Urban Physical Examination and Hypernetwork Analysis for Shenzhen, China: A Livability-Driven Sustainable Development Study

Abstract

Rapid global urbanization has intensified the need for cities to transition from growth-oriented models to sustainable development frameworks that prioritize livability, environmental quality, and social equity, positioning urban physical examination as an essential methodology for guiding this transformation. This study analyzes the spatial–temporal evolution of Shenzhen’s sustainable urban transformation from 2020 to 2024, employing urban physical examination methodologies combined with hypernetwork analysis to evaluate livability enhancement strategies. The research develops an economic vitality index incorporating urban points of interest density, Habitat Environment Index, and land surface temperature. Through spatial optimization analysis and hypernetwork modeling, the study examines the evolution of Shenzhen’s economic vitality and sustainable development patterns, with a particular focus on the impacts of economic centralization on regional sustainability and habitability. Results show Shenzhen’s economic vitality index increased by 10.47% from 2020 to 2024. However, regional disparities persist, with western and central regions displaying higher vitality than eastern coastal areas. The hypernetwork analysis reveals clustering patterns in livable spaces, with connectivity indicators ranging from 3.75 to 3.86. The uneven distribution of public facilities in Longgang and Yantian Districts highlights the need for improved resource allocation. These findings provide evidence-based support for sustainable urban space strategies in rapidly developing cities, emphasizing the importance of equitable resource allocation and community-centered planning approaches.

1. Introduction

The pursuit of livable cities through rational land use and sustainable urban space development has become paramount in contemporary urban planning discourse. As global urbanization accelerates, cities worldwide face the dual challenge of accommodating growing populations while maintaining environmental quality and social equity [1,2]. Shenzhen, as China’s pioneering reform city and innovation hub, exemplifies both the opportunities and challenges inherent in rapid urban transformation [3]. The city’s four-decade evolution from a fishing village to a global metropolis presents valuable insights into sustainable urban development strategies and rational land use practices. The concept of rational land use encompasses the efficient allocation of urban space to maximize community benefits while minimizing environmental impacts [4]. This approach recognizes that sustainable urban spaces must balance economic development with social equity and environmental stewardship. Urban physical examination emerges as a critical tool for assessing the effectiveness of land use policies and identifying opportunities for creating more livable communities [5]. By systematically evaluating various urban systems and functions, this methodology provides evidence-based insights for optimizing spatial configurations and enhancing community sustainability. The challenge of creating truly livable cities extends beyond traditional economic metrics to encompass comprehensive quality of life indicators [6]. In this context, the application of the economic vitality Index (EVI) has gained importance. The EVI scoring system not only considers traditional GDP growth but also incorporates multiple dimensions, including employment rates, innovation capacity and industrial structure. Historically, urban development models have overemphasized economic growth at the expense of comprehensive and coordinated local development. The introduction of hypernetworks offers a novel analytical perspective to better understand and address these complex issues [7]. Through examining interactions across various spatial scales and functional domains, hypernetwork approaches reveal the underlying mechanisms that contribute to sustainable urban development [8]. Central to this study is the innovative integration of Hypernetwork Analysis with data from Urban Physical Examinations, which forms a three-tiered framework—multi-source data fusion, spatial network modeling, and community validation feedback—enabling a deeper understanding of urban dynamics and providing a promising, replicable methodology for evidence-based urban planning in dynamic environments. This study addresses the critical question of how cities can achieve sustainable development through rational land use while enhancing community economic vitality and promoting equitable access to urban amenities. The research contributes to the growing body of knowledge on sustainable urban space planning by demonstrating how advanced analytical methods can inform evidence-based policy decisions for creating more livable cities.

2. Review of Related Research

2.1. The Application of Urban Physical Examination in Cities

The urban physical examination, as a systematic method for assessing urban health, is theoretically rooted in the concept of healthy cities, introduced by the World Health Organization in 1986. With the growing influence of sustainable development, the scope of urban health check-ups has expanded to encompass the broader concept of urban health and well-being. This framework emphasizes that urban physical examinations should prioritize both the physical and mental health of residents, alongside their quality of life. To achieve this, urban physical examinations typically span multiple dimensions, including economic, social, and environmental factors, each with distinct weights and development indicators [9,10,11]. These factors include infrastructure, public services; environmental quality in neighborhoods, and residents’ overall quality of life, including housing quality and satisfaction [12]. Several key trends have emerged in urban physical examinations as urbanization deepens and technological advancements accelerate. The evaluation of a city’s health requires interdisciplinary integration, drawing from urban planning, ecology, sociology and public health [13]. The concept of digital twins for cities, made possible through virtual city models, allows for real-time monitoring and predictive simulations of actual urban conditions [14]. Moreover, to enhance the precision of urban physical examinations, innovative methods such as human-scale urban morphology evaluation, which integrates street view imagery, Points of Interest (POI) data and traditional statistical data, have been introduced [15]. Additionally, with the advancement of Internet of Things (IoT) and 5G technologies, it will soon be possible to develop real-time monitoring platforms powered by a city’s brain [16]. Furthermore, utilizing high-resolution remote sensing and street view imagery, urban physical examinations are expected to provide multi-scale assessments from macro to micro-levels [17]. Another critical area for future exploration is the use of scientific methods in urban data collection, which will enhance public understanding and participation in the urban audit process [18]. Urban studies are increasingly moving towards multi-source data integration, high-precision spatial analysis, and intelligent assessments.

2.2. The Economic Vitality Assessment

Economic vitality is a crucial indicator of urban development. The methods for assessing economic vitality have long been a subject of academic inquiry [19,20]. Traditional approaches primarily focus on macroeconomic indicators such as GDP and population growth. However, relying on a single metric is no longer sufficient to fully capture a city’s economic vitality, given the growing complexity of urban economic systems [21]. In recent years, scholars have sought more comprehensive and multidimensional approaches to assess economic vitality. Night light data, which are strongly correlated with economic activity intensity, have emerged as an effective proxy [22]. By combining night light data with land use and population distribution, a multidimensional model for evaluating urban economic vitality was developed [23]. Furthermore, integrating POI data and social media activity has allowed for the construction of an Economic Vitality Index that reflects urban functional diversity and human activity intensity [24,25,26]. Moreover, the dynamic coupling relationship between economic vitality and urban network structure has been explored from a complex systems perspective. Despite significant advancements in these assessment methods, challenges remain. These include the integration of heterogeneous multi-source data, balancing quantitative and qualitative approaches, and accounting for spatio-temporal dynamics in urban systemscite.

2.3. Hypernetwork Analysis in Urban Planning and Spatial Optimization

In urban studies, hypernetwork theory is a cutting-edge approach to the study of complex systems and has garnered significant attention and applications in recent years. C. Berge pioneered the foundation of this theory by introducing the concept of hypergraphs [27], which later evolved into a higher-order network framework, enabling hypernetworks to capture more accurately the multidimensional relationships within complex systems [28]. Hypernetwork theory was initially applied to analyze the spatial structure of cities, aiming to uncover the intricate interconnections between various urban elements in urban studies [29]. Urban resilience and adaptability assessment benefits significantly from hypernetwork modeling approaches that integrate natural, social, and built environment subsystems [30]. Smart city development evaluation increasingly employs hypernetwork frameworks to assess the integration of physical infrastructure, information systems, and community networks [31]. These applications demonstrate the potential of hypernetwork analysis for supporting comprehensive urban planning and sustainable development strategies. The advantages of hypernetwork theory include its capacity for integrating heterogeneous data sources and analyzing synergistic effects between different urban components. This approach provides valuable insights for optimizing spatial configurations, enhancing community connectivity, and promoting sustainable urban development patterns that contribute to overall livability.

