A Multi-Dimensional Assessment of Pocket Park Landscapes: Insights from Scenic Beauty Estimation and Analytic Hierarchy Process in Dadukou District, Chongqing

Abstract

Pocket parks are small green spaces that significantly enhance urban livability, particularly in densely populated cities. To leverage this advantage, Chongqing has committed to establishing 100 pocket parks by 2024. This study assesses the landscape quality of six pocket parks in Chongqing’s Dadukou District, employing Scenic Beauty Estimation (SBE) and Analytic Hierarchy Process (AHP). The results of the SBE method identify four main factors—spatial hierarchy, plant diversity, landscape harmony, and color richness—that show a strong correlation with scenic beauty (R = 0.947, R² = 0.897). AHP analysis reveals that landscape function is the dominant criterion, accounting for 66.86% of the total weight, with ecological function and service function contributing 21.44% and 8.82%, respectively. Notably, plant color richness emerges as the most significant factor, with a comprehensive weight of 0.1509, emphasizing its critical role in enhancing visual appeal. Based on these findings, this study recommends increasing plant diversity, optimizing color design to improve visual appeal, and implementing sustainable, low-maintenance strategies. This research integrates both quantitative and qualitative approaches, offering strategies to refine pocket park designs and establish a framework to enhance green spaces in densely populated urban areas, not only in China but potentially globally, promoting environmental sustainability and user satisfaction.

1. Introduction

Urban development faces the challenge of accommodating growing populations in limited spaces, making the creation of large-scale parks difficult. The COVID-19 pandemic has heightened the demand for parks [1], which have proven vital in improving physical and mental health while reducing disease transmission [2,3]. Access to green spaces mitigates the negative impacts of urban density [4], promoting well-being through physical activity, social interaction [5], and stress relief [6,7,8]. However, in dense urban centers, particularly in mountainous cities, creating expansive green spaces is challenging due to limited land and fragmented landscapes [9]. The narrow gaps between buildings and lack of open areas weaken the human-nature connection [10]. The disappearance of everyday nature experiences can have significant implications for the well-being of urban residents [11]. Wilson’s “Biophilia Hypothesis” posits that humans possess an inherent predisposition to establish connections with nature and diverse life forms [12]. Positive past and present nature experiences can evoke various emotions, such as affinity, interest, and indignation, motivating people to engage in nature-protective behaviors [13]. These tendencies are crucial for fostering emotional and psychological well-being [11,14,15]. In contemporary densely populated urban landscapes, the emphasis has shifted toward creating compact, easily accessible green spaces, exemplified by pocket parks. These micro-green spaces serve the dual purpose of furnishing verdant areas and leisure zones within the urban fabric, seamlessly integrating natural components into the constructed milieu, thereby adhering to the tenets of biophilic design. Kellert, as one of the pioneers of biophi-lic design, defines it as “an intentional attempt to meet people’s need to connect with natural systems and processes in the contemporary built environment, improving their health, work productivity, and well-being [16]”. He argues that biophilic design fosters beneficial interactions between humans and nature, resulting in “positive environ-mental impacts” [17]. Biophilic design extends beyond the mere incorporation of flora and arboreal elements, encompassing the assimilation of natural patterns, materials, and ecological processes in urban environments. It further contemplates the congruence of these elements with human behaviors, emotional responses, and social dynamics, thereby fostering an immersive and interactive natural experience within architectural design [18]. Pocket parks afford urban residents the invaluable opportunity to engage with and derive insights from the natural environment, substantially augmenting the well-being of city inhabitants and playing a pivotal role in fostering the development of sustainable and habitable urban landscapes.

Pocket parks, known for their significant public accessibility, are small green spaces created by repurposing urban land, typically in commercial and residential areas [19,20]. They generally range from 1 to 3 acres in size [21,22]. The United States, the United Kingdom, and Australia have gained extensive experience in designing and managing pocket parks, initially focusing on their physical attributes, such as size and facilities. Recent research has expanded to include their contributions to ecological services and sustainability, such as mitigating urban heat island effects and managing rainwater [23,24]. Additionally, eye-tracking technology has been applied to optimize park design and enhance user experience [25]. In pocket park design, ecological functions and aesthetic factors are closely linked, shaping the spatial quality and user experience. Ecological functions, such as air purification, noise reduction, and temperature regulation, improve urban livability [26,27]. Aesthetic factors, including visual appeal, plant diversity, and color composition, influence users’ perceptions and well-being [28,29]. Enhancing both aspects can maximize ecological benefits while fostering a stronger sense of place and satisfaction, ultimately promoting the sustainable use of pocket parks and social interaction. Pocket parks have also received widespread attention for promoting community interaction, improving residents’ physical and mental health, and enhancing social cohesion [30,31,32,33,34]. Recent studies show that China has become the second-largest country in pocket park research, significantly surpassing the United Kingdom, with about half of the studies coming from Asia, followed by Europe. As China shifts toward high-quality urban development, pocket parks have become an integral part of high-density city planning. In 2022, China launched a national plan to build 1000 pocket parks [35], and in 2023, Chongqing, a mountainous city, planned to build 100 pocket parks to improve the residents’ quality of life [36].

In landscape evaluation, the multifunctional attributes and visual aesthetics of pocket parks have emerged as the principal subjects of study. Peschardt and Stigsdotter [34] identified factors such as psychological restorative potential, vibrant landscape colors, and spatial harmony as crucial in influencing user experiences in small green spaces. Two prevalent methodologies in this realm are Scenic Beauty Estimation (SBE) and AHP. The SBE method, which is grounded in psychophysics, is considered one of the most accurate tools for landscape assessments [37]. It assesses scenic images and quantifies collective aesthetic preferences, emphasizing key elements like naturalness, spatial qualities, and visual focal points [38,39]. Conversely, the AHP method excels in structured, multi-level weighting analyses, facilitating systematic evaluations of landscape functions and ecological values. This method is widely utilized in planning and policymaking [40,41]. While the SBE method prioritizes visual perception and user experience in evaluating spaces such as urban parks [42] and waterfront parks [43], the AHP method offers a quantitative framework suitable for applications in garden design, scenic area development, residential landscaping, and roadside greenery [44,45]. By integrating these methodologies, researchers can combine subjective user experiences with rigorous quantitative analyses, thereby providing a comprehensive and reliable framework for landscape assessment.

