Association between Land Use and Urban Vitality in the Guangdong–Hong Kong–Macao Greater Bay Area: A Multiscale Study

Abstract

Urban vitality, which indicates the development level of a city and the quality of life of its residents, is a complex subject in urban research due to its diverse assessment methods and intricate impact mechanisms. This study uses multisource data to evaluate the urban vitality of the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) across social, economic, cultural, and environmental dimensions. It analyzes the spatial distribution characteristics of urban vitality and examines the relationships between urban vitality and land use at both regional and city scales. The results indicate that the urban vitality in the GBA generally exhibits a spatial distribution pattern of a high central density and a low peripheral spread, where built-up areas and cropland emerge as key influencing factors. Cities with different developmental backgrounds have unique relationships between land use and urban vitality. In high-vitality cities, the role of the built-up area diminishes, and natural ecosystems, such as wetlands, enhance vitality. In contrast, in low-vitality cities, built-up areas boost urban vitality, and agriculture-related land types exert a lower negative or even positive effect. This research contributes to the understanding of the spatial structures of urban vitality related to land use at different scales and offers insights for urban planners, builders, and development managers in formulating targeted urban vitality enhancement strategies at the regional collaborative and city levels.

1. Introduction

The concept of urban vitality was first proposed more than 60 years ago and has been considered a prerequisite for safe and successful cities [1]. As research on this topic continues to deepen, urban vitality is commonly used to describe the attractiveness, diversity, and prosperity of urban spaces due to human activities [2], and it is an important indicator of the level of a city’s urban development and the quality of life of its residents. Vibrant urban spaces can attract more frequent and diverse socioeconomic activities, enhance people’s subjective perceptions of urban spaces, drive the influx of capital and personnel, and promote the sustainable development of cities.

Urban vitality includes various aspects, including the economy, society, culture, and environment. Currently, there is no unified standard to assess urban vitality, and related research commonly focuses on urban morphology and socioeconomic factors. Early assessments relied heavily on questionnaires, interviews, and field surveys to obtain the public’s perceptions and evaluations of urban vitality. These sources of information are highly subjective, inefficient, and commonly only applicable at the microscale of case studies [3,4]. In recent years, internet big data with geospatial information has been considered effective in reflecting the density of people’s socioeconomic activities [5], and it has become a new approach to quantitatively assess urban vitality [6,7]. Lu et al. [8] calculated the urban vitality of Beijing and Chengdu based on social media check-in data; they analyzed the impact of six indicators: socioeconomic status, transportation accessibility, building density and development intensity, spatial function, urban form, and landscape quality. Big data containing spatial information, such as public transport card data, mobile signal data, Wi-Fi hotspot data, and consumer venue review data, have also been applied to estimate urban vitality [9]. However, these big data sources exhibit limitations due to their specific user groups, which may lead to inaccurate assessment results. For example, nearly 80% of the user base of Sina Weibo, which is a mainstream social media platform in China, are people born in the 1990s, and the majority of the users are female [10].

Urban vitality assessments based on a single indicator struggle to accurately reflect the multiple aspects of urban vitality. Consequently, many scholars have attempted to construct a comprehensive assessment system for urban vitality from various perspectives. For example, building upon Jacobs’ theory, Gomez-Varo et al. [11] established an urban vitality assessment framework with 22 indicators across six dimensions—concentration, functional diversity, contact opportunities, building diversity, accessibility, and border vacuums. Jin et al. [12] decomposed urban vitality into three components—urban form, urban function, and urban society. Lai et al. [13] selected 14 indicators across five dimensions—population, residential, business, facility, and quality—to comprehensively evaluate urban vitality, and they tested and estimated how local vibrancy and physical activity relate to real estate characteristics.

