Landscape Scene Sequences of Park View Elements Facilitate Walking, Jogging, and Running: Evidence from 3 Parks in Shanghai

Abstract

With the growing awareness of public health, urban parks have increasingly become popular venues for physical activities due to their accessibility and pleasant landscapes, among which walking, jogging, and running dominate. This study innovatively integrates exercise trajectory data from the Strava platform and semantic segmentation technology to analyze the interaction mechanisms among park view elements, physical activities, and physiological responses, based on empirical data from three representative parks in Shanghai. This study includes the following: (1) acquiring hotspot exercise paths and physiological data (heart rate and speed) of walking, jogging, and running users through the open Strava platform; (2) conducting semantic segmentation on real-word photos of three case parks to extract 17 types of park elements; (3) applying Spearman’s correlation analysis to reveal the differential impacts of park elements on physiological responses under walking, jogging, and running behaviors, demonstrating that combinations of elements such as trees, water bodies, fences, and sky influence exercise performance; and (4) constructing scene modules for site attraction, training improvement, and restorative relaxation for walking, jogging, and running, and proposing phased landscape scene sequence strategies to provide quantitative guidance for health-oriented park planning and design. This study breaks through the limitations of traditional subjective evaluations by coupling objective physiological data with spatial elements, offering novel insights for optimizing the exercise functionality of urban green spaces.

1. Introduction

1.1. Park View, Physiological Response, and Walking, Jogging, and Running

With the growing public awareness of health, urban green spaces—particularly well-designed parks—have increasingly become vital environments that support physical activity and well-being. Much evidence has confirmed that parks with esthetic environments, convenient accessibility, and rational spatial layouts can promote public health by encouraging physical activity and enhancing social interaction [1,2,3]. Extensive epidemiological evidence has verified that activities in green environments can enhance physical activity levels [4], reduce the risk of cardiovascular diseases [5], and support mental health [6]. Among these physical activities, three economical and accessible forms of fitness—walking (3.5–6.0 km/h, HR < 100 bpm), jogging (6.0–8.0 km/h, 100 ≤ HR < 140 bpm), and running (≥8 km/h, HR ≥ 140 bpm)—are the preferred choices for fitness enthusiasts, accounting for over 60% of physical activities in urban parks [7]. These walking types offer flexible and low-cost health benefits: walking activates the parasympathetic nervous system and improves mood [8]; jogging enhances cardiopulmonary function through aerobic metabolism [9]; and running stimulates BDNF (brain-derived neurotrophic factor) secretion, which supports cognitive and emotional health [10]. However, the effectiveness of these activities depends not only on the exercise itself but also on the interaction between exercise types and their surrounding landscape environment [11]. Therefore, scientific research should at least cover (1) the landscape environment; (2) walking, jogging, and running behaviors; and (3) physiological response. Landscape environment refers to park view elements such as trees, rivers, and sky, and various scenes composed of multiple elements [12,13]. Walking, jogging, and running are physical activities performed at different speeds. Physiological response includes speed, HR(heart rate), breath, subjective fatigue, etc. [14]

In general, exercise training tends to be regular and goal-oriented. Regardless of whether the goal is to alleviate stress through walking or improve physical fitness through running, their behavioral characteristics can be divided into three core stages: site attraction, training improvement, and restorative relaxation. Each stage includes specific physiological and psychological demands to improve individual exercise performance. The landscape characteristics in different scenes affect the performance and physiological feedback of walkers, joggers, and runners at each stage. Therefore, the park environment suitable for walking, jogging, and running should provide a variety of scenes to promote these activities. The landscape scene sequences should follow the principles of exercise training and correspond to site attraction, training improvement, and restorative relaxation [15,16,17].

