Measuring Vitality and Spatial Efficiency of Public Spaces in Commercial Complexes: A Multi-Source Data-Driven Analysis in Guangzhou, China

Abstract

The accurate measurement and optimization of spatial vitality inside commercial complexes has become crucial for sophisticated urban governance as urban growth moves from rapid expansion to quality-oriented stock augmentation. This research creates a multifaceted assessment methodology that incorporates systemic connectedness (transportation synergy), spatial performance (public activity and social efficacy), and spatial supply (human–land linkages and arrangement). We used a stratified purposive sample of 20 business complexes spread across eight districts in Guangzhou, a typical high-density megacity. In order to understand the underlying mechanisms of spatial vitality, we measured important indicators including the Polycentricity Index (α) and the Spatial Performance Index (β) using a mixed-methods approach that included K-means clustering, multinomial logit regression, and Structural Equation Modeling (SEM). Four important insights are shown by our findings. 1. The paradox of density and efficiency: The notion that high-density development inevitably ensures lively public space is called into question by the lack of a significant linear correlation between the Floor Area Ratio (FAR) and spatial performance (r = 0.32, p > 0.05), despite a core–periphery gradient in development intensity. 2. Structural Supply Demand Mismatch: Although overall spatial performance is strong (β = 0.81 ± 0.07), there is a notable shortfall in cultural and artistic venues, where young adults’ demand (0.27) is 145% greater than supply (0.11). 3. Polycentric Networking vs. Transport Polarization: While spatial structures show a networked polycentric pattern (mean α = 6.40), transportation synergy is affected by core–periphery polarization, which results in “vitality islands” in the periphery. 4. Dual-Path Driving Mechanisms: According to SEM results, cultural spaces have a considerable indirect impact (39.7% mediation) by boosting brand uniqueness and “cultural capital,” while composite plaza spaces have a strong direct effect on commercial performance (γ = 0.682). Based on these findings, we suggest distinct optimization strategies: aging projects need climate-responsive design interventions; growing areas should create family-oriented consumption ecosystems; and core districts should give priority to cultural “IP” integration. For the planning and revitalization of commercial land use in high-density global environments, this study offers a solid analytical framework and practical insights.

1. Introduction

Commercial complexes are functionally evolving from single-purpose consumption venues to integrated “urban public space hubs” in the global shift from incremental urban growth to stock renewal [1]. Their role in promoting social engagement and forming urban identity has grown in importance as essential urban nodes that integrate social contact, cultural experience, and community services [2]. However, given China’s high-density urbanization, this transition poses significant practical challenges [3].

Using Guangzhou as a case study, the macro-level commercial spatial structure has changed to a “one-core, multi-point” framework under the direction of the Guangzhou Commercial Network Layout Plan (2020–2035), which is the main hub of the Guangdong-Hong Kong-Macao Greater Bay Area [4]. However, there are several micro-level issues that these complexes’ public areas must deal with, such as hyper-density, functional homogenization, and stalling spatial quality. According to empirical data, the average Floor Area Ratio (FAR) of Guangzhou’s typical commercial complexes is 6.50 ± 2.17, indicating a high level of spatial polarization [5,6]. Ironically, there has not been a corresponding improvement in spatial vitality as a result of this increase in physical density. The “Density–Efficacy Paradox”—in which high-intensity development without human-centered design may actually suppress social interaction due to disproportionate spatial scales or poor environmental quality—is echoed in many projects that suffer from “scale without vitality” or “flow without dwell time [7,8]”.

The research now in publication presents a variety of viewpoints on commercial vitality, but a cohesive analytical framework is still problematic [9]. Early research, based on retail geography, concentrated on customer behavior and macro-accessibility [10,11]. Research on “place-making” and social performance changed as a result of the “humanistic turn” in urban studies. Montgomery (1998) offered the “activity-accessibility-comfort” triad [12], whereas Whyte (1980) recognized microenvironmental characteristics that promote interaction [13]. Space syntax and polycentric theory have been used to examine social flows from a morphological point of view [14]. At the same time, Bertolini’s (2012) “node-place model” offered a framework for comprehending how urban attractions and transportation hubs interact together [15].

Despite these developments, a crucial study gap still exists: current studies frequently split into macro-locational analysis or micro-behavioral observations, missing a multifaceted, measurable framework that incorporates transport synergy, performance feedback, and spatial supply. In the complex environment of high-density Asian cities, this fragmentation makes it challenging to identify the structural restrictions and generative mechanisms of vitality [16]. In order to close this gap, this study develops an integrated analytical framework for Guangzhou with three developmental goals in mind:

1.

Create a Multidimensional Measurement System: Incorporate systemic connectedness (accessibility and synergy), usage performance (public activity and social benefits), and spatial form (development intensity and polycentricity) into a comparable evaluation index [17].

2.

Examine Internal Mechanisms and Spatial Differentiation: Use structural equation modeling (SEM), K-means clustering, and spatial analysis to measure vitality patterns and test supply-demand conflicts and driving variables empirically.

3.

Offer Differentiated Optimization Pathways: Create tiered solutions for commercial complexes of different kinds and locations, from operational management to spatial design interventions.

This study makes two contributions. In theory, it provides a cross-disciplinary operational tool by bridging social performance theory, spatial morphology, and transport synergy [18]. In practical terms, it provides evidence-based support for human-centered design and improved governance in the Greater Bay Area, ultimately fostering an inclusive, resilient, and sustainable urban living environment.

2. Materials and Methods

In order to methodically decipher the multifaceted traits and generative processes of public space vitality, this study makes use of a multi-level research approach [19]. Conceptual framework development, case selection, multi-source data integration, and thorough model design are the steps in the methodology’s organized technical pathway.

2.1. Case Selection Procedure and Sample Features

We used a stratified purposive sampling technique to guarantee the regional representativeness of our results and enable a thorough mechanistic investigation. This method provides a solid basis for comparison analysis and driver identification by methodically capturing structural changes across three crucial dimensions: spatial location, dominating function, and development scale.

2.1.1. Sampling Structure and Selection Guidelines

The sample procedure followed four fundamental guidelines to reduce selection bias and increase spatial diversity:

1.

Spatial Stratification: This study area was divided into three tiers in accordance with the Guangzhou Territorial Spatial Master Plan (2021–2035): the Central Transition Zone (Haizhu and Liwan districts), the Core Functional Zone (Tianhe and Yuexiu districts), and the Peripheral Emerging/Expansion Zone (Baiyun, Panyu, Huangpu, and Huadu districts). From established central business districts (CBDs) to emerging urban periphery, this demarcation guarantees the inclusion of a whole urban spectrum.

2.

Functional Typology: Four prevalent development models were represented by the purposefully chosen samples: office-driven (such as commercial podiums of Grade-A towers), commerce-driven (such as large-scale shopping malls), residential-supporting (such as amenities serving high-density communities), and transport hub-driven (such as Transit-Oriented Developments (TODs) integrated with aviation or rail networks).

3.

Scale Gradient: The sample set effectively reflects a range of urban carrying capacities by encompassing a thorough gradient of built environments, from community-level developments (<100,000 m²) to regional-level hubs (>500,000 m²).

4.

