Spatiotemporal Influence of Urban Park Landscape Features on Visitor Behavior

Abstract

The relationship between visitor behavior and the park landscape is complex. Influences of some park features on visitor behavior are unconfirmed and change with time and space, which always confuses landscape architects and park managers. These spatiotemporal dependent effects were less studied. To understand this influence, an empirical study was conducted in 10 representative parks across the West Lake Scenic Area in Hangzhou, China. A system for Observing Play and Recreation in Communities was applied to record visitor behavior on weekdays and weekends, which provides a non-contact approach for an objective and comprehensive dataset. Spearman correlation analysis, Wilcoxon rank-sum test, and a mixed linear model were used for data analysis. The results revealed that spatiotemporal differences between different visitor behaviors. Park location and visiting date affected visitors’ behavior patterns. Landscape features influenced visitor behavior in various ways depending on their context. Landmarks served as gathering points, and the dual effect of vegetation cover varied significantly among visitors. Not all visitors preferred places with high vegetation cover. Social space was more suitable for visitors engaging in planned behaviors, such as exercise and recreation. Based on these findings, the study proposed several strategies for park sustainable design and management.

1. Introduction

As a major component of urban green spaces, urban parks provide many benefits to locals [1], such as reducing visitors’ physical and mental stress [2], improving public health [3], and providing numerous ecosystem services [4]. Urban parks also provide many suitable spaces for visitors to participate in a variety of activities, such as exercise, recreation, and relaxation [5]. Most research has focused on visitors’ motivations, preferences, satisfaction levels, travel patterns, and their influencing factors in urban parks. It has been reported that park visitors could be motivated by seeking relaxation, experiencing nature, educational and cultural activities, and children’s activities [6]. Park size [7], activity facilities [8], and public transportation accessibility [9] are the main factors affecting park usage efficiency. Visitors’ individual characteristics play a significant role in the relationship between the destination and their in-tended behavior, and variations in visitors’ perceptions influence their behavior [10]. There is a complex relationship between visitor behavior, features, and expectations. For instance, tourists select specific parks that align with their preferred activities and have an environment that they are comfortable with [11]. Exploring the relationship between visitor behavior and park features may enhance visitor experiences and guide park design and management.

Several studies have focused on the influencing factors of some specific visitor behaviors: visiting path selection, duration, and taking pictures are all of great importance for the spatiotemporal behavior of tourists [12]. According to Demand Theory, the planned behavior of visitors is place-dependent; for example, visitors interested in planning parties will tend to choose open spaces. On the contrary, one of the behavioral mechanisms that enables the realization of private activities is to keep one’s body in a closed and quiet environment, which makes resting facilities placed in secluded areas very popular. In addition, Albert mentioned the term “behavioral environment”, which expresses the idea that for any given place, there are specific features of each space that closely and systematically correspond to the behaviors that occur within it [13]. Thus, landscape features of the area regulate visitors’ behavior, such as activity selection and path characteristics [14]. The safety of a place is also one of the factors influencing visitor behavior, such as floods, wildfire, criminal rate, and pandemics [15,16]. Similarly, vegetation coverage has different effects across different seasons [7] and visitors. Some people prefer greener places, while others feel unsafe with high vegetation coverage [17]. In addition, other park features, such as water, vistas, management, biodiversity, and human impacts are most often considered to influence visitor behavior [18]. Sigit-Dwiananto demonstrated that increasing public facilities can increase visitor behaviors, such as sun bathing, reading, and other light physical activities [19].

In most studies, tourist data was obtained through semi-structured interviews or social media and then explored the factors that influence visitors’ behavior from various perspectives. Landscape features have a close relationship with visitors’ behavior. For example, urban park facilities work in a variety of ways for different genders and ages [20], leading to the differences of their preferred parks in size, color, presence of water bodies, and the attractiveness of vegetation [6]. Specific landscape features evenly have an impact on visitors’ behaviors, however, the linkages are rarely studied. Moreover, subjective emotions or unclear expressions from respondents can have a substantial impact on the accuracy of the data, which can further impact the consistency of the results. Secondly, the resulting data are not comprehensive, with over-representation of young generation but less response available for seniors who do not prefer smart devices. Third, owing to the differences in visitors’ willingness to participate in interviews, most studies used approachable people to conduct the interviews. Moreover, the questionnaire method is limited by small sample sizes and low temporal and spatial resolution [21]. Thus, observing tourist behavior from the perspective of an observer is crucial in understanding the relationship between landscape features and visitor behavior in a more objective manner. This study used the System for Observing Play and Recreation in Communities (SOPARC) to address the aforementioned problems effectively.