1.2. Research Gaps

In recent years, interdisciplinary research has laid a preliminary foundation for exploring the relationships among the landscape environment; walking, jogging, and running behaviors; and physiological response. For example, studies have employed GPS tracking systems and ActiGraph accelerometers to analyze associations between energy expenditure (calories burned), exercise intensity, and activity frequency [18,19]. Multispectral remote sensing and machine learning techniques are used to analyze park view elements [20,21] and summarize the positive effects of walking, jogging, and running on BMI (body mass index), HR, systolic blood pressure, diastolic blood pressure, and other physiological indicators [22,23]. GPS and VGI information are used to interpret the semi-open exercise data of online platforms such as Strava [24,25], define a reasonable amount of walking in parks [26], and analyze the influence of landscape environment on the perceptions and behavioral intentions of walkers, joggers, and runners [27,28]. The continuity, resilience, flatness, and esthetics of park trails are evaluated from the perspective of walking effects, and the trail combinations suitable for different walking types are found [29]. Researchers can capture real-time physiological responses associated with walking, jogging, and running intensity, thereby enabling interdisciplinary investigations into how the landscape environment regulates health [30]. In addition, some scholars have applied computer vision techniques to analyze geotagged green space images, quantifying the landscape elements that affect self-perception, thus facilitating exploration from macro-scale spatial sequences to micro-scale environmental features [31].

However, there are still some research gaps. In terms of landscape element selection, existing studies mostly focus on urban streets, neighborhood roads, and green spaces, etc., while neglecting the specific characteristics and internal spatial information of park environments [32,33]. In terms of the selection of physiological indicators, existing studies have primarily focused on subjective feelings such as pleasure and satisfaction [34]. However, these subjective elements are often uncontrollable and descriptive, whereas objective physiological indicators like HR, speed, and duration are rarely used to explore the landscape influence mechanism. A large number of studies on the correlation between landscape environment, landscape behavior, and physical health have confirmed that a favorable landscape environment and positive landscape behaviors can promote physical and mental health [35,36]. These studies mainly suggest improving the ecological environment through landscape behaviors to bring indirect health benefits [37,38]. However, few studies have explored how the landscape environment directly affects individuals to induce positive physiological responses. Research in this area often focuses on the influence mechanisms between individual park view elements and physiological indicators, such as the effects of different water and plant landscapes on human physiological indicators (e.g., blood pressure, pulse, and brain waves) [39,40], neglecting the impacts of combination of park view elements on walking physiological indicators. Moreover, most of the studies focus on theories, with less emphasis on planning and design practice.

Few studies can answer the following questions: Which park view elements affect the landscape behavior of walking, jogging, and running behaviors in parks? What is the influence mechanism among them? Which elements and their combinations may affect the HR and speed during walking, jogging, and running? How do we construct a suitable landscape environment for different walking types through related park view elements? What kind of park scenes can stimulate preferences for walking, jogging, and running, and promote various states of these activities? These questions may be answered by a comprehensive quantitative study on landscape environment, walking, jogging, and running behaviors, and physiological response.

1.3. Research Framework

Based on the above, this study takes three representative parks in Shanghai as an example, exploring the relationship among landscape environment, walking, jogging, and running behaviors, and physiological response by using 3 walking types, 2 physiological response indicators crawled from Strava, and 17 park view elements obtained from park view images (PVIs) taken in the park, and scientifically proving that “favorable landscape environment and positive landscape behaviors can promote the public health”. The contents include the following: (1) verify the practicability of mass exercise data and establish a set of scientific methods to interpret park view elements; (2) identify the park view elements and their relationships that affect the physiological response of walking, jogging, and running; (3) construct scene modules for site attraction, training improvement, and restorative relaxation from the perspective of landscape architecture, and propose the corresponding landscape scene sequences of park view elements.