Data Verifiability: To ensure the empirical reliability of this study, priority was given to locations that allowed methodological cross-validation through geographical analysis, on-site observations, and longitudinal survey data.

2.1.2. Spatial Features and Sample Composition

For the empirical analysis, twenty representative business complexes spread over eight administrative districts were chosen (

Table 1. Basic Attributes and Spatial Characteristics of Research Samples.

No.	Sample Name	District	Scale (104 m2)	FAR	Density (%)	Dominant Type
1	GBA Tech & Finance Center	Huangpu	31.18	6.32	40.13	Industry–Office
2	Guangzhou Knowledge City	Huangpu	9.51	8.90	33.88	Industry–Office
3	Teemall	Panyu	39.50	6.00	45.00	Commerce–Driven
4	Grandview Mall	Tianhe	37.13	6.58	59.60	Commerce–Driven
5	Yuehai Financial Center (Plot B1-1)	Tianhe	25.10	9.35	31.30	Office–Driven
6	Liwan Xinhe Plaza	Tianhe	10.81	6.12	42.30	Industry–Office
7	TaiKoo Hui	Tianhe	45.76	10.40	50.30	Commerce–Driven
8	Guangzhou IFC	Tianhe	45.00	12.03	66.70	Office–Driven
9	Yuexiu Central Plaza	Yuexiut	36.70	9.70	61.70	Mixed–Use
10	Liwan Link Plaza	Liwan	42.11	5.00	48.30	Residential–Supporting
11	Liwan Hey Plaza	Liwan	15.04	4.20	47.20	Residential–Supporting
12	R&F Haizhu City	Haizhu	12.54	10.97	66.10	Residential–Supporting
13	Xinyicheng Plaza	Haizhu	32.78	6.10	28.10	Residential–Supporting
14	Design Capital	Baiyun	101.00	4.34	41.14	TOD
15	Yuesheng Plaza	Baiyun	71.60	2.62	16.76	TOD
16	Wanda Plaza	Panyu	50.34	5.50	67.20	Commerce–Driven
17	Wanbo Aoyuan International Center	Panyu	15.23	5.00	45.00	Commerce–Driven
18	Haiyin Youyicheng	Panyu	20.50	4.80	35.80	Commerce–Driven
19	Teemall Outlet Park	Huadu	23.34	2.70	45.00	Leisure–Entertainment
20	Junyi Wanbang Plaza	Huadu	26.88	7.50	28.50	Leisure–Entertainment
Statistical Features	Total Samples: 20	8 Districts	Mean: 33.52, SD: 21.37	Mean: 6.50, SD: 2.17	Mean: 44.85, SD: 14.22	6 Types Covered

Note: FAR = Floor Area Ratio. Scale data sourced from GZ Municipal Housing and Urban–Rural Development Bureau.

). Key morphological characteristics such as construction scale (95,100 to 1,010,000 m²), floor area ratio (FAR) (2.62 to 12.03), and building density (16.76% to 67.20%) show considerable internal variance in these samples. For examining the non-linear connections between spatial form and social performance, this intrinsic variation offers a perfect empirical baseline.

There is clear regional differentiation amongst the samples, as seen in

Table 2. Development Features and Prevalent Commercial Complex Types by Administrative District.

District	Dominant Type (Proportion)	FAR Range	Scale (104 m2)	Spatial–Economic Characteristics
Tianhe	Office-Driven (72.3%)	≥10.0 (Extremely High)	≤35 (Super–Large)	CBD Functional Agglomeration, Highest Output per Land Area
Yuexiu/Huangpu	Mixed-Use/Industry-Office	7.5~10.0 (High)	≥35 (Super–Large)	Mature Urban Area Composite Functional Agglomeration
Haizhu/Liwan	Residential-Supporting (63.5%)	5.0~7.5 (Medium–High)	15~25 (Medium)	Population Density-Driven Development
Baiyun	Transport Hub–Driven	2.5~5.0 (Medium–Low)	25~35 (Large)	TOD-Oriented Flow Economy Model
Panyu	Commerce–Driven	5.0~7.5 (Medium)	25~35 (Large)	Moderate Intensive Development of Secondary Commercial Centers
Huadu	Leisure–Entertainment	≤5.0 (Low)	25~35 (Large)	Proportion of Inefficient Land Use Reaches 38%

. The functional agglomeration typical of a central business district (CBD) is reflected in the high-intensity, office-driven typologies with peak FAR values that characterize developments in the Tianhe district. On the other hand, samples from the districts of Haizhu and Liwan show population-driven traits, with a preponderance of medium-sized residential complexes. The need for the multidimensional evaluation framework created in the next sections is highlighted by this spatial polarization.

2.2. Gathering Data from Multiple Sources and Building Indicator Systems

2.2.1. Sources and Processing of Data

We used a multisource data integration approach that included four main data categories in order to guarantee the validity, repeatability, and analytical depth of this study:

• Spatial Morphology and Functional Data: To calculate important morphological indicators, such as the floor area ratio (FAR) and building density, architectural design drawings (in CAD format) for each of the 20 examples were obtained. Concurrently, the functional mix of each complex was measured and examined across two tiers: major functional categories and particular business formats, using refined points of interest (POIs) data acquired through the A Map API (

  Table 3. Sample Functional Mix Index.

  No.	Sample Name	Major Category Index	Sub-Category Index
  1	GBA Tech & Finance Center	4	5
  2	Guangzhou Knowledge City	3	5
  3	Teemall	4	9
  4	Grandview Mall	5	9
  5	Yuehai Financial Center (Plot B1-1)	3	6
  6	Liwan Xinhe Plaza	3	7
  7	TaiKoo Hui	4	6
  8	Guangzhou IFC	5	7
  9	Yuexiu Central Plaza	6	8
  10	Liwan Link Plaza	4	6
  11	Liwan Hey Plaza	3	5
  12	R&F Haizhu City	3	6
  13	Xinyicheng Plaza	3	9
  14	Design Capital	6	11
  15	Yuesheng Plaza	5	6
  16	Wanda Plaza	5	7
  17	Wanbo Aoyuan International Center	5	7
  18	Haiyin Youyicheng	3	6
  19	Teemall Outlet Park	5	6
  20	Junyi Wanbang Plaza	3	7
  Statistical Features	Total Samples: 20	Mean: 4.1	Mean: 7.2

  ).
• Accessibility Data: An integrated macro-micro assessment method was used. The built-up area within a 30 min drive-time isochrone for each administrative district was determined at the macro level using network analysis based on Guangzhou’s urban road network data [20]. The number of public transportation nodes (bus stops and subway stations) within a 300 m radius of each complex, as well as the number of physical pedestrian access points that directly connect the complex to these nodes, were measured in order to evaluate the multimodal transit integration capacity at the micro level (

  Table 4. Macro Analysis of Transport Accessibility by District in Guangzhou.

  No.	District	30 Min Accessibility (Sq km)	% of Built-Up Area
  1	Yuexiu	40	16
  2	Tianhe	38	17
  3	Haizhu	35	14
  4	Liwan	32	15
  5	Baiyun	25	7
  6	Panyu	23	8
  7	Huangpu	21	6
  8	Huadu	20	5
  9	Nansha	19	4
  10	Zengcheng	18	5
  11	Conghua	16	3
  Statistical Features	Total Districts: 11	Mean: 25.5	Mean: 8.7

  and

  Table 5. Micro-Transport Connectivity Data of Research Samples.