Numerous factors affect urban vitality, and the influencing mechanisms are complex. Many studies have investigated the relationship between land use and urban vitality, but these studies have largely focused on land-use patterns in urban built-up areas, and there is a lack of research on different land-use types at larger scales. Research on urban vitality in built-up areas indicates that the urban form, built environment, and land use significantly affect urban vitality. Built environment factors, such as population density, mix of points of interest, and metro stations, were significantly associated with urban vibrancy, whereas building density, street intersections, urban greenery, and bus stations were not [14]. Jiang et al. [15] indicated that tall, large-area multifunctional buildings, road network density, and the density of transportation and public facilities had a significantly positive impact on urban vitality. Zhang et al. [16] considered compact urban blocks, diverse architecture, mixed land use, and high-rise buildings to be the main characteristics of vibrant neighborhoods. However, we posit that urban vitality is not solely associated with the morphological structure of the built-up areas, but may also be subject to influences from land use patterns at broader scales. For instance, research has indicated that grasslands, forests, and wetlands promote surrounding housing prices by providing ecosystem services, and help attract population aggregation, which is integral to urban vitality [17].

Despite extensive exploration of the composition of urban vitality and its influencing factors in existing studies, the varying assessment methods, complex influence mechanisms, and confusion of assessment indicators and influencing factors have caused difficulties in drawing unified conclusions from related research. For example, factors such as population, housing prices, and POIs (points of interest) are assessment indicators of urban vitality in some studies [18,19,20] but influencing factors in other studies [21,22]. This inconsistency indicates that the research has not provided clear guidance on the spatial differentiation mechanisms of urban vitality.

At the urban scale, rapid urbanization has introduced a significant influx of people and capital to urban areas, fostered sound urban planning and construction, which in turn attracts more resources, and created a beneficial cycle of enhanced urban vitality. However, the mismatches among urban expansion, the demands of people’s production and living, and economic development have caused a decline in or even loss of urban vitality; this issue is a growing challenge for many cities. Ghost cities with numerous vacant houses, typical examples of low-vitality urban spaces, have emerged in many Chinese cities in recent years [12,23,24]. Factors such as the uneven distribution of resources among cities, differences in industrial development, cultural homogenization, and population migration have caused an imbalance in the spatial distribution of urban vitality on a regional level and at even larger scales [25,26].

At the regional scale, urban agglomerations, as advanced forms of urbanization, serve as new territorial units for countries to engage in global competition and occupy a pivotal position within the global urban system [27]. Currently, Chinese urban agglomerations exhibit a pattern of diversity of developmental stages among cities [28]. Identifying the spatial distribution of urban vitality at the regional scale and conducting comparative studies on cities at various development stages are crucial. Such efforts are vital for fostering coordinated regional development and enhancing the overall urban vitality within these agglomerations. However, most related studies typically focus on case studies of individual cities, which are often developed cities with high levels of vitality, such as Beijing, Shanghai, and Shenzhen. The spatial distribution mechanisms of urban vitality under different urban development backgrounds at a regional scale remain unclear.

The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) has one of the highest degrees of openness and economic vitality in China. Building a vibrant world-class urban agglomeration is one of the developmental goals of the GBA; therefore, analyzing the spatial construction of urban vitality in the GBA and conducting comparative studies of the relationship between land use and urban vitality in cities at various developmental stages can offer important references for targeted urban development and regional collaborative development. This study takes the urban agglomeration of the GBA as the research object, comprehensively assesses its urban vitality from different dimensions using multisource data, and analyzes the association between land use and urban vitality at both the regional and urban scales. This study focuses on the following questions: (1) How is urban vitality spatially distributed in the GBA? (2) What is the association between land use and urban vitality at various scales? (3) Are there differences in the association between land use and urban vitality among cities with diverse urban development backgrounds?

2. Materials and Methods

2.1. Study Area

The GBA comprises two Special Administrative Regions, namely Hong Kong and Macao, and nine cities in Guangdong Province: Guangzhou, Shenzhen, Zhuhai, Foshan, Huizhou, Dongguan, Zhongshan, Jiangmen, and Zhaoqing (Figure 1). The total area of the GBA is approximately 56,000 km², the population exceeds 86 million, and the GDP exceeds CNY 13 trillion [29]. However, due to tightening constraints, the uneven spatial distribution of high-quality resources, increasing ecological and environmental pressures, and a gradual decline in demographic advantages, the GBA faces pronounced issues of unbalanced spatial development, which are characterized by the phenomena of “over-concentration and under-sparsity” and “over-rich and under-poor”.

