partly cloudy in Taipei. The lowest daily sunshine duration was 0 h, and the highest daily sunshine duration was 11 h. The total number of days with sunshine during the Expo was 106. The average daily rainfall duration was approximately 0.81 h (standard deviation [SD] = 2.37); the lowest daily rainfall duration was 0 h, and the highest daily rainfall duration was 14 h. The total of rainy days during the Expo was 52. Table 4 Analysis of daily visitor attendance on proximity to the closing date.
Proximity to the closing date
*
p ≤ 0.001.
Last week before closing Second to the last week before closing Third to the last week before closing Four to the last week before closing Other weeks from the opening
Mean
F
153,647.50
29.14*
98,619.86 79,023.29 71,101.14 46,724.06
To enable us to better understand the association with each predictor and the number of visitors to the Flora Expo, a regression analysis was conducted with the number of weeks into the Expo, the day of the week, long holiday, proximity to the closing date, temperature, rainfall and sunshine duration used as predictors in the model. Two different functional forms, additive or multiplicative, were suggested by Cooper et al. (1993) to measure the associations of various factors with daily use. For additive functions, changes in use are estimated in the absolute number of visitors. In the case of multiplicative models, changes in use are estimated by multiplying the coefficients (multiplier) in visitor numbers at a specific time (Dwyer, 1988). Both a linear regression and a semilogarithmic multiplicative model were considered. The linear regression analysis shows a significant association between the day of the week and daily visitor attendance to the Flora Expo. On Saturdays and Sundays, the average numbers of visitors were 16,336 and 18,113 higher than on weekdays, respectively. The effects of long holidays and proximity to the closing date were both significantly increased visitor attendance. During the long holidays, the number of visitors was 40,754 higher than on normal days. In the weeks before the Expo closed, the number of visitors on the last week before closing was 86,277 higher than at 22 weeks from the opening. The numbers of visitors on the second, third and fourth to the last weeks before closing were approximately 46,412, 21,712 and 18,122 higher than at 22 weeks from the opening, respectively. The number of visitors increased by 1435 with each 1 ◦ C increase in temperature at 12 noon, and the number of visitors also increased by 895 with each 1 h increase in sunshine duration. The number of visitors decreased by 1232 with each 1 h increase in rainfall duration. The total variance explained by the model was 79% (Table 5). Comparing the predicted values with the actual visits, the mean prediction error of the model was 93 visitors, and these two values were highly correlated (R = 0.89, p ≤ 0.01). In the semi-logarithm model, the factor exp(B) value for the constant was shown as 19,661.64, which meant that, on average, 19,662 visitors could be expected on a weekday with the temperature at 12 noon at the freezing point, no rainfall, no sunshine, not during long holidays, and not in the four weeks before the closing. The coefficients for the independent variables could be converted to multipliers with this multiplicative form. If it was a Saturday or a Sunday, visitor numbers would increase by 35% or 33% (factor 1.35/factor 1.33). Visitor attendance would increase by 69% (factor 1.69) on long holidays. The number of visitors would increase by 120% (factor 2.20) on the last week before closing; the visitor numbers would increase by 94% (factor 1.94), 35% (factor 1.35) and 36% (factor 1.36) on the second, third and fourth to the last weeks before the closing, respectively. Each additional degree Celsius of the temperature at 12 noon would increase the number of visitors by 4% (factor 1.04), each additional hour of sunshine by 2% (factor 1.02). The visitor numbers would decrease by 3% (factor 0.97) with each additional hour of rainfall. The total variance explained by the model was 71% (Table 5). The mean prediction error between the predicted visits and the actual visits was 1327 visitors, and these two values were also highly correlated (R = 0.90, p ≤ 0.01). Although there were significant correlations among some variables as shown in Table 2, the coefficients were relatively low, which suggesting confound effect might not be a serious problem for the model. Besides, the values of variance inflation factors (VIF) shown in Table 5 are all under 5, suggesting no multicollinearity among variables. The explanatory variables for the final models were selected by examining the correlations, t-values, and the values of VIF among variables. Both models accurately reflect overall
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Table 5 Regression for daily visits to the Flora Expo. Linear model Estimate B (Constant) Saturday Sunday Long holiday Last week before closing Second to the last week before closing Third to the last week before closing Fourth to the last week before closing Temperature at noon Hours of rainfall Sunshine duration R2 Adjusted R2 F * ** ***
12,173.16 16,335.62 18,113.47 40,754.42 86,276.95 46,411.74 21,711.72 18,122.19 1435.38 −1232.39 894.74
Semi-logarithmic model t 2.63** 5.89*** 6.63*** 7.67*** 9.56*** 9.56*** 4.32*** 3.75*** 5.55*** −2.92** 2.87** 0.79 0.78 60.88***
VIF
Estimate B
t
1.06 1.06 1.09 1.07 1.05 1.13 1.04 1.69 1.13 1.52
9.89 0.30 0.29 0.52 0.79 0.66 0.30 0.31 0.04 −0.03 0.02
103.02*** 5.22*** 5.05*** 4.74*** 4.22*** 6.60*** 2.89** 3.11** 6.69*** −3.64*** 2.43* 0.71 0.70 39.64***
exp(B) 19,661.64 1.35 1.33 1.69 2.20 1.94 1.35 1.36 1.04 0.97 1.02
VIF 1.06 1.06 1.09 1.07 1.05 1.13 1.04 1.69 1.13 1.52
p ≤ 0.05. p ≤ 0.01. p ≤ 0.001.
