A Multi-Level Analytical Framework for Street Spatial Elements and Its Vitality Mechanisms: A Case Study of Seats on Pingdeng Street, Zhengzhou

Abstract

Street seating serves as a critical medium for stimulating spatial vitality and holds substantial design value in the refined planning of commercial upgrading and quality enhancement in aging districts. As urban regeneration and the optimization of existing built environments have become dominant paradigms in global urban development, the improvement of street quality—given its role as the primary setting for everyday public life—has increasingly depended on the fine-grained design and precise regulation of micro-scale environmental elements. This study takes Pingdeng Street in Zhengzhou, China, and its 33 seating installations as an empirical case. A multi-level analytical framework—comprising the seating ontology level, the seating space level, and the street environment level—was developed to quantitatively examine the relationships between multi-level spatial elements and street vitality intensity. Through correlation and regression analyses, the study systematically investigated the mechanisms by which seating-related elements at different levels influence street vitality. The results indicate that the Green View Index (GVI) is the core driver of street vitality, with the most significant enhancement observed when GVI ranges between 28% and 35%. The synergistic coupling of multi-level seating elements is essential for maximizing street vitality, while optimization pathways vary across different functional seating types. In design practice, high-comfort seating with backrests is recommended, with seating continuity controlled within 0.63–0.90. Seating spaces should adopt moderately enclosed spatial forms, such as eave-covered areas, and be supplemented with adequate lighting facilities. At the street environment level, a GVI of 28–35% and spatial openness of 9–18% should be maintained. The multi-level analytical framework and quantified indicator thresholds established in this study offer a new perspective on the mechanisms linking seating and street vitality. The findings provide a scientific theoretical basis and offer context-sensitive design guidance for the refined renewal of aging urban districts under comparable conditions.

1. Introduction

Streets, as the core carriers of urban public life, not only serve transportation functions but also sustain residents’ everyday social interactions and interpersonal communication [1]. The level of vitality in street space not only directly reflects urban quality of life and spatial quality, but has also become an important indicator for assessing a city’s capacity for sustainable development [2]. As emphasized by Mayiam H. Whyte in The Social Life of Small Urban Spaces, genuine street vitality does not arise from the sheer volume of pedestrian flow, but is instead manifested in people’s staying behavior, perception, and interactive experiences [3]. Jan Gehl further argues in Life Between Buildings that spatial vitality emerges from spontaneous and social activities, and that the arrangement and design of street facilities—particularly seating—directly shape people’s willingness to engage with public space and their degree of participation [4]. As the smallest unit and a key medium supporting staying behavior, street seating, although modest in scale, may provide physical support for rest, delineate activity territories, extend the duration of stay, and facilitate social interaction, thereby becoming a critical node for activating street space vitality.

Within the field of urban spatial research, scholars have examined the determinants of street vitality from multiple dimensions. In terms of research scale and core themes, early studies primarily focused on macro-scale characteristics of the urban built environment. Using methods such as GIS-based spatial analysis [5] and big data mining [6], these studies have demonstrated the significant effects of factors including the built environment [7], street network accessibility [8], and functional mix and development intensity [9] on street vitality. As research has progressed, micro-scale spatial perception and environmental experience have emerged as key areas of interest [10], complementing macro-scale approaches. A growing body of literature suggests that the activation of street vitality depends not only on macro-level functional configurations but is also closely associated with micro-level visual quality and perceived comfort [11], thereby enhancing the overall systematicity and explanatory power of related studies. In addition, the integrated application of methods such as machine learning [12], environmental behavior observation [13], and questionnaire surveys [14] has further promoted the development of street vitality research toward greater quantification and precision.

From the perspective of research scope expansion, studies on street vitality have gradually shifted from a singular focus on physical spatial analysis toward an interdisciplinary integration of the material environment and human perception [15]. At the level of physical space, existing research has concentrated on objective factors such as street interface continuity [16], greenery configuration [17], and the completeness of street facilities [18]. At the level of human perception, subjective dimensions—including emotional experiences [19], social needs [20], and behavioral preferences [21]—have been incorporated into analytical frameworks, forming a comprehensive logical chain linking objective environments, subjective perception, behavioral responses, and vitality generation. The Urban Design Quality (UDQ) framework developed by Ewing and colleagues systematically elucidates the mechanisms through which micro-scale design attributes relate to pedestrian behavior and satisfaction, using multidimensional indicators such as imageability, human scale, and enclosure [22]. In contrast, Gehl’s human-scale theory emphasizes that the prosperity of public life depends on the alignment between street design and pedestrians’ sensory experiences and behavioral needs [3].

However, notable gaps remain in the existing literature. On the one hand, dedicated studies examining the relationship between street seating and spatial vitality are relatively scarce. In most cases, seating is merely mentioned as an auxiliary component of street furniture [23] without a detailed examination of its underlying mechanisms. On the other hand, current research tends to focus primarily on the functional and formal design of seating [24], while lacking a systematic, hierarchical deconstruction of seating-related elements. Moreover, urban design guidelines in many cities generally overlook quantitative guidance on seating layout patterns [25], resulting in seating configurations in practice that are often superficial and ineffective in stimulating spatial vitality. In addition, much of the existing research relies on single data sources or single-dimensional analytical approaches [26], failing to reveal the synergistic relationships among seating attributes, surrounding spatial conditions, and overall street environmental quality. Consequently, such studies provide limited support for fine-grained and evidence-based street regeneration practices.

The core of the above-mentioned research gaps lies in the absence of an analytical framework capable of systematically integrating the multidimensional influence factors of street seating, thereby failing to elucidate the hierarchical logic through which seating operates from its intrinsic design, to spatial adaptation, and further to environmental synergy. Grounded in environmental behavior studies and urban spatial theory, the activation of spatial vitality by street seating is not the result of isolated effects of single factors, but rather emerges from the progressive and synergistic coupling of seating attributes, spatial conditions, and the broader street environment [27]. Therefore, there is an urgent theoretical and practical need to develop a multi-level analytical framework that integrates multi-source data to quantitatively examine the associations between factors at different levels and spatial vitality, as well as to identify key indicator thresholds.

Grounded in environmental behavior studies and urban spatial theory, this study develops a multi-level analytical framework comprising the seating-object level, the seating-space level, and the street-environment level. Taking Pingdeng Street in Zhengzhou, China, and 33 distributed characteristic seating installations as an empirical case, the study systematically examines the associations between seating-related factors at different levels and spatial vitality. By integrating field surveys, web-based data collection, and semantic segmentation techniques, the research quantitatively analyzes the relationships between multi-level factors and the intensity of spatial vitality, and identifies key threshold values and synergistic effects across levels. This study aims to address existing gaps in micro-scale analyses of the relationship between street furniture and spatial vitality, and to provide theoretical support and operational design guidance for the fine-grained renewal of livable streets in the context of urban regeneration, thereby contributing to the high-quality development of people-centered urban public spaces.