2.2. Urban Vitality Assessment

Based on the understanding from existing research, urban vitality is widely considered a multidimensional composite parameter that includes various facets related to the city’s attractiveness and the intensity of population activities, such as society, economy, culture, and environment [30]. Consequently, this study constructed a comprehensive system to assess urban vitality using multisource data from these four dimensions. Considering the representativeness and availability of the data, we assessed urban vitality across each dimension using multisource data. Table 1 lists the data in this study and their descriptions.

• Social vitality

Population density is directly related to the frequency and diversity of the social activities of urban residents and is an important indicator to measure the social vitality of a city. Therefore, this study used population data (0–128,753 people/km²) to calculate social vitality as follows:

(1)$S_i={\displaystyle \frac{P_i}{A_i}}$

where $S_i$ is the social vitality value of region $i$, $P_i$ is the population count of region $i$, and $A_i$ is the area of region $i$.

• Economic vitality

Housing prices are closely linked to the level of urban economic development [31]. Because housing prices are easily spatialized, they can effectively represent the economic vitality of a city. Nighttime light data are also a common means of assessing the level of urban economic development [32,33]; however, nighttime light intensity is not always directly correlated with the intensity of economic activity. In this study, the mean values of housing prices (2008.84–1,360,000.00 yuan/m²) and nighttime light intensity data (0.32–945.61) were used to compute the economic dimension of urban vitality (Equation (2)).

(2)$E_i={\displaystyle \frac{H_i+N_i}{2}}$

where $E_i$ is the economic vitality value of region $i$, $H_i$ is the normalized housing price of region $i$, and $N_i$ is the normalized nighttime light intensity of region $i$.

• Cultural vitality

POI data have characteristics, such as richness, accuracy, timeliness, and diversity, that can intuitively reflect the quantity and spatial distribution of various urban facilities; thus, POI data offer a powerful tool to study urban vitality [34]. This study used the kernel density of cultural POIs (0–12192376.00) to represent the cultural vitality ($C_i$) of a city, including categories such as science education, tourist attractions, leisure and entertainment, and sports and fitness.

• Environmental vitality

Urban parks form an essential part of the urban environment, represent the main green spaces within cities, and effectively improve the quality of the urban ecological environment [32]. This study used the kernel density of urban park POIs (0–58,199.87) to represent the environmental vitality ($D_i$) of a city.

• Comprehensive urban vitality

The values of social, economic, cultural, and environmental vitality were equally weighted and summed to obtain a comprehensive urban vitality value (Equation (3)). In addition, before these calculations, to standardize the scale differences among various data types, $S_i$, $H_i$, $N_i$, $C_i$, and $D_i$ were normalized to a scale of 0–1.

(3)$V_i={\displaystyle \frac{S_i+E_i+C_i+D_i}{4}}$

Table 1

Data collection and processing.

Variables	Evaluation Data	Description and Data Source
Social vitality	Population density	Population data with a 1-km spatial resolution from the LandScan Global Population Database [35].
Economic vitality	Housing price	Including the neighborhood’s name, price, area, latitude and longitude; these data were collected utilizing a web crawler from mainstream real estate transaction websites (URLs: https://www.lianjia.com, https://www.ke.com and https://hk.centanet.com; accessed on 5 May 2023).
	Nighttime light value	Annual nighttime light remote sensing data from 2023 [36].
Cultural vitality	Density of cultural POIs	POI data about culture- and environment-related locations, including categories such as science and education, tourist attractions, leisure and entertainment, and sports and fitness. These data were collected utilizing the API interface provided by Gaode Map.
Environmental vitality	Density of urban park POIs	POI data for urban parks, collected utilizing the API interface provided by Gaode Map.
Land use area	Land use data	Land-cover data with a 10-m spatial resolution from 2021 [37].

All of the data were converted to a raster format in ArcGIS 10.5. Subsequently, 2 km × 2 km grid layers were established, and all of the relevant data were resampled to a uniform spatial resolution of 2 km.