fluctuation patterns, sudden increases in the number of visitors during long holidays, and the dramatically increasing number of visitors in the weeks preceding the Expo’s closing date (Fig. 4). Discussion and conclusions Recreation use patterns of urban green spaces This study verifies several factors associated with variation in number of daily visitors to the Flora Expo. Snowball (2004) demonstrates that the location of festivals affects the type of visitor and the length of stay. A festival near a large city is more likely to attract day and short-stay visitors than a festival in an isolated area. Arnberger (2006) also suggested that use density would be higher for innerurban forests than peri-urban ones. The Flora Expo was located in Taipei’s city center, so similar high use density was observed. The daily visits to the Flora Expo on Saturdays were different from Sundays, and the ratio of weekend use to weekday use was about 1.4, which accords with Arnberger’s (2006) result in the inner-urban forest. In comparison with the use on normal days, the daily visits on long holidays were significantly higher, which accords with Van
Wagtendonk’s (1981) and Lim and McAleer’s (2001) results in visitor numbers on national or school holidays. Although people tend to travel during holidays (Hamal, 1996), empirical studies that examine the effect of long holidays on the number of daily visitors are lacking. The results of current study fill the gap of this research niche, which demonstrated that during holidays that are longer than three days, such as Chinese New Year and Peace Memorial Day, the number of visitors at the exhibition increased significantly. Moreover, empirical studies have proven that the closing effects were highly significant, not only in marketing and consumer behavior (Auter and Moore, 1993; Verhallen and Robben, 1994; Brannon and Brock, 2001; Kauffman and Wang, 2001; Oh et al., 2009; Curras-Perez et al., 2011), but also in festivals held in urban parks. The average daily visitor numbers in the third and the fourth weeks before closing were approximate 20,000 more than the average daily numbers during the preceding 22 weeks. The increased amount of average daily visit in the second to the last week before closing doubled the average daily numbers of visits in the last two weeks, which was approximate 40,000 more than the average daily numbers in the preceding 22 weeks. The increased average amount of visitors in the last week before closing also doubled the increased average daily visits in the second
Fig. 4. Comparison of the model forecasting results vs. Flora Expo visitor data.
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to last week before closing, which was approximately 80,000 more than the average daily numbers during the preceding 22 weeks. A geometric increase in visits occurred during the last four weeks of the 26-week exhibition. However, the closing effects in shorter or longer duration of exhibitions might not be as long as four weeks, and more comparisons among different festivals could be made in future studies. Among Flora Expo visitors, 53% came from Taipei (Lin et al., 2011). The study site is in the center of a major city and there is no major attraction nearby; 87% of respondents indicated that the Flora Expo was their main destination (Lin et al., 2011). Additionally, transportation to and from the Flora Expo was so convenient that local visitors did not have to pre-plan their trips. Variation in visitor attendance was thus more sensitive to weather conditions, such as temperature, sunlight and rainfall. The temperature at noon was found to be a significant predictor for visitor numbers in this study. Allcock (1989), Dwyer (1988) and Dwyer et al. (1990) have discussed the association of season and weather with visitor numbers. Dwyer (1988) noted that visitor attendance increased when there is a temperature increase above the monthly average during cold months. However, during hot months, the number of visitors may decrease as the temperature rises above the monthly average. This study demonstrates that during the Flora Expo, the temperature positively correlated with the number of daily visitors, which accords with Dwyer’s (1988) results for cold seasons. The reason might be that the Taipei Flora Expo ran from November to April, which coincided with the winter and spring transition in Taipei. The daily temperature during the exhibition ranged from 8.61 ◦ C to 24.73 ◦ C, which is a comfortable temperature transition from cold to cool. Since the seasonal use pattern and the weather are correlated, Dwyer (1988) and Dwyer et al. (1990) measured daily weather variables as differences from monthly averages in their models to separate out the associations with daily weather and season (month). In the current study, the association between temperature and daily visits were not varied with month, as there are not major seasonal effects. The current result indicates when the temperature at noon rose by 1 ◦ C, the number of visitors increased. Interpretation of model selection In terms of model selection, Dwyer (1988) and Dwyer et al. (1990) used semi-logarithmic models to model daily recreational