2. Materials and Methods

2.1. Study Area and Research Objects

Against the backdrop of urban regeneration and the optimization of existing urban stock as dominant trends in contemporary urban development, street space serves not only as a conduit for transportation but also as a fundamental public realm that accommodates everyday life and social interaction. The enhancement of street spatial quality increasingly depends on the fine-grained investigation and calibrated design of micro-scale environmental elements, such as street furniture configuration, spatial enclosure patterns, interface continuity, and the visibility of greenery. These subtle elements operate in combination and may substantially influence patterns of spatial use and the mechanisms underlying vitality generation. Accordingly, identifying key influencing factors and their threshold ranges at a quantifiable indicator level is essential for developing replicable and transferable approaches to refined street design. Such evidence-based identification may also provide a scientific foundation for improving the overall quality of urban public spaces.

This study takes Pingdeng Street in Zhengzhou, Henan Province, China, as the research area (Figure 1). Located in the core area of the old city of Zhengzhou, Pingdeng Street is functionally characterized by a residential-dominated composition supplemented by commercial services. Its spatial form features mixed pedestrian–vehicular traffic and continuous street frontages, representing a typical form of living street primarily oriented toward residential use while accommodating commercial activities. Streets similar to Pingdeng Street, characterized by continuous street interfaces and ground-floor commercial uses, are relatively common in Chinese cities, although their specific configurations may vary across different contexts. As a representative case of urban regeneration practice in Zhengzhou, the spatial renewal strategies adopted on Pingdeng Street align with the prevailing urban development paradigm in China, which emphasizes stock-based regeneration and quality improvement. In recent years, as urban regeneration has been widely promoted across Chinese cities, improving the quality of pedestrian environments has become a key focus. Within this process, the provision and rational arrangement of seating facilities constitute an important component, highlighting the practical relevance of research in this area. The challenges encountered and experiences accumulated during the transformation of Pingdeng Street therefore provide empirically grounded insights that may inform the renewal of comparable streets. The research objects consist of 33 seating installations with diverse forms distributed along Pingdeng Street (Figure 2). These seating facilities are not arranged independently; rather, they are integrated with spatial elements such as plazas and building interfaces, forming a cohesive and organically structured system that serves as an important spatial carrier of street vitality. The integrated design approach observed on this street—namely, the combination of street furniture with building frontages and landscape elements—demonstrates the potential for application in similar spatial and socio-economic contexts, and provides an appropriate empirical case for examining the intrinsic relationship between street furniture and spatial vitality.

2.2. Research Framework

This study provides quantitative references that may inform the refined design of street seating influences spatial vitality. Building upon a critical review and selective adaptation of existing indicator systems related to street spatial vitality, the study proposes an outward-expanding analytical structure comprising three hierarchical levels: the seating entity level, the seating–space level, and the street environment level (Figure 3). Through on-site data collection and iterative refinement, this framework enables a systematic and context-specific identification of seating-related factors enables a comprehensive identification of seating-related factors at different spatial levels that affect spatial vitality within the street context. The detailed research procedure is illustrated in Figure 4.

2.3. Data Collection and Processing of Seating-Related Indicators

To comprehensively examine the influence of street seating on spatial vitality, this study constructed a case-based multi-level seating spatial system consisting of the seating entity level, the seating–space level, and the street environment level. This framework was designed to analyze the mechanisms through which seating-related indicators at different spatial scales affect spatial vitality. The seating-related indicators identified and extracted in this study include seating type, seating continuity, spatial configuration, lighting facilities, green view index, and spatial openness. These indicators constitute the independent variables in the regression analysis.

2.3.1. Street View Image Acquisition and Pre-Processing

To obtain seating-related data for analysis, a standardized procedure was adopted to collect street-view images. All images were captured using a Canon EOS 70D camera (Canon Inc., Tokyo, Japan) under clear weather conditions during three time periods—morning (8:00–10:00), afternoon (14:00–16:00), and evening (19:00–21:00)—in order to control for the effects of lighting conditions on image quality. Shooting locations were precisely positioned at the center of each seating unit, and the camera height was fixed at 1.1 m to simulate the average eye level of an adult in a seated posture. The camera was operated in aperture-priority mode with the aperture set to f/8, while the sensitivity (ISO) was controlled within the range of 200–400 to ensure clear images under varying lighting conditions [28]. The captured raw images were stored in RAW format with a resolution of 1920 × 1080 pixels.

To avoid the limitations associated with a single viewing angle, a 360° panoramic image was obtained at each sampling point. From this panorama, four 90° directional static images (front, back, left, and right), oriented toward the primary directions of seating use, were extracted. This approach simulates the surrounding visual field of users while seated and enables a comprehensive capture of spatial enclosure, visual landscape, and facility conditions around the seating [29]. After image acquisition, all raw images were uniformly preprocessed using Adobe Lightroom Classic (version 12.0). Lens distortion and chromatic aberration were automatically corrected based on the camera profile. White balance was adjusted by selecting neutral regions within the images using the white balance eyedropper tool to ensure accurate color reproduction. Exposure, contrast, shadows, and highlights were adjusted according to histogram information, with the aim of maximizing the dynamic range while avoiding highlight clipping and excessive shadow loss [30]. All adjustment parameters were kept consistent and applied to all images within the same batch through the batch synchronization function. Following the above preprocessing procedures, the images were uniformly exported in JPEG format with a resolution of 1920 × 1080 pixels for subsequent semantic segmentation and quantitative analysis.

2.3.2. Quantification of Indicators Based on Semantic Segmentation

To objectively extract the green view index, spatial openness, and lighting facilities, semantic segmentation techniques were employed to perform pixel-level analysis of the pre-processed street view images. The DeepLabV3+ model, which has demonstrated strong performance in urban scene understanding, was used to accurately identify elements such as vegetation, sky, and lighting facilities within the images [31]. Based on the segmentation results, quantitative values of seating-related indicators were calculated. For example, the green view index was computed as the proportion of pixels classified as vegetation relative to the total image area [32].