2.3. Statistical Analysis

Spatial autocorrelation analysis serves as a crucial tool for evaluating the extent of spatial clustering among the variables. In the scope of this study, both global Moran’s I and local Moran’s I statistics were used to quantify the characteristics of spatial clustering pertaining to urban vitality. The global Moran’s I index fluctuates between −1 and 1, and values approaching 1 signify strong positive spatial autocorrelation, whereas those near −1 signify strong negative spatial autocorrelation. The local indicators of spatial association (LISA) were used to test the significance of local Moran’s I to visualize the spatial clusters. Hotspot analysis was applied to identify areas of urban vitality that exhibited statistically significant cold and hot spots.

This study used Spearman correlation and regression models to explore the relationships between various land-use types and urban vitality, including both ordinary least squares (OLS) and geographically weighted regression (GWR). GWR is a local linear regression technique that accounts for spatially varying relationships; it is adept at elucidating the local spatial dynamics and heterogeneity among variables. Using the variance inflation factor (VIF), collinearity diagnostics were performed prior to conducting the regression analysis. Data processing and analytical procedures were executed using ArcGIS 10.5 and R Studio 4.0.

3. Results

3.1. Spatial Distribution of Urban Vitality

3.1.1. Urban Vitality of Different Dimensions

• Social vitality

The mean social vitality value for the GBA is 0.028. The areas with high levels of social vitality are predominantly concentrated in the southern and central regions of the GBA, including Dongguan, Shenzhen, Macau, Guangzhou, Foshan, and Zhongshan. Macau exhibits the highest average social vitality at 0.185. In contrast, the eastern and western parts of the GBA, including Zhaoqing, Huizhou, and Jiangmen, exhibit lower levels of social vitality with a scattered distribution of vitality points, among which Zhaoqing has the lowest mean social vitality at 0.007 (Figure 2).

• Economic vitality

The mean economic vitality value for the GBA is 0.034. The areas with high economic vitality are primarily concentrated in the southern and central parts of the GBA, including Hong Kong, Shenzhen, Macau, Dongguan, and Guangzhou; Hong Kong has the highest average economic vitality at 0.220. The southwestern and eastern regions of the GBA, which mainly comprise Jiangmen, Huizhou, and Zhaoqing, have comparatively lower economic vitality, and Zhaoqing has the lowest mean economic vitality at 0.015 (Figure 2).

• Cultural vitality

The mean cultural vitality value for the GBA is 0.087. Areas with high levels of cultural vitality are predominantly located in the central region of the GBA, including Guangzhou, Foshan, Dongguan, and Shenzhen; Dongguan has the highest mean cultural vitality at 0.418. Regions with lower levels of cultural vitality are mainly in the western part of the GBA, particularly Zhaoqing and Jiangmen; Zhaoqing has the lowest mean cultural vitality at 0.007 (Figure 2).

• Environmental vitality

The mean environmental vitality for the GBA is 0.138. The areas with high levels of environmental vitality are largely in the center of the GBA, including Guangzhou, Foshan, Zhongshan, Dongguan, Shenzhen, Hong Kong, and Macau; Shenzhen has the highest mean environmental vitality at 0.742. Regions with comparatively low environmental vitality are primarily in the western and eastern parts of the GBA, including Jiangmen, Zhaoqing, and Huizhou; Zhaoqing has the lowest mean environmental vitality at 0.012 (Figure 2).

3.1.2. Comprehensive Urban Vitality

The mean comprehensive urban vitality value for the GBA is 0.072. The areas with high levels of urban vitality are predominantly concentrated in the southern and central parts of the GBA, including Guangzhou, Foshan, Zhongshan, Dongguan, Shenzhen, Hong Kong, and Macau; Shenzhen has the highest mean urban vitality at 0.308. Areas with lower urban vitality are mainly in the western part of the GBA, such as Jiangmen and Zhaoqing; Zhaoqing has the lowest mean urban vitality at 0.010. Two cores of high urban vitality are formed at the junction of Guangzhou and Foshan and in the southwestern part of Shenzhen (Figure 2).

Figure 2

Spatial distribution of urban vitality. (a) social vitality; (b) economic vitality; (c) cultural vitality; (d) environmental vitality; (e) comprehensive urban vitality.