The present study attempted to investigate how landscape features of urban parks affect visitor’s behavior by answering the following questions: (1) what were the spatiotemporal patterns of visitor behavior in urban parks? (2) what are the dominant landscape features driving visitors’ behaviors in urban parks? Based on previous studies, we explored the intrinsic connections and spatiotemporal patterns in visitor behavior and further detected the main park features that drove these behaviors.

2. Materials and Methods

2.1. Study Site

The West Lake Scenic Area in Hangzhou is the largest urban park in China, included in the World Heritage List by UNESCO in 2011 [22]. The West Lake Scenic Area is located west of Hangzhou, Zhejiang Province, centered at 30°14′45″ N, 120°08′30″ E, with a total area of 60 km². Over 18 million tourists visited the West Lake Scenic Area, the associated tourism income reached 35 billion CNY in 2019 [23]. The large number of domestic and international tourists provided sufficient samples for this study. There are several types of parks in the West Lake Scenic Area, including heritage, recreation, and leisure parks. The high diversity of urban parks in the West Lake Scenic provides a high-quality platform for our study. We selected 10 parks with various sizes, shapes, and types surrounding West Lake (Figure 1), that offered free admission (

Table 1. Description of the studied urban parks.

No.	Park’s Name	Shape	Surrounding Environment	Description
1	Gu Shan	Block	Mountain and lake	The largest island in the West Lake; is not only a scenic spot but also a place of cultural relics.
2	Bai Causeway	Ribbon	Lake on both sides	Built to commemorate the famous poet Bai Juyi, it is one of the ten scenic spots of West Lake and is named “Broken Bridge with Snow”.
3	First Park	Ribbon	Central Business District and lake	The green corridor of The West Lake Cultural Landscape, located on the eastern shore of West Lake, close to the CBD of Hangzhou.
4	Liulangwenying Park	Block	Recreation and Viewing Park	Formerly known as the Southern Song Dynasty Imperial Garden, which was named for the willow trees and warbler’s song.
5	Wu Mountain	Block	Mountain and lake	Located the southeast of West Lake and is a mountain park known for its ruins.
6	Leifeng Pagoda	Block	Pagoda and lake	One of the “Ten Views of West Lake” and one of the nine famous pagodas in China. Located on Sunset Mountain on the south shore of West Lake Scenic Area, with great scenery and best seen at sunset.
7	Flower View Fish Park	Block	Lake	Leisure and recreational park with flowers, a harbor, and fish as the main features.
8	MAO’s Home Port	Ribbon	Traditional residence and lake	Highlighting the landscape features of the harbor and waterfront residence, with beautiful and peaceful scenery.
9	Su Causeway	Ribbon	Lake on both sides	A wooded embankment running through the north and south scenic areas of West Lake, with willow trees planted on both sides and famous for its spring scenery
10	Quyuanfenghe Park	Block	Temple and lake	Located in the northwest corner of West Lake, with lotus flowers planted around the courtyard and many pavilions.

). There are also various temples, pagodas, pavilions, and museums in these parks.

2.2. Data Collection

We selected 10 features that represent the natural and landscape features of these parks (

Table 2. Description of landscape features in urban parks.

Landscape Types	Landscape Features	Unit	Description
Natural landscape features	Plant richness	Species	Number of vascular plant species.
	Largest vegetated patch area	m2	Area of largest vegetated patch.
	Vegetation cover	%	Coverage of plant canopy.
	Old trees	Number	Individual old and valuable trees.
	Waterfront	—	Waterfront properties connected with the observation area of a waterbody, which were classified into none, small water bodies, large water bodies, and West Lake (the central and largest waterbody).
Artificial landscape features	Social space	Number	Large open space footprint for social services, such as pavilions, connecting corridors, and outdoor cafes.
	Seating facilities	Number	Seats, stone benches, and other seating facilities.
	Recreational facilities	Number	Recreational facilities, sport facilities, and artistic sketches.
	Public service facilities	Number	Huts, booths, or facilities that provide food, shopping, and public health services.
	Landmark	Number	Monuments, status, steles, and fountains.