  No.	Sample Name	No. of Transport Stops	No. of Access Points
  1	GBA Tech & Finance Center	5	7
  2	Guangzhou Knowledge City	3	6
  3	Teemall	7	7
  4	Grandview Mall	9	20
  5	Yuehai Financial Center (Plot B1-1)	4	10
  6	Liwan Xinhe Plaza	7	6
  7	TaiKoo Hui	8	15
  8	Guangzhou IFC	9	14
  9	Yuexiu Central Plaza	7	10
  10	Liwan Link Plaza	12	16
  11	Liwan Hey Plaza	10	9
  12	R&F Haizhu City	5	10
  13	Xinyicheng Plaza	8	9
  14	Design Capital	12	21
  15	Yuesheng Plaza	8	31
  16	Wanda Plaza	7	11
  17	Wanbo Aoyuan International Center	7	8
  18	Haiyin Youyicheng	4	6
  19	Teemall Outlet Park	3	8
  20	Junyi Wanbang Plaza	3	9
  Statistical Features	Total Samples: 20	Mean: 6.7	Mean: 11.8

  ).
• User Behavior and Perception Data: Structured questionnaire surveys and methodical on-site behavioral mapping were used to gather this data [21]. In order to comprehensively capture activity kinds, usage intensity, and demographic features inside public places, behavioral mapping was carried out on regular weekdays and weekends. A 5-point Likert scale was used in the questionnaire survey, which produced 821 valid responses, to quantitatively assess 26 important factors affecting consumer choice and spatial experience. It also recorded behavioral variables like activity patterns and dwell time (Table 6, Table 7 and Table 8).
• Qualitative Management and Operational Data: 40 representative tenants and the commercial management directors of 12 sample projects participated in semi-structured, in-depth interviews. In addition to facilitating methodological triangulation, the generated textual data offered a more thorough mechanistic understanding of the quantitative results.

Table 6. Core Analysis of User Behavioral Characteristics N = 821.

Analysis Dimension	Category	Proportion (%)
Dwell Time	≥3.0 h	11.27
	2–3 h	32.70
	1–2 h	41.60
	0.5–1 h	11.40
	≤0.5 h	3.03
Primary Activity	Shopping	25.20
	Dining	16.30
	Leisure	17.50
	Entertainment	20.50
	Cultural Services	7.40
	Others	13.10

Table 6. Core Analysis of User Behavioral Characteristics N = 821.

Analysis Dimension	Category	Proportion (%)
Dwell Time	≥3.0 h	11.27
	2–3 h	32.70
	1–2 h	41.60
	0.5–1 h	11.40
	≤0.5 h	3.03
Primary Activity	Shopping	25.20
	Dining	16.30
	Leisure	17.50
	Entertainment	20.50
	Cultural Services	7.40
	Others	13.10

Table 7. Cross-Analysis of Dwell Time and Primary Activity (%).

Activity Type/Dwell Time	≥3.0 h	2.0–3.0 h	1.0–2.0 h	0.5–1.0 h	≤0.5 h
Shopping	36.30	31.20	18.70	11.00	0.00
Dining	8.60	17.60	21.60	11.30	3.40
Leisure	16.50	11.30	9.60	14.40	51.00
Entertainment	9.70	15.70	14.80	11.20	8.00
Cultural Services	5.90	6.40	8.50	27.00	21.00
Tourism	17.70	10.80	17.90	14.70	0.00
Business Affairs	2.70	3.20	5.30	5.30	12.00
Childcare/Children’s Activities	29.00	35.00	18.00	12.00	6.00
Others	2.60	3.80	3.60	5.10	4.60

Note: Long dwell time (≥3 h) is strongly associated with shopping and childcare activities; short dwell time (≤0.5 h) primarily reflects leisure or pass-through behavior. N = 821. Data reflects the strong association between long dwell times and childcare/shopping.

Table 7. Cross-Analysis of Dwell Time and Primary Activity (%).

Activity Type/Dwell Time	≥3.0 h	2.0–3.0 h	1.0–2.0 h	0.5–1.0 h	≤0.5 h
Shopping	36.30	31.20	18.70	11.00	0.00
Dining	8.60	17.60	21.60	11.30	3.40
Leisure	16.50	11.30	9.60	14.40	51.00
Entertainment	9.70	15.70	14.80	11.20	8.00
Cultural Services	5.90	6.40	8.50	27.00	21.00
Tourism	17.70	10.80	17.90	14.70	0.00
Business Affairs	2.70	3.20	5.30	5.30	12.00
Childcare/Children’s Activities	29.00	35.00	18.00	12.00	6.00
Others	2.60	3.80	3.60	5.10	4.60

Note: Long dwell time (≥3 h) is strongly associated with shopping and childcare activities; short dwell time (≤0.5 h) primarily reflects leisure or pass-through behavior. N = 821. Data reflects the strong association between long dwell times and childcare/shopping.

Table 8. Multi-Dimensional Assessment of Decision-Making Factors for Guangzhou Commercial Complex Consumers N = 821.

No.	Key Factor	Score (0–5)	Category
1	Transport Convenience	4.44	Location & Accessibility
2	Location Perception	4.06	Location & Accessibility
3	Architectural Design	3.91	Physical Spatial Environment
4	Pedestrian Flow Layout	3.66	Physical Spatial Environment
5	Stair/Escalator Layout	3.72	Physical Spatial Environment
6	Air Conditioning & Ventilation	3.06	Physical Spatial Environment
7	Cleanliness/Hygiene	3.06	Physical Spatial Environment
8	Seating/Resting Facilities	2.94	Physical Spatial Environment
9	Parent–Child Rooms	1.66	Physical Spatial Environment
10	Wayfinding/Signage System	3.41	Physical Spatial Environment
11	Tenant Mix/Business Format Combination	3.44	Function & Content
12	Local Guangzhou Characteristics	3.66	Function & Content
13	Food & Beverage Offerings	3.31	Function & Content
14	Retail Brand Strength	3.22	Function & Content
15	Anchor Stores/Key Tenants	3.00	Function & Content
16	Membership System	2.44	Function & Content
17	Residential Needs (e.g., proximity)	1.36	Function & Content
18	Tourism Needs	2.31	Function & Content
19	Office Needs	1.09	Function & Content
20	Digital Experience	3.09	Experience & Service
21	Humanized Services	2.91	Experience & Service
22	Children’s Play Facilities	2.34	Experience & Service
23	Experiential Scenes/Spaces	3.44	Experience & Service
24	Themed Marketing Events	3.53	Operation & Activities
25	Promotion/Sales Intensity	3.19	Operation & Activities
26	Community & Social Media Engagement	3.59	Operation & Activities
27	Cultural Activities/Events	3.78	Operation & Activities

Note: Location & Accessibility (4.25) > Operation & Activities (3.52) > Physical Environment (3.18) > Experience & Service (2.94) > Function & Content (2.65).