2. Materials and Methods

2.1. Research Area

Considering the popularity, use frequency, and service scope of parks in various administrative districts of Shanghai, this study selects the following three representative parks as research areas: Century Park in Pudong New Area, Minhang Sports Park in Minhang District, and Huangxing Park in Yangpu District (Figure 1). These parks are well-located and easily accessible, combining natural landscapes such as forests, lakes, and lawns with artificial facilities, providing both citizens and tourists with convenient spaces for recreation and fitness. In terms of functional attributes, they offer a variety of functions, including recreation, fitness, and science popularization education, catering to the demands of citizens of different ages and interests. Additionally, they have become the first choice of local residents for daily fitness due to their excellent facilities and comfortable environment.

Referring to the records on Strava (USA), there are hundreds of records about these three parks with abundant empirical data. As one of the largest parks in Shanghai, Century Park covers an area of 140 hm² and attracts a large number of fitness enthusiasts. Minhang Sports Park spans approximately 45 hm² and offers many sports facilities to meet the diverse sporting requirements. Huangxing Park covers an area of 60 hm², integrates cultural elements with green space, and serves as an important recreation venue for residents in the Yangpu District. Therefore, the selection of these three parks as research areas is representative and universal, providing empirical support for the study of park walking, jogging, and running in Shanghai and other cities.

2.2. Methods and Steps

Considering the data volume and the feasibility of the research, this study attempts to use the Strava platform to obtain public data for the exploration of universal laws. Existing studies have already utilized data from wearable devices and open platforms for research on the behavioral analysis of physical activities within cities. Moreover, compared with the sensor devices in laboratories, the data from crowdsourcing platforms such as Strava strike a balance between spatial granularity (GPS trajectories) and scalability, and can rapidly capture the natural movement behaviors of diverse populations. Meanwhile, due to the complex environment of the case parks, in order to reduce the biases of human observers, semantic segmentation technology, which is capable of achieving pixel-level classification in complex scenes, is selected to obtain the park view elements.

The overall research consists of four steps: data acquisition and positioning, data processing, data analysis, and landscape scene construction. (1) Crawl the walking data of users in three parks, respectively, including trail and physiological response data, and conduct point-by-point shooting of park view data according to the hot path. (2) Perform semantic segmentation on the obtained panoramic photos to obtain the pixel ratios of each park view element, geographically match the walking user data with the park landscape element data, and screen and classify them into three categories: walking, jogging, and running. (3) Overlay the path heat maps of the three types of walking paths separately. Orientally identify the scene characteristics of the preferred paths for each activity based on the heat maps, conduct Spearman correlation analysis, and calculate the impacts of various park view elements on HR and speed within different groups. (4) Based on the impact of park view elements on the preference and physiological response of walking, jogging, and running behaviors, we constructed landscape scene modules from the perspective of landscape architecture planning and design, focusing on the needs of site attraction, training improvement, and restorative relaxation, and developed landscape scene sequences that cater to different walking, jogging, and running behaviors.

2.3. Data Collection

2.3.1. Park View Image (PVI) Collection and Positioning

The PVIs of Century Park, Minhang Sports Park, and Huangxing Park were collected by the Inst360 One X2 panorama camera (Insta360 Co., Ltd., Shenzhen, China). To ensure the consistency of PVI data and encompass diverse physical environmental components, photographs were taken at 20 m intervals along selected trails [41]. The camera was mounted on the photographer’s head to minimize interference from the figure on PVI elements. Data collection was conducted between November and December 2024 to maintain the seasonal stability of deciduous vegetation [42]. After excluding the position errors and blurry images, a total of 1264 PVI samples were obtained, of which 736 samples matched the walking trails with exercise data.

The collected PVIs were analyzed through semantic segmentation using Python 3.8. Physical elements within the PVIs were classified and quantified based on the 150 categories in the ADE20K dataset (Microsoft, Redmond, WA, USA; Figure 2). These categories were consolidated into broader classifications, with elements accounting for less than 1% of pixels excluded. Finally, 17 types of elements were selected: wall, building, sky, floor–road–earth, tree, grass–plant, sidewalk–path, person, car, water, fence–railing, signboard, bench, streetlight–pole, ashcan, altitude, and slope gradient. After semantically segmenting the panoramic image for each location, the proportion of each element in the image is obtained. The GPS coordinates of the park view element proportion data corresponding to each location were mapped onto the park trail.