), including natural and artificial landscape features. Natural landscape features were plant richness, largest vegetated patch area, vegetation cover, old trees, and waterfronts. Further, we divided the park facilities into social areas, seating facilities, recreational facilities, public service facilities, and landmarks. Based on Smith’s classification of people’s behavior [24], we classified four visitors’ behaviors into two categories: planned behavior and unplanned behavior. Exercise and recreation were defined as planned behaviors and these had a clear aim, fixed location, and schedule before the behavior occurred, whereas rest and walks were treated as random, unplanned activities. Additionally, we individually recorded and analyzed traffic behaviors within the observation area (

Table 3. Classification of visitor behavior.

Behavior Group	Behavior Type	Description
Planned behavior	Exercise	Physical exercise and sport activities, such as running, playing Kungfu, and dancing.
	Recreation	Social and recreational activities, such as singing, playing local opera, playing chess, and fishing.
Unplanned behavior	Rest	Take a rest without significant body movements, such as sitting, lying down, or chatting.
	Stroll	Enjoy the scenery and relax with slowly walking speed.
Other	Transportation	Quickly pass through the observation area by bicycle or bus.

).

Data was collected in Autumn (average temperature: 10–22 °C), 2020 to avoid disturbance from extreme weather. Sixteen round sampling plots with a radius of 20 m were randomly selected in each park. The interval between plots was greater than 100 m. Then, the landscape features within each plot were recorded in a clockwise direction. The number of visitors and duration for each behavior type in the plot were recorded using the System for Observing Play and Recreation in Communities (SOPARC). SOPARC is a non-participatory transient time sampling observation method that can be effectively used to detect the activity patterns of different park features [25] and is also commonly used in the study of physical activity [26] and environmental behavior [27,28]. The data on visitor behavior was collected independently on weekdays and weekends to cover temporal variations [29]. A total of 53,313 visitors were recorded in 320 sampling plots with an average observation time of 20 min.

2.3. Statistical Analysis

Spearman’s correlation analysis was used to study the association between behavioral types and their temporal variation. Wilcoxon rank tests and ANOVA were applied to test the differences in visitor behavior across different parks and visiting dates. We then used a linear mixed-effects model to analyze the effects of park features on the number of visitors of each behavior type, with the random effects of park identities and visiting dates. The best-fit model was finally obtained using stepwise regression. All variables were standardized before the analysis. All statistical analyses were performed using R version 3.6.3.

3. Results

3.1. Interactions between Different Behavior Types

• The average duration of the behaviors were varied significantly among the parks studied (Figure 2), that transportation accounted for the shortest time, whereas exercise and recreation accounted for the longest duration. Spearman’s correlation analysis revealed that the ratio of behavior types could significantly influence each other. (Figure 3). Spearman correlation analysis at the upper-right of Figure 3, respectively tested the correlation between different types of visitor behaviors on weekdays, weekends and in total. Scatter plot at lower-left showed the observed ratio of behavior at each sampling plot. Box plots at the right edge and histograms at the bottom compare the ratios on different visiting dates. Transportation and strolling both caused significant negative effects on all other behaviors, with higher correlation coefficients on weekends than weekdays. A significant positive correlation was observed between the ratios of exercise and recreational behaviors. Surprisingly, there was no significant difference in the ratio of each behavior between weekdays and weekends, despite their stronger correlations on weekends.

3.2. Spatiotemporal Variation in Visitor’s Behavior

• The total number of visitors to these parks was double on weekends compared to weekdays (p < 0.001) (Figure 4); however, the Wilcoxon test indicated that there was no difference in the average duration (p = 0.345). In the pairwise comparisons of behavior types, there was no significant difference in the number of visitors who engaged in exercise and recreation between weekends and weekdays (p_exercise = 0.647, p_recreation = 0.316) or the average duration (p_exercise = 0.289, p_recreation = 0.238). For rest and strolling, the numbers on weekdays was significantly lower than that on weekends (p_rest = 0.002, p_stroll < 0.001).
• The results from the ANOVA further demonstrated that both park identity and visiting date have significant impacts on visitor behavior (

  Table 4. ANOVA for park identity and visiting date affecting the visitor amount with different behavior types.