2.3.3. Indicator Acquisition Based on Field Survey

For complex spatial attributes that could not be fully identified through two-dimensional image recognition, indicators such as seating type, seating continuity, and spatial configuration were recorded and coded through systematic field surveys conducted by the research team. A classification system was established based on seating materials and functional forms, and on-site assessments were conducted accordingly. The distances and angles between adjacent seating units were measured to calculate spatial continuity between seats [33]. By integrating on-site measurements with street view image analysis, the specific spatial types in which the seating was located were identified and recorded [34].

The core independent variables of this study focus on street seating and the spatial qualities directly associated with it. To systematically quantify the six indicators—seating type, seating continuity, spatial configuration, lighting facilities, green view index, and spatial openness (

Table 1. Definition and formula of seat-related indicators.

Variable Name	Formula	Expression	Definition
Seat type	—	A 5-level quantification standard was adopted: seats with backrests were assigned 5 points; those incorporating small tables were assigned 4 points; seats without backrests were assigned 3 points; foldable seats were assigned 2 points; and integrated bench-type seating (e.g., along planter edges) was assigned 1 point.	Characterize the functional configurations and comfort attributes of seating, and quantify the supporting capacity of different seat types for people’s willingness to sit.
Seat continuity	$I_i=\left|{\displaystyle \frac{\mathit{COS}{\theta }_i}{d_i}}\right|$	${\theta }_i$ denotes the angle formed between the i-th seat and the two adjacent seats in front and behind, and $d_i$ represents the distance between the i-th seat and the preceding seat.	Used to measure the continuity and clustering characteristics of seating arrangements within street spaces.
Seat space morphological	—	A six-level quantitative scoring system was adopted: overhanging spaces were assigned 1 point; recessed spaces were assigned 2 points; platform spaces were assigned 3 points; hybrid spaces were assigned 4 points; setback spaces were assigned 5 points; and open spaces were assigned 6 points.	Characterize the enclosure and shelter features of the space surrounding seating areas, and quantify the impact of spatial morphology on the comfort level of people’s lingering.
Lighting facilities	$L_i$ = The number of lighting facilities	—	Quantify the actual number of lighting facilities within a 5-m radius around the seating area, including street lights, landscape lights, and supplementary lighting from shops, to reflect the nighttime usability and safety conditions of the seating space.
Green view index	$G_i={\displaystyle \frac{{\sum }_{k=1}^n\left(\frac{s_{l,i}}{S_{p,i}}\right)}{N_{photo,j}}}$	$N_{photo,j}$ represents the number of valid images collected at the j-th seating space, $s_{l,i}$ denotes the area of green vegetation in the i-th image, and $S_{p,i}$ is the total area of the i-th image.	Indicates the proportion of green vegetation visible in the human field of view, reflecting the visibility of natural elements within the street environment.
Spatial openness	$O_i={\displaystyle \frac{{\sum }_{k=1}^n\left(\frac{s_{b,i}}{S_{p,i}}\right)}{N_{photo,j}}}$	$N_{photo,j}$ represents the number of valid images collected at the j-th seating space, $s_{b,i}$ denotes the area of the sky region in the i-th image, and $S_{p,i}$ is the total area of the i-th image.	Analyzed the proportion of the sky area within the field of view, representing the degree of spatial openness perceived by a person at a specific seating location.

)—a combined approach of field survey documentation and street view image computation was adopted. This approach was intended to obtain objective and replicable environmental data, thereby constructing the independent variable dataset for subsequent statistical analysis (Figure 5).

2.4. Data Collection and Processing of Spatial Vitality Intensity

Spatial vitality intensity represents the most direct manifestation of spatial use efficiency and the occurrence of social interaction. Spatial vitality is reflected in people’s participation in social activities and conceptually denotes the degree of crowd aggregation [35]. In this study, spatial vitality intensity is defined as the degree and frequency of various behavioral activities exhibited by people within a specific spatiotemporal context. To systematically and objectively quantify this key dependent variable while avoiding the limitations associated with a single data source, this study integrates web-based open data scraping, semantic segmentation-based image processing, and quantitative statistical analysis. Through this multi-method approach, objective indicators of spatial vitality on Pingdeng Street were extracted to ensure data comprehensiveness, accuracy, and methodological rigor.

2.4.1. Image Data Collection

Pingdeng Street was selected as the study area. An observation network covering the entire street segment and key seating nodes was established, and online data were synchronously collected to construct a multi-source vitality assessment dataset integrating both offline and online information.

(1)

Field survey statistics: Research team members conducted on-site observations and photography during three time periods—morning (8:00–10:00), afternoon (14:00–16:00), and evening (19:00–21:00). They recorded the total pedestrian flow passing each observation point, the number of people staying within seating areas and adjacent spaces, and the primary types of activities observed.

(2)

Online image scraping: A Python-based web crawler (Python 3.11) was employed to collect publicly available images from social media platforms, including Douyin, Xiaohongshu, and Weibo [36]. Using “Pingdeng Street” and related landmarks as keywords, images containing relevant tags or textual descriptions were retrieved. A large dataset of valid images was subsequently constructed. These images, voluntarily shared by users, provide visual evidence of actual patterns of spatial use.

2.4.2. Quantification of Image Data Based on Semantic Segmentation

The images obtained through web scraping were pre-processed and manually annotated to construct a labeled dataset containing elements such as crowds, facilities, and environmental scenes (Figure 6). A semantic segmentation model (DeepLabV3+) was introduced to train and analyze the pre-processed image samples, enabling pixel-level segmentation and precise identification of different target elements. This approach distinguished human subjects from street facilities and built environment background elements, thereby accurately identifying crowd aggregation areas, locations of behavioral activities, and the number of active individuals within different seating nodes along Pingdeng Street [37].

Based on the recognition results, behavioral activities were systematically classified. Core categories included seat-related behaviors such as sitting and resting, social interaction, leisure-oriented photo-taking, and consumption-related experiences. Non-seating-related behaviors, such as passing through and brief standing observation, were separately labeled.

Using the proportion of crowd-related pixels and the spatial extent extracted through semantic segmentation, quantitative calculations were conducted to determine the number of participants, the frequency of behavioral activities, and the spatial distribution range of activities within each sample. These measures collectively constitute the fundamental indicators for assessing spatial vitality intensity.