In Chongqing, a prototypical high-density mountainous city, the complexities of its terrain and high population density underscore the urgent necessity to optimize pocket park landscapes. This study focuses on six representative pocket parks within the Dadukou District, employing a combined approach of SBE and AHP to systematically analyze the key factors impacting landscape quality, establish a multidimensional evaluation framework, and develop optimization strategies. The research objectives are to: (a) Identify and classify the principal factors influencing the quality of pocket park landscapes and explore their shared characteristics. (b) Develop a comprehensive evaluation model for landscape quality utilizing the SBE and AHP methodologies. (c) Provide actionable design recommendations for high-density mountainous urban areas by assessing typical parks and offering both scientific insights and practical guidance for improving urban green space development. This study endeavors to bridge the existing gap in evaluating and optimizing pocket parks in high-density mountainous urban settings. By doing so, this study aims to contribute an innovative theoretical framework and practical design solutions to support the future planning and management of urban green spaces.

2. Materials and Methods

2.1. Study Area Overview

Chongqing, situated in the inland region of southwestern China, experiences a subtropical humid monsoon climate characterized by distinct seasons and is recognized as one of China’s “new first-tier cities”. As a mountainous city, Chongqing’s pocket parks exploit its unique terrain to foster diverse spatial forms and feature distinctive features. These parks are categorized by their location into block-type, slope-type, and under-bridge type (

Table 1. The location classification of Chongqing Pocket Park.

Spatial Location Type	Plan Diagram	Site Diagram
Slope
Block
Under-bridge

), designed to facilitate daily community activities, utilize elevation changes for landscape creation, and repurpose under-bridge spaces. In terms of accessibility, pocket parks are classified as open, semi-open, or enclosed, each offering distinct visual experiences. Regarding topography, they are differentiated into flat, gentle slope, and steep slope types (

Table 2. Classification of Pocket Park Spatial Undulation.

Types of Spatial Undulations	Degree of Undulation	Section Diagram	Site Diagram
Flat Land	0~5 m
Gentle Slope	5~10 m
Steep Slope	≥10 m

), each displaying unique terrain characteristics [10]. With their flexible layouts, pocket parks not only address residents’ everyday needs but also enhance the urban landscape in high-density areas, functioning as small-scale public spaces that reflect the unique qualities of mountainous urban settings.

Chongqing’s Dadukou District has made substantial progress with its “Park Dadukou” initiative. By December 2024, the district had established 80 parks of varying sizes, achieving a green land ratio of 40.31%, a green coverage rate of 50.8%, and an average of 25 square meters of park green space per capita [46]. From the launch of Chongqing’s latest pocket park plan in 2023 until October 2024, the Dadukou District constructed 13 new pocket parks, leading the central urban districts of Chongqing in terms of the number of newly built parks. These developments showcase a diverse array of pocket-park types.

This study focuses on the Dadukou District as the primary research area (Figure 1), conducting a comprehensive landscape evaluation and analysis of six representative pocket parks constructed following the introduction of the 2023 Pocket Park Guidelines. These parks, which are characterized by prolonged and frequent public use, are listed in

Table 3. List of six pocket parks in Dadukou District, Chongqing.

Park	Site Photos	Area (m2)	Location	Location Type	Space Type	Terrain Type
Steel Park		8000	Yuejin Village Subdistrict, Gangtie Road, Hao’er Mountain Top	Slope	Open	Steep Slope
Longqiao Pocket Park		2000	No. 102 Diaoyuzui Avenue, Longqiao Garden Community	Block	Open	Gentle
Vegetable Commune		500	Between No. 60 Songqing Avenue and Jinxing Complex Building	Block	Open	Gentle
Xingfuli Park		9500	Baijia Garden Community, Jiansheng Town	Block	Closed	Gentle
Happy Yuli Garden		2300	Yuejin Village Subdistrict, Gangtie Community	Slope	Semi-Open	Gentle Slope
Huangjue Memory Park		2500	Next to Huangjue Building, Ganghua Road	Slope	Open	Steep Slope

.

2.2. Develop a Park Scenic Quality Assessment System

The results of the SBE method are influenced by the intrinsic characteristics of the landscape and the aesthetic preferences of the evaluators. This method integrates the measurement of aesthetic attitudes with a quantitative evaluation of landscape components, employing a numerical model to assess and predict landscape quality with high sensitivity [47]. The research procedure for implementing the SBE method is as follows:

• Field surveys were conducted during the summer months (July–August), taking into account seasonal and practical considerations. July was identified as the optimal month for photography to ensure favorable weather conditions. Photographs were captured between 8:30–11:30 a.m. and 12:00–4:30 p.m., leveraging the best possible lighting conditions. The camera was positioned at a height of 1.6 m, equivalent to the human eye level, to standardize the visual perspective. In each park, three photographs were taken from various positions and angles to minimize the impact of extraneous elements, such as people or the surrounding environment, on the visual quality of the landscapes (Figure S1).
• After completing the field data collection, the research team developed, distributed, and collected the questionnaires in August 2024. The survey participants comprised 48 landscape professionals and 49 non-professionals. A seven-point scale was utilized to assess scenic beauty as the primary evaluation metric. A total of 106 questionnaires were returned, of which 97 were considered valid, resulting in a response efficiency rate of 91.5%. The standardization formula for the SBE method is as follows:

  $$Z_{ij}={\displaystyle \frac{R_{ij}-\overline{R_j}}{{\mathrm{S}}_j}}, Z_i={\sum }_j{\displaystyle \frac{Z_{ij}}{N_j}}$$
  These variables are used to evaluate and standardize scenic beauty. R_ij represents the raw score given by the jth evaluator for the ith landscape, which reflects the evaluator’s direct judgment. R_j is the average score assigned by the evaluator across all landscapes, which helps adjust for differences in scoring standards among evaluators. S_j is the standard deviation of all scores given by the evaluator, indicating the consistency or variability of the ratings. Z_ij is the standardized score given by the jth evaluator for the ith landscape, calculated to eliminate differences in scoring scales and variability. Finally, Z_i is the overall standardized score for the ith landscape, representing its comprehensive scenic beauty. These variables enable the model to objectively compare the aesthetic qualities of different landscapes.
• Decomposition of Landscape Elements: Research on pocket park landscapes has provided valuable insights, with scholars examining key elements from various perspectives. Luks [20] analyzed the influence of cultural elements on the identity of Detroit’s pocket parks, elucidating the intricate relationship between culture and landscape design in this context. Marcus et al. [22] investigated how plant diversity influences the composition of urban open-space plant communities. Forsyth et al. [48] emphasized the importance of recreational facility layout and spatial openness in enhancing user experiences in small parks. Peschardt et al. [34] identified essential factors such as layering, harmony, naturalness, and color diversity, highlighting their contributions to creating an inviting atmosphere and promoting health. Additional studies by Wang et al. [42] demonstrated how the integration of water features with plant landscapes enhances the visual appeal of Nanjing’s waterfront parks, while Li et al. [49] assessed factors such as shading, planting techniques, plant health, ornamental value, and the green view index in their analysis of plant landscape quality in Nanning’s floral parks. Given the challenges posed by Chongqing’s topography, water elements are rarely incorporated into its pocket parks. This research synthesizes findings from prior studies and focuses on landscape evaluation to identify 11 landscape factors suitable for evaluating pocket parks in Dadukou District, Chongqing (

  Table 4. Decomposition of plant landscape scenic beauty factors in pocket parks in Dadukou District, Chongqing.