  Behavior Type	Factor	Sum Square	F-Value	p-Value
  Recreation	Park identity	39,045	8.691	<0.001
  	Visiting date	345	0.69	0.407
  	Residual	152,249	—	—
  Exercise	Park identity	3991	5.133	<0.001
  	Visiting date	266	3.081	0.080
  	Residual	26,345	—	—
  Rest	Park identity	20,080	7.424	<0.001
  	Visiting date	2838	9.444	0.002
  	Residual	91,657	—	—
  Stroll	Park identity	749,981	12.2	<0.001
  	Visiting date	133,415	19.53	<0.001
  	Residual	2,083,170	—	—
  Transportation	Park identity	1,316,067	23.91	<0.001
  	Visiting date	179,576	29.36	<0.001
  	Residual	1,865,211	—	—
  Total	Park identity	4,695,466	25.38	<0.001
  	Visiting date	769,436	37.43	<0.001
  	Residual	6,269,852	—	—

  ). The most attractive parks around West Lake, the Su Causeway, and Bai Causeway, as well as the park close to downtown on the east coast of the lake, had significantly higher visitor numbers and durations than the other parks. On weekends, the visiting status (numbers and duration) was better than that on weekdays in each park, especially in Leifeng Pagoda, where several Buddhist temples, iconic attractions, and food services are distributed (Figure 4). We also found that park identity and visiting date better explained the number of visitors engaging in transportation and strolling (R²_{transportation} = 44.5%, R²_stroll = 29.8%) than rest, recreation, and exercise (all R² were less than 20%).

3.3. Effects of Landscape Features in Parks on Visitor Behavior

• A linear mixed model was applied to analyze the effects and contributions (

  Table 5. Effects of landscape features in urban parks on visitor behavior by linear mixed models controlling the influence from park identity and visiting data.

  Behavior Type	Factor	Estimated Coefficient	Sum Square	F-Value	p-Value
  Exercise	Vegetation cover	1.15	369.22	4.80	0.029
  	Social space	1.53	643.68	8.38	0.004
  	Landmark	2.31	1536.40	20.00	<0.001
  Recreation	Seating facilities	3.58	3669.30	8.30	0.004
  	Social space	5.47	8434.90	19.08	<0.001
  	Landmark	3.61	3744.40	8.47	0.004
  Rest	Seating facilities	6.67	12,478.60	49.98	<0.001
  	Social space	3.47	3496.10	14.00	<0.001
  	Recreational facilities	−1.81	972.30	3.89	0.049
  Stroll	Plant richness	14.47	49,683.00	7.75	0.006
  	Old tree	−9.46	25,610.00	4.00	0.046
  	Vegetation cover	−1.48	59,460.00	9.28	0.002
  	Waterfront	17.22	53,206.00	8.30	0.004
  Transportation	Largest vegetation patch area	−9.82	26,954.00	4.54	0.034
  	Old tree	−9.03	23,243.00	3.92	0.049
  	Social space	−9.94	28,651.00	4.83	0.029

  The landscape features appearing in the table were obtained from the best-fit model using stepwise regression. Bold features indicate the dominant features that influence visitor behavior.

  ) of landscape features in parks on visiting behavior, controlling for the interference from park identity and visiting data as random effects. The results indicated that visitor behaviors were driven by the quality of landscape features and visiting purposes.
• Landmarks, social space, and vegetation cover positively affected planned behaviors, such as exercise. The number of landmarks was the dominant factor, and all three features influenced the amount and duration of visitor exercises. However, social space was the most important feature for recreating visitors, followed by landmarks and seating facilities. Seating facilities and social spaces were the main landscape features accelerating rest, whereas recreational facilities inhibited it. Higher plant richness and waterfronts could improve the environmental quality for strolling; however, vegetation cover and the number of old trees had negative impacts. Largest vegetation, patch area, number of old trees, and social space had stronger negative effects on transportation than other behaviors. The landscape features affecting rest and recreation were relatively consistent but with slightly different contribution rates.