use. The results indicated that multiplicative models provide better fits than additive models. However, the present study compared an additive model using actual visitor numbers with a multiplicative model using the natural logarithm of the number of visitors as dependent variable. The linear regression model shows that with changing weather or day and its location within the week/Expo, visitor attendance on a particular day of the Flora Expo would be altered by an absolute amount. The semi-logarithm model revealed that visitor numbers would be expected to be altered by a multiplier. The explanatory power of our linear regression model (79%) was relatively better than that of the semi-logarithmic model (71%), and there was also a lower mean prediction error in the linear regression model. Consequently the additive model was recommended for our estimation. Examining the observed and predicted attendance shown in Fig. 4, both models performed well in predicting daily visits to the Flora Expo. However, divergences seemed to exist between the observed and the predicted attendance over a number of consecutive days in mid-January and mid to late February. While no special event is held during this period, it is suspected that it might be associated with the school holidays. MidJanuary was the end of semester in Taiwan, and fewer field trips were arranged in this period. Mid- to late February was the winter vacations period for school. Further studies of these factors would be useful.
The results in Table 3 indicate a weak linear association between daily visits and the number of the week. However, when all of the other explanatory variables were added, its association with the numbers of weeks became insignificant (Table 5), which indicates that the correlation with all of the other explanatory variables replaced its association with the number of weeks. However, there was no correlation between the number of weeks and any of the weather variables, which meant that the results were not confounded by the correlation between the weeks and weather conditions. Moreover, the closing effects were highly significant, which empirically verified the existence of a closing effect in festivals held in urban parks such as the Flora Expo. In addition, the association between long holiday and daily visit is confirmed in the study. Applications of urban park management The study used a linear regression model to predict the number of daily visitors to the six-month Flora Expo. One of the strengths of this study is the detailed attendance data of all 171 days event period. The results could be applied to other sites which also have similar conditions. The results indicate that the number of daily visitors increased on Saturdays, Sundays and long holidays, when the temperature was relatively high and when sunshine duration was long. The visitor number decreased when hours of rainfall increased. Additionally, the study confirmed the presence of a closing effect during festivals in urban parks: when the end of the period open to visitors approaches, the number of visitors significantly increases. These findings may provide useful information for the operation and management of similar festivals and help managers to accurately assess and predict the number of visitors and estimate potential revenues from entrance fees. When large numbers of visitors are expected, managers can arrange manpower and other resources in advance to provide better services for visitors, such as assigning additional staff and/or volunteers on weekends and long holidays, arranging additional shuttle bus service near the closing time, or arranging facility maintenance schedules. Moreover, a large number of visitors may increase traffic congestion and conflicts over recreational resources or activities (Arnberger, 2006; Arnberger and Eder, 2007), which could threaten visitors’ experience. Therefore, administrators can better prepare for large visitor numbers by route planning or visitor capacity control to maintain the recreational quality of urban parks. In addition, different marketing, advertising or promotional strategies can be implemented to attract more visitors when fewer visitors are expected. When festivals are held in urban parks, other complementary management may be needed. For example, local residents could enjoy free admission before 7 a.m., which can minimize the impact of the Expo on their regular recreational use. The park use pattern and attitudes of the local residents before and during the Expo were tracked. Some users kept using these parks for leisure activities as usual, and some users used other neighborhood parks temporarily (Lin et al., 2011). Although most of the local residents believed that the Flora Expo is beneficial in environmental, economic, and social ways, and the inconvenience during the Expo was temporary and acceptable, it is suggested that the space competition between festivals and public open green spaces should be considered and monitored when using a free accessible urban park for similar activities. Acknowledgements The authors want to thank two anonymous reviewers for their useful comments and editing which have greatly improved this paper.