2.4. Limitations of Existing Research

Despite extensive research in hypernetwork analysis, existing literature still contains several critical gaps. First, most urban livability assessment studies are limited to single-dimensional analysis, lacking comprehensive integration of environmental quality, social equity, and spatial accessibility [9,10]. Second, traditional land use assessment methods primarily rely on static indicators, making it difficult to capture the dynamic processes and spatial evolution characteristics of urban development [13]. Furthermore, existing hypernetwork analysis applications are largely concentrated on theoretical construction, with relatively limited application cases in urban practice, particularly in community-scale livability assessment [28]. Existing research also faces challenges in data integration. Most studies rely on single data sources or traditional statistical data, lacking effective integration of multi-source heterogeneous data [15,17]. Meanwhile, in terms of spatial analysis precision, there exists a disconnect between macro-scale analysis and micro-level community needs, making it difficult to provide precise guidance for specific community planning [18], leading to gaps between research results and actual community needs. This study responds to the critical need for integrated models that go beyond traditional economic analysis by filling these gaps through a methodology that connects spatial vitality relationships at the network level (hypernetwork analysis) with ground-level validation data at the community level (urban physical examination). Based on the deficiencies in existing research, this study constructs a comprehensive urban livability assessment framework, aiming to address the above-mentioned shortcomings through multidimensional integration and dynamic analysis. The core innovation of this framework lies in combining urban physical examination methods with hypernetwork analysis, forming a three-tiered progressive analytical system of multi-source data fusion–spatial network modeling–community validation feedback. First, at the data level, this study integrates remote sensing imagery, points of interest data, nighttime light data, and field survey data, constructing a multidimensional evaluation indicator system covering environmental quality, spatial functions, and social activities [32,33]. This multi-source data fusion method can more comprehensively reflect the complex connotations of urban livability, overcoming the limitations of single-indicator assessment. Second, at the methodological level, this study innovatively applies hypernetwork theory to urban spatial analysis, depicting complex associations between communities through constructing node-hyperedge relationship models [34]. This method not only identifies the spatial distribution of high-livability areas but also reveals functional dependencies and synergistic effects between different communities, providing a network perspective for rational land use planning. Finally, at the validation level, this study ensures the practicality and operability of research results through in-depth community field surveys and stakeholder feedback. Through comparative analysis of the development model differences between Longgang District and Yantian District, this study can identify livability improvement strategies under different development paths, providing replicable experiences for other similar cities (Figure 1).

3. Study Area and Methods

3.1. Study Area

This study examines Shenzhen from 2020 to 2024, focusing on the Yantian and Longgang districts (Figure 2). Shenzhen holds a unique position in China’s urbanization as a pioneer of reform and opening-up, exemplifying rapid urban and industrial development. Yantian features rich cultural and tourism resources, covering about 72 km²; with a population of roughly 243,600, featuring a territorial structure dominated by coastal ports, hilly terrain, and urban-rural interfaces. while Longgang serves as an industrial and technological hub, encompassing around 388 km²; and home to over 4 million people, with a territorial structure that includes expansive industrial zones, suburban expansions, and emerging tech parks. The building fabric across Shenzhen shows varying consistency: central and western areas (including parts of Longgang) exhibit modern high-rise consistency with glass-and-steel skyscrapers and planned residential complexes, while eastern districts like Yantian retain a mix of older low-rise structures from its fishing village origins alongside newer developments, leading to heterogeneous urban morphology influenced by rapid industrialization. Analyzing these districts enables understanding of Shenzhen’s urban infrastructure and environmental management. The high transparency of local community agencies in data sharing provide robust support for this research.

3.2. Data Sources

The data sources for this research include remote sensing imagery, online datasets, and field surveys. The remote sensing data primarily comprises Landsat 8 imagery and nighttime light data, spanning the period from 2020 to 2024. The Landsat 8 data (LC08L2SP121044 02T1) were obtained from the United States Geological Survey (https://www.usgs.gov/, accessed on 30 April 2024). The night light data utilized in this study originates from the National Oceanic and Atmospheric Administration’s Global Vcmslcfg V2 C. Average dataset (https://eogdata.mines.edu/products/vnl/, accessed on 30 April 2024). Data processing was conducted using QGIS software (version 3.26.2—Buenos Aires), employing techniques such as clipping, raster calculations, and resampling. The urban POI data covers key sectors, including residential, service, commercial, educational, and municipal facilities. This dataset was collected in October 2019 via the Amap Open Platform (https://lbs.amap.com/api/loca-v2/intro, accessed on 10 December 2019). After data cleaning and the elimination of duplicates, 900,000 data points were retained. The urban physical examination, conducted between May and August 2024, focused on community, residential, and housing aspects within the neighborhoods of Shatoujiao Street in Yantian District and Bantian Street in Longgang District (Figure 3). The collected data encompasses building configurations, environmental quality, property management, safety performance, functionality, and intelligent upgrades This information reflects the quality of Shenzhen’s urban living environment, the effectiveness of community governance, the standard of property services, the safety and functionality of housing, and the advancements in smart city development.

This is accomplished through the use of QGIS software for spatial data preprocessing, plane mapping, and attribute enhancement. Ultimately, all data undergo preprocessing and standardization to ensure accuracy and comparability.

3.3. Research Methods

The methods utilized in this study are categorized into three distinct phases. The first phase entails data preprocessing and classification. In the second phase, the filtered data is identified and categorized. Core regions are extracted, and a hypernetwork is subsequently constructed based on this foundation. The final stage involves spatial analysis and evaluation of the constructed hypernetwork.

3.3.1. Data Preprocessing

Sustainable urban development can be comprehensively evaluated across several dimensions, including the ecological environment, quality of life, and economic vitality. To accurately measure these facets, this study selects Shenzhen’s Green Space Index (GSI), Human Settlements Index (HSI) and POI to reflect the city’s ecological and environmental quality, assess the livability of residential areas, and gauge regional economic development. The selection of these indicators was guided by their complementary roles in capturing multifaceted urban vitality: POI density serves as a proxy for economic activity and functional diversity (e.g., commercial and service hubs); GSI quantifies ecological quality through vegetation coverage; HSI evaluates human-environment interactions for livability; and LST measures thermal stress from urbanization, which can impact sustainability. These were chosen based on data availability, spatial relevance to Shenzhen’s rapid development context, and alignment with established literature [24,33]. These indicators collectively illustrate the overall level of sustainable urban development and the potential Economic Vitality Index (EVI). Concurrently, a detailed analysis of the current situation in urban regions will be conducted alongside a physical examination of the urban environment. The study aims to identify existing problems and challenges in regional development while proposing targeted recommendations and strategies for improvement (

Table 1. Urban multivariate data preprocessing.