2.4.3. Calculation of Spatial Vitality Intensity

To achieve a systematic evaluation of spatial vitality intensity, this study integrates multi-source data derived from field survey statistics and online image scraping, combined with image quantification results obtained through semantic segmentation, to construct a comprehensive calculation model. The model is designed to synthesize the spontaneous activity intensity reflected in online images with the spatial characteristics of behavioral occurrences, thereby generating a composite and scientifically grounded spatial vitality intensity index [38]. The specific calculation model is presented as follows:

$$V={\displaystyle \frac{{\sum }_{i=1}^n\left(n_i{\omega }_t{\omega }_n\right)}{S_j{N}_{photo,j}}}$$

In the model, (N_photo,_j) denotes the number of valid images collected for the (j)-th seating space, including both field survey photographs and web-scraped images; (n_i) represents the number of individuals accurately identified in the (i)-th image through semantic segmentation; (S_j) refers to the area of the (j)-th seating space measured during the field survey; (ω_t) denotes the weight assigned to behavioral activity types; and (ω_n) indicates the weight assigned to seating spatial domains.

Drawing on the classification of activity types proposed by Jan Gehl in Life Between Buildings, human behaviors are categorized into three types: necessary activities, spontaneous activities, and social activities. Based on Edward T. Hall’s theory of interpersonal distance presented in The Hidden Dimension, the spatial domain surrounding seating is divided into five categories: seating domain (within 0.2 m), intimate distance (0.2–0.5 m), personal distance (0.5–1.2 m), social distance (1.2–3.7 m), and public distance (beyond 3.7 m).

Based on statistical samples of human activities on Pingdeng Street, and integrating the Analytic Hierarchy Process with expert judgment, different weights were assigned to three activity types: ω_t (necessary) = 0.10, ω_t (spontaneous) = 0.45, and ω_t (social) = 0.45. The influence weights of the five spatial domains on the intensity of spatial vitality were set at 0.21, 0.27, 0.18, 0.24, and 0.10, respectively. To ensure the robustness of these weights, a sensitivity analysis was further conducted, showing that (ρ > 0.9) variations within a reasonable range do not affect the overall ranking trend of spatial vitality intensity (see Appendix A for details).

Through this model, field survey data, web-scraped data, and semantic segmentation-based quantification results are comprehensively integrated. The resulting spatial vitality intensity values for each seating space along Pingdeng Street capture the dynamic characteristics of offline spatial use while leveraging large-scale online data to enhance statistical reliability. This provides a robust empirical foundation for subsequent analyses of the relationship between street seating and spatial vitality.

3. Results

3.1. Seating-Related Indicators and Spatial Vitality Intensity

3.1.1. Spatial Vitality Intensity

Based on the spatial vitality intensity calculation model, this study computed the spatial vitality intensity of 33 seating locations along Pingdeng Street. The results are presented in Figure 7.

The results indicate that the maximum spatial vitality intensity of seating along Pingdeng Street is 6.5, the minimum is 1.6, the mean value is 3.85, and the standard deviation is 1.43. This suggests substantial variation in vitality intensity among different seating nodes, with a relatively high degree of dispersion. Among them, 13 seating locations exhibit spatial vitality intensity values higher than the mean (3.85), accounting for approximately 39.39% of the total. From a spatial distribution perspective (Figure 8), seating with relatively high spatial vitality intensity is predominantly arranged along the eastern side of Pingdeng Street, whereas seating with lower vitality intensity tends to cluster on the western side of the street. Notably, the maximum spatial vitality intensity value (6.5) is approximately four times the minimum value (1.6). This pronounced disparity intuitively demonstrates that, even under the same macro-level street environment, differences in seating design, spatial configuration, and environmental design may lead to markedly divergent vitality outcomes.

According to the spatial location of the seating and their direct association with surrounding building functions, the 33 seating locations were classified into three types: Commercial Dining-Associated (CDA), Cultural Experience-Associated (CEA), and Independent Public Recreation (IPR) (

Table 2. Descriptive statistics of seat space classification, quantity distribution and spatial vitality intensity index.

Type	Count/Rate	SVI-Max	SVI-Min	SVI-Average
Commercial & dining affiliated	16/48.5%	6.5	2.4	4.4
Cultural experience affiliated	9/27.3%	6.5	2.4	3.8
Independent public recreation	8/24.2%	4.8	1.6	2.8

). This classification reflects the spatial context at the street-environment level in which the seating is embedded. In terms of proportional distribution, Commercial Dining-Associated seating constitutes the primary component of resting facilities along Pingdeng Street, accounting for 48.5% of the total. This distribution is consistent with the positioning of the street as a livable, consumption-oriented urban corridor. Cultural Experience-Associated and Independent Public Recreation seating account for 27.3% and 24.2% of the total, respectively. Together, these three types form a diversified resting network characterized by commercial dominance, cultural supplementation, and public recreational support along Pingdeng Street.

The CDA seating type exhibits the highest mean spatial vitality intensity (4.4), indicating that commercial functions serve as a primary driving force in attracting crowds and generating staying activities. Although the maximum spatial vitality intensity of CEA seating is comparable to that of the CDA type, its mean value is lower and its vitality intensity demonstrates greater variability. This fluctuation may be attributed to the selective and context-specific nature of cultural experience activities. In contrast, the IPR seating type records the lowest mean spatial vitality intensity (2.8), which is consistent with its role as a carrier of non-consumptive and spontaneous activities. Despite its relatively modest vitality intensity, IPR seating provides essential resting spaces for local residents and contributes to the cultivation of community atmosphere, thereby possessing irreplaceable social value.

3.1.2. Seating-Related Indicators

A comparison of the mean values of seating-related indicators across different functional types (

Table 3. Average values of seat-related indicators for different functional types of seats.

Variable Name	Commercial & Dining Affiliated	Cultural Experience Affiliated	Independent Public recreation
Seat type	4.19	4.33	2.25
Seat continuity	0.67	0.83	1.47
Seat space morphological	2.69	2.89	5.38
Lighting facilities	0.88	0.67	0.13
Green view index	0.31	0.30	0.22
Spatial openness	0.14	0.14	0.22

) indicates distinct characteristics among the three categories. CDA seating scores relatively high on indicators such as lighting facilities and green view ratio. In contrast, IPR seating demonstrates higher mean values in seating continuity, seating spatial configuration, and spatial openness. These results indicate clear differences in spatial attributes and environmental features among the three seating types.

At the seating-object level, the mean values for seating type in both the CDA and CEA categories exceed 4, indicating that these seating types generally adopt higher-comfort designs, such as seats equipped with small tables or backrests. This configuration aligns closely with the operational logic of commercial and cultural venues, which aim to enhance consumption experiences and encourage longer stays. In contrast, the IPR seating type records a substantially lower mean value of 2.25, suggesting that it primarily consists of basic functional forms, including backless benches or even simpler planter-style seating. Such designs emphasize cost efficiency, ease of maintenance, and universal accessibility. Notably, the IPR category demonstrates the highest mean value for seating continuity (1.47), indicating a more dispersed spatial distribution. By comparison, CDA and CEA seating exhibit lower continuity values, suggesting that they are more frequently arranged in short distances or small clusters. These configurations typically form well-defined resting corners in front of commercial establishments, reinforcing a stronger sense of territoriality and maintaining close integration with adjacent business spaces.