2.3.2. Exercise Data Collection

The data collection for walking, jogging, and running behaviors primarily utilized available data from internal park trails. A total of 58 exercise trajectories across three parks were crawled from Strava, with external loop trails excluded. However, some trails in Century Park were too short, and the data were insufficient and did not match the data of the other two parks. To ensure experimental accuracy, 10 target trails in Century Park, 6 in Minhang Sports Park, and 5 in Huangxing Park were selected for correlation analysis after a secondary screening. Additionally, user data with abnormal speeds, such as unusually fast or slow records, was excluded. Ultimately, 189,321 pieces of exercise data from 905 users were collected using Python. Based on the data from these 21 trails, the trails were categorized into three groups to generate heatmaps for walking, jogging, and running behaviors in subsequent analyses.

2.3.3. Physiological Response Data Collection and Positioning

The valid physiological response data, including HR and speed, were mined on Strava (

Table 1. Physiological response group data.

Data Grouping	HR (beats/min)			Speed (km/h)
	<100	100~140	>140	<3.5~6	6~8	>8
Amount of valid data for Century Park	218	6202	29,243	678	2731	32,272
Amount of valid data for Minhang Sports Park	1396	6196	22,381	2727	8632	18,614
Amount of valid data for Huangxing Park	12	3830	25,172	2524	9795	16,698

). A point-by-point positioning program was developed based on the exercise trails, which transformed the original exercise data points into line features. The target trails were then positioned on the map using ArcGIS Pro 3.8 software to generate exercise data points.

2.4. Data Processing and Analysis

In this study, the independent variables included 17 park view elements, namely wall, building, sky, floor–road–earth, tree, grass–plant, sidewalk–path, person, car, water, fence–railing, signboard, bench, streetlight–pole, ashcan, altitude, and slope gradient, which were selected through semantic segmentation of PVIs. Dependent variables included HR and speed [43,44]. The dependent variables, including HR and speed, were preprocessed through the STDEVP function, AVERAGE function, and ABS function. The standard deviation, average, and absolute value of the speed data were calculated. After screening out the abnormal values, the meaningless interference data with a value of 0 was removed, and the remaining data were integrated again to obtain 44,572 pieces of HR and speed data.

Considering the multicollinearity problem, the OLS trial calculation of HR and speed showed that the VIF values of sky, floor–road–earth, tree, grass–plant, and sidewalk–path were all greater than 10. To avoid the multicollinearity problem of park view elements, Spearman correlation coefficient was used to analyze and explore the influence mechanism between park view elements and physiological response of walking, jogging, and running [45]. The correlation data were visualized using Matplotlib 3.3.3 and Seaborn 0.11.1 toolkits in Python, and the relationships between variables were presented as a matrix heatmap.

3. Results

3.1. Mechanism of Park View Elements Affecting Walking, Jogging, and Running Behavior

A total of 21 pieces of heatmap exercise data for internal trails in parks were crawled on Strava. They were divided into three exercise trajectories (walking, jogging, and running) according to HR and speed, and then superposed to the corresponding heatmaps (Figure 3, Figure 4 and Figure 5).

3.1.1. Characteristics of Walking Attraction Places

The openness of space is a key characteristic that attracts walkers. The high sky view factor (SVF) improves natural lighting and air circulation, reducing the sense of crowding, and enhancing the comfortableness of the walking environment. The ground is mainly paved with cement bricks, providing a stable and comfortable walking experience. Waterscape and green space in the site are accessible, and natural elements are integrated to beautify the environment and generate extra healing effects. The diverse colors add artistic appeal and visual impact to the site, making the walking environment more attractive. The big loop route of Jingtian Lake in Century Park, the splendid lakeside water belt in the north of Minhang Sports Park, and the paths around the lake in Huangxing Park are highly popular among walkers, joggers, and runners.