[Figure omitted. See PDF]

3.2. Spatial Clustering Characteristics of Urban Vitality

The results of the global spatial autocorrelation analysis of urban vitality in the GBA show a Moran’s I value as high as 0.96, with a p-value below 0.001, which indicates a very strong spatial autocorrelation in the distribution of urban vitality. Thus, high urban vitality values positively influence the urban vitality of neighboring areas. The local indicators of spatial association (LISA) analysis reveals that the distribution of urban vitality in the GBA is primarily characterized by two types of clusters: “high–high clusters” and “low–low clusters.” Specifically, the area of the high–high cluster regions is 5615.39 km², i.e., 10.13% of the total area; the area of the low–low cluster regions is 15,362.60 km², i.e., 27.73% of the total area. The high–low cluster and low–high cluster regions account for only 2.10 km² and 7.37 km², respectively. They are primarily distributed at the borders of Zhuhai, Macau, Shenzhen, and Hong Kong. More specifically, the junction of Guangzhou–Foshan and the areas of Hong Kong, Shenzhen, and Dongguan form two major high–high cluster regions. The areas in the eastern and northwestern parts of the GBA, including Zhaoqing, the eastern part of Jiangmen, and the northwestern part of Huizhou, form low–low cluster regions (Figure 3).

The hotspot analysis of urban vitality reveals a distinct dual-core phenomenon in the GBA: the Guangzhou–Foshan urban vitality core and the Hong Kong–Shenzhen–Dongguan urban vitality core (Figure 4). The interconnection of these two major urban vitality cores gives the GBA an overall core–periphery spatial pattern of urban vitality distribution, where central areas, such as Guangzhou, Foshan, Shenzhen, Dongguan, Hong Kong, and Macau, exhibit higher urban vitality, and peripheral areas show weaker vitality. The hotspot area covers 9670.73 km², which accounts for 17.45% of the total area. The coldspot area covers 27,309.94 km², which accounts for 49.29% of the total area. Peripheral cities, such as Zhaoqing, Jiangmen, Zhongshan, Zhuhai, and Huizhou, also have relatively obvious urban vitality hotspots. However, these hotspots are disconnected from the hotspots in the surrounding areas, which results in a localized “island” phenomenon. The “vitality islands” in these cities are mostly their central urban areas.

3.3. Association between Land Use and Urban Vitality

First, we categorized all land use types into categories of built-up areas and non-built-up areas, and then conducted a Spearman correlation analysis with urban vitality for each category. The analysis reveals that both built-up areas (R² = 0.66, p < 0.001) and non-built-up areas (R² = −0.66, p < 0.001) are significantly correlated with urban vitality. The results of the ordinary least square (OLS) model indicate that under a global pattern that does not consider the role of geographical relationships, at a scale of 2 km × 2 km, land use in the GBA significantly influences urban vitality (R² = 0.46, p < 0.001) (Table 2). Specifically, the areas of cropland, wetlands, water bodies, built-up areas, and bare land significantly affect urban vitality. Wetlands, built-up areas, and bare land areas have significant positive effects on urban vitality: built-up areas exert the strongest influence, with a β value of 0.6547, whereas the positive effects of wetlands and bare land are relatively weak (0.0162 and 0.0313, respectively). The area of cropland and water bodies has a significant negative effect on urban vitality, where cropland has a larger influence, with a β value of −0.1310. The area of grassland and shrubland does not significantly affect urban vitality.

Based on the results of the geographically weighted regression (GWR) model, the mean values and proportions of the positive and negative values of the regression coefficients were organized (Table 2). Hence, different land-use types have varying associations with urban vitality, and there are clear differences in the proportion of positive and negative effects. The areas of grassland and built-up areas mainly affect urban vitality positively; in particular, built-up areas have a positive effect, accounting for about 99.95%. The areas of cropland, shrubland, wetlands, water bodies, and bare land mainly negatively affect urban vitality; cropland has the highest proportion of negative effects at 88.36%, followed by shrubland at 67.46%.