  No.	Landscape Element	Landscape Element
  1	Shade Coverage (X1)	Exposed to Sunlight	——	Shaded
  2	Landscape Hierarchy (X2)	Lack of Layers	——	Rich in Layers
  3	Green View Index (X3)	Low Green Coverage	——	High Green Coverage
  4	Planting Methods (X4)	Formal Planting	——	Informal Planting
  5	Plant Growth Condition (X5)	Poor Growth	——	Healthy Growth
  6	Ornamental Characteristics of Plants (X6)	Non-Ornamental	——	Highly Ornamental
  7	Plant Diversity (X7)	Single Plant Species	——	Rich Plant Species
  8	Landscape Harmony (X8)	Inharmonious	——	Harmonious
  9	Color Richness (X9)	Monotonous Colors	——	Rich Colors
  10	Landscape Naturalness (X10)	Unnatural	——	Natural
  11	Spatial Characteristics (X11)	Enclosed	——	Open

  ).
• Establishment of the Evaluation Model: Multiple linear regression analysis was performed using Excel and SPSS software (version 26.0). The backward elimination method was employed to iteratively exclude insignificant landscape factors, culminating in the retention of the most influential factors affecting the scenic beauty scores. Subsequently, the relationship between the SBE calculation results and the retained landscape factors was analyzed. This analysis facilitated the construction of a final evaluation model.

2.3. Analytical Hierarchy Process Method (AHP)

The AHP method is utilized to ascertain the weight of each factor through hierarchical multi-criteria analysis. This approach simplifies complex problems by categorizing them into multiple levels, thereby allowing subjective judgments and qualitative analyses to be quantified and articulated in an objective manner [50,51,52]. The research procedure is as follows:

• Construction of a Comprehensive Plant Landscape Evaluation System: To enhance the scientific validity and reliability of the evaluation system, 28 preliminary indicators were initially selected [49,53,54,55,56,57,58,59,60]. A panel of 15 experts from fields such as landscape design, ecology, and landscape economics—each with an average of over 10 years of professional experience—was convened. The Delphi method was employed in two rounds to refine the indicators. In the absence of direct evidence, the Delphi method provides a scientific decision-making tool to ensure the scientific validity and reliability of the results [61]. The Delphi method has been widely applied to establish evaluation systems and determine indicators. By repeatedly consulting a group of independent experts, it systematically collects and synthesizes opinions to reach a consensus and make forecasts, ultimately leading to more objective and comprehensive conclusions [62]. In the first round, the experts anonymously rated the importance of the 28 indicators using a questionnaire. The coefficient of variation was calculated to assess the consensus level among experts, with indicators showing a coefficient below 0.25 being considered to have reached an agreement [63,64]. After screening, 20 indicators were retained. During the second round, experts received feedback on the results of the first round and were requested to refine their rankings and ratings of the indicators. Through further analysis of the weighted average scores and coefficients of variation, 15 final evaluation indicators were established (Table 5). The finalized indicators were categorized into four criteria: landscape function, ecological function, service function, and economic benefits. Landscape function includes five factors, ecological function comprises four, service function also encompasses four, and economic benefits are represented by two factors. Collectively, these categories create a multi-level, comprehensive evaluation framework for pocket park landscapes in Dadukou District.

Table 5. Evaluation system for pocket park landscapes in Dadukou District, Chongqing.

Criterion Layer (B)	Factor Layer (C)	References
B1 Landscape Function	C1 Plant Color Richness	Peng (2018) [53]
	C2 Seasonal Variation in Plant Appearance	Ye, Y. (2016) [54]
	C3 Green View Index	Yin, X et al. (2013) [55]
	C4 Environmental Cleanliness	Nordh et al. (2013) [56]
	C5 Landscape Style Compatibility with Surroundings	Feng, X. et al. (2013) [57]
B2 Ecological Function	C6 Plant Diversity	Robinson (2006) [58]
	C7 Community Nativeness	Wu, S (2017) [59]
	C8 Plant Health Condition	Li, L et al. (2020) [49]
	C9 Low Maintenance of Plant Landscape	Zheng, W.K. et al. (2023) [60]
B3 Service Function	C10 Spatial Safety	Nordh et al. (2013) [56]
	C11 Comfort of Facilities and Landscape	Pasha et al. (2013) [65]
	C12 Targeted Services for Specific User Groups	Ma, X. et al. (2022) [66]
	C13 Educational Value of Plant Landscape	Li, L et al. (2020) [49]
B4 Economic Benefits	C14 Initial Site Renovation Cost	Zheng, W.K. et al. (2023) [60]
	C15 Post-Renovation Maintenance Cost	Zheng, W.K. et al. (2023) [60]

Table 5. Evaluation system for pocket park landscapes in Dadukou District, Chongqing.

Criterion Layer (B)	Factor Layer (C)	References
B1 Landscape Function	C1 Plant Color Richness	Peng (2018) [53]
	C2 Seasonal Variation in Plant Appearance	Ye, Y. (2016) [54]
	C3 Green View Index	Yin, X et al. (2013) [55]
	C4 Environmental Cleanliness	Nordh et al. (2013) [56]
	C5 Landscape Style Compatibility with Surroundings	Feng, X. et al. (2013) [57]
B2 Ecological Function	C6 Plant Diversity	Robinson (2006) [58]
	C7 Community Nativeness	Wu, S (2017) [59]
	C8 Plant Health Condition	Li, L et al. (2020) [49]
	C9 Low Maintenance of Plant Landscape	Zheng, W.K. et al. (2023) [60]
B3 Service Function	C10 Spatial Safety	Nordh et al. (2013) [56]
	C11 Comfort of Facilities and Landscape	Pasha et al. (2013) [65]
	C12 Targeted Services for Specific User Groups	Ma, X. et al. (2022) [66]
	C13 Educational Value of Plant Landscape	Li, L et al. (2020) [49]
B4 Economic Benefits	C14 Initial Site Renovation Cost	Zheng, W.K. et al. (2023) [60]
	C15 Post-Renovation Maintenance Cost	Zheng, W.K. et al. (2023) [60]