3.1.2. Characteristics of Jogging Attraction Places

The sites that attract joggers share a range of pleasant environmental characteristics, including scenic natural landscapes, high green view factor, and diverse plant combinations. They provide a tranquil ecological environment for joggers to enjoy nature during exercise and enhance their sense of pleasure. For example, Minhang Sports Park offers a broad view along the paths around the lake, with layered surrounding landscapes. This pleasant environment attracts people to engage in physical activities. Similarly, the trail design of the Jingtian Lake loop route in Century Park is also varied. The open landscape is not only interesting but also enables joggers to exercise across different terrains. This helps improve the jogging effect, facilitating a smooth transition from walking to jogging, and enhancing exercise intensity and effect.

3.1.3. Characteristics of Running Attraction Places

The sites that attract runners should meet special requirements. With its strong sense of depth, a circular track offers runners a visual feeling of extending forward and a sense of regularity, motivating them to keep running [46]. Professional rubber tracks, known for their excellent elasticity and shock absorption, can effectively reduce the impact on joints during running. The continuous layout of the trails ensures that runners can maintain a smooth run over long distances without sudden interruptions. Additionally, a safe distance is usually maintained between the track and the waterscape to avoid any potential risks during running, as shown by the route around the lake in Huangxing Park (Figure 5c). Well-designed drainage facilities and quick use recovery after rain further guarantee the running experience. Furthermore, the track should be marked with distance indicators to assist in recording and planning running distances, adding both interest and challenge. As for the planting space combinations, the areas with the highest running popularity in Figure 4c and Figure 5c are densely forested and arranged with tall trees on both sides of the path and grassy slopes, creating an enclosed and scenic environment that attracts runners to exercise.

3.2. Mechanism of Park View Elements Affecting Physiological Response

To explore the relationships among park view elements, heart rate (HR), and speed, a correlation matrix heatmap was generated based on dual filtering of HR and speed data across walking, jogging, and running. In the heatmap (Figure 6), positive correlations are shown in red and negative correlations in blue, with darker shades representing stronger correlation coefficients.

3.2.1. Mechanism of Park View Elements Affecting Physiological Responses During Walking

(1)

HR

During walking, only the wall and the signboard were positively correlated with HR, and no park view elements had a negative effect on HR. As walking was a low-intensity exercise, obvious obstacles and information selection had a significant impact on the HR changes in walkers.

(2)

Speed

During walking, the higher the proportion of tree and floor–road–earth, the higher the speed. That is, better greening and pavement conditions were beneficial in enhancing the intensity of walking. The higher the proportion of sidewalk–path, signboard, building, sky, etc., the lower the speed. This indicated that the obstacles could easily affect the speed of walkers.

3.2.2. Mechanism of Park View Elements Affecting Physiological Responses During Jogging

(1)

HR

During jogging, a higher proportion of trees is associated with an increase in HR. This suggests that an increase in the green–looking ratio, represented by trees, effectively supports the aerobic exercise environment during the transition from walking to jogging. The higher the proportion of altitude, sky, and car, the lower the HR. Similarly to the walking mechanism, the addition of sky helped create an open and relaxing environment, preventing the HR from increasing too fast during exercise.

(2)

Speed

During jogging, the addition of altitude, person, and ashcan could easily increase the speed. The gathering or companion of joggers helped maintain or even improve the speed. The higher the proportion of floor–road–earth, the lower the speed. This indicated that jogging had a greater demand for greening coverage surfaces.

3.2.3. Mechanism of Park View Elements Affecting Physiological Responses During Running

(1)

HR

During running, the higher the proportion of sky, signboard, sidewalk–path, and fence–railing, the higher the HR. Compared with walking and jogging, obstacles or information selection elements had a greater impact on the running rhythm and caused the unstable HR. The higher the proportion of tree, floor–road–earth, grass–plant, etc., the lower the HR. The enhancement of green space through a combination of trees, shrubs, and grasslands can help prevent the HR from rising too quickly.