At the seating-space level, IPR seating exhibits a notably high mean value for seating spatial configuration (5.38), with spatial forms predominantly characterized as open. This indicates that such seating is typically located in open settings, such as green spaces and plazas, where spatial boundaries are relatively indistinct and diverse, spontaneous activities are encouraged. In contrast, the mean values for seating spatial configuration in the CDA and CEA categories are lower, generally falling between platform-based and hybrid forms. This suggests that these seating types more often rely on building interfaces to create semi-outdoor transitional spaces with a clear sense of enclosure and shelter, thereby fostering a secure and tranquil atmosphere conducive to staying activities. The CDA category records the highest mean value for lighting facilities, directly reflecting the operational demands of dining and bar-related businesses for nighttime use and a secure ambient environment. Lighting thus serves as a critical element in stimulating nighttime vitality. By comparison, IPR seating is largely devoid of dedicated lighting facilities, relying primarily on natural daylight, which makes it difficult to sustain vitality during evening hours.

At the street-environment level, both CDA and CEA seating demonstrate relatively high green view ratios. This is typically attributable to carefully arranged potted plants, flower boxes, or façade-integrated greenery in front of commercial establishments, which are intended to enhance environmental aesthetics and perceived comfort. Although IPR seating is often located within green spaces, its green view ratio is slightly lower. This may be related to the specific orientation of the seating, the presence of low-lying lawns, or the inclusion of extensive hard paving within the visible field. In terms of spatial openness, IPR seating exhibits higher values, consistent with its placement in open and expansive sites. By contrast, CDA and CEA seating show lower levels of spatial openness, largely because they are positioned adjacent to building façades or beneath overhanging eaves. Such spatial conditions create a visually more enclosed and relatively private environment.

3.2. Correlation and Regression Analysis Between Seating-Related Indicators and Spatial Vitality Intensity

To quantitatively examine the mechanisms through which seating-related indicators influence spatial vitality intensity, this study conducted Pearson correlation analysis followed by multiple linear regression analysis. The analyses were based on 33 seating locations and aimed to identify the patterns of association between each variable and spatial vitality intensity, as well as to determine the key predictive factors with explanatory power after controlling for other variables.

3.2.1. Correlation Analysis: The Influence of Seating at Different Levels on the Intensity of Spatial Vitality

To examine the association patterns between seating-related indicators at different levels and the intensity of spatial vitality, this study employed six seating-related variables as independent variables and spatial vitality intensity as the dependent variable to conduct Pearson correlation analysis. The analysis aimed to determine whether significant linear relationships exist between each variable and vitality intensity, as well as to identify the direction of these relationships. The results are presented in

Table 4. Pearson correlation analysis of relevant indicators and spatial vitality intensity.

Variable Name	Spatial Vitality Intensity
	Correlation Coefficient	p-Value
Seat type	0.466 **	0.006
Seat continuity	−0.446 **	0.009
Seat space morphological	−0.484 **	0.004
Lighting facilities	0.560 **	0.001
Green view index	0.812 **	0.000
Spatial openness	−0.700 **	0.000

Note: ** indicates statistical significance at the 1% level.

.

The results indicate that all six independent variables are statistically significant at the p < 0.01 level, confirming their close associations with spatial vitality intensity.

At the seating-object level, seating type shows a significant positive correlation with spatial vitality intensity (r = 0.466, p < 0.01), suggesting that seating with higher levels of comfort and design complexity is more likely to be located in areas with greater vitality. In contrast, seating continuity exhibits a significant negative correlation with spatial vitality intensity (r = −0.446, p < 0.01), indicating that more dispersed seating arrangements are generally associated with lower vitality levels. Further examination of the data distribution indicates that spatial vitality reaches its optimal level when seating continuity is maintained within the range of 0.63–0.90, whereas excessively dense or overly dispersed layouts tend to weaken vitality.

At the seating-space level, lighting facilities demonstrate a significant positive correlation with spatial vitality intensity (r = 0.560, p < 0.01), highlighting the importance of lighting in sustaining vitality, particularly during nighttime conditions. In contrast, spatial configuration shows a significant negative correlation with vitality intensity (r = −0.484, p < 0.01), indicating that more open seating spaces tend to exhibit lower vitality levels. This suggests that seating environments with a stronger sense of enclosure are more conducive to enhancing spatial vitality.

At the street-environment level, the green view ratio exhibits an exceptionally strong positive correlation with spatial vitality intensity (r = 0.812, p < 0.001), representing the most strongly associated variable and underscoring the central role of the seating environment in stimulating vitality. The vitality-enhancing effect is most pronounced when the green view ratio falls within the range of 28–35%. Conversely, spatial openness is strongly negatively correlated with vitality intensity (r = −0.700, p < 0.001), with an optimal range of 9–18%. This finding suggests that, within the specific context of Pingdeng Street, excessively open visual conditions are associated with lower vitality levels. It further supports the argument that overly open spaces, lacking a sense of enclosure, are less likely to foster crowd aggregation and sustained activity.

Analysis of the correlation coefficient matrix among the independent variables (Figure 9) indicates a series of significant interrelationships. These findings indicate that the variables are not isolated factors; rather, they exhibit complex patterns of co-occurrence and interaction within the actual spatial environment.

A strong negative correlation is observed between green view ratio and spatial openness (r = −0.746, p < 0.01), indicating that, within the context of this study, areas characterized by higher levels of greenery tend to exhibit lower spatial openness and a stronger sense of enclosure. Conversely, more open spaces generally contain comparatively less vegetation. This key relationship suggests that naturalness and enclosure are closely coupled spatial attributes within the street context under investigation.

Several variables are significant associated with both spatial openness and green view ratio. Seating continuity shows a strong positive correlation with spatial openness (r = 0.697, p < 0.01) and a significant negative correlation with green view ratio (r = −0.556, p < 0.01), indicating that more continuous seating arrangements are typically located in areas with higher openness and less greenery. Similarly, spatial configuration demonstrates a strong positive correlation with spatial openness (r = 0.714, p < 0.01) and a significant negative correlation with green view ratio (r = −0.630, p < 0.01), suggesting that more regularized spatial forms tend to correspond to open environments with limited vegetation. Taken together, these interrelationships indicate that the elements of the street seating system do not operate independently. Rather, they interact in a mutually reinforcing and constraining manner, collectively shaping the formation and enhancement of spatial vitality.