• Determining the Weights of Evaluation Indicators: Initially, 25 experts were invited to assess the relative importance of 15 evaluation indicators (e.g., plant color richness and seasonal variation) using a pairwise comparison method on a scale of 1–9. The scores were then averaged to construct a comprehensive judgment matrix. Subsequently, the maximum eigenvalue (λ_max) and corresponding weight vector were calculated using eigenvalue decomposition. A consistency test is performed, involving the calculation of the consistency index (CI = (λ_max – n)/(n – 1)) and the consistency ratio (CR = CI/RI), where RI represents the random consistency index. If CR < 0.1, the matrix is deemed consistent; otherwise, adjustments are necessary, and the process is repeated [67]. Finally, the normalized matrix is employed to determine the final weights of each indicator, thus providing a scientific foundation for the comprehensive evaluation.
• The Comprehensive Plant Landscape Evaluation Index is calculated to quantify the overall quality of plant landscapes based on weighted evaluation factors. Each evaluation factor is assigned a weight (W_i) derived from the AHP, which reflects its relative importance in the assessment. Additionally, each factor is scored (F_i) based on its performance, with scores of 3, 5, 7, and 9 representing low, moderate, good, and excellent quality, respectively. The comprehensive evaluation index (B) is calculated as the sum of the products of the weights and corresponding scores, expressed by the following formula [68,69]:

  $$B=\sum _{i=1}^n{W}_i{F}_i$$
• To standardize the evaluation, the Comprehensive Evaluation Index (CEI) is derived using the following formula:
  $$CEI={\displaystyle \frac{S}{S_0}}\times 100\%$$

  where S represents the actual score obtained from the evaluation and S₀ is the ideal maximum score representing perfect performance [70]. The CEI provides a percentage-based measure of plant landscape quality, enabling standardized comparisons across different sites. Based on the CEI, plant landscapes are classified into four grades: Grade I (CEI > 80%), Grade II (70% ≤ CEI ≤ 80%), Grade III (60% ≤ CEI ≤ 70%), and Grade IV (CEI < 60%). The last CEI for each bag area was calculated using the mean of three pictures, providing a brief and accurate landscape assessment.

3. Results and Analysis

3.1. Reliability and Validity Analysis of the Questionnaire

George and Mallery [71] provide specific classifications for Cronbach’s Alpha, commonly accepted thresholds include a minimum value of 0.7 for acceptable reliability, 0.8 for good reliability, and 0.9 or higher for excellent reliability. In this study, the reliability coefficient for the scenic beauty data is 0.94, surpassing the 0.9 threshold, which signifies that the data possesses high reliability. Furthermore, the Kaiser-Meyer-Olkin (KMO) value is a metric utilized to determine the appropriateness of data for factor analysis. It assesses whether the correlations among variables are sufficiently strong to justify the extraction of potential common factors. Here, the KMO value is 0.883, which significantly exceeds the minimum requirement of 0.6 [72], indicating that the variable structure is robust and the results of the factor analysis are highly reliable.

3.2. Scenic Beauty Scores and Evaluation Model

3.2.1. Analysis of SBE Scores for Each Park

After standardizing the values, typical prices for environment photos were established.

Table 6. SBE scores of six pocket parks in Dadukou District.

Park	Sample No.	SBE Score	SBE Mean Score	Park	Sample No.	SBE Score	SBE Mean Score
Vegetable Commune	1	0.0022	0.0063	Longqiao Pocket Park	10	−0.0134	−0.0258
	2	0.0111			11	−0.0527
	3	0.0057			12	−0.0114
Xingfuli Park	4	0.0743	0.0973	Huangjue Memory Park	13	0.1784	0.2821
	5	0.1155			14	0.2875
	6	0.1021			15	0.3803
Happy Yuli Garden	7	0.2703	0.3517	Steel Park	16	0.1228	0.1974
	8	0.4249			17	0.0624
	9	0.3600			18	0.4069

ranks the locations of Happy Yuli Garden, Huangjue Memory Park, Steel Park, Xingfuli Park, Vegetable Commune, and Longqiao Pocket Park based on their scenic charm from highest to lowest. Five parks registered standardized SBE scores above zero, while one park recorded a score below zero. The narrow range of SBE scores suggests that the plant landscapes in the pocket parks of Dadukou District, Chongqing, demonstrate limited variation, indicating consistently high quality, aesthetic appeal, and equitable resource distribution.

According to the SBE evaluation results, Happy Yuli Garden ranks highest with a standardized mean SBE score of 0.3517, and sample No. 8 (Figure 2a), achieving 0.4249. The park excels in providing a comfortable user experience, primarily through its extensive tree canopy coverage, which offers effective shading in hot weather. The well-defined landscape layering, which includes a mix of trees, shrubs, and grasses, creates a visually rich and spatially deep environment. This extensive green coverage forms a cohesive and continuous green space. Moreover, the park incorporates a diverse range of plant species, including trees, shrubs, ground cover plants, and lawns, which contribute to ecological stability and enhance aesthetic appeal. Brightly colored ground surfaces in yellow, blue, and red provide a striking contrast to the greenery, adding vibrancy and playfulness to the space.

Huangjue Memory Park, with a standardized SBE mean score of 0.2821 and sample No. 15 scoring 0.3803 (Figure 2b), exhibits strengths in several key areas. The park offers shaded walkways bordered by dense trees that create a pleasant and cool environment conducive to relaxation. The integration of pathways, fences, and vegetation enhances the three-dimensional visual appeal and produces a dynamic effect. Consistent greenery distribution fosters a natural and serene ambiance, while the park’s sloped terrain introduces depth and visual interest, balancing privacy with accessible and interactive pathways.

In contrast, the Vegetable Commune and Longqiao Pocket Park show significantly lower performance with standardized SBE mean scores of 0.0063 and −0.0258, respectively. Sample No. 11 from Longqiao Pocket Park recorded the lowest score of −0.0527 (Figure 2c). Challenges, such as sparse tree cover and uneven shading, compromise the comfort and usability of these spaces, exposing them to harsh conditions. The monotony of the vegetation, often confined to shrubs or ground cover, leads to flat and uninspiring visuals. Additionally, limited greenery, excessive hardscaping, and poor maintenance reduce their aesthetic appeal, with some plants appearing to wilt. Specifically, the Vegetable Commune (Figure 2d) suffers from an enclosed design that isolates it from the surrounding urban context, rendering it disconnected and lacking vitality.