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References Ahmed, F., Pretorius, L., 2010. Mega-events and environmental impacts: the 2010 FIFA World Cup in South Africa. Alternation 17 (2), 274–296. Allcock, J.B., 1989. Seasonality. In: Witt, S.F., Moutinho, L. (Eds.), Tourism Marketing and Management Handbook. Prentice Hall, New York, pp. 387–392. Arnberger, A., 2006. Recreation use of urban forests: an inter-area comparison. Urban For. Urban Green. 4 (3–4), 135–144. Arnberger, A., Eder, R., 2007. Monitoring recreational activities in urban forests using long-term video observation. Forestry 80 (1), 1–15. Arnberger, A., Hinterberger, B., 2003. Visitor monitoring methods for managing public use pressures in the Danube Floodplains National Park, Austria. J. Nat. Conserv. 11 (4), 260–267. Auter, P.J., Moore, R.L., 1993. Buying from a friend: a content analysis of two teleshopping programs. Journal. Quart. 70 (2), 425–436. Baron, R.R.V., 1975. Seasonality in Tourism: A Guide to the Analysis of Seasonality and Trends for Policy Making. The Economist Intelligence Unit, London. Baum, T., Hagen, L., 1999. Responses to seasonality: the experiences of peripheral destinations. Int. J. Tour. Res. 1 (5), 299–312. Baumann, R., Matheson, V.A., Muroi, C., 2009. Bowling in Hawaii: examining the effectiveness of sports-based tourism strategies. J. Sports Econ. 10 (1), 107–123. Becken, S., 2012. Measuring the effect of weather on tourism: a destination and activity-based analysis. J. Travel Res. 52 (2), 156–167. Brandenburg, C., Matzarakis, A., Arnberger, A., 2007. Weather and cycling – a first approach to the effects of weather conditions on cycling. Meteorol. Appl. 14 (1), 61–67. Brannon, L.A., Brock, T.C., 2001. Limiting time for responding enhances behavior corresponding to the merits of compliance appeals: refutations of heuristic-cue theory in service and consumer settings. J. Consum. Psychol. 10 (3), 135–146. Burns, P., Holden, A., 1995. Tourism: A New Perspective. Prentice Hall, London/New York. Butler, R.W., 1994. Seasonality in tourism: issues and problems. In: Seaton, A.V. (Ed.), Tourism: The State of the Art. John Wiley & Sons, Chichester, pp. 332–339. Butler, R.W., 2001. Seasonality in tourism: issues and implications. In: Baum, T., Lundtorp, S. (Eds.), Seasonality in Tourism. Pergamon, Amsterdam, pp. 5–21. Collins, A., Jones, C., Munday, M., 2009. Assessing the environmental impacts of mega sporting events: two options? Tour. Manage. 30 (6), 828–837. Cooper, C., Fletcher, J., Gilbert, D., Wanhill, S., 1993. Tourism: Principles and Practice. Pitman, London. Cranz, G., 1982. Politics of Park Design: A History of Urban Parks in America. MIT Press, MA. Curras-Perez, R., Ruiz-Mafe, C., Sanz-Blas, S., 2011. What motivates consumers to teleshopping? The impact of TV personality and audience interaction. Market. Intell. Plann. 29 (5), 534–555. Deery, M., Jago, L., 2010. Social impacts of events and the role of anti-social behaviour. Int. J. Event Festiv. Manage. 1 (1), 8–28. Dines, N.T., Brown, K.D., 2002. Time-saver Standards for Landscape Architecture (CD-ROM), 1st ed. McGraw-Hill, New York. Dwyer, J.F., 1988. Predicting daily use of urban forest recreation sites. Landsc. Urban Plann. 15 (1–2), 127–138. Dwyer, J.F., Schroeder, H.W., Buck, R.L., McGinty, D., 1990. Modeling daily boating use at a new urban lake. In: More, T.A., Donnelly, M.P., Graefe, A.R., Vaske, J.J. (Eds.), Proceedings of the 1990 Northeastern Recreation Research Symposium. Northeastern Forest Experiment Station, Radnor, PA, pp. 175–180. Finger, R., Lehmann, N., 2012. Modeling the sensitivity of outdoor recreation activities to climate change. Climate Res. 51, 229–236. Gelan, A., 2003. Local economic impacts: The British Open. Ann. Tourism Res. 30 (2), 406–425. Getz, D., 1991. Festivals, Special Events, and Tourism. Van Nostrand Reinhold, New York. Hall, C.M., Page, S.J., 2006. The Geography of Tourism and Recreation: Environment, Place and Space, 3rd ed. Routledge, London. Hamal, K., 1996. Modeling domestic holiday tourism demand in Australia: problems and solutions. Asia Pac. J. Tour. Res. 1 (2), 35–46.