Table 8. Multi-Dimensional Assessment of Decision-Making Factors for Guangzhou Commercial Complex Consumers N = 821.

No.	Key Factor	Score (0–5)	Category
1	Transport Convenience	4.44	Location & Accessibility
2	Location Perception	4.06	Location & Accessibility
3	Architectural Design	3.91	Physical Spatial Environment
4	Pedestrian Flow Layout	3.66	Physical Spatial Environment
5	Stair/Escalator Layout	3.72	Physical Spatial Environment
6	Air Conditioning & Ventilation	3.06	Physical Spatial Environment
7	Cleanliness/Hygiene	3.06	Physical Spatial Environment
8	Seating/Resting Facilities	2.94	Physical Spatial Environment
9	Parent–Child Rooms	1.66	Physical Spatial Environment
10	Wayfinding/Signage System	3.41	Physical Spatial Environment
11	Tenant Mix/Business Format Combination	3.44	Function & Content
12	Local Guangzhou Characteristics	3.66	Function & Content
13	Food & Beverage Offerings	3.31	Function & Content
14	Retail Brand Strength	3.22	Function & Content
15	Anchor Stores/Key Tenants	3.00	Function & Content
16	Membership System	2.44	Function & Content
17	Residential Needs (e.g., proximity)	1.36	Function & Content
18	Tourism Needs	2.31	Function & Content
19	Office Needs	1.09	Function & Content
20	Digital Experience	3.09	Experience & Service
21	Humanized Services	2.91	Experience & Service
22	Children’s Play Facilities	2.34	Experience & Service
23	Experiential Scenes/Spaces	3.44	Experience & Service
24	Themed Marketing Events	3.53	Operation & Activities
25	Promotion/Sales Intensity	3.19	Operation & Activities
26	Community & Social Media Engagement	3.59	Operation & Activities
27	Cultural Activities/Events	3.78	Operation & Activities

Note: Location & Accessibility (4.25) > Operation & Activities (3.52) > Physical Environment (3.18) > Experience & Service (2.94) > Function & Content (2.65).

2.2.2. Vitality Measurement Indicator System for Public Spaces

This study develops a multilayer, multidimensional evaluation methodology for public space vitality in order to overcome the drawbacks of conventional, unidimensional indicators like pedestrian foot traffic. This paradigm, which is based on the “space-function-behavior-transportation” synergy idea, leads to the creation of the Public Space Vitality Index (PSVI) (

Table 9. Commercial Complex Public Space Vitality Measurement Indicator System.

Target	Criterion	Indicator	Indicator & Calculation Method	Data Source
Public Space Vitality (V)	Human–Land (A)	FAR (A1)	Ratio of total floor area to site area.	Field survey
		Scale (A2)	Total construction floor area	Statistics Bureau
	Spatial Structure (B)	Polycentricity (B1)	Activity node α-index	Space syntax
		Efficacy (B2)	β-index based on 5 dimensions	Questionnaire
	Public Relation (C)	Activity Intensity (C1)	Dwell time& daily visitor density	Commerce Bureau
		Diversity (C2)	Shannon index for formats	Field survey
	Transport (D)	Accessibility (D1)	Number of stops/points (300 m)	AMap API
		Road–Land Synergy (D2)	Area covered by 30 min commute isochrone. High level defined as ≥40 km2	Network analysis method, Territorial spatial planning maps

).

Eight distinct indicators and four criterion levels make up the system:

1.

Spatial Supply Layer (A): Describes the site’s physical base and level of development, including the floor area ratio (A1) and construction scale (A2).

2.

The internal spatial structure and service quality are described by the Functional Organization Layer (B). It consists of a public space efficacy score (B2), which is based on facility audits and behavioral observations, and a polycentricity index (B1), which is generated from space syntax analysis.

3.

Use Layer (C) Performance: Captures the true diversity and intensity of usage. This layer consists of a business format variety index (C2) and spatial activity intensity (C1), which combines visitor dwell time and density.

4.

Transportation Synergy Layer (D): Assesses the effectiveness of links to the larger urban system, including macro-regional accessibility (D2) and micro-transport connectivity (D1).

Min-max normalization (range standardization) was used to process all raw indicator data in order to remove dimensional effects and guarantee data comparability. The dataset was ready for the ensuing thorough integrated modeling thanks to this fundamental phase.

2.3. Model Building and Analytical Techniques

2.3.1. Thorough Vitality Assessment: Model of Entropy Weight-TOPSIS

This study uses a hybrid evaluation model that combines the entropy weight approach with the order of preference by similarity to ideal solution (TOPSIS) technique to objectively integrate the multidimensional indicators and compute the complete PSVI. In order to successfully mitigate any biases inherent in subjective weighing, this methodology first applies the entropy weight method to objectively assign weights based on the degree of dispersion (information entropy) of each indicator’s observed values. The Euclidean distance between each sample and the positive ideal solution—the best values for each indicator—and the negative ideal solution—the worst values—is then determined using the TOPSIS approach. The standardized PSVI score, which ranges from 0 to 1, is the resulting relative closeness degree (C i). Lastly, Jenks natural breaks optimization is used to classify the PSVI values into four vitality tiers: high, reasonably high, medium, and low. This allows for unambiguous spatial representation and comparison analysis.

2.3.2. Econometric Analysis of Influence Mechanisms at Multiple Levels

This work uses a progressive, multilevel econometric modeling approach to thoroughly analyze the intricate behavioral logic and systemic mechanisms driving vitality generation:

1.

Behavioral Decision Modeling at the Micro Level (Mixed Logit Model): The combined “dwell time–consumption” selections obtained from the questionnaire are used as the discrete dependent variable in a mixed logit model. Individual socioeconomic characteristics, functional mix, and spatial environment satisfaction scores are examples of independent factors. This model makes it possible to precisely quantify the marginal effects that different spatial and economic factors have on user behavioral decisions by accounting for unobserved variation in individual preferences. As a result, it makes it easier to pinpoint important operational and design factors that promote effective space usage.

2.

Macro-Level System Path Analysis (Structural Equation Modeling): A structural equation model (SEM) is created in order to empirically validate the fundamental theoretical pathway of “spatial form → functional organization → environmental perception → behavioral vitality.”

Using the previously identified indicators as observed variables, this model integrates hidden variables, namely Spatial Form, Functional Mix, Transport Accessibility, Environmental Perception, and Comprehensive Vitality. In order to accomplish two main goals, the model is fitted and validated using maximum likelihood estimation (MLE): (1) to measure the standardized coefficients of the influence paths between latent variables; and (2) to thoroughly examine the importance of Functional Mix and Environmental Perception as mediating variables in the pathway through which Spatial Form and Transport Accessibility influence Comprehensive Vitality.

2.4. Roadmap for Technology

The overarching research design is synthesized and visualized in a comprehensive technical roadmap (Figure 1). This framework outlines the entire analytical process, from gathering and processing data from several sources to building measurement and mechanistic models. It ensures the rigor, systematic coherence, and repeatability of this investigation by culminating in the formulation of vitality evaluation outcomes and the clarification of underlying influence mechanisms.