(2)

Speed

During running, an increase in the floor–road–earth, tree, and slope gradient is more likely to enhance speed. The combination of green paths and these elements contributes to a decrease in heart rate, effectively improving overall running performance. The higher the proportion of altitude, signboard, and fence–railing, the lower the speed. The obstacles affected the speed allocation and increased the HR, which was not conducive to maintaining a healthy running state.

4. Landscape Scene Sequences Facilitating Walking, Jogging, and Running Behavior

Based on the heatmap and correlation analysis results of walking, jogging, and running behaviors in three parks, it can be known that the park view elements affecting the physiological indicators during walking, jogging, and running are different from each other. In conventional exercise training, the willingness to walk, jog, and run often appears first, which is a site attraction. Walking then begins, marking the start of the exercise process, which represents the training improvement phase. After the set goal is finalized, exercisers need to relax and restore their HR, which is restorative relaxation. According to the feedback mechanism of walking, jogging, and running behaviors in different park view elements, the landscape scene sequences are generated through the combinations of three walking types (walking, jogging, and running) and three stages (site attraction, training improvement, and restorative relaxation). These sequences are one of some patterns of park scenes, which serve as a reference for subsequent park landscape planning and design.

4.1. Walking-Friendly Landscape Scene Sequences

Scene of site attraction: The scene offers a broad view, with accessible green spaces and waterscape that stimulate walkers to explore and stroll. The trails are mainly paved with concrete to ensure stability and comfortableness. Resting seats and public service facilities are provided along the trails, allowing walkers to walk with ease for a long time (Figure 7a).

Scene of training improvement: During this period, it is essential to maintain stable HR and speed. The enclosures and the number of signboards should be reduced to prevent the HR from increasing. Shrubs can be planted to reduce the enclosure of tall trees, properly narrow paths can help slow down the speed of walkers, and wooden plank roads can be paved to make walking interesting (Figure 7b).

Scene of restorative relaxation: At the end of walking, the scene transitions from a natural, narrow setting to an artificial, open space. The combination of artificial architectural enclosure with open hardscape can be properly selected to form a connecting scene (Figure 7c).

4.2. Jogging-Friendly Landscape Scene Sequences

Scene of site attraction: Beautiful natural landscape and green looking ratio are maintained to offer joggers a pleasant experience and promote the exercise effect. Additionally, resting pavilion, benches, and fitness equipment are provided to facilitate joggers’ resting and stretching (Figure 7d).

Scene of training improvement: After beginning jogging, the HR increases slowly and is maintained between 100~140 beats/min. Continuous and soft-enclosed green space is suitable for continuous jogging, and it can be planted with tall trees to maintain both HR and speed. Jogging paths should primarily be made of concrete, with variations in space opening and closing to enhance the enjoyment and exploration of jogging. Visual focal points such as sculptures or featured landscapes can be set at the trail corners to stimulate joggers to keep jogging (Figure 7e).

Scene of restorative relaxation: At the end of jogging, the space should be moderately open so as to improve the SVF, reduce the HR and speed, provide hard open spaces such as squares for people to relax, and set a certain number of seats to facilitate resting after exercise (Figure 7f).

4.3. Running-Friendly Landscape Scene Sequences

Scene of site attraction: The scene that attracts runners has a strong sense of depth, with a combination of trees, shrubs, and grasslands, which are somewhat separated from the waterscape. The optimal track is the annular rubber one with a few forks, which ensures continuous running (Figure 7g).