Name	Formula	Description
Green Space Index	$$GSI={\displaystyle \frac{NDVI-NDV{I}_{MIN}}{NDV{I}_{MAX}-NDV{I}_{MIN}}}$$ (1)	The $NDVI$ is calculated using the infrared and visible light bands of remote sensing imagery. $NDV{I}_{MIN}$ is the minimum NDVI value for locations with the least vegetation coverage. $NDV{I}_{MAX}$ is the maximum $NDVI$ value for locations with the densest vegetation. where higher values indicate greater vegetation coverage [35].
Human Settlements Index	$$HSI={\displaystyle \frac{1-N{D}_{MAX}+V_I}{1-V_I+N{D}_{MAX}+V_I·N{D}_{MAX}}}$$ (2)	$N{D}_{MAX}$ is $NDV{I}_{MAX}$, the area with the densest vegetation coverage. $V_I$ indicates night light intensity, which is used to assess the density of human activities [36].
POI Kernel Density	$${\int }_{poi}\left(x\right)={\displaystyle \frac{1}{P_{oi}·h}}\sum _{i=1}^{P_{oi}}K\left({\displaystyle \frac{x-X_i}{h}}\right)$$ (3)	K is the kernel function, h is the search radius, and x refers to the location of POI. $X_i$ represents the specific spatial positions of POI, with $X_i$ forming a set of locations centered around the search radius. POI represents the number of sample points [37].
land surface temperature	$$LST={\displaystyle \frac{BT}{1+\lambda ·\frac{BT}{C_2}}}·ln\left(E\right)$$ (4)	$BT$ is Top of Atmosphere Brightness Temperature. $\lambda$ represents Wavelength of emitted radiance. E is Land Surface Emissivity. $C_2$ is 14,388 uk. h is Plank’s constant. s represents Boltzmann’s constant. c is Velocity of light [38].
Economic Vitality Index	$$EVI=PO{I}_{Density}+GSI+HIS+LST$$ (5)	$PO{I}_{Density}$ is the density of POI. $LST$ is used to assess the thermal conditions of the land, influencing vegetation health [39].
Data Standardization	$$X^{\prime }={\displaystyle \frac{X-\mu }{\sigma }}$$ (6)	X is the original data, $\mu$ is the mean, and $\sigma$ is the standard deviation [40].

).

The GSI serves as a measure of a city’s ecological and environmental quality (Equation (1)). It evaluates the greening of urban areas and the overall ecological environment, incorporating factors such as urban green space area, distribution, and quality. The Normalized Difference Vegetation Index (NDVI) reflects the health of the urban ecosystem and its capacity for sustainable development [32]. The HSI assesses the quality and livability of the residential environment within a region or city (Equation (2)). It integrates NDVI and nighttime light data to provide a quantitative analysis reflecting the overall quality of life and sustainable development of a region. This index can guide the city toward more livable and sustainable development while assessing the current state of the residential environment [33]. The EVI is a comprehensive indicator that reflects the vitality of a region’s economic system and its capacity for sustainable development. It integrates multiple indicators across various dimensions, including POI (Equation (3)), HSI, GSI, and land surface temperature (LST) (Equation (4)). The EVI is constructed using an additive approach after min-max standardization (Equation (6)) to ensure comparability across heterogeneous scales, with equal weighting applied by default for simplicity and interpretability. This method allows the index to be sensitive to changes in any dimension while avoiding undue complexity, though it assumes equal importance among indicators; alternative weighting (e.g., via principal component analysis) could be explored in future studies to account for varying real-world impacts. Through quantitative analysis and data statistics (Equation (5)), it reflects the overall economic health and development potential of a region, highlighting current economic performance while providing insights into future trends and potential challenges. Data preprocessing and standardization techniques are employed to ensure comparability and accuracy (Equation (6)). Data preprocessing includes missing value imputation, outlier detection, and data smoothing. Standardization is applied to eliminate dimensional differences among various indicators, enabling comparisons on a uniform scale.

3.3.2. Hypernetwork Construction

This study is grounded in a hypernetwork model. The hypernetwork H can be expressed as $H=(V,E)$, where V denotes the set of nodes and E represents the set of hyperedges [34]. Each node v in V symbolizes an area of economic vitality, while each hyperedge e in E signifies a higher-order relationship among a group of economic vitality areas, determined by a specific indicator. This model integrates a wide array of data from urban assessments, encompassing local buildings, facilities, and services, by selecting data that reflect the highest economic vitality indices. The determination of hyperedge connection strengths (Equation (7)) establishes a set of hyperedge relationships based on data clusters, employing clustering algorithms (Equation (8)) as the fundamental method for constructing the hypernetwork (Table 2). The objective of this approach is to elucidate the interaction mechanisms between various indicators and their influence on regional sustainable development.

3.3.3. Calculation of Hypernetwork Index

The study analyzed the structure and dynamics of the hypernetwork by examining various metrics, including degree centrality (DC), information centrality (IC), network diameter, network density, and average degree. This comprehensive approach enabled researchers to gain insights into the network’s overall properties and behavior (Table 3). DC (Equation (9)) and IC (Equation (10)) are employed to assess the significance of nodes within the hypernetwork. DC reflects the number of directly connected nodes, while IC indicates the efficiency of information flow across the network. Together, they illuminate the influence of key nodes and the shifting centers of economic activity. The network diameter (Equation (11)) represents the maximum length of the shortest path within the network, reflecting its overall scale and the adaptations of urban economic spatial structure. A reduction in network diameter may signify a spatial concentration of economic activity. Network density (Equation (12)) describes the ratio of actual connections to all possible connections, reflecting economic connectivity. Furthermore, the average degree (Equation (13)) represents the mean degree of all nodes, indicating the network’s overall connectivity and the general level of economic activity. An increase in the average degree may suggest a strengthening of economic connections. A comprehensive analysis of these network indicators facilitates a deeper understanding of the complexity and multi-dimensional interactions within the hypernetwork. It provides a theoretical foundation and practical guidance for analyzing urban development, formulating public policies, and exploring complex systems.

Table 2. Hypernetwork Construction Method.

Name	Formula	Description
Clustering analysis	$$Q={\displaystyle \frac{1}{2m}}\sum _{i,j}\left[A_{ij}-{\displaystyle \frac{k_i{k}_j}{2m}}\right]\delta \left(c_i,c_j\right)$$ (7)	Q is the modularity, m is the total number of edges in the network, $A_{ij}$ represents the elements of the adjacency matrix, $k_i$ and $k_j$ are the degrees of nodes i and j, respectively, $c_i$ and $c_j$ denote the communities to which nodes i and j belong, and $\delta (c_i,c_j)$ is the Kronecker delta function [41].
Hyperedge connection strengths	$$S_{ij}={\displaystyle \frac{{\sum }_{k=1}^n{w}_k·sim\left(x_{ik},x_{jk}\right)}{{\sum }_{k=1}^n{w}_k}}$$ (8)	Here, n is the number of nodes and l is the centrality of a path from node i to j [42].