3. Results

The empirical results for the 20 commercial complex samples in Guangzhou are methodically presented in this section. The Public Space Vitality Index (PSVI) and the created multidimensional evaluation framework serve as the foundation for the analysis’s four main elements, which are development intensity, spatial structure, utilization efficacy, and economic vitality. It also thoroughly investigates the peculiarities of their spatial differentiation and the underlying mechanisms that underlie these patterns.

3.1. Development Intensity: Empirical Support for the “Density–Efficacy Paradox” and Zonal Differentiation

The commercial complexes in Guangzhou have a clear core–periphery zonal structure in terms of development intensity.

With an average construction scale of 335,200 m², the sample mean for the floor area ratio (FAR) is 6.50 (standard deviation = 2.17), suggesting a general pattern of high-intensity development. From the Tianhe CBD (mean FAR = 11.2) to the center urban transition areas (Yuexiu and Haizhu; FAR = 7.5–8.5) and suburban new towns (Baiyun and Panyu; FAR = 5.0–6.5), the FAR gradually decreases spatially until it reaches its lowest point in the outlying districts (Huadu; FAR ≈ 3.8). The traditional urban land rent gradient model is exactly in line with this spatial distribution.

The study’s main conclusion is that there is a significant “density–efficacy paradox.” The FAR and the public space efficacy index have a slight and statistically negligible positive association, according to Pearson correlation analysis (r = 0.32, p > 0.05).

This empirical finding strongly implies that high-quality, high-performing public spaces are not always the outcome of simply increasing physical development density. As a result, it emphasizes how important it is to move away from density-driven development paradigms and toward a focus on logical functional configuration and spatial form optimization.

3.2. Three Differentiation Typologies and Polycentric Networks in Spatial Structure

The average activity node index (α-index) for the sample set is 6.40, according to the space syntax analysis. This value is much greater than that of conventional, stand-alone commercial buildings, indicating that a polycentric network structure has become a common spatial feature of Guangzhou’s business complexes.

Three archetypal spatial structure typologies were found based on the spatial differentiation of the α-index (Figure 2):

1.

Core Composite Typology: Mostly found in developed urban centers like the districts of Tianhe and Yuexiu. These initiatives successfully create a comprehensive three-dimensional network by achieving a profound functional mix through vertical integration and horizontal extension. The Grandview Mall, for example, has a three-dimensional, stacked structure that combines retail, leisure, and cultural tourism (α > 8.0). Similarly, Yuexiu district complexes use elevated pedestrian skywalks (α = 6.8 ± 0.6) and underground commercial streets to methodically enhance multilevel connections.

2.

Emerging Hub Typology: These complexes, which are concentrated in areas like Baiyun and Panyu, are usually anchored by emerging commercial districts or transit hubs. Along the main pedestrian flow corridors, their spatial nodes are positioned in a sequential, linear arrangement. The Baiyun district’s transit-oriented development (TOD) projects have the highest index values (α > 10.0), whilst Panyu’s Wanbo commercial district has established a unique secondary network of functional centers (α = 8.3 ± 0.4).

3.

Transitional Primary Typology: Mostly seen in Liwan and Haizhu districts’ earlier projects or early-stage urban revitalization initiatives. These complexes have weak internode connection and a comparatively simple spatial organization. As a result, their polycentric networks (α = 3.6 ± 0.3) are still in the early stages of formation.

3.3. Overall Medium–High Level of Usage Efficacy with Structural Imbalances

The public spaces of Guangzhou’s commercial complexes have a moderately high overall efficacy, as indicated by the sample mean for the public space efficacy index (β) of 0.81 (±0.07). Nonetheless, there are notable internal structural abnormalities (

Table 10. Component Assessment of Public Space Efficacy (β Index) for Representative Commercial Complexes.

No.	Name/Influencing Factor	Composite Plaza	Green Ecology	Leisure/Social Interaction	Culture & Art	Children/Family	Comprehensive β Index
1	GBA Tech & Finance Center	0.10	0.10	0.15	0.25	0.30	0.90
2	Guangzhou Knowledge City	0.08	0.21	0.28	0.22	0.00	0.79
3	Teemall	0.31	0.21	0.28	0.00	0.00	0.80
4	Grandview Mall	0.32	0.24	0.28	0.00	0.00	0.84
5	Yuehai Financial Center (Plot B1-1)	0.23	0.16	0.21	0.00	0.33	0.93
6	Liwan Xinhe Plaza	0.22	0.15	0.21	0.00	0.00	0.58
7	TaiKoo Hui	0.11	0.23	0.06	0.12	0.21	0.73
8	Guangzhou IFC	0.31	0.11	0.17	0.00	0.17	0.76
9	Yuexiu Central Plaza	0.31	0.21	0.14	0.00	0.06	0.72
10	Liwan Link Plaza	0.18	0.23	0.34	0.18	0.00	0.93
11	Liwan Hey Plaza	0.21	0.20	0.23	0.20	0.12	0.96
12	R&F Haizhu City	0.32	0.24	0.21	0.14	0.00	0.91
13	Xinyicheng Plaza	0.24	0.21	0.23	0.00	0.18	0.86
14	Design Capital	0.22	0.15	0.21	0.13	0.11	0.82
15	Yuesheng Plaza	0.26	0.18	0.20	0.00	0.15	0.79
16	Wanda Plaza	0.21	0.23	0.19	0.00	0.00	0.63
17	Wanbo Aoyuan International Center	0.22	0.18	0.15	0.17	0.00	0.72
18	Haiyin Youyicheng	0.23	0.24	0.21	0.13	0.00	0.81
19	Teemall Outlet Park	0.21	0.22	0.19	0.00	0.17	0.79
20	Junyi Wanbang Plaza	0.18	0.21	0.18	0.05	0.17	0.77
	Citywide Average	0.21	0.19	0.20	0.11	0.14	0.81

, Figure 3). The main conclusions are summarized as follows:

Efficacy Components’ Advantages and Disadvantages: As the fundamental forces behind spatial vitality, basic functional areas like composite plazas and leisure/social interaction spaces often have higher efficacy (average ratings of 0.21 and 0.20, respectively). On the other hand, with an average efficacy score of just 0.11, cultural and creative areas show the greatest inadequacy. However, particular case studies show significant possibilities for focused development; for example, the Grandview Mall’s cultural and artistic area was 42% more effective once a museum was strategically added.

Severe Supply Demand Imbalance Among Important User Groups:

1.

Youth Cultural Demand Deficit: Questionnaire analysis targeting the core 18–35-year-old demographic reveals an intense demand for cultural and artistic experiences (scored at 0.27). When compared to the current average supply level (0.11), this results in a whopping 145% deficit, which is the most significant supply–demand mismatch in this research area.

2.

Lack of Functional Spaces Focused on Families: The average dwell time of family groups decreases by about 35% in complexes with severe deficiencies in children’s activity areas (β < 0.10). The possibility of conversion to consumption is directly reduced by this decline.

3.

“Uneven Development” in Functional Configuration: A lot of initiatives follow the pattern of “excellence in single metrics, but holistic imbalance.” The Yuehai Financial Center and TaiKoo Hui, for instance, are office-driven complexes that score highly on greening and recreational areas but completely lack cultural, artistic, and kid-friendly activities. As a result, their functional configurations continue to be out of step with the various, actualized needs of the larger user base.