Scene of training improvement: After running begins, the speed can be improved by properly increasing the trail slope. The soft enclosure around the road needs to be improved, and SVF is also required. In addition, wide running paths and a moderate number of signboards can help raise the heart rate to an aerobic level. Transparent railings can be added to enclose the path, divide the two-way trails, increase the width of trails, and set up signboards such as energy consumption and trail length along the way. Moreover, the green elements that can reduce the HR need to be controlled reasonably (Figure 7h).

Scene of restorative relaxation: The proportion of green elements can be gradually increased by planting trees, shrubs, etc. The landscape space can be enriched, and the space view factor can be gradually expanded to help stabilize the HR (Figure 7i).

5. Discussion

5.1. Differences in the Mechanisms of Park View Elements’ Effect on Physiological Responses

The correlation among HR, speed, and park view elements varies in different walking types. At lower speeds during walking and jogging, fewer park view elements significantly affect HR. However, as speed increases during running, the number of park view elements strongly correlated with HR increases significantly. For example, as SVF increases, the HR slows down during jogging. However, it increases the HR during fast running. Therefore, planting trees around the track can help stabilize the HR and improve exercise efficiency. In addition, there are fewer park view elements that enhance speed, while more elements tend to slow down speed. This may imply that park view elements can distract walkers, joggers, and runners and make them reduce their speed. When walking, floor–road–earth is positively correlated with the speed. When jogging, it is negatively correlated with speed. This indicates that a spacious ground can enhance the walking speed. When running, the correlation coefficient of park view elements related to speed is relatively low, indicating that when exercising at a high speed, speed is mainly controlled by the runners, and the influence of park view elements is relatively small.

5.2. Impact of Training Stage Differences on the Landscape Scene Sequences of Park View Elements

The landscape scene sequence framework, which consists of three key stages, can help us comprehensively understand how a park environment evolves during sports activities. At the stage of site attraction, spaces are created to encourage people to start walking. These areas should be esthetically pleasing and easily accessible, featuring natural characteristics such as water elements and greenery to stimulate people’s interest. In contrast, training improvement requires more functional spaces to support sustained physical activities and create an environment, such as paths and natural hedges, to facilitate concentration and HR control. It will not cause distraction and discomfort while encouraging exercise. At the stage of restorative relaxation, spaces such as an open square or a bench are required for relaxation and restoration to reduce HR and relieve stress.

The concept construction of landscape scene sequences aims to offer a practical framework for park designers to optimize each stage of the walking process. A scene is constructed by integrating specific park view elements to attract people to start walking, strengthen the walking experience, and optimize the health benefits of walking.

5.3. Impact of Safety Environment on Walking, Jogging, and Running Behaviors

Perceived safety is a critical factor influencing physical activities in parks. This study identified safety-related landscape elements (e.g., fences and vehicles) through semantic segmentation and revealed their effects on physiological responses across different walking types. For instance, fence–railing exhibited a positive correlation with HR during running, likely due to physical barriers disrupting exercise rhythm. In the jogging group, the car shows a negative correlation with HR. This is likely due to the fact that the car occupies the space of the activity path or increases the environmental complexity, thus suppressing the exercise intensity. These results indicate that safety-related rigid boundary elements can directly affect the physiological state of athletes through physical or visual interference.

Perceived safety encompasses not only objective environmental features but also subjective psychological evaluations. Existing studies have shown that lighting conditions, vegetation permeability, and visibility of emergency facilities in parks affect users’ sense of security and change their sustained willingness to walk or run. For example, paths with insufficient lighting at night or overly dense vegetation will reduce the sense of security of walkers, causing them to avoid such areas [47]. Methodological limitations in data collection restricted this study from incorporating indirect safety factors (e.g., lighting, vegetation density), potentially resulting in incomplete interpretations of safety mechanisms. Future research should integrate multidimensional safety metrics, such as perceived safety data from surveys or real-time sensor-based monitoring (e.g., illumination levels), to enable precise analysis of how safety environments impact walking and running behaviors.