4. Discussion

4.1. Various Spatiotemporal Patterns across Behavior Types

• In urban parks, visitor behavior is interacted with each other [30]. Spearman’s correlation analysis revealed a significant negative correlation between transportation and other behaviors. Areas with more transportation were generally perceived by visitors as being exposed to higher traffic and its associated risks, resulting in a relatively low percentage of exercise and recreational activities in these areas. Additionally, there was a significant inverse correlation between strolling and other behaviors. This may be due to the fact that strolling requires relatively less space for activities and more natural landscapes, which contrasts with other behaviors. Several studies have affirmed that more attractive and aesthetic walking environments contribute to improving walking behavior, especially among the elderly [31,32,33]. A positive correlation between the proportion of visitors with exercise and recreation may be a result of the similar requirements for larger open spaces in these two activities. Furthermore, there was no significant difference in visitor behavior between weekdays and weekends, indicating that the interaction between various visitor behaviors are consistent regardless of the day of the week. This was consistent with the fact that behavior patterns of tourists at moderate and low densities do not vary and the utilization of space does not change significantly [34].
• Visitor numbers for recreational and sports activities were stable across different dates, indicating that these visitors constituted a confirmed group composed of local retired elders, who did not exhibit lifestyles that varied weekdays and weekends. West Lake Scenic Area was the most famous urban park and World Heritage in China. The purpose of most visitors is to be close to nature and enjoy the lake and mountain views. We found the space and facilities supporting recreation and sport were limited, and always occupied by the same groups of local elders. Thus, the extra young visitors on the weekend did not enhance the exercise and recreational activities. However, the number of visitors who engaged in rest and strolling on weekends was higher than on weekdays, which was owing to visitors either being employed or including children. ANOVA further demonstrated that park identity significantly affected all visiting behaviors. Reputation, accessibility, and service convenience were the main features driving park selection by visitors [35].

4.2. Roles of Landscape Features in Parks for Visitor’s Behavior

• As it is the largest urban park in China, massive numbers of domestic and international visitors arrived daily. Distinct landmarks provided a clear gathering point for these visitors to form a group. Thus, landmarks were the most important factor increasing planned behavior, which served as anchors in the visitor’s mental representation of the chaos and crowded physical environment [36].
• Vegetation cover and social space also determine the occurrence of planned behaviors. Vegetation cover had different effects on planned and unplanned behaviors. Higher vegetation cover provided shade and fresh air and improved exercise efficiency. However, it had a negative effect on strolling. The strollers preferred open spaces with diverse flowering shrubs and herbs, such as the parks of the Su Causeway and Bai Causeway. However, a high density of trees may increase fear of potential criminal risks due to obstruction of view [17], despite the potential for improved nature-based experiences and greater well-being benefits [37]. In addition, a larger social space provided more space for singers and dancers and easily gathered a certain audience with sufficient seating facilities. Thus, convenient seating facilities are positively associated with rest [4], which also increases the total number of visitors [3]. In contrast, noise from recreational and exercise activities had a negative impact on rest.
• Visitors may perform both negative and positive tasks in the same environment [38]. Visitors decided how they behaved by assessing the trade-off between the positive and negative effects of the same landscape features (

  Table 6. Trade-off in landscape features for visitor’s activities in urban park.

  Landscape Types	Park Features	Positive Effects	Negative Effects
  Natural landscape features	Number of plant species	Colorful and natural experience	Aesthetic fatigue
  	Largest vegetation patch area	Natural experience	Poor transportation
  	Vegetation cover	Shade and clean air	Mosquitoes and insecurity
  	Old and valuable trees	Historic	Space limitation for activities
  	Waterfront Conditions	Broader and beautiful view; Coolness	Mosquitoes and insecurity
  Human landscape features	Social Space	Interactivity	Noisy
  	Seating facilities	Rest	Space limitation
  	Recreational facilities	Recreation and exercise support	Noisy
  	Public Service Facilities	Convenient tours	Crowded and noisy
  	Landmark	Gathering points	Noisy and space limitation