3.4. Morphological Clustering and Gradient Patterns in the Association of Economic Vitality

The 20 examples were divided into four different spatial morphological typologies using a K-means cluster analysis. These typologies show a strong link with the public space vitality index (PSVI) (Figure 4,

Table 11. Examination of the Association between Economic Vitality and Spatial Morphology Clustering.

Morphological Type	Proportion	Spatial Characteristics	Typical Distribution	Average PSVI	Vitality Level
Low-rise Enclosed	19.10%	Indoor-outdoor permeability, strong sense of agglomeration	Tianhe-Yuexiu-Haizhu Core Belt	0.85	High Vitality
High-rise Enclosed	6.33%	Vertical mix, multi-level connectivity	Urban Core Areas	0.82	Relatively High Vitality
Linear Sequential	35.20%	Layout along corridors, efficiency-oriented	Emerging Hub Areas	0.76	Medium-High Vitality
Single-core Agglomerated	39.37%	Simple structure, single function	Transitional Development Areas	0.61	Medium-Low Vitality

).

1.

Low-rise Enclosed and High-Rise Enclosed Typologies: These designs, which are primarily found in urban core locations, have the highest average PSVI scores (0.85 and 0.82, respectively), making them high-vitality zones. Positive “eyes on the street” effects and strong opportunities for social engagement are greatly aided by their spatial features, particularly indoor–outdoor permeability and vertical functional mixing.

2.

Linear Sequential Typology: This layout, which is efficiency-focused and has an average PSVI of 0.76, indicating medium–high vitality, is frequently found in developing hub regions.

3.

Single-Core Agglomerated Typology: This typology has the lowest average PSVI (0.61, indicating medium–low vitality) and is concentrated in transitional development zones. Its mono-functional features and straightforward structural design naturally limit the creation of spatial liveliness.

A clear vitality gradient appears throughout this study area based on the PSVI’s spatial distribution:

Tianhe district (driven by commercial agglomeration effects) and Panyu district (driven by a dual culture tourism and commerce concept) are the anchors of Tier 1 (PSVI ≥ 0.80).

Tier 2 (0.60 ≤ PSVI < 0.80): Consists of a few developments spread around the districts of Yuexiu, Haizhu, and Baiyun.

Tier 3 (PSVI < 0.60): Located in districts like Liwan and Huadu, these projects are primarily traditional or monofunctional.

Differences in morphological efficiency are further explained by analyzing building density (ε). Through high building density, high-vitality developments (such as the Guangzhou International Finance Center, ε = 66.7%; Panyu Wanda Plaza, ε = 67.2%) successfully accomplish spatial intensification and vitality agglomeration. On the other hand, land utilization in low-vitality projects (like Baiyun Yuesheng Plaza, ε = 16.8%) is noticeably inefficient. Together, our results show that by arranging pedestrian flows and influencing user experiences, spatial morphology both directly contributes to and significantly impacts the production mechanisms of economic vitality. During the stock regeneration stage of urban development, this produces solid empirical evidence to direct targeted spatial optimization.

4. Discussion

This part seeks to clarify the generative mechanisms of public space vitality and pinpoint important structural inconsistencies, building on the previous empirical findings. We suggest a tiered optimization approach that is adapted to the larger urban spatial pattern using structural equation modeling (SEM) and systematic analysis.

4.1. Verification of Vitality Driving Mechanisms and Determination of Important Discrepancies

This study reveals the tripartite driving factors and underlying structural conflicts that govern the creation of public space vitality within Guangzhou’s commercial complexes by combining SEM and multiple regression analyses.

4.1.1. Multidimensional Driving Mechanisms: Quantitative Validation

With a strong model fit (CFI = 0.93, RMSEA = 0.048), the SEM analysis confirmed the intricate mechanisms by which public space efficacy affects commercial success.

The findings show a key threshold effect and two complementing driving routes (Figure 5, Table 12):

1.

Direct Functional Driving Route: Commercial performance is directly and significantly improved by composite plaza areas, which serve as essential social and activity hubs (standardized path coefficient γ = 0.682, p < 0.001). This pathway contributes almost 60.3% of the overall effect, highlighting the essential function of physical environment as a “activity container.”

2.

Indirect Cultural Capital Conversion Pathway: The main method that cultural and creative places contribute is through indirect effects, which produce a substantial mediating influence that makes up 39.7% of the overall impact. These areas indirectly increase economic value by developing a distinctive brand image, strengthening location identification, and encouraging client loyalty. This provides empirical support for the strategic necessity and conversion value of “cultural capital” in urban commercial settings.

3.

Nonlinear Efficacy Conversion Threshold: According to the analysis, there is a strong nonlinear increase in the marginal benefit on foot traffic and consumption conversion after the public space efficacy index (β) exceeds a key threshold of 0.75. This result demonstrates that attaining “sufficiently high” spatial efficacy is a crucial prerequisite for unlocking exponential economic rewards and provides a precise quantitative standard for spatial quality optimization.

Figure 5. Model of the Driving Path from Public Space Efficiency to Commercial Performance. *** p < 0.001|Dual pathways: functional (γ=0.682) & cultural (39.7%), ↑ Threshold > 0.75.

Figure 5. Model of the Driving Path from Public Space Efficiency to Commercial Performance. *** p < 0.001|Dual pathways: functional (γ=0.682) & cultural (39.7%), ↑ Threshold > 0.75.

Table 12. Examination of Driving Routes from Commercial Performance to Spatial Efficacy.

Driving Pathway Type	Typical Space Type	Path Coefficient (γ)	Effect (%)	Mechanism of Action
Direct Functional Driving	Composite Plaza	γ = 0.682 ***	60.3	Core social hub
Indirect Cultural Conversion	Cultural & Artistic	Significant	39.7	Brand image shaping
Efficacy Conversion Threshold Effect	Comprehensive	β > 0.75	-	Nonlinear growth.

Note: *** indicates p < 0.001.

Table 12. Examination of Driving Routes from Commercial Performance to Spatial Efficacy.

Driving Pathway Type	Typical Space Type	Path Coefficient (γ)	Effect (%)	Mechanism of Action
Direct Functional Driving	Composite Plaza	γ = 0.682 ***	60.3	Core social hub
Indirect Cultural Conversion	Cultural & Artistic	Significant	39.7	Brand image shaping
Efficacy Conversion Threshold Effect	Comprehensive	β > 0.75	-	Nonlinear growth.

Note: *** indicates p < 0.001.

4.1.2. Identifying Fundamental Structural Inconsistencies

1.

Youth Cultural Consumption’s Supply-Side Deficit: The core 18–35 group has a demand intensity of 0.27 for cultural and artistic events, which is a huge 145% deficiency when contrasted to the existing average supply level (0.11), as the efficacy analysis makes clear. The functional layout of the majority of current projects, which are still mostly dominated by homogenized retail, contrasts sharply with this demographic’s strong demand for experiential consumption—such as “cultural check-ins” and “artistic socializing.”

2.