733
Hartmann, R., 1986. Tourism, seasonality and social change. Leisure Stud. 5, 25–33. Highama, J., Hinch, T., 2002. Tourism, sport and seasons: the challenges and potential of overcoming seasonality in the sport and tourism sectors. Tour. Manage. 23 (2), 175–185. Jiang, S., Du, Y., Sun, L., 2011. Analysis of mode choice performance among heterogeneous tourists to Expo Shanghai 2010. Commun. Comp. Inform. Sci. 135, 154–164. Kauffman, R.J., Wang, B., 2001. New buyers’ arrival under dynamic pricing market microstructure: the case of group-buying discounts on the internet. J. Manage. Inform. Syst. 18 (2), 157–188. Kim, S.S., Chon, K., Chung, K.Y., 2003. Convention industry in South Korea: an economic impact analysis. Tour. Manage. 24 (5), 533–541. Koenig-Lewis, N., Bischof, E.E., 2005. Seasonality research: the state of the art. Int. J. Tour. Res. 7 (4–5), 201–219. Lee, C.-K., Taylor, T., 2005. Critical reflections on the economic impact assessment of a mega-event: the case of 2002 FIFA World Cup. Tour. Manage. 26 (4), 595–603. Lee, C.-K., Song, H.-J., Mjelde, J.W., 2008. The forecasting of International Expo tourism using quantitative and qualitative techniques. Tour. Manage. 29 (6), 1084–1098. Lim, C., McAleer, M., 2001. Monthly seasonal variations: Asian tourism to Australia. Ann. Tour. Res. 28 (1), 68–82. Lin, Y.-J., Chang, C.-Y., Chen, H.-M., Cheng, C.-K., Lin, B.-S., 2011. 2010 Taipei International Flora Exposition – Landscape Benefit Evaluation and International Academic Conference Report. National Taiwan University, Taipei. Mallen, C., Stevens, J., Adams, L., McRoberts, S., 2010. The assessment of the environmental performance of an international multi-sport event. Eur. Sport Manage. Quart. 10 (1), 97–122. Moore, W.R., 2010. The impact of climate change on Caribbean tourism demand. Curr. Issues Tour. 13 (5), 495–505. Neuvonen, M., Sievänen, T., Tönnes, S., Koskela, T., 2007. Access to green areas and the frequency of visits – a case study in Helsinki. Urban For. Urban Green. 6 (4), 235–247. Oh, H., Kim, H.-C., Hong, K.-W., 2009. A dynamic perspective of meeting planners’ satisfaction: toward conceptualization of critical relevancy. Tour. Manage. 30, 471–482. 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. J. Nat. Conserv. 11 (4), 297–308. Reverté, F.G., Izard, O.M., 2011. The role of social and intangible factors in cultural event planning in Catalonia. Int. J. Event Fest. Manage. 2 (1), 37–53. Ritchie, J.R.B., Beliveau, D., 1974. Hallmark events: an evaluation of a strategic response to seasonality in the travel market. J. Travel Res. 13 (2), 14–20. Ritchie, J.R.B., Smith, B.H., 1991. The impact of a mega-event on host region awareness: a longitudinal study. J. Travel Res. 30 (1), 3–10. Scott, D., Jones, B., Konopek, J., 2007. Implications of climate and environmental change for nature-based tourism in the Canadian Rocky Mountains: a case study of Waterton Lakes National Park. Tour. Manage. 28 (2), 570–579. Snowball, J.D., 2004. Interpreting economic impact study results: spending patterns, visitor numbers and festival aims. S. Afr. J. Econ. 72 (5), 1076–1084. Van Wagtendonk, J.W., 1981. The effect of use limits on backcountry visitation trends in Yosemite National Park. Leisure Sci. 4 (3), 311–323. Verhallen, T.M.M., Robben, H.S.J., 1994. Scarcity and preference: an experiment on unavailability and product evaluation. J. Econ. Psychol. 15, 315–331. Vries, S.de, Goossen, M, 2002. Modelling recreational visits to forests and nature areas. Urban For. Urban Green. 1 (1), 5–14. Xu, M., Yang, L., Cao, M., 2009. Strategies for managing complex visitor flow issues at Shanghai World Expo 2010. In: 2009 IEEE International Conference on Service Operations, Logistics and Informatics, the IEEE Intelligent Transportation Systems Society, Informs Service Science Section, Chicago, Illinois, pp. 592–597. Zhang, Y., 2011. Daily visitor volume forecasts for Expo 2010 Shanghai, China. In: Wang, F.-Y., Berté, S., Chen, Y. (Eds.), International IEEE Conference on Intelligent Transportation Systems. The IEEE Intelligent Transportation Systems Society, Washington, DC, pp. 496–500.