5.4. Limitation

(1)

Users’ physical health and exercise experience may influence their HR and speed, and thus affect the correlation with park view elements. The crawlable data from Strava includes not only the data of walking, jogging, and running behaviors and physiological response, but also the data of users’ age and weight. Future studies could supplement corresponding correlation analyses and conduct more in-depth research through questionnaires and actual measurements.

(2)

The landscape scene sequences constructed in this study are ideal, and their specific applications may be influenced by geographical conditions, temperature, and humidity. Although the exercise and GPS data in the study were standardized to minimize the impact of potential variables, and the investigation period was limited to reduce seasonal variation, external factors such as lighting, vegetation density, and park crowding may vary between seasons and influence the performance of walkers, joggers, and runners. Future research could consider integrating these factors to explore their potential impacts on the results and to construct more flexible and practical landscape scene sequences.

(3)

Due to the limitations of the Strava platform, this study could not account for variations in physical activity caused by time-of-day differences in temperature, crowd dynamics, and environmental perception (e.g., running is more common in the morning, while walking is more prevalent in the evening). In future research, time-segmented field experiments involving recruited volunteers could be conducted to explore the influence of more nuanced factors such as seasonality, time of day, user demographics, and body weight categories on walking, jogging, and running behaviors. This would contribute to the development of more refined and adaptive park view element sequences.

6. Conclusions

This study explores the coupling relationship among landscape environment, walking, jogging, and running behaviors, and physiological response by combining the semi-open exercise data from Strava with PVIs. The main conclusions of this study are as follows: (1) There are differences in spatial characteristics that attract walking, jogging, and running. Walking tends to occur in relatively open spaces, emphasizing stable and comfortable paving, open views, and visual esthetics. Jogging is strongly influenced by environmental features, requiring more diverse landscape compositions and quiet ecological settings. Running is constrained by higher physical demands, with professional rubber tracks and uninterrupted long-distance routes being preferred by runners. (2) Spearman correlation analysis examined the relationships between HR, speed, and 17 park view elements across walking, jogging, and running. For low-intensity walking, HR and speed were more influenced by visual coherence than ecological complexity. The walking speed is greatly affected by elements that obstruct vision, such as sidewalk–path, signboard, building, and sky. In jogging, tree showed a positive correlation with HR, while altitude, sky, and car exhibited negative correlations. Additionally, altitude, person, and ashcan were positively associated with jogging speed. For high-intensity running, the predictability of the path was more critical. Visual and spatial obstacles such as sky, signboard, sidewalk–path, and fence–railing were positively correlated with increased HR, while elements like altitude, signboard, and fence–railing were negatively associated with speed, potentially disrupting pacing.

Similarly to previous studies, the significant impact of landscape environment on walking, jogging, and running behaviors has been recognized. This study also provides new insights:

(1)

Research areas: Diversify the study on the simple relationship between landscape environment and walking, jogging, and running behaviors, add objective physiological response data, and explore the coupling relationship among them from the perspective of physical training.

(2)

Data collection: Park view data have always been in shortage on the public street view open platform. In this study, we conducted field photography to supplement the park view databases for Century Park, Minhang Sports Park, and Huangxing Park. Meanwhile, we collected hourly and point-based data for each user and spatially mapped them to walking, jogging, and running trails, enhancing the granularity of data from open network platforms. We have developed a research model that combines Strava’s semi-open exercise data with PVIs.

Based on the analysis results, this study developed scene modules corresponding to the walking, jogging, and running behaviors, namely site attraction, training improvement, and restorative relaxation. This organic transformation was from discrete park view elements to integrated landscape environments.

This study offers a novel perspective for aligning landscape environment design with the behavioral demands of walking, jogging, and running, enriching the research framework on health-oriented landscape planning. The findings provide actionable insights for landscape architects, supporting the rational allocation of landscape resources to enhance both user experience and health benefits in urban parks. These scene-based strategies can more effectively promote and support diverse forms of physical activity within public green spaces.