Table 2. Hypernetwork Construction Method.

Name	Formula	Description
Clustering analysis	$$Q={\displaystyle \frac{1}{2m}}\sum _{i,j}\left[A_{ij}-{\displaystyle \frac{k_i{k}_j}{2m}}\right]\delta \left(c_i,c_j\right)$$ (7)	Q is the modularity, m is the total number of edges in the network, $A_{ij}$ represents the elements of the adjacency matrix, $k_i$ and $k_j$ are the degrees of nodes i and j, respectively, $c_i$ and $c_j$ denote the communities to which nodes i and j belong, and $\delta (c_i,c_j)$ is the Kronecker delta function [41].
Hyperedge connection strengths	$$S_{ij}={\displaystyle \frac{{\sum }_{k=1}^n{w}_k·sim\left(x_{ik},x_{jk}\right)}{{\sum }_{k=1}^n{w}_k}}$$ (8)	Here, n is the number of nodes and l is the centrality of a path from node i to j [42].

Table 3. The hypernetwork analysis index.

Name	Formula	Description
Degree Centrality (DC)	$$D_{Ci}=\sum _{j=1}^n{A}_{ij}\left(i\ne j\right)$$ (9)	$A_{ij}$ represents the hypergraph link matrix, where i and j denotes nodes. This centrality shows the local connectivity relationships around each node, indicating that a higher node degree results in a greater influence on neighbouring nodes [43].
Information Centrality (IC)	$$\overline{I_i}={\displaystyle \frac{n}{{\sum }_{j=1}^n\frac{1}{I_{ij}}}}$$ (10)	Here, n is the number of nodes and l is the centrality of a path from node i to j [42].
Network Diameter	$$D=\underset{i,j}{max}{d}_{ij}$$ (11)	The maximum distance between any two nodes in the network is represented as $d_{ij}$, which indicates the spatial distance between the two nodes [44].
Network Density	$$N_{De}={\displaystyle \frac{2L}{n^2-n}}$$ (12)	L represents the actual number of hyperedges in the graph, while n denotes the number of nodes [45].
Average Degree	$$〈k〉={\displaystyle \frac{1}{N}}\sum _{i=1}^N{k}_i$$ (13)	The degree $k_i$ of node i in the network represents the number of edges directly connected to node i [46].

Table 3. The hypernetwork analysis index.

Name	Formula	Description
Degree Centrality (DC)	$$D_{Ci}=\sum _{j=1}^n{A}_{ij}\left(i\ne j\right)$$ (9)	$A_{ij}$ represents the hypergraph link matrix, where i and j denotes nodes. This centrality shows the local connectivity relationships around each node, indicating that a higher node degree results in a greater influence on neighbouring nodes [43].
Information Centrality (IC)	$$\overline{I_i}={\displaystyle \frac{n}{{\sum }_{j=1}^n\frac{1}{I_{ij}}}}$$ (10)	Here, n is the number of nodes and l is the centrality of a path from node i to j [42].
Network Diameter	$$D=\underset{i,j}{max}{d}_{ij}$$ (11)	The maximum distance between any two nodes in the network is represented as $d_{ij}$, which indicates the spatial distance between the two nodes [44].
Network Density	$$N_{De}={\displaystyle \frac{2L}{n^2-n}}$$ (12)	L represents the actual number of hyperedges in the graph, while n denotes the number of nodes [45].
Average Degree	$$〈k〉={\displaystyle \frac{1}{N}}\sum _{i=1}^N{k}_i$$ (13)	The degree $k_i$ of node i in the network represents the number of edges directly connected to node i [46].

4. Research Results

4.1. Spatial Evolution of Economic Vitality Within Sustainable Urban Development Framework

The spatio-temporal evolution of economic vitality within Shenzhen’s sustainable urban development framework reveals the effectiveness of rational land use strategies in promoting both economic growth and community well-being. The economic vitality index (EVI) integrates multiple indicators, including the density distribution of Points of Interest (POI), the Green Space Index (GSI), the Human Settlements Index (HIS), and Land Surface Temperature (LST), to evaluate the economic vitality and developmental potential of various regions within Shenzhen. Comparing the color changes in the EVI across different years, from green (indicating high EVI values) to yellow and brown (indicating low EVI values), illustrates the significant developments and potential challenges faced by Shenzhen over the past five years (Figure 4).

Shenzhen’s EVI exhibits an upward trajectory from 2020 to 2024, reflecting the city’s resilience and sustained developmental potential. The highest EVI values escalated from 1.67465 in 2020 to 1.84995 in 2024, marking an increase of 10.47%. This signifies a continuous rise in the economic intensity of high vitality areas within the city. Concurrently, the lowest EVI climbed from 0.00929014 in 2020 to 0.0857677 in 2024, demonstrating even more pronounced growth. This shows that Shenzhen’s remarkable progress in economic development, urban construction and environmental enhancement In particular, the peak EVI value in 2021 reached 1.74272, surpassing both 2020 and 2022, showcasing the city’s rapid recovery and robust rebound following the pandemic. However, a temporary decline occurred in 2022 and 2023, with the highest values recorded at 1.6353 and 1.4834, respectively. This may reflect the economic challenges or external shocks experienced over the preceding two years. In 2023, the EVI peak further declined to 1.4834, representing the lowest level in five years. Changes in the macroeconomic landscape or temporary adjustments during the city’s transformation process may account for this decrease. In 2024, the EVI not only fully recovered but also reached a five-year high of 1.8500. This significant resurgence indicates that Shenzhen has adeptly navigated previous challenges, resulting in a robust revival of economic vitality and signaling the onset of a new upward cycle in urban development.

The spatial distribution of EVI in Shenzhen reveals marked regional disparities. The western and central areas tend to exhibit greater vibrancy, while the eastern coastal regions experience comparatively lower levels of economic activity. The green areas (with high EVI) are predominantly concentrated in the city center (such as Futian and Nanshan) and the main commercial areas from 2020 to 2024. Moreover, the green areas have gradually expanded, indicating a steady enhancement in economic vitality within these areas. Conversely, the eastern and northern regions are primarily marked by yellow and brown, reflecting relatively low EVI values and signaling a lag in economic vitality. Notably, the spatial distribution of EVI in 2023 and 2024 demonstrates a tendency for some initially low EVI regions (such as Guangming and Pingshan) to deepen in green, further corroborating the overall improvement in Shenzhen’s economic vitality and the progressive balance of its development, implying a gradual increase in economic vitality in these regions.

The dynamic process of urban development and its potential challenges are mirrored in the year-on-year fluctuations of Shenzhen’s EVI. From 2020 to 2024, Shenzhen’s EVI values consistently exhibit an upward trend, showcasing urban resilience in addressing external environmental shifts and internal developmental challenges. The EVI reached an unprecedented high in 2024. However, this does not imply that Shenzhen’s development will be free from bottlenecks or difficulties. Certain regions continue to exhibit low economic vitality, underscoring the issue of unbalanced regional economic development, which may impede the overall economic coordination of Shenzhen. With the rapid expansion of the city, there is an accompanying increase in pressure on the environment and resources, while fluctuations in the HIS can affect the city’s overall habitability. Furthermore, the ecological environment may face challenges stemming from changes in LST. Consequently, future development in Shenzhen should prioritize sustainability by optimizing urban planning, enhancing infrastructure, and promoting green development to effectively address potential challenges.