This study generated 40 × 40 grid vegetation density heatmaps for sample 8 from the highly rated Happy Yu Li Garden and sample 18 from Steel Park after removing sky interference using Photoshop 2023 and processed the images with GIS technology (Figure 3). In Steel Park, sample 18 exhibits a scattered distribution of vegetation, with higher-density areas concentrated in the northwest and central regions. The northwest area is characterized by tall trees paired with low shrubs, while the central region consists primarily of evergreen plants and colorful ground cover. The southeastern region is relatively sparse, with an overall Vegetation Density Index (VDI) of 0.57. In contrast, Happy Yu Li Garden, located at the center of a sloped enclosure, has more evenly distributed vegetation in Sample 8, which is mainly composed of various ground cover plants and shrubs. The higher areas are surrounded by trees. The area with a density greater than 0.6 occupies 68.4% of the total area, with a VDI of 0.72 and 74.2% green pixel coverage, significantly higher than Steel Park’s 61.5%. Both sites feature more than 50% green-leaf plants, with a mix of trees, shrubs, and ground cover, along with a certain proportion of colorful-leaved plants that enhance color richness. Hard paving accounts for less than one-quarter of the area. The tall trees on both sides provide excellent shading effects and extend the visual perspective, creating a layered appearance and earning the highest evaluation. The two lower-ranked sites have poorer color richness, with a predominance of evergreen or deciduous green plants and a higher proportion of hard paving, resulting in a monotonous landscape. These sites require optimized vegetation configuration, multi-layered greening, improved green infrastructure, and enhanced plant maintenance management to increase their ecological value and sustainability in high-density mountainous urban environments.

Through meticulous research and systematic observation of the six parks, it was discerned that parks with a greater proportion of green spaces generally offer a more immersive and diverse natural experience. The configuration of leisure amenities and paving designs exerts a substantial influence on individuals’ visual perception. Kaczynski et al. advocate for the strategic placement of seating within parks to accommodate the social congregation and interactive requirements of park visitors [73]. Cohen et al. also propose that the installation of fitness equipment can increase the park’s appeal [74].

Through on-site observation, in Steel Park and Huangjue Memory Park, which feature a variety of landscape sculptures, people are more likely to choose to climb to high points to enjoy the view or take photos of the cultural landscape sculptures. These facilities and landscapes provide diverse interactive spaces, enhancing the biophilic qualities of the parks. In Happy Feather Li Garden, the colorful pavings and varied leisure facilities (such as trampolines and slides) attracted many families to visit, especially parents with children gathering in the park. The ample and varied seating arrangements make the design of this space not only enhance the park’s recreational appeal but also promote social interactions among residents, creating opportunities for meaningful interactions between families and the natural environment. Nevertheless, the Longqiao Pocket Park has several design shortcomings. It suffers from a scarcity of mature trees, inadequate plant maintenance, and expansive open areas dominated by monotonous, gray paving. The bridge-shaped landscape element is situated at the periphery of the park, leaving the central zone devoid of features and is visually unremarkable. This absence of a prominent focal point discourages visitors from lingering, as they instinctively traverse the vacant space without pause, thereby impeding opportunities for communication and interaction within the local community. The original design intent of the Vegetable Commune was to create a vegetable planting and educational nursery, allowing residents to experience the joy of nature and cultivation and enhance their understanding of natural production processes. However, owing to insufficient maintenance, the vegetation within the nursery deteriorated, thereby diminishing its intended design significance and compromising both the park’s visual allure and the efficacy of its biophilic design principles. The Vegetable Community is enclosed by 2-m-high cement brick walls on three sides, which diminishes the park’s aesthetic appeal and makes the already small space feel even more confined.

These observations indicate that park biophilic design should not only focus on incorporating natural elements but also ensure proper maintenance and a rational layout of functional areas to guarantee its long-term appeal and ecological benefits.

The six pocket parks analyzed in this study vary in size from 500 to 9500 m². Correlation analysis reveals a positive relationship between park size and scenic beauty scores, with larger parks generally achieving higher scores. However, this correlation is not statistically significant. The adaptability of pocket parks to various sizes—from compact to large-scale areas—has been pivotal in their widespread implementation. Their characteristics as “pocket-sized” and “infill green” spaces enable them to flourish in urban environments, effectively meeting residents’ needs for green spaces and significantly enhancing urban quality of life [75,76].

3.2.2. Establishing the Evaluation Model

Using SPSS 26.0, a multiple linear regression analysis was conducted on the scenic beauty scores, with the quantified values of the 11 landscape evaluation factors as independent variables. The stepwise regression (backward elimination) method was employed, resulting in the exclusion of seven less relevant factors and the retention of four significant factors: spatial hierarchy, plant diversity, landscape harmony, and color richness (

Table 7. Summary of the backward elimination model for landscape elements.

Number of Iterations	R	R2	Adjusted R2	Standard Estimation Error
1	0.950 a	0.902	0.723	0.1178
2	0.950 b	0.902	0.763	0.1091
3	0.950 c	0.902	0.792	0.1022
4	0.949 d	0.901	0.813	0.0967
5	0.949 e	0.900	0.831	0.0921
6	0.947 f	0.897	0.841	0.0893
7	0.944 g	0.891	0.846	0.0878
8	0.938 h	0.880	0.843	0.0886

^a. Predictor Variable: X₁₁, X₁₀, X₉, X₄, X₇, X₆, X₃, X₂, X₅, X₈, X₁. ^b. Predictor Variable: X₁₁, X₁₀, X₉, X₇, X₆, X₃, X₂, X₅, X₈, X₁. ^c. Predictor Variable: X₁₀, X₉, X₇, X₆, X₃, X₂, X₅, X₈, X₁. ^d. Predictor Variable: X₁₀, X₉, X_7, X₆, X₂, X₅, X₈, X₁. ^e. Predictor Variable: X₁₀, X₉, X₇, X₆, X₂, X₅, X₈. ^f. Predictor Variable: X₁₀, X₉, X₇, X₆, X₂, X₈. ^g. Predictor Variable: X₁₀, X₉, X₇, X₂, X₈. ^h. Predictor Variable: X₉, X₇, X₂, X₈.

). The regression model yielded a multiple correlation coefficient (R) of 0.947, indicating a strong positive relationship between the independent variables (landscape factors) and dependent variable (SBE scores). The coefficient of determination (R²) was calculated to be 0.897, suggesting that the model accounts for 89.7% of the variance in the SBE scores, thus highlighting the substantial impact of these factors on scenic beauty. Furthermore, the adjusted R² was determined to be 0.841, which adjusts for the number of predictors and sample size, providing a more conservative and reliable measure of the model explanatory power. This adjustment prevents overestimation that might result from the inclusion of additional variables. The high adjusted R² indicates that the four retained factors collectively explain approximately 84.1% of the variance in the SBE scores. This model demonstrates a high level of fit and statistical significance, providing a robust scientific basis for the design and optimization of pocket park landscapes.