3.2.2. Regression Analysis: Key Indicators of the Impact of Seating on Spatial Vitality Intensity

Building upon the correlation analysis, a multiple linear regression model incorporating all six independent variables was constructed to further clarify the effects of seating-related indicators on spatial vitality intensity. The results of the regression analysis are presented in

Table 5. Results of multiple linear regression analysis.

	Unstandardized Coefficient (B)			Standardized Coefficient (Beta)			t	p	Collinearity Diagnostics
	B		Std. Error		Beta				VIF	Tolera
Constant	2.778		18.995		-		0.146	0.885	-	-
Seat type	−0.520		1.546		−0.047		−0.336	0.739	1.711	0.584
Seat continuity	2.803		4.015		0.105		0.698	0.491	2.024	0.494
Seat space morphological	1.617		1.578		0.160		1.025	0.315	2.173	0.460
Lighting facilities	3.345		3.989		0.113		0.838	0.409	1.609	0.622
Green view index	160.527		41.391		0.670		3.878	0.001 **	2.668	0.375
Spatial openness	−113.302		65.509		−0.353		−1.730	0.096	3.715	0.269
R2	0.709
Adjusted R2	0.642
F	F (626) = 10.552, p = 0.000
D-W	2.149

Note: ** indicates statistical significance at the 1% level.

.

The results indicate that the overall model is statistically significant (F(6, 26) = 10.552, p = 0.000 < 0.01). The coefficient of determination (R²) is 0.709, and the adjusted R² is 0.642, suggesting that the six independent variables collectively explain approximately 64.2% of the variance in spatial vitality intensity, reflecting a satisfactory model fit. Collinearity diagnostics show that the variance inflation factor values for all variables are below 5, indicating no serious multicollinearity issues. The Durbin–Watson (D–W) statistic is 2.149, which is close to the ideal value of 2, suggesting that the residuals are not autocorrelated and that the sample data are independent and reliable.

The regression coefficients and significance tests reveal that only the green view ratio demonstrates a statistically significant positive predictive effect on spatial vitality intensity (p = 0.001 < 0.01). The remaining variables do not reach statistical significance (p > 0.05). Although these variables fail to meet the strict threshold of statistical significance, their potential contributions to vitality enhancement may be indirectly manifested through synergistic interactions with core factors such as green view ratio, or may operate through more complex causal pathways.

The regression analysis further validates the key threshold ranges identified across different levels. At the seating-object level, high-comfort seating types with backrests are recommended, and seating continuity should be maintained within the range of 0.63–0.90. At the seating-space level, enclosed configurations should be adopted and complemented with adequate lighting facilities. At the street-environment level, a green view ratio of 28–35% and a spatial openness range of 9–18% should be ensured. Together, these quantified criteria constitute the core conditions for effectively stimulating spatial vitality.

4. Discussion

4.1. Construction of a Multi-Level Analytical Framework for Street Spatial Elements

In response to the functional fragmentation [39] and element discreteness [40] observed in existing street element studies, as well as the insufficient exploration of the relationship between street elements and spatial vitality, this study constructs a multi-level analytical framework comprising the seat entity level–seat spatial level–street environment level. This framework is grounded in field observations and empirical data from Pingdeng Street. On the one hand, under the same macro street environment, the maximum vitality intensity (6.5) among 33 seating spaces is more than four times the minimum value (1.6), indicating that differences in seat design, spatial adaptation, and environmental configuration are key factors driving vitality variation. On the other hand, seat type and seat continuity constitute the fundamental variables at the entity level; seat space morphological and lighting facilities represent key elements at the spatial level; while green view index and spatial openness characterize the visual quality of the environmental level. Pearson correlation analysis demonstrates that all three categories are significantly associated with spatial vitality intensity (Table 4), confirming the scientific validity of the hierarchical framework. Specifically, seat type (r = 0.466, p < 0.01) and lighting facilities (r = 0.560, p < 0.01) positively influence vitality at their respective levels, while the green view index (r = 0.812, p < 0.001) exhibits the strongest correlation. A key contribution of this framework lies in transcending the conventional treatment of street furniture as isolated elements and establishing a hierarchical analytical logic from micro-scale facilities to meso-scale spaces and macro-scale environments [41].

The methodological significance of this framework lies in its provision of a potentially transferable research paradigm that requires contextual calibration: a wide range of street elements (such as art installations, temporary commercial facilities, or greening configurations) may be systematically deconstructed within this framework following the three-tier structure of entity–space–environment. The hierarchical logic of the framework is grounded in empirical validation. The research data indicate that indicators at different levels contribute unevenly to spatial vitality, and optimization at a single level is insufficient to maximize vitality. Instead, a multi-scalar, synergistic analytical perspective is required. The establishment of this paradigm provides a methodological foundation for advancing street space research from fragmented element-based analysis toward a systematic investigation of coupled mechanisms [42], and also offers an analytical tool for subsequent studies to compare the roles of elements across different cultural contexts and street types.

4.2. Logic of Element Selection and Driving Mechanisms Across Spatial Typologies

The three types of seating on Pingdeng Street exhibit significant differences in spatial vitality intensity. CDA seats show the highest average vitality (4.4), IPR seats the lowest (2.8), while CEA seats rank in between but demonstrate the greatest variability (maximum 6.5, minimum 2.4). These differences indicate that the driving factors of vitality vary systematically across spatial contexts. CDA seats achieve the highest scores in lighting facilities (0.88) and green view index (0.31), aligning with their commercial logic of serving consumers and extending nighttime use. IPR seats score highest in seat continuity (1.47) and spatial openness (0.22), consistent with their placement in open-site environments. CEA seats perform best in seat type, reflecting the dual demand for comfort and symbolic meaning in cultural-experiential spaces. These findings demonstrate that the selection and configuration of elements should correspond, in most cases, to the functional positioning of specific spatial types, rather than relying on generalized design templates [43].

This finding indicates a core principle of street element research: the vitality effect of any element depends on its degree of adaptation to specific spatial types. The theoretical significance of this principle lies in shifting street-vitality research from seeking universal patterns to understanding context dependence—acknowledging that the driving factors of vitality may systematically differ across different spatial types. For commercially oriented spaces, priority should be given to configuring elements such as lighting facilities and greening that support consumption behaviors and extend usage duration. For community-oriented spaces, greater attention should be paid to balancing factors such as seat continuity and spatial openness, which influence everyday lingering and social interaction. For cultural-experiential spaces, emphasis should be placed on strengthening the synergy between the comfort and symbolic meaning embodied in seat type. This logic is not limited to seating studies but may be extended to the vitality-oriented optimization of other street furniture elements, thereby providing a theoretical foundation for function-specific and categorized street renewal strategies.