4.2. The Spatial Distribution of EVI Nodes

The study identifies the concentration of economic activities across various regions and their evolving trends by conducting a clustering analysis of spatial nodes derived from the highest EVI values in Shenzhen between 2020 and 2024. This analysis further explores the characteristics of Shenzhen’s economic development over these five years (Figure 5).

From an urban perspective, EVI has demonstrated a significant spatial agglomeration effect throughout the five-year period. In 2020, the distribution of nodes was relatively dispersed; however, certain areas exhibited notable concentrations, particularly the clusters of red nodes in the northwest and green nodes in the southeast, indicating heightened economic vitality in these regions. By 2021, the original five clusters had transformed into four, intensifying this agglomeration effect. This transformation reflects sustained growth in economic activity, especially in the northwest, with the red nodes and the central region with the green nodes. In 2022 and 2023, shifts occurred within the clustering regions, with node distributions in certain areas beginning to converge towards the center. By 2024, a significant trend towards dispersion emerged, particularly characterized by a substantial decrease in the number of red nodes in the northwest, which may signify fluctuations in economic vitality within this region over the year.

Conducting a time series analysis of the node statistics reveals future trends in economic vitality development in Shenzhen. Statistics indicate that 2022 was a pivotal turning point, as the number of multiple nodes peaked that year before declining in the subsequent two years. The marked reduction in red nodes may suggest a decrease in EVI in the northwest region. Conversely, other areas, such as those represented by the red and yellow nodes, demonstrate varying degrees of recovery in 2024, indicating that economic activity in these regions may have entered a new cycle of growth.

In summary, Shenzhen’s economic development from 2020 to 2024 reveals significant regional disparities. These changes warrant greater emphasis in future development strategies, particularly focusing on industrial upgrading and innovation-driven initiatives in areas experiencing declining economic vitality, to achieve coordinated economic development across the city.

4.3. Temporal Evolution Characteristics of the EVI Hypernetwork Structure

Figure 6 illustrates the evolution of the EVI hypernetwork in Shenzhen from 2020 to 2024, reflecting the dynamic changes in the city’s economic structure and vitality. Each hypernetwork comprises nodes (representing the highest EVI values) and hyperedges of varying colors, where the nodes signify peak EVI values and the hyperedges depict the relationships among these values. The hypernetwork’s structure and its evolution reflect the complexity and level of integration within Shenzhen’s economic system. From 2020 to 2024, the shape and distribution of the hyperedges (colored regions) have undergone significant transformation. In 2020, the hypernetwork appeared relatively dispersed, with distinctly defined boundaries, indicating a strong independence among economic subsystems. However, as time progresses—particularly by 2024—the hypernetwork has become denser and more intricate, with regional boundaries gradually blurring. This evolution demonstrates the strengthening of connections between EVIs across different regions in Shenzhen, reflecting the interregional integration of economic activities and an enhancement of systemic cohesion. Such changes in the hyperedges signify a dynamic reorganization of economic clusters and an increase in collaborative innovation. The changing topological structure of the hypernetwork reveals the dynamic characteristics of Shenzhen’s economy. The number of nodes may indicate a consolidation or reorganization of economic indicators and key entities, decreasing from 70 in 2020 to approximately 50 in 2024. The dynamic adjustments in clusters of economic activity are mirrored in the color variations of the hyperedges. In 2020, the boundaries of the differently colored areas were distinct, signifying relatively independent economic activities. By 2024, the overlap among colored areas increased, indicating a strengthened synergistic effect among different economic sectors. Additionally, changes in the distribution density of nodes within the hyperedges reflect adjustments in the internal structures of various economic subsystems. For instance, the distribution of nodes within hyperedges became denser in the 2024 hypernetwork, illustrating tighter internal connections within EVI subsystems, potentially leading to more robust clustering. The increased complexity of hypernetwork development and the heightened degree of hyperedge overlap suggest that Shenzhen’s economy is transitioning to a more advanced systemic and integrated development model. The blurring of boundaries between regions indicates a deep integration of livable environments, land development intensity, and economic growth, fostering new growth in the EVI. Enhanced resource allocation efficiency and improved collaborative innovation capabilities are evident in the increased density of connections between nodes. However, this highly integrated economic structure may pose new challenges, such as the rapid transmission of economic fluctuations. Therefore, Shenzhen needs to establish more flexible and resilient economic governance mechanisms to address potential systemic risks and ensure sustainable economic growth while promoting high-quality economic development.

4.4. Analysis Results of the EVI Hypernetwork Indicators

EVI is typically closely associated with several indicators, including network density, DC, IC, network diameter, and average degree. A higher density signifies more frequent economic activity within the city, leading to denser resource flows and stronger economic links. The DC and IC highlight the significance of specific key nodes within the overall economic network. The EVI has exhibited considerable changes in the hypernetwork indicator data from 2020 to 2024 (

Table 4. The numerical statistics for various indexes of the hypernetwork.

Years	Network Density	Degree Centrality	Information Centrality	Average Degree	Network Diameter
2020	0.1019	0.005181	0.005181	3.845	15
2021	0.1039	0.005263	0.005263	3.863	14
2022	0.0929	0.004673	0.004695	3.85	16
2023	0.0993	0.005051	0.005076	3.818	15
2024	0.139	0.007194	0.007194	3.752	14

).

Firstly, network density has fluctuations between 2020 and 2024, suggesting that economic activity in Shenzhen has become increasingly concentrated and frequent over these years. There was a slight increase in connectivity between nodes, with a network density of 0.1019 in 2020, rising marginally to 0.1039 in 2021. However, reflecting a potential weakening of economic links among certain regions, network density fell to 0.0929 in 2022. Notably, it rebounded significantly to 0.1390 in 2024, indicating that the connections between nodes in the economic network have become much denser, thereby suggesting a further enhancement in economic vitality.

Secondly, there have been annual fluctuations in both DC and IC. In 2020, the average DC stood at 0.005181, with the average IC matching this value, indicating relatively low connectivity and importance of nodes within the network that year. In 2021, both indicators rose, reflecting an enhancement in the influence of specific nodes within the network. However, in 2022, both metrics declined, with the average DC dropping to 0.004673 and the average IC to 0.004695, possibly linked to a decrease in the concentration of economic activities during that year. By 2024, both centrality indices experienced significant increases: DC and IC each reached 0.007194, marking a substantial rise compared to 2020 and 2022. This signifies a notable enhancement in the influence of the most crucial nodes within the economic network. Thus, in 2024, certain nodes (key EVI regions) have gained prominence within the overall EVI network and are poised to serve as core regions driving the city’s economic vitality.