Underperformance of Climate-Responsive Design: The assessed contribution of current green areas to thermal comfort is just 0.15, given Guangzhou’s typical hot and humid subtropical climate (average annual temperature of 22 °C and relative humidity of 78%). This is much less than the theoretical potential value of 0.22. This disparity reveals a crucial gap between aesthetic form and environmental performance, since a significant percentage of “passive” or solely decorative landscape designs are ineffective at reducing summer heat stress.

3.

Functional Mix: Breadth at the Expense of Depth: The homogeneity index for particular subcategories is still disproportionately high (θ = 6.3 ± 1.1), despite the sampled complexes performing well in terms of the richness of major business categories (φ = 4.2 ± 0.6). Additionally, the percentage of deep-experiential and cultural formats—which are essential for successfully increasing visitor dwell time—is typically less than 20%. This illustrates how present functional configurations lack depth of experience and distinctiveness on a systemic level (

Table 13. Comparison of Commercial Complex Spatial Vitality Features by District.

District	Typical Projects	Avge. Dwell (h)	Daily Footfall (104)	Rent ¥/m2/Day	Night-Time Consumption(%)	Core Drivers
Tianhe	TaiKoo Hui, Grandview	≥3.0	15–25	30–90	45	Business, metro hub
Panyu	Teemall, Chimelong	≥2.5	12–18	15–35	40	Tourism flow
Yuexiut	Central Plaza	≤2.0	10–15	20–50	38	Cultural IPs
Haizhu	Xinyicheng Plaza	≤2.0	8–12	15–40	35	Family
Baiyun	Design Capital, Yuesheng Plaza	≥2.0	5–8	15–25	25	TOD hub
Liwan	Link Plaza, Hey Plaza	≤2.0	6–10	10–30	30	Cultural IPs
Huangpu	Wanda Plaza	≤2.0	4–7	10–20	20	Family
Huadu	Outlet Park	≤2.0	3–6	8–15	18	Airport economy

).

4.2. Spatial Differentiation-Based Tiered Optimization Strategy System

This study suggests a three-tiered, coordinated strategy framework—macro-regional direction, meso-typological optimization, and micro-efficacy enhancement—to successfully handle the aforementioned driving factors and structural contradictions. This multiscalar strategy is clearly in line with the general commercial spatial pattern of gradient diffusion and core polarization in Guangzhou.

4.2.1. Macro-Level: Control of Differentiated Zonal Development

Each region’s unique developmental stages and fundamental structural issues must be taken into consideration when implementing targeted planning controls and policy interventions (Table 14).

1.

Core Polarization Zones (such as Tianhe and Yuexiu core areas): Overdevelopment and excessive traffic load demands are the main sources of conflict in these locations. The developmental strategy has to shift from incremental expansion to functional replacement and stock quality improvement. Enforcing strict floor area ratio (FAR) caps (e.g., FAR ≤ 12.0) and implementing transferable development rights (TDRs) to encourage the conversion of construction intensity into superior public spaces and cultural amenities are two ways to operationalize this transformation.

2.

Possible Improvement Zones (such as developing regions in Baiyun and Panyu): Functional homogenization and a clear lack of spatial identity are the main issues in these zones. Land-grant requirements that require a minimum threshold (≥30%) for unique theme forms (e.g., family-oriented, cultural-creative, or sports-centric spaces) should be incorporated into interventions. Targeted fiscal subsidies should be added to these regulations in order to actively direct differential development.

3.

Peripheral Transition Zones, such as the periphery of Nansha and Huadu: Chronic inadequacies in vitality and inefficient spatial sprawl are characteristics of these areas. To stop disorderly expansion, TDR methods must be applied in concert with strategic industry-introduction agreements. In order to stimulate commercial vitality through industrial integration, planners must simultaneously proactively establish unique anchor roles, such as exhibition-trade centers and hubs for technology innovation.

Table 14. Differentiated Zonal Development Control Strategies.

Zone Type	Representative Areas	Core Problems	Control Strategy	Policy Tools
Core Polarization Zone	Core areas of Tianhe, Yuexiu	Over-development, traffic congestion	FAR cap control (FAR ≤ 12.0)	TDR, Functional replacement incentives
Potential Enhancement Zone	Emerging areas of Baiyun, Panyu	Functional homogenization, lack of distinctiveness	Thematic format ratio requirement (≥30%)	Land grant condition binding, Fiscal subsidies
Peripheral Transition Zone	Outer areas of Huadu, Nansha	Inefficient sprawl, low vitality	TDR + Functional implantation	TDR mechanism, Industry-introduction agreements

Table 14. Differentiated Zonal Development Control Strategies.

Zone Type	Representative Areas	Core Problems	Control Strategy	Policy Tools
Core Polarization Zone	Core areas of Tianhe, Yuexiu	Over-development, traffic congestion	FAR cap control (FAR ≤ 12.0)	TDR, Functional replacement incentives
Potential Enhancement Zone	Emerging areas of Baiyun, Panyu	Functional homogenization, lack of distinctiveness	Thematic format ratio requirement (≥30%)	Land grant condition binding, Fiscal subsidies
Peripheral Transition Zone	Outer areas of Huadu, Nansha	Inefficient sprawl, low vitality	TDR + Functional implantation	TDR mechanism, Industry-introduction agreements

4.2.2. Meso-Level: Enhancing Land Use and Transportation Cooperation

Deepening TOD Standards: Stricter coordination indicators must be put in place to guarantee true synergy between commercial operations and transportation hubs. For example, if a commercial development next to a transit station has a floor area ratio (FAR) of at least 4.0, it must also meet the following requirements: a road network density of at least 8.0 km/km², a land-use functional mix (entropy value) of at least 0.7, and the availability of at least three seamless pedestrian access points that connect directly to the station.

Accurate Injection of Functions with Regional Characteristics:

Promote the combination of “commerce + R&D + exhibition” by setting up specialized professional venues including shared laboratories, technology showrooms, and pitch centers in the Innovation Industry Zones (Huangpu and Nansha districts).

Historical and Cultural Zones (Liwan and Yuexiu districts): Promote “local cultural translation” by using modern design languages to adapt historic features, such Xiguan houses and Lingnan gardens, into immersive consuming environments.

Create an integrated ecosystem with “themed attractions, distinctive commerce, and resort accommodation” for Cultural Tourism and Leisure Zones (Panyu and Huadu districts) in order to efficiently extend the consumption chain.

4.2.3. Micro-Level: Accurate Improvement of Spatial Effectiveness

Three feasible micro-level optimization projects are suggested to address the structural flaws that have been precisely discovered (

Table 15. Precise Micro-Level Spatial Efficacy Enhancement Initiatives.

Optimization Focus	Current Problem	Target Value	Key Technical Measures	Expected Benefits
Cultural Capital Conversion	Low cultural space efficacy (0.11)	Increase to 0.15–0.20	Art curation, IP implantation, digital exhibitions	Footfall increase 15–25%,Brand premium 20–30%
Family-Friendly Ecosystem Construction	Insufficient child-friendly spaces, short dwell time	Extend dwell time by 30–50%	Child-friendly facilities, parent rest areas, related format layout	Family consumption increase 25–40%
Climate-Responsive Design	Low thermal comfort contribution (0.15)	Improve by 1–2 levels	Vertical greening, evaporative cooling, wind-guiding design	Summer perceived temperature reduction 1.5–2.5 °C

):

Cultural Capital Conversion Initiative: By implementing strategic interventions like art curation, local intellectual property (IP) implantation, and digital interactive exhibitions, this initiative seeks to raise the severely low efficacy of cultural spaces (0.11) to 0.15–0.20. A 20–30% rise in brand premium and a 15–25% increase in pedestrian foot traffic are anticipated results.