The spatial association between land-use types and urban vitality can be divided into two patterns: a core–periphery progressive distribution and a high–low interspersed scattered distribution. Cropland, grassland, built-up areas, and water bodies exhibit a core–periphery progressive distribution, and the areas of cropland, grassland, and water bodies tend to have increasing influence from the center to the periphery, whereas the influence of built-up areas decreases from the center to the periphery. The areas of shrubland, wetlands, and bare land exhibit a high–low interspersed scattered distribution pattern (Figure 5).

The association between land-use types and urban vitality also varies in the cores of influence. Overall, the Guangzhou–Foshan junction and the Hong Kong–Shenzhen area are important core regions. The influence of different land types on urban vitality shows distinct dual-core patterns. The negative effect cores of cropland and the positive and negative effect cores of grassland and shrubland are predominantly situated at the junction of Guangzhou and Foshan or within the Hong Kong and Shenzhen areas. The effects of wetlands, bare land, built-up areas, and water bodies on urban vitality exhibit a clear “multi-core” pattern (Figure 5).

3.4. Comparative Analysis of Different Cities

Independent OLS regression models were fitted for each city to assess the association between land use and urban vitality. Table 3 shows the results.

In all of the cities, the built-up areas exhibit a highly significant positive effect on urban vitality. In 80% of the cities, the built-up areas have more influence than other land types. The built-up areas of Zhuhai have the smallest effect on urban vitality (with a β value of 0.1492), and the built-up areas of Zhaoqing have the greatest effect (with a β value of 0.7633). With decreasing urban vitality, the positive effect of the area of built-up areas on urban vitality gradually increases.

The area of cropland shows a highly significant negative effect on urban vitality in every analyzed city. Dongguan experiences the greatest negative effect of the cropland area on urban vitality (with a β value of −0.2406), and Huizhou experiences the smallest effect (with a β value of −0.0490). With decreasing urban vitality, the negative effect of cropland area on urban vitality tends to diminish.

In cities with higher levels of urban vitality, the area of grassland mainly exhibits a significant negative effect on urban vitality, except in Hong Kong, which has a β value of 0.2111 for grassland. In cities with lower levels of urban vitality, the area of grassland mainly shows a significant positive effect, and when the vitality decreases, this positive effect weakens; the lowest impact is in Zhaoqing with a β value of 0.0461.

The shrubland area exerts a significant negative effect on urban vitality in 40% of the cities: Hong Kong and Zhuhai have a larger negative effect with β values of −0.1343 and −0.1446, respectively, Shenzhen and Jiangmen have a weaker negative effect, and there is no significant impact on other cities.

The wetland area significantly affects urban vitality in 60% of the cities. The greatest positive effect is in Shenzhen, which has the highest level of urban vitality (with a β value of 0.3980), and the strongest negative effect is in Zhaoqing, which has the lowest urban vitality (with a β value of −0.2357). Overall, when urban vitality decreases, the effect of the wetland area on urban vitality shows a trend of decreasing positive effects and increasing negative effects.

The area of water bodies significantly affects urban vitality in 70% of the cities, mainly showing a negative effect, except in Zhaoqing. The greatest negative effects on urban vitality are observed in Dongguan and Zhuhai (where the β values are −0.2344 and −0.2327, respectively), and a significant positive effect is observed in Zhaoqing (with a β value of 0.0954).

The area of bare land significantly affects the urban vitality in half of the cities. It tends to show a positive effect in cities with higher vitality, and the greatest positive effect is in Hong Kong, with a β value of 0.1110. In cities with lower vitality, it tends to show a negative effect, and the greatest negative effect is in Zhuhai, with a β value of −0.1024. The effect in Zhaoqing is weaker, with a β value of 0.0196.