A multiple linear regression analysis was performed on the four landscape factors retained, as detailed in

Table 8. Regression coefficients of scenic beauty factors in pocket parks.

Coefficients	Effect Coefficients	Standard Error	Beta	t	Sig.	Tolerance	VIF
X2	0.221	0.066	0.481	3.358	0.005	0.450	2.223
X7	0.185	0.055	0.363	3.370	0.005	0.796	1.256
X8	0.094	0.044	0.216	2.168	0.049	0.930	1.075
X9	0.276	0.073	0.513	3.768	0.002	0.499	2.003

. All independent variables demonstrated tolerance values greater than 0.1, suggesting minimal multicollinearity among the variables [77]. Tolerance quantifies the correlation between one independent variable and the rest of the independent variables, with values closer to 1 indicating a lower correlation and thus, greater independence in influencing the dependent variable. Additionally, all independent variables exhibited Variance Inflation Factor (VIF) values below 10, and specifically below 2.5, further validating the absence of significant multicollinearity. VIF, which is the reciprocal of tolerance, measures the extent to which the variance of an estimated regression coefficient increases due to multicollinearity. A VIF below 10 is typically considered acceptable, while a VIF below 2.5 denotes excellent independence among variables [77,78].

Additionally, the linear relationships between the independent and dependent variables are significant (Sig. < 0.05), allowing the establishment of the following linear equation:

SBE = −0.139 + 0.276X₉ + 0.221X₂ + 0.185X₇ + 0.094X₈

According to the data presented in Table 8, all landscape factors demonstrate a positive correlation with SBE scores, where larger coefficients signify a more substantial impact on scenic beauty. Among these factors, color richness exhibits the highest coefficient at 0.276, indicating its predominant influence on scenic beauty. In contrast, landscape harmony has the smallest coefficient at 0.094, suggesting a relatively lesser impact. This model effectively elucidates the primary variances in SBE scores and provides a solid scientific basis for enhancing the scenic beauty in the design of pocket parks.

3.3. Analysis of Evaluation Indicator Weights

The weights of each evaluation factor, along with their comprehensive weights, were derived from an analysis of the scores provided by 25 experts and the execution of a consistency test. The findings presented in

Table 9. Evaluation indicators and weight values for the plant landscape of pocket parks.

Criterion Layer (B)	Criterion Layer Weight Value	Factor Layer (C)	Factor Layer Weight Value	Comprehensive Weight Value
B1 Landscape Function	0.6686	C1 Plant Color Richness	0.2257	0.1509
		C2 Seasonal Variation in Plant Appearance	0.1345	0.0899
		C3 Green View Index	0.1268	0.0848
		C4 Environmental Cleanliness	0.0979	0.0655
		C5 Landscape Style Compatibility with Surroundings	0.0837	0.0560
B2 Ecological Function	0.2144	C6 Plant Diversity	0.0840	0.0180
		C7 Community Nativeness	0.0441	0.0095
		C8 Plant Health Condition	0.0446	0.0096
		C9 Low Maintenance of Plant Landscape	0.0417	0.0089
B3 Service Function	0.0882	C10 Spatial Safety	0.0331	0.0029
		C11 Comfort of Facilities and Landscape	0.0183	0.0016
		C12 Targeted Services for Specific User Groups	0.0189	0.0017
		C13 Educational Value of Plant Landscape	0.0180	0.0016
B4 Economic Benefits	0.0289	C14 Initial Site Renovation Cost	0.0139	0.0004
		C15 Post-Renovation Maintenance Cost	0.0150	0.0004

delineate the relative importance of each factor within the comprehensive evaluation system. These results offer quantifiable insights into the priority and influence of individual landscape factors, facilitating a more structured approach to park design and evaluation.

Data from Table 9 illustrate the weight distribution among the criteria layers in the evaluation of pocket park plant landscapes in Chongqing. The weights rank as follows: landscape function (0.6686), ecological function (0.2144), service function (0.0882), and economic benefit (0.0289). This distribution emphasizes the paramount importance of visual and scenic effects in the design of plant landscapes for pocket parks, with landscape function receiving the highest weight.

Within the landscape function category, plant color richness holds the highest weight at 0.1509, underscoring the essential role of color coordination in enhancing visual experience and overall landscape appeal. The landscape design also prioritizes dynamic visual changes and environmental harmony, focusing on seasonal variation (0.089) and the green view index (0.088). The ecological function category highlights the significance of plant diversity and community nativeness, emphasizing ecological stability and the use of local plant species. This category also emphasizes sustainability and management efficiency, as reflected in the importance of plant health and low-maintenance requirements.

In the service function category, spatial safety is identified as the primary concern, addressing the need to balance facility comfort with various user requirements. For economic benefits, which receive the lowest weight, post-maintenance, and initial renovation costs are identified as critical factors in achieving cost-effective and durable landscape solutions. Managing functionality with economic feasibility remains a crucial component of the comprehensive evaluation system.

3.4. Calculation of Comprehensive Plant Landscape Evaluation Index

The comprehensive plant landscape evaluation index and corresponding grades for the six pocket parks are presented in

Table 10. Comprehensive Evaluation Index and Rating of Plant Landscapes in Pocket Parks.

Park	Comprehensive Evaluation Index B	Comprehensive Evaluation Index (%)	Grade
Huangjue Memory Park	4.06	89.88	I
Steel Park	3.96	87.65	I
Happy Yuli Garden	3.94	87.24	I
Xingfuli Park	3.48	77.11	II
Vegetable Commune	3.21	71.02	II
Longqiao Huayuan Pocket Park	3.01	66.61	III

. Based on the evaluation data, the parks are ranked from highest to lowest as follows: Huangjue Memory Park, Steel Park, Happy Liyu Garden, Xingfuli Park, Vegetable Commune, and Longqiao Huayuan Pocket Park. Among these, three parks are classified as Grade I (CEI > 80%), indicative of high-quality plant landscapes. Two parks are categorized as Grade II (70% ≤ CEI ≤ 80%), representing good-quality landscapes, while one park falls into Grade III (CEI < 70%), which is indicative of average-quality landscapes.

Using SPSS for correlation analysis and regression modeling, this study investigated the relationship between CEI and SBE scores of various parks, as outlined in

Table 11. Results of correlation analysis between standardized SBE mean score and CEI.

Analysis	R	R2	Adjusted R2	Sig.
Correlation Analysis (SBE and CEI)	0.936 a	0.877	0.846	0.006

^a. Predictor Variable: (Constant), Comprehensive Evaluation Index (%).