Family-Friendly Ecosystem Construction Initiative: This initiative calls for the systematic arrangement of safe play areas, parent–child rooms, designated rest and observation areas, and cooperative retail and food and beverage (F&B) establishments in order to address the lack of kid-friendly spaces and increase family dwell times. The goal is to enhance family dwell time by 30–50% in order to boost associated consumption by 25–40%.

Initiative for Proactive Climate-Responsive Design: Urban design must go beyond just cosmetic greening in order to enhance thermal comfort in Guangzhou’s hot and muggy climate. Strict promotion of active environmental control technologies is necessary, such as wind-guiding wall designs, evaporative mist cooling, and vertical greening. The goal is to lower the summertime perceived outdoor temperature by 1.5–2.5 °C, greatly improving public spaces’ environmental appeal.

5. Conclusions

In order to integrate geographical supply, spatial performance, and system connectivity, this study develops and uses a multidimensional analytical framework. It analyzes the vitality qualities and clarifies the generative mechanisms of public spaces in Guangzhou’s commercial complexes using a mixed-methods approach that blends an entropy-weight TOPSIS model with structural equation modeling (SEM). Below are specific findings, policy implications, study limitations, and future research directions.

5.1. Principal Findings and Scholarly Input

Three interconnected aspects summarize the main conclusions of this study, which together contribute to the theoretical understanding of commercial spatial vitality in high-density urban environments:

Empirical Disclosure of the “Density–Efficacy Paradox” and Reexamination of Spatial Patterns: This study verifies that there is a clear core–periphery gradient in the development intensity of Guangzhou’s commercial complexes. However, the econometric study reveals a crucial paradox: the Public Space Efficacy Index (β) and the floor area ratio (FAR) have only a weak and statistically negligible positive connection (r = 0.32, p > 0.05). The linear assumption that high-intensity development inevitably ensures high vitality is empirically challenged by this. As a result, it indicates that the developmental logic of urban commercial spaces urgently has to change from depending on the “scale dividends” of incremental expansion to seeking the “quality dividends” of stock enhancement through structural optimization.

Quantitative Diagnosis of Supply Demand Deficits and Structural Imbalances: This study achieves a rigorous quantitative evaluation of spatial social performance by operationalizing the Public Space Efficacy Index (β). The findings show that acute structural supply-demand imbalances continue even when overall efficacy is reasonably good (β = 0.81 ± 0.07). Most notably, the core youth population (18–35 years old) has a demand intensity of 0.27 for cultural and creative places, which is significantly higher than the present supply level of 0.11 and results in a 145% gap. Additionally, the study provides a precise quantitative benchmark for focused quality interventions by identifying a critical threshold (β > 0.75) at which spatial efficacy starts to exert a significant nonlinear impact on economic returns.

Empirical Validation of Dual-Pathway Vitality Generation and Cultural Capital Conversion: A composite mechanism for vitality generation is outlined by the SEM analysis. The physical basis of vitality is the “direct functional driving” pathway (anchored by composite plaza spaces, γ = 0.682). Concurrently, value leapfrogging is sparked by the “indirect cultural capital conversion” pathway, which is mediated by cultural and artistic places and accounts for 39.7% of the mediating effect. This provides a solid theoretical foundation for transforming commercial complexes from uniform “consumption containers” into highly distinctive “cultural destinations.” It also empirically supports the enormous conversion potential of “cultural capital” within commercial spaces.

5.2. Policy and Practical Implications

Based on these findings, this research suggests three forward-thinking approaches to the management and enhancement of commercial areas in densely populated areas:

A Change in Planning Control Paradigm: From “Intensity Indicators” to “Performance Covenants”: Conventional planning frameworks depend too heavily on intensity indicators like building density and FAR. Instead, planners could incorporate minimum functional ratios (such as cultural spaces ≥ 15%) and public space performance indicators (such as the β index) directly into land-grant conditions or urban renewal agreements, so creating “performance covenants.” Transferable development rights (TDR) mechanisms can be used in core regions to achieve comprehensive territorial enhancement by directly connecting development intensity to improvements in public space quality.

Synergistic Upgrades in Urban Design: Building an Integrated “Form–Function–Transportation–Climate” Framework: To stress integrated systemic synergy, architectural design must go beyond simple morphological development. High-permeability road networks (density ≥ 8.0 km/km²), a high functional mix (entropy value ≥ 0.7), seamless multimodal transit integration, and proactive climate-responsive design (e.g., vertical greening, wind channeling, and advanced shading) should all be required by design standards linked to high development intensity (e.g., FAR ≥ 4.0). This is especially important for maintaining year-round spatial vibrancy and reducing Guangzhou’s hot and muggy climate.

Fundamental Renewal and Remodeling of Operations: From “Space Leasing” to “Scene Operation” and “Cultural Value-Added”: Initiatives for stock regeneration must switch from upgrading hardware to curating experiential content. To incentivize operators to incorporate art curation and intangible cultural asset revival, policymakers should implement “cultural value-added” incentive systems (such as FAR bonuses or specialized funding). Concurrently, the methodical development of a “child-friendly–family rest–associated consumption” ecology can significantly release latent consumption potential by extending the stay time of high-yield family demographics by 30–50%.

5.3. Research Restrictions and Prospects for the Future

Although this study provides valuable insights, it recognizes some methodological and data constraints that set the direction for future research:

Theoretical Expansion: Combining Spatial Configuration Theory with Multisensory Environmental Perception: To more accurately examine pedestrian movement and interaction patterns via the lenses of spatial topology and visual connectivity, future research should integrate space syntax and visual graph theory. Additionally, expanding quantitative assessments to include multisensory environmental factors (such as lighting, thermal comfort, and acoustics) will fully reveal the integrated influence of environmental perception on spatial vitality.

Methodological Innovation: Building a Multisource Big Data Dynamic Monitoring Platform: Future studies should incorporate mobile signaling data, Wi-Fi probes, user-generated content (UGC) from social media, and Internet of Things (IoT) sensor networks to overcome the static constraints present in cross-sectional data. The development of a dynamic, long-term monitoring database will be made easier by this integration. This strategy will allow a methodological jump from static evaluation to dynamic process simulation and predictive policy scenario forecasting when paired with cutting-edge tools like agent-based modeling (ABM).

Realistic Extension: Examining Universal Standards for Typified Public Areas: The study’s analytical approach can be modified and used for other types of urban public spaces, like waterfront promenades, historic districts, and important transit hubs. Evaluating its explanatory capacity in various scenarios can help create standards for vitality assessment that are applicable to all situations. In the end, this will offer methodical, empirically supported, and extremely practical strategies for fostering more inclusive, resilient, and spatially just urban living environments.