Finally, the structural characteristics of the network are also reflected in the average degree and network diameter. From 2020 to 2024, the average degree remained relatively stable, fluctuating between 3.752 and 3.863. This stability suggests that the average number of connections per node has remained constant, implying that residents have relatively equal access to public services and housing within the city. The network size exhibited slight fluctuations over the five years, peaking at 16 in 2022, while remaining between 14 and 15 in the other years. A larger value indicates longer distances between the two most distant nodes, potentially suggesting reduced accessibility for certain remote areas, which could adversely affect the quality of life and comfort in those regions. Conversely, the network diameter in 2024 measured 14, a smaller figure that may signify improved overall connectivity within the city. This suggests a reduction in travel time for residents moving from one area to another, thereby enhancing the city’s quality of life.

In conclusion, the EVI hypernetwork displays intricate changes between 2020 and 2024. Regional disparities and dynamic shifts in economic vitality are mirrored in fluctuations in network density and centrality indicators. The significant increases in these indicators in 2024 may indicate the centralization of economic activity and rapid development in specific regions during that year. However, the changes in average degree and network diameter highlight the necessity of focusing on the diversity of connections between nodes and the overall stability of the network to ensure balanced and sustainable economic development in the future.

4.5. Analysis Results of the Hypernetwork and Urban Physical Examination

Studying the application of urban physical examination and hypernetwork analysis methods in Longgang District and Yantian District of Shenzhen reveals distinct marginalization characteristics in their urban development processes. Longgang District, a significant industrial hub in Shenzhen, has experienced some improvement in its economic vitality; however, it continues to grapple with challenges such as inadequate public services and pronounced disparities in environmental quality. In contrast, while Yantian District relies on tourism and port-related economic development, it exhibits relatively singular economic vitality, deficient public facilities, and limited regional connectivity. Consequently, not only do the economic structures of these two districts differ, but various factors such as social resource distribution, infrastructure development, and regional cooperation are also reflected in the discrepancies in node and network connectivity pertaining to economic activities and resource allocation between them (Figure 7).

As an established industrial district, Longgang showcases low node density and degree centrality in the hypernetwork analysis, signifying its weak economic ties to the core areas of the city. This is corroborated by data from the urban physical examination, which indicates severe deficiencies in public services such as healthcare and education, particularly in remote areas like Bantian and Nanwan, where residents report low satisfaction levels regarding infrastructure. The scarcity of public resources, coupled with poor hypernetwork connectivity, perpetuates a vicious cycle that impedes the enhancement of regional economic vitality. Simultaneously, significant differences in environmental quality exist between industrial and residential regions within Longgang. Regions such as Pinghu and Pingshi, which have a high concentration of industrial activity, have significant pollution problems and low HSI values. Conversely, Longgang Central City and its surrounding residential areas have benefited from urban renewal and transformation, leading to improved living conditions. This spatial differentiation is manifested in the hypernetwork structure as an imbalance in node functionality, adversely affecting the network’s overall efficiency and stability.

Yantian District, endowed with abundant port and tourism resources, displays signs of marginalization, as evidenced by low degree centrality and network density. Data from the urban physical examination reveal an uneven distribution of public facilities in Yantian District, with medical and educational resources being relatively scarce, reflecting the district’s weak integration into the economic network. Despite the flourishing logistics industry at Yantian Port, the district’s overall economic vitality remains inadequate, hindering effective integration into Shenzhen’s core economic network. Additionally, while rich in tourism resources, Shatoujiao Street in Yantian District suffers from significant shortcomings in its residential environment, creating a dissonance between the tourist experience and the quality of life for its residents. This contradiction is evident in the hypernetwork structure as a singularity of node functionality, restricting resource flow and limiting the diversification of economic development.

Therefore, the marginalization phenomena observed in Longgang and Yantian districts underscore the importance of inter-regional interaction and balanced resource distribution in enhancing the overall economic vitality of the city.

5. Discussion

5.1. Discussion of Methodology

This study utilizes the Economic Vitality Index (EVI) data of Shenzhen from 2020 to 2024, synthesizing information across various dimensions [21], including the density distribution of Points of Interest (POI), Healthcare Infrastructure Services (HIS), and Land Surface Temperature (LST). Spatial statistical analysis techniques are employed to delineate the characteristics of urban economic vitality and to develop a spatio-temporal evolution model. Building upon this foundation, spatial node transformation and clustering analyses of the highest EVI values are conducted to construct a hypernetwork. The study investigates the spatial distribution characteristics, temporal development trends, and interrelationships between regions regarding Shenzhen’s economic vitality by calculating hypernetwork indicators. Ultimately, the research findings are verified and refined through the integration of urban physical examination data and feedback from community management departments, ensuring the accuracy and applicability of the analytical results. Consequently, this methodology not only observes overarching trends but also identifies local anomalies, providing a crucial foundation for a deeper understanding of the spatial dynamics of Shenzhen’s economic development.

5.1.1. The EVI and Multidimensional Assessment Methods

The EVI employed in this study offers a multidimensional perspective for a comprehensive analysis of urban development. The construction of the EVI integrates several key indicators, including the density distribution of POI, HIS, and LST. This fusion of multidimensional data effectively captures various facets of urban economic vitality [9]. The density distribution of POI reflects the extent of spatial agglomeration of economic activities [15], while HIS indicates the level of urban livability, and LST is closely linked to the urbanization process and environmental quality. The EVI encompasses not only the tangible aspects of economic development but also the intangible elements and sustainable development potential of the city, based on a thorough consideration of these indicators [11]. This study utilized standardization techniques to ensure comparability and validity of indicators across different dimensions. Simultaneously, time series analysis was employed to monitor the dynamic changes in Shenzhen’s economic vitality from 2020 to 2024, revealing development trends and critical turning points. The strength of this approach lies in its capacity to provide a comprehensive and dynamic framework for assessing urban economic vitality, establishing a crucial foundation for a deeper understanding of the spatial characteristics of Shenzhen’s economic development.

5.1.2. The Application of Spatial Statistical Techniques in Economic Vitality Research

The application of spatial statistical analysis techniques has significantly enhanced the study’s capacity to analyze the spatial distribution of economic vitality [17]. By employing Geographical Information System technology to spatially visualize the EVI, a series of heat maps have been generated, illustrating the spatial distribution of economic vitality. These heat maps offer a visual representation of economic vitality levels across various areas of Shenzhen, utilizing a color gradient that ranges from green (indicating high EVI values) to brown (indicating low EVI values). Additionally, spatial interpolation techniques were employed to estimate EVI values for unsampled regions, resulting in a continuous economic vitality surface encompassing the entire city [18]. These methodologies facilitate the identification of spatial dependence and heterogeneity in economic vitality, revealing patterns such as generally higher vitality in the western and central regions, while the eastern coastal areas exhibit comparatively lower vitality. This approach provides a robust framework for understanding the spatial dynamics of urban economic development.