. The correlation analysis yielded a Pearson correlation coefficient of 0.936, with a significance level of 0.006 (p < 0.01), demonstrating a strong positive correlation between the two indices. This result implies that parks with higher SBE scores generally exhibit higher CEI scores, underscoring the significant influence of visual aesthetics on overall park evaluation. Furthermore, the scatter plot and regression line in SPSS illustrated a clear linear relationship between the SBE and CEI scores (Figure 4). The regression model’s R² was 0.877, indicating that 87.7% of the variation in CEI scores is explained by the SBE scores, reflecting a high level of model accuracy.

As depicted in Figure 4, the data points on the fitted scatter plot are evenly distributed, predominantly clustering around the regression line with no significant outliers, indicating a robust fit of the regression model. The analysis shows that high-scoring parks, such as Happy Yuli Garden and Huangjue Memory Park, excel in both SBE and CEI scores. Conversely, parks with lower scores, like Longqiao Pocket Park, exhibit suboptimal performance in both metrics. These observations underscore the critical importance of visual aesthetics, including plant diversity and color richness, in enhancing the overall quality of parks. These findings provide valuable insights and quantitative support for the enhancement and design of future parks.

4. Discussion

4.1. Research Background and Spatial Quality Evaluation

This study identified spatial hierarchy, plant diversity, landscape harmony, and color richness as the primary factors influencing the aesthetic appeal of pocket parks through a comprehensive examination of six pocket parks in Dadukou District, Chongqing. Among these factors, color richness exerted the greatest influence, consistent with previous research emphasizings the importance of visual features in landscape perception [34,79,80]. Peschardt and Stigsdotter observed that a conducive environment and enhancement of health significantly rely on factors such as color diversity. This research reinforced the essential perspective that color enhances the attractiveness of pocket park environments [34]. A study assessing winter plant landscapes in Beijing’s urban parks, utilizing the SBE-SD methodology, analyzed 66 plant landscapes and established significant correlations between aesthetic quality and factors such as plant species diversity, landscape hierarchy, color richness, winter viewing characteristics, and growth conditions of winter plants [80].

The geographic, cultural, and social contexts of the Dadukou area have significantly influenced the design and usage patterns of pocket parks, contributing to the regional specificity of this research. Due to the terrain, many of the pocket parks in this area are situated on slopes, street corners, or beneath bridges. For instance, Huangjue Memory Park and Steel Park make full use of steep terrain, creating rich, layered green spaces. This design approach contrasts sharply with that in flat cities, such as Beijing and Shanghai, where pocket parks typically feature regular geometric layouts. In contrast, the pocket parks in Chongqing emphasize the vertical use of space and multi-layered visual experiences. Therefore, the applicability of the research findings to flat cities requires further verification.

However, Hong Kong, another mountainous and high-density city, shares many similarities with Chongqing, particularly in utilizing terrain to create unique pocket park spaces. A typical example is the Pak Tsz Lane Park, located in the bustling central district of Hong Kong. This 1600-square-meter park combines its terraced geographic location with a closed public space design, successfully integrating recreational, entertainment, and educational elements into a multifunctional public space [81]. This is highly similar to Chongqing’s Huangjue Memory Park, especially in terms of its terraced layout and spatial layering. Both parks break traditional park layouts through innovative spatial use and landscape design, generating dynamic and multi-layered visual experiences.

This comparison suggests that Chongqing’s design concepts are highly applicable to mountainous, high-density cities, particularly in terms of vertical space utilization, layered green spaces, and multifunctional integration. These design approaches can be effectively applied not only in Chongqing but also in other mountainous cities such as Hong Kong. In the context of land scarcity, dense populations, and the growing demand for public spaces, Chongqing’s design solutions offer valuable insights into the design of other cities. Cross-city comparisons further indicate that the design concepts proposed in this study not only contribute to enhancing the aesthetic quality of landscapes in mountainous cities but also provide guidance for pocket park planning and design in other high-density cities worldwide, particularly in terms of spatial optimization, functional integration, and multi-layered greenery.

4.2. Research Findings and Analysis of Influencing Factors

The AHP analysis indicates that the significance of landscape function in assessing pocket park plant landscapes far exceeds that of ecological function, service function, and economic benefits. This outcome aligns with research on urban park landscape evaluation, indicating that park design is significantly influenced by visual and landscape effects [45]. Highlighting these components in design would significantly enhance the overall quality of parks and fulfill individuals’ pursuit of beauty, as the diversity of plant colors and seasonal variations in the landscape profoundly affect visual experiences and aesthetic appeal of parks. Plant diversity and the indigenous characteristics of plant communities are considered essential for fulfilling the requirements of ecological balance and sustainable development in densely populated metropolitan areas [22,48,82]. While native plants are better suited to local conditions, which lowers maintenance costs and supports natural succession in ecosystems, plant diversity helps to establish stable ecosystems.

The study identified a positive correlation between park area and scenic attractiveness; however, this correlation was not statistically significant. This may pertain to the uniqueness of pocket parks, where the quality of the environment is largely contingent upon a meticulous design. Pocket parks primarily cater to the daily recreational requirements of local residents, in contrast to larger parks. Jia Shuhan’s study of Luobinwang Park in Yiwu indicates that visitors’ experiences are significantly shaped by cultural landscapes and spatial architecture, rather than the park’s size [82]. For instance, Steel Park integrates aspects of industrial heritage, such as scrap steel sculptures and industrial-style seating, thereby enhancing the park’s historical and regional identity. In contrast, pocket parks in Shanghai’s Huangpu district typically reflect the city’s “Shanghai style” culture [83], whereas neighborhood pocket parks in Guangzhou are often characterized by the Lingnan garden style [84]. Pocket parks may yield exceptional landscape experiences in limited places through innovative spatial designs, strategic plant combinations, and the use of landscape elements. Certain successful pocket parks have created multi-tiered landscapes and incorporated distinctive features within limited spaces, thereby enhancing the site’s allure and attractiveness [75,76].

When comparing high-scoring parks, such as Happy Yuli Garden, with low-scoring parks, such as Longqiao Huayuan Pocket Park, the fundamental principles of park design become evident. Parks with high ratings excel in road surfacing, spatial design, and plant arrangements. Happy Yuli Garden, for example, offers effective shading through suitable plant combinations; diverse landscape layers foster a pleasant spatial atmosphere; and vibrant ground paving contrasts with the flora, so enhancing vitality. Parks with low scores, such as Longqiao Huayuan Pocket Park, exhibit issues that detrimentally impact the overall landscape quality and user experience. These include insufficient color coordination and functional configuration deficiencies, characterized by singular plant varieties, monotonous landscapes, and incomplete facilities.