4.3. Systematic Analysis of Impact Factor Extraction and Mechanisms Based on Multi-Source Data Integration

This study contributes to a replicable technical pathway for extracting influencing factors. By integrating field surveys, web-based image crawling, and deep learning–based semantic segmentation, a multi-source data fusion approach was implemented. Semantic segmentation techniques were employed to accurately quantify green view index, spatial openness, and the number of lighting facilities. Field investigations captured complex spatial attributes such as seat type, seat continuity, and seat space morphological, while online image data expanded the sample breadth for spatial vitality assessment. The innovation of this technical approach lies in overcoming the limitations of single data sources: field surveys ensured the accuracy of spatial attributes, semantic segmentation enabled objective quantification of visual perception indicators, and web image data compensated for the spatiotemporal constraints of traditional survey methods [44].

At the mechanism analysis level, multiple linear regression indicates that the six independent variables jointly explain 64.2% of the variance in spatial vitality intensity, and the overall model is statistically significant. Among them, green view index is the only variable that demonstrates a significant positive predictive effect on spatial vitality (β = 0.670, p = 0.001). This finding, together with the correlation coefficient (r = 0.812), suggests the potentially central role of greenery perception in stimulating vitality. However, in the regression model, none of the five variables—seat type, continuity, spatial form, lighting facilities, and spatial openness—reached statistical significance, contrasting with the significant relationships observed in the correlation analysis. This discrepancy may reflect the particular characteristics of living streets: within the context of fine-grained urban renewal, the influence of greenery visibility on vitality may outweigh the design attributes of the seating itself. For living streets such as Pingdeng Street, people’s decisions to pause or linger are often guided more by the quality of street greenery than by the comfort of the seating. This does not diminish the importance of seating design; rather, it highlights the hierarchical relationship among elements in generating vitality: visual quality at the environmental level constitutes the core condition for attracting stays, while design quality at the object level serves to enhance this effect. Further analysis of the correlation matrix among independent variables (Figure 9) indicates strong interdependencies: green view index is highly negatively correlated with spatial openness (r = −0.746, p < 0.01); seat continuity is significantly positively correlated with spatial openness (r = 0.697, p < 0.01); and seat space morphological is significantly negatively correlated with green view index (r = −0.630, p < 0.01). These relationships indicate that spatial elements do not operate independently in real-world settings but are interwoven and co-occurring. Areas with high green view index tend to exhibit lower spatial openness, while continuous seating layouts are more common in highly open spaces. Therefore, the vitality-enhancing effects of variables such as seat type and lighting facilities may be indirectly realized through the core factor of green view index or function within multi-element synergies. The influence mechanism of street elements on vitality is thus not a simple linear relationship but the result of multi-factor coupling. Future research should introduce nonlinear approaches, such as structural equation modeling and machine learning, to further explore transmission pathways and interaction effects among elements [45].

This finding indicates the hierarchical nature of street-vitality generation: greenery visibility at the environmental level constitutes the core condition for attracting lingering, while design qualities at the object and spatial levels (such as seat type and lighting facilities) function to reinforce this effect. When the quality of the street greenery environment is insufficient, the benefits of design optimizations may be masked. Practically, this insight provides guidance for street renewal prioritization: under resource constraints, ensuring the quality of street greenery should take precedence, upon which secondary elements such as seating design and lighting configuration may be optimized.

4.4. Translating Design Strategies into Quantitative Threshold Guidelines for Urban Renewal

Based on quantitative evidence from the 33 seating spaces on Pingdeng Street, this study extracts key design thresholds for elements at different hierarchical levels. These thresholds may inform urban street design guidelines in similar contexts, providing technical guidance for the renewal of livable streets.

Seat entity Level: Seat type and seat continuity directly influence users’ willingness to sit and duration of stay, forming the foundational conditions for spatial vitality generation. The study finds that high-comfort seat type, such as those equipped with backrests or small tables, generally correspond to higher spatial vitality intensity. By providing stable physical support and functional extension, such seating reduces users’ psychological defensiveness and encourages spontaneous and social behaviors—particularly resting and interpersonal interaction—which are key behavioral carriers of spatial vitality enhancement. Seat continuity shows a significant negative correlation with spatial vitality, and correlation analysis identifies its optimal range as 0.63–0.90. When seat continuity is excessively high, individual privacy is compressed, potentially generating a sense of crowding and reducing willingness to stay. Conversely, when continuity is too low, opportunities for social interaction decrease substantially, making it difficult to form aggregation effects. A moderately continuous seating arrangement balances individual autonomy and interactive potential, achieving equilibrium between solitude and social engagement. This finding provides direct design guidance for seating layouts in commercially affiliated and cultural-experiential street spaces [42].

Seat Space Level: The spatial morphology and facility configuration surrounding seating indirectly influence spatial vitality by shaping environmental comfort and duration of use [46]. Correlation analysis indicates that spaces characterized by stronger enclosure—such as overhanging eaves and recessed spatial forms—tend to exhibit higher vitality intensity. These configurations create semi-enclosed gray spaces through partially sheltered building interfaces, providing practical functions such as shade and rain protection while fostering an environment that balances openness and privacy. By reducing external disturbances, such settings are more conducive to prolonged stays and social interaction [47]. Lighting facilities emerge as a critical factor in extending the temporal dimension of space use. Commercially affiliated seating areas show significantly higher provision rates of lighting facilities than other types, accompanied by correspondingly higher nighttime vitality intensity. This suggests that lighting stimulates nighttime economic activity and social engagement by enhancing safety and atmosphere. However, the vitality-enhancing effect of lighting facilities exhibits variability, potentially influenced by factors such as illumination level, color temperature, and spatial arrangement [48].

Street Environment Level: The visual landscape characteristics of the street environment constitute key determinants of spatial vitality, among which the green view index plays the most prominent role. Regression analysis indicates that green view index has a significant positive predictive effect on spatial vitality (β = 0.670, p = 0.001), with a high correlation coefficient of 0.812—the strongest among all variables. The results show that when the green view index ranges between 28% and 35%, spatial comfort and users’ willingness to stay increase markedly. In contrast, spatial openness is significantly negatively correlated with spatial vitality (r = −0.700, p < 0.01), with an optimal range of 9–18%. This finding differs from the conventional assumption that more open spaces inherently generate higher vitality. As Pingdeng Street is a traditional living street in the old city, excessively open spaces with insufficient enclosure make it difficult to form aggregation anchors. Moderately open spaces, by contrast, help define territorial boundaries, enhance users’ sense of belonging, and facilitate social interaction. These results highlight the importance of scale appropriateness in old-city street design [49] and provide empirical evidence against the indiscriminate pursuit of large-scale and overly open spatial forms in urban renewal practice.