5.1.3. Hypernetwork Analysis: Exploring the Complex Relationship Networks of Economic Vitality

The introduction of hypernetwork analysis presents an innovative research perspective for investigating the intricate relationship networks of Shenzhen’s economic vitality [28,31]. This study utilizes the transformation of spatial nodes corresponding to the highest EVI values. The continuous EVI surface is discretized into a series of representative nodes, which signify local peaks of economic vitality and indicate the centers of economic dynamism within the city. Concurrently, community detection algorithms have been employed to analyze clusters of economic vitality within the network, identifying regions with analogous economic characteristics. Building upon this foundation, a hypernetwork model was constructed to illustrate the interrelationships between the nodes. The hyperedges of the hypernetwork encapsulate the interactions between nodes, influenced by factors such as spatial distance and the strength of economic ties. Analyzing the topological structure of the hypernetwork unveils the complexity and degree of integration within Shenzhen’s economic system. For instance, the network density metric reflects the frequency of economic activities, while degree centrality and information centrality highlight the significance of key nodes within the overarching economic network. Additionally, metrics such as average degree and network diameter were calculated to evaluate the connectivity and efficiency of the network. This hypernetwork analysis method not only captures the static distribution of economic vitality but also elucidates the dynamic processes of economic interaction, providing new analytical tools for comprehending the systemic and synergistic aspects of Shenzhen’s economic development.

5.2. Discussion of Results

By conducting a comprehensive analysis of Shenzhen’s EVI from 2020 to 2024, utilizing methods such as spatio-temporal evolution models and hypernetwork analysis, this study aims to uncover the dynamic characteristics and underlying mechanisms of economic vitality in Shenzhen, thereby providing a scientific foundation for sustainable urban development and coordinated regional growth. The findings reveal that Shenzhen’s EVI demonstrates an overall upward trajectory from 2020 to 2024, accompanied by significant spatial variability. The distribution of economic vitality across regions exhibits pronounced gradient variations and agglomeration effects, resulting in a distinctive urban economic geography. Consequently, Shenzhen is undergoing a complex process of economic restructuring, with different regions pursuing new development pathways informed by their unique characteristics and advantages.

5.2.1. The Evolution of Economic Vitality Demonstrates an Intensification of Agglomeration Effects and Regional Disparities in Shenzhen

Drawing on the EVI data from 2020 to 2024 and its subsequent hypernetwork analysis, Shenzhen’s economic activities are distinctly marked by a trend of centralization. Specifically, both the network density and centrality metrics exhibit substantial improvement in 2024, indicating a gradual concentration of economic vitality in particular regions. This phenomenon may stem from rapid development propelled by industrial upgrading, technological innovation, or policy initiatives. Notably, the increase in network density signifies an acceleration of resource flows within the city, resulting in tighter economic interconnections. These concentrated areas have emerged as the core drivers of Shenzhen’s economic advancement. However, this agglomeration effect may also contribute to widening disparities in economic vitality across regions, as some peripheral areas may struggle to reap the developmental benefits enjoyed by central regions [24]. This differentiated development model could further intensify economic imbalances within the city, posing challenges to the overall sustainability of economic growth.

5.2.2. The Comfort of Shenzhen’s Living Environment Has Been Relatively Maintained Amid Rapid Urbanization

Despite the trend of centralization in Shenzhen’s economic activities, an analysis of the average degree and network diameter reveals that the city has successfully maintained a commendable balance regarding the comfort of its living environment, as supported by network theory in urban studies, where average degree serves as a proxy for system-wide connectivity and resource distribution equity (e.g., [8], who link uniform node degrees in hypernetworks to balanced urban resource flows [31], demonstrating correlations between network topology and social equity in smart city frameworks). Empirically, this is validated by our urban physical examination data, where stable average degrees align with resident-reported equitable access to amenities in Longgang and Yantian Districts, though disparities in remote areas suggest areas for improvement; future studies could employ targeted equity surveys for further empirical corroboration. The relative stability of the average degree indicates that, although economic vitality is concentrating in specific regions, the distribution of public services and amenities remains fairly equitable, with connections and interactions among various residential areas are sustained at a reasonable level. Simultaneously, the network diameter has fluctuated only slightly over the past five years and decreased in 2024, which reflecting an enhancement in the overall accessibility of the city and the efficiency of resource allocation. This trend contributes to alleviating the issues of convenience and comfort associated with the centralization of economic activity, thereby enhancing the overall quality of life within the city.

5.2.3. Hypergraph Analysis Reveals the Complexity and Interconnectedness of Economic and Living Environment Development in Shenzhen

By transforming the EVI into a hypernetwork and employing various analytical indicators such as network density, degree centrality (DC), and information centrality (IC), a more profound insight into Shenzhen’s development trends—both in economic and living environment aspects—can be unveiled. These network indicators not only illustrate the spatial distribution and evolving trends of economic activities but also illuminate the strength of connections between different regions and their significance within the broader urban network [26,29]. For instance, the increase in centrality measures implies that certain regions are assuming an increasingly pivotal role in propelling urban economic development. However, this may also exacerbate regional imbalances within the city. Conversely, average degree and network diameter serve as robust metrics for evaluating the comfort of the living environment, indicating that Shenzhen enjoys commendable accessibility to public resources and services, despite the pronounced effects of economic agglomeration.

5.3. Discussion of Case

The trajectory of Shenzhen’s economic development not only embodies the characteristics of urban transformation within the framework of China’s modernization but also offers invaluable lessons for cities in other developing countries, serving as a pioneer of China’s reform and opening-up and a model of rapid urbanization. Through the integration of spatial data analysis utilizing a hypernetwork model and urban diagnostic research, a comprehensive study of neighborhoods in Longgang District and Yantian District of Shenzhen was undertaken. This research unveiled the differences and similarities in the development of various communities, thereby establishing a scientific foundation for crafting more targeted urban development policies.

5.3.1. Multidimensional Interpretation and Insights of Urban Development Indicators

The overall living environment in the city is favorable; however, imbalances in the distribution of public facilities persist [6]. While most communities are equipped with amenities such as property management, waste sorting, public activity spaces, and green areas that improve residents’ quality of life, there are notable disparities in the provision of public facilities across different communities, impacting the convenience of daily life for some residents [7]. Some communities are well-served with educational resources, including multiple primary and secondary schools, while others are limited, forcing residents to seek schooling outside their neighborhoods, thereby highlighting the unequal distribution of educational resources. Additionally, a widespread shortage of parking in older communities underscores the inadequacies in planning for supporting facilities during the city’s rapid development. Furthermore, there is a need to enhance residents’ awareness of fire safety, as common issues such as blocked corridors and fire escapes with stored items hinder safe evacuation. Therefore, strengthening urban planning and resource allocation to ensure equitable access to public services and facilities across all communities is essential for future urban development.

5.3.2. Innovations in Urban Governance and Practices for Sustainable Development

Innovations in urban governance and practices for sustainable development are continually being advanced through diverse measures aimed at enhancing residents’ quality of life and the level of urban development. Regarding property management, many communities have adopted comprehensive systems that encompass various facets, such as daily maintenance and security management, thereby creating a safe and comfortable living environment for [25]. Environmental protection has emerged as a critical issue in urban development, with the widespread implementation of waste sorting underscoring the city’s proactive commitment to sustainability. Simultaneously, there has been a focus on the rational planning and utilization of public spaces. Most communities offer public activity areas and green spaces for residents to socialize and enjoy their leisure time, which not only enriches the living environment but also fosters community cohesion and resident interaction. The comprehensive execution of these measures reflects the city’s overall advancement in governance innovation and sustainable development.