With a significance of 0.006 < 0.01, the correlation test between the assessment results of the AHP technique and the SBE method revealed a great consistency between the two evaluation approaches [85]. Combining the SBE and AHP data, both corroborate and jointly expose the main determining elements of the quality of pocket parks. While the AHP technique defines the weights of each factor by expert evaluation, thereby offering a complete scientific basis for landscape design and optimization, the SBE method identifies the main elements of landscape beauty from the perspective of public aesthetics. While other studies have primarily concentrated on a single approach or a few elements, this study includes several landscape components and their interrelationships, therefore providing a more methodically organized and comprehensive view for the assessment of pocket park landscapes. This mix gives great support for improving landscape quality and helps one to more precisely understand the benefits and drawbacks of park surroundings.

5. Conclusions

This study employed two methodologies, SBE and AHP, to assess the landscapes of six pocket parks located in the Dadukou District of Chongqing. The evaluation yielded a series of scientifically significant findings.

In the evaluation of landscape beauty using the SBE methodology, comprehensive field surveys, questionnaire distribution, and subsequent data processing were employed to calculate the SBE scores for each park. Observations indicated minimal variation in plant landscapes across the parks; however, quality differences were evident. High-performing parks, such as Happy Yuli Garden and Huangjue Memory Park, were distinguished by their shading coverage, layered landscapes, color harmony, and effective spatial arrangements. Conversely, parks with lower scores, such as Vegetable Commune and Longqiao Park, exhibited deficiencies in greening, landscape design strength, and integration with their environments. Multiple linear regression analysis identified four principal factors—spatial layering, plant diversity, landscape harmony, and color richness—that were significantly correlated with landscape beauty (R = 0.947, R² = 0.897), with color richness exerting the most substantial influence. These findings, derived from robust statistical and analytical techniques, offer a quantifiable framework for identifying the critical elements in the design of pocket park landscapes.

The AHP was utilized to develop a comprehensive evaluation index system for plant landscapes. Following two rounds of expert Delphi surveys and a detailed multi-step weighting process, the specific weight values for each criterion and factor layer were finalized. The findings reveal that landscape function is paramount, holding the highest weight of 0.6686, with the richness of plant color proving especially impactful at 0.1509. This emphasizes the critical role of visual and landscape aesthetics in the design of pocket park vegetation, highlighting the importance of color combinations in enhancing overall visual appeal. Additionally, the study assigned weights to ecological function, service function, and economic benefits, thus providing a detailed framework to address the multifaceted functional needs of pocket parks.

The correlation analysis between the evaluation results of both methods, with a significance level of 0.006, which is less than the threshold of 0.01, confirms their strong consistency. This demonstrates the study’s effectiveness in identifying the critical factors that influence the landscape quality of pocket parks, as perceived through public aesthetics (SBE) and AHP. This approach offers a holistic and systematic scientific perspective on landscape evaluation, overcoming the limitations of previous studies that typically relied on a single method or a limited range of factors. Moreover, this study addresses the landscape evaluation and optimization of pocket parks in high-density mountainous cities, thereby filling a notable gap in the existing research. Whereas previous investigations have predominantly focused on flat urban environments or large parks, this research specifically addresses the unique landscape evaluation requirements of mountainous city pocket parks, characterized by their distinct topography and spatial features. The findings of this research enhance the theoretical framework for evaluating urban green space landscapes and provide crucial support for developing pocket parks in mountainous urban settings.

6. Recommendations

This study employs the SBE and AHP methodologies to assess the landscapes of six pocket parks located in the Dadukou District of Chongqing. The results provide vital insights for enhancing the design of pocket parks. The recommendations of this study are directed towards improving landscape quality, more effectively serving urban residents, and facilitating the development of urban green spaces.

• Optimize landscape design by incorporating biophilic principles: The SBE results reveal that factors such as spatial layering, plant diversity, landscape harmony, and color richness are crucial for aesthetic appeal, with color richness being the most significant. In the design process, it is imperative to incorporate natural elements that foster a deeper human-nature connection, such as strategically positioning vegetation of diverse heights and leveraging the park’s topographical features to create visually engaging vertical layers, particularly within Dadukou’s mountainous terrain. Enhancing plant diversity through a balanced composition of trees, shrubs, and grasses, while selecting species that are well-adapted to the local environment, will contribute to ecological resilience and biodiversity enhancement. Furthermore, integrating biophilic design principles, such as the use of natural materials and organic patterns, into the park layout can reinforce the symbiotic relationship between individuals and the natural world. This can be achieved by emphasizing color coordination, using complementary or analogous colors, and incorporating color into elements like pavements, seating, and lighting to create a harmonious and visually appealing environment.
• While no significant correlation between park size and aesthetic appeal was found, the limited size of pocket parks demands efficient and meticulous planning. Functional areas should be strategically designed to optimize space usage. For instance, combining fitness zones with landscape elements can optimize spatial efficiency. Furthermore, park designs should be tailored to their specific locales and surrounding contexts to address the varied needs of different user demographics. Considering the significance of public aesthetics, as evidenced by the SBE results, community engagement is indispensable. Employing participatory approaches, such as surveys and community workshops, is crucial to ensure that the design resonates with residents’ preferences. Additionally, implementing a comprehensive long-term maintenance strategy, encompassing routine plant care practices like pruning, irrigation, fertilization, and pest management, is essential to preserving plant vitality and sustaining the park’s aesthetic integrity.

These initiatives will not only prolong the park’s visual attractiveness but also deliver superior outdoor spaces for residents, enrich urban green infrastructure, and establish a benchmark for the development of pocket parks in other urban settings.

7. Limitations

While this study offers valuable insights into the evaluation of pocket park landscapes, it is constrained by several limitations. First, the analysis was confined to six pocket parks in the Dadukou District of Chongqing, limiting the generalizability of the findings. Regional variations in geography, culture, and economy may influence landscape characteristics, suggesting that future research should expand the sample size using scientific sampling methods to enhance its broader applicability.

Additionally, the evaluation methods employed are inherently subjective. In the SBE process, aesthetic preferences vary among evaluators, and the AHP expert ratings may reflect personal biases. To improve accuracy, increasing the diversity of SBE evaluators and integrating objective indicators is recommended. Implementing techniques such as fuzzy hierarchical analysis within the AHP could further mitigate subjectivity.

Moreover, this study does not account for some influencing factors, such as the social and cultural functions of parks and user behavior, which significantly shape landscape perceptions. Future research should incorporate these aspects, including cultural events and interactions, to develop a more comprehensive evaluation system. This approach will support the enhanced planning and design of pocket parks, ultimately enriching the urban quality of life.