At the practical level, the quantitative thresholds provided by this study may be translated into technical specifications for street-design guidelines. In the renewal of living streets, greenery visibility should be maintained between 28% and 35%, and spatial openness between 9% and 18%. These measurable and verifiable indicators offer an objective basis for evaluating and optimizing design proposals, facilitating a shift in street renewal from regulatory compliance toward performance-based control. Furthermore, the differentiated optimization pathways identified for seats of different functional types provide empirical support for classification-based renewal strategies: CDA seats should balance high comfort, adequate lighting, and elevated greenery visibility; CEA seats should harmonize cultural expression with spatial comfort; and IPR seats should maintain inclusivity while prioritizing enhanced nighttime lighting conditions.

4.5. Limitations

This study also has several limitations. Firstly, the spatial-vitality data partly rely on publicly available images from social media platforms. Although multi-source validation and semantic segmentation were applied, selective bias may still exist. User posting preferences could introduce age-related bias, with behaviors of younger and frequent social-media users potentially overrepresented, while activities of older adults and children may be underestimated. Platform recommendation algorithms may also affect content visibility, as images from popular check-in locations are more likely to be promoted and retrieved, potentially leading to an overestimation of vitality at certain seating nodes. Although this study calibrated the data using field observations, it remains difficult to fully eliminate such biases. Future research could integrate more objective, real-time data sources—such as Wi-Fi probe data [50] and mobile signaling data [51]—to enhance the timeliness and accuracy of spatial-vitality assessments and enable refined analyses across different population groups.

Secondly, the case study of Pingdeng Street represents a living street, characterized primarily by small- and medium-sized commercial dining and cultural-experience functions, with distinct patterns of human activity. The identified indicator thresholds and associated patterns are applicable within similar living-street contexts, but their generalizability to other settings, such as commercial streets or historical-cultural districts, requires further validation. A cross-city comparative study by Galaktionova revealed that the relationship between street vitality and the built environment varies significantly across urban contexts—the association between compact street morphology and vitality in Barcelona is stronger than in the more dispersed urban structure of Warsaw [52]. Therefore, the indicator thresholds identified in this study are most suitable as reference benchmarks for the renewal of comparable living streets, rather than universal standards for all street types. For other street typologies, the findings may serve as hypotheses or points of reference, but they should be verified and calibrated according to the specific context. Future research could develop a more adaptable, classification-based guidance system through comparative studies across different street types.

Finally, the generation of street vitality is a dynamic and multifaceted process [53]. Beyond the indicator variables examined in this study, it is also influenced by factors such as functional land-use distribution [54], climatic conditions [55], and nighttime lighting [56]. Under different climatic conditions, environmental factors such as temperature, humidity, and sunlight affect the comfort of outdoor stays: in high-temperature conditions, areas with natural ventilation and shading are more attractive, whereas in cooler conditions, sunlit areas are preferred. Additionally, the orientation of seating is an important consideration. Nighttime lighting exhibits a dual effect: moderate illumination may enhance spatial legibility and extend street usage, particularly around commercial zones and transportation hubs; conversely, excessive or inappropriate lighting (e.g., high color temperature or strong glare) may cause light pollution, reducing residents’ willingness to be outdoors at night and potentially disrupting circadian rhythms [57]. Notably, the impact of lighting is not determined solely by illuminance values; its spatial distribution, color temperature, uniformity, and contrast with the surrounding environment collectively shape perceived visual quality, thereby influencing people’s nighttime lingering behavior and spatial choices. Future research could further integrate these multidimensional factors, employing qualitative methods such as interviews, activity logs [58], and environmental tracking to more comprehensively elucidate the underlying mechanisms linking seating spaces to street vitality.

5. Conclusions

This study takes 33 seating spaces on Pingdeng Street in Zhengzhou as empirical cases and constructs a multi-level analytical framework consisting of the seat entity level, seat space level, and street environment level. Through this framework, it systematically indicates the mechanisms by which seat-related street elements stimulate spatial vitality. The research not only identifies key indicator thresholds and synergistic effects at the empirical level, but also contributes to a transferable methodological paradigm, providing both theoretical support and practical guidance for refined street space renewal. The main conclusions are as follows:

(1)

Within the context of living streets such as Pingdeng Street, the GVI at the street environment level represents a key driving factor of vitality enhancement. The vitality-promoting effect is most pronounced when GVI ranges between 28% and 35%.

(2)

The synergistic coupling of multi-level elements is critical to maximizing spatial vitality. At the seating ontology level, high-comfort types with backrests should be selected, with seat continuity controlled within 0.63–0.90. At the seating space level, enclosed spatial forms such as eave-covered spaces should be adopted and supplemented with lighting facilities. At the street environment level, GVI should be maintained within 28–35%, and spatial openness should be controlled within 9–18%.

(3)

Distinct optimization pathways are required for different functional seating types. Seating associated with commercial catering should prioritize high comfort, adequate lighting provision, and a relatively high GVI. Culturally experiential seating should balance cultural expression with spatial comfort. Independent public recreational seating should enhance environmental adaptability while maintaining inclusiveness.

This study not only provides context-specific quantitative reference ranges provides directly applicable quantitative standards for the refined design of street seating, but its multi-level analytical logic may also be extended, with appropriate validation, to vitality enhancement research on other street furniture, such as public art installations and temporary commercial facilities. The theoretical contribution of this study lies in proposing a transferable multi-level analytical framework encompassing object, spatial, and environmental dimensions, rather than providing universal numerical standards. Its practical significance is demonstrated in showcasing an evidence-based design approach grounded in multi-source data and quantitative analysis. Future studies may further expand comparative analyses across different functional street types and incorporate more dynamic data and segmented population analysis to improve the theoretical framework and practical guidelines for enhancing street spatial vitality, thereby supporting the high-quality, people-oriented development of urban public spaces [59]. The transferability of this methodological approach offers potential theoretical tools and technical support for systematic street space renewal. As urban regeneration shifts from demolition-led redevelopment to fine-grained urban stitching, design decision-making grounded in multi-source data and quantitative analysis may become a critical pathway for improving spatial quality.