5.3.3. Future Development Trends and Policy Recommendations

Optimizing the configuration of public facilities and enhancing urban adaptability are pivotal directions for future urban development, requiring integration of hypernetwork insights with participatory governance mechanisms that prioritize community voices in planning processes. First, balanced urban planning policies must prioritize rectifying the uneven distribution of public facilities, particularly essential infrastructure such as schools to ensure that all communities have equitable access to fundamental public services. Beyond facility provision, hypernetwork analysis revealing connectivity disparities (network density: 0.0929–0.139; degree centrality: 0.004673–0.007194) suggests establishing “connectivity corridors” in marginalized districts like Longgang and Yantian, integrating multi-modal transit and institutional partnerships that strengthen inter-regional connections across the urban system. Second, to tackle parking challenges urban regeneration initiatives should incorporate the development of additional parking facilities and actively explore innovative models such as shared parking, to effectively alleviate parking pressures. However, field surveys revealed that residents possess context-specific knowledge regarding underlying causes—storage inadequacies, regulatory gaps—that quantitative indicators cannot capture. Institutionalizing Community Planning Councils with decision-making authority over local land use allocations and quarterly participatory budgeting sessions would ensure technical analyses are interpreted through lived experience. It is also crucial to elevate residents’ awareness of fire safety through community outreach and educational programs in schools, thereby reducing the risk of fire resulting from inappropriate behaviors. Safety interventions should emerge from collaborative problem-identification where neighborhood committees comprising residents, property managers, and officials co-diagnose issues and co-design contextually appropriate solutions. Finally, urban planning should proactively anticipate the new demands, arising from population growth and evolving lifestyles. Adequate land should be reserved for public facilities and adaptable spaces to enhance the overall flexibility of the city. Given rapid structural transitions identified (node reduction from 70 to 50, increased hyperedge overlap), adaptive zoning frameworks including “flexible-use districts” in high-vitality zones and participatory urban diagnosis workshops where community researchers collaborate with planners to co-interpret monitoring data are essential. The ultimate measure of rational land use is not optimization of abstract network metrics but enhancement of lived experience as defined by residents themselves, necessitating extended ethnographic engagement and democratization of planning knowledge in future research.

5.4. Limitations and Future Perspectives

This study offers a comprehensive analysis of Shenzhen’s economic vitality from 2020 to 2024; however, it has certain limitations. First, although a multidimensional EVI assessment method has been employed, constraints remain concerning the data sources for some key indicators. Second, while spatial statistical analysis and hypernetwork analysis techniques have been utilized to unveil spatial distribution characteristics and complex relational networks, potential remains for further refinement in addressing dynamic interactions between regions and long-term trends. Additionally, feedback from community management departments has been incorporated, but its breadth and depth may not sufficiently capture the realities of each region. Furthermore, our analysis highlights inherent conflicts among the economic, environmental, and social dimensions at stake in urban sustainability. For instance, pursuing economic vitality through industrial and commercial expansion (as seen in Longgang district) often leads to environmental degradation, such as elevated land surface temperatures and reduced green space indices, while exacerbating social inequities through uneven access to amenities in marginalized areas like Yantian district. From our perspective, these conflicts arise from competing priorities: economic growth drives short-term gains but undermines long-term environmental resilience and social cohesion. To govern these upstream of tools like EVI and hypernetwork analysis, we advocate for preemptive strategies including integrated policy frameworks that embed multi-stakeholder dialogues (e.g., involving residents, planners, and industries) from the planning stage, regulatory incentives for green innovation, and mitigate trade-offs. Future studies could expand on this by empirically testing governance models in diverse urban contexts to achieve true equilibrium among these dimensions.

Future research should focus on optimizing data acquisition and processing methods to enhance the accuracy and applicability of the findings. More comprehensive coverage of dynamic changes in urban economic vitality can be achieved by integrating high-precision, multidimensional data sources, and strengthening data quality control. It is also advisable to further develop and refine spatial statistical and hypernetwork analysis techniques, particularly by incorporating artificial intelligence methods to address dynamic interactions between regions; thereby enhancing predictive and analytical capabilities. Moreover, future studies should strive to increase the level of engagement and feedback from community management departments to ensure that research findings are more representative and practically applicable. These enhancements will enable the research to provide more robust scientific evidence for the sustainable development of Shenzhen and other cities.

6. Conclusions

This study demonstrates the effectiveness of integrating economic vitality assessment with sustainable urban space evaluation through rational land use analysis. This study investigates the dynamic characteristics and underlying mechanisms of Shenzhen’s economic vitality through a spatio-temporal analysis of the Economic Vitality Index (EVI) from 2020 to 2024, utilizing urban physical examinations, multidimensional evaluation methods, spatial statistical analysis, and hypernetwork analysis techniques. The research findings reveal that Shenzhen’s economic vitality index exhibits a consistent upward trend from 2020 to 2024; however, significant spatial disparities persist, with economic vitality demonstrating a clustering effect while regional inequalities continue to grow. The comfort of the living environment remains relatively stable throughout the rapid urbanization process. Consequently, a comprehensive analysis of the evolution of Shenzhen’s economic vitality provides robust scientific evidence for sustainable urban development and coordinated regional growth.

Shenzhen’s economic vitality demonstrated substantial growth over the study period, with the highest EVI values increasing by 10.47% from 1.67465 in 2020 to 1.84995 in 2024. However, this growth occurred unevenly across the city, with western and central regions (particularly Futian and Nanshan districts) consistently exhibiting higher vitality than eastern coastal areas. The hypernetwork analysis revealed intensifying economic clustering, with network density increasing from 0.1019 in 2020 to 0.139 in 2024, alongside rising degree centrality and information centrality metrics both reaching 0.007194 in 2024. These findings confirm that rational land use strategies can effectively promote economic growth, but spatial concentration of economic activity creates significant challenges for equitable urban development. Despite pronounced economic clustering effects, the city maintained relatively stable livability conditions during rapid urbanization. The average degree remained consistent between 3.75 and 3.86 throughout the study period, while network diameter decreased to 14 in 2024, indicating improved overall accessibility and resource allocation efficiency. This demonstrates that sustainable urban space planning can balance economic development with livability enhancement when properly implemented.

The urban physical examination of Longgang and Yantian Districts revealed critical implementation gaps in achieving equitable access to sustainable urban amenities. Both districts exhibited marginalization characteristics despite different development pathways—Longgang as an industrial hub and Yantian as a port and tourism center. Low node density and degree centrality in both districts indicated weak connections to core economic areas, while field surveys documented significant deficiencies in healthcare, education, and public facilities. These disparities underscore that economic vitality growth alone is insufficient for comprehensive sustainable urban development without deliberate attention to equitable resource distribution and community-centered planning approaches.

This comprehensive analysis of Shenzhen’s economic vitality evolution provides scientific evidence for sustainable urban development and coordinated regional growth. Future research should prioritize the development of integrated frameworks that simultaneously optimize economic vitality and sustainable urban space outcomes. Enhanced community participation mechanisms and governance innovations will ensure that rational land use strategies effectively serve diverse community needs while promoting long-term urban sustainability.