  ) in these parks across the West Lake Scenic Area. The trade-off between naturality, aesthetics, accessibility, security, and disturbance drove the spatial distribution of these different behaviors in these parks. Considering the spatiotemporal patterns of visitors’ behaviors and field-based observations, we found that the visitors with planned behaviors were mostly local old residents near the parks, with strong spatial variation but less temporal variation. Sufficient social spaces and facilities were the dominant park features that supported these planned behaviors. The trade-off between park features strongly affected unplanned behaviors, especially the trade-off between safety and natural experience. The negative correlation between transportation and other behaviors also supported that safety was a dominant factor in deciding visitors’ behavior in the park. Unlike the Western tourists who preferred to sit on the grassland, more Chinese visitors selected seating facilities for rest. Planning the sitting space was important for resting visitors in parks. The trade-off between potential criminal risk and natural experience from the vegetation cover always determines the route selection of the strollers.

4.3. Suggestions for the Planning and Design of Public Parks

• Tourists’ landscape preference was deriving the preference values of different landscapes from their perceptions [39]. Visitors would select their behaviors based on the trade-off between benefits and risks from landscape features in urban parks. For example, reducing fences, shrubs, and terrain changes can effectively deal with public safety issues in park design [40]. This shows that the landscape features of a park can also induce or discourage certain behaviors by influencing users’ psychological perceptions. Bandura’s “tripartite reciprocal determinism” further states that tourists’ perceptions are shaped by and, in turn, shape their environment and behaviors [41]. Similarly, with the increasing number of people travelling, tourist behavior also has an increasing impact on the environment [42]; thus, research on visitors’ behavior plays an important role in the maintenance of the environment to some extent.
• Based on the results of this study, we propose strategies for urban park designers. First, the scientific configuration of landmarks is important for a large park, which can support visitors’ congregating activities. Furthermore, landmark-based wayfinding has enormous potential for application purposes, such as route guidance systems and signage [31], and a clear route guidance and signage system would lead visitors to a suitable space for specific behaviors. Second, designers should consider the dual utility of vegetation and design it more skillfully. The trails passing through dense forests would increase a single visitor’s fear of criminal activity. However, a visitor group prefers greenspaces that are more connected to nature. On the other hand, in terms of landscape ecology, visitors crossing dense forests may cause negative “edge effects” on internal biodiversity. Finally, more rational functional zoning is a way to reduce conflicts arising between different behaviors from blurred functional positioning [41]. In this study, we found synergies and trade-offs between different visitor behaviors. A suitable design of multifunctional parks should have clear zoning, open places, and necessary facilities for exercise and rest, and monitoring and alarm devices in densely vegetated areas. Designers should reconsider the results and how they can be interpreted from the perspective of previous studies and working hypotheses. These findings and their implications should be discussed in the broadest context possible. Landscape features do not only influence visitors’ physical behavior but also their mental emotions [43].
• Therefore, future research should focus on the effects of landscape features on visitors’ physical and mental health from individual and public levels, and explore how to regulate and practice the trade-off of landscape features in park design.
• In addition, SOPARC is a fundamental and user-friendly tool to quantify park visitor behaviors and characteristics. Furthermore, SOPARC is a visual, observation-based, non-contact approach, which was not restricted by distance, and easily captured all the visitors without consent. However, SOPARC relies on the observer’s subjective judgment of visitor activities, which might introduce some errors. It also cannot capture the psychological and emotional characteristics of visitors. Although SOPARC can provide some quantified data, it cannot provide a deep understanding and insight into visitors’ perceptions and attitudes toward the park. Therefore, more appropriate methods need to be developed for visitor behavior research in the future.

5. Conclusions

Urban parks are great places for humans living in cities to be close to nature. Understanding the relationship between the landscape features of urban parks and visitors’ activities is an important way to enhance the happiness of urban residents and improve urban planning and design. In this study, we found that the resting and strolling of visitors in parks exhibited significant spatiotemporal variation; however, exercise and recreational activities only varied among parks. Some given landscape features had both promoting and restrictive effects on visitor behavior. Landmarks and social spaces were the dominant demands for exercise and recreation restricted resting. Seating facilities supported recreation and rest. Strolling was restricted by higher vegetation cover but promoted by higher plant richness. Our findings suggest that landscape architects should consider the synergies and trade-offs of park landscape features for different visitor behaviors.