4. Discussion

The spatial distribution of urban vitality in the GBA is uneven. Overall, the central and south-central regions exhibit higher levels of urban vitality across all of the dimensions, whereas the peripheral areas in the east and west have relatively low levels of urban vitality (Figure 2). We believe that the reason is a combination of the natural environment, historical culture, policies, and other factors that have gradually led to the development of a core area in the central and south-central regions of the GBA. The core area is characterized by economic prosperity, population aggregation, a rich culture, and high urban environmental quality. In contrast, urban development in the peripheral areas is lagging. For example, in terms of the natural environment, the central cities of the GBA benefit from the geographical advantages of the Pearl River estuary and excellent port conditions. In terms of historical culture, Guangzhou was one of the world’s largest foreign trade ports during the Tang Dynasty, and Hong Kong and Macau have developed a fusion of Chinese and Western cultural characteristics due to modern colonial governance. In terms of policy, Shenzhen is one of China’s first special economic zones, and Hong Kong and Macau maintain the capitalist and free economic systems of the colonial period under the “one country, two systems” framework. In addition, the assessment of urban vitality in this study is affected by the size of the city. Hong Kong and Macau have relatively small urban areas, leading to a more homogeneous spatial distribution of urban vitality and a relatively high average level of urban vitality.

The results of the spatial autocorrelation analysis reflect the spatial heterogeneity of urban vitality in the GBA (Figure 3). Local indicators of spatial association show that the junction of Guangzhou and Foshan and the Hong Kong–Shenzhen–Dongguan area have formed obvious cores of high urban vitality aggregation (Figure 3). This is related to the long-term close urban cooperation practices in these two areas [38]. In approximately 2000, Hong Kong, Shenzhen, Guangzhou, and Foshan gradually established a series of legislative frameworks for urban cooperation, such as the Memorandum of Enhancing Shenzhen–Hong Kong Cooperation, which was signed in 2004, and the formal signing of the strategic cooperation agreement between Guangzhou and Foshan in 2009. These cities have cooperated in terms of urban planning, transportation infrastructure, industrial collaboration, and environmental protection. Thus, they have gradually formed a synergistic development space that is closely connected in space, functionally matched, culturally connected, and densely populated in terms of personnel exchanges [39], which leads to a high–high cluster distribution of urban vitality in these spaces.

The hotspot analysis results further confirm the effectiveness of the collaborative development of cities in the GBA (Figure 4). In February 2019, China introduced the Outline Development Plan for the Guangdong–Hong Kong–Macao Greater Bay Area, which positioned Hong Kong–Shenzhen, Guangzhou–Foshan, and Macau–Zhuhai as regional growth poles to transform and optimize the regional spatial pattern and achieve polycentric development. This study shows that the Hong Kong–Shenzhen and Guangzhou–Foshan growth poles have formed a spatial connection through Dongguan and created a concentrated urban vitality hotspot area. In contrast, the Macau–Zhuhai area has not formed a clear urban vitality growth pole, and intercity cooperation must be further strengthened (Figure 4).

Zhaoqing, Jiangmen, Zhongshan, and Huizhou, which are located on the periphery of the GBA, exhibit only small-scale scattered urban vitality hotspots in the city centers, and there is a lack of spatial connections with the core urban vitality hotspot areas (Figure 4). In addition, there are large areas of urban vitality coldspots in Zhaoqing, Jiangmen, and Huizhou, which will be key areas for improving urban vitality in the GBA in the future. The coordination of policies and planning connections among the scattered hotspot areas and between cold- and hotspot areas will be effective in optimizing the functional layout of the region, promoting the coordinated development of cities in the region, and enhancing the overall urban vitality of the GBA.

The built environment provides the necessary space and infrastructure for urban economic, social, and cultural activities. Its impact on urban vitality is a focus of related research. Urban form, transportation conditions, land-use combinations, and other factors affect urban vitality. Urban form factors (such as the block sizes, building density, building height, and age of the buildings) and transportation conditions (such as accessibility, road network density, and transportation station density) are closely related to urban vitality [40,41]. Related studies have produced diverse results, but all of the studies have confirmed the important influence of the built environment on urban vitality. The built-up area is the carrier of the built environment, so in this study, a larger built-up area corresponds more to urban vitality (Figure 5, Table 2). In contrast to the promoting effect of built-up areas on urban vitality, the expansion of farmland and ecological land is generally negatively correlated with the intensity of socioeconomic activities [42,43,44,45]. The results of this study confirm these views, where the areas of cropland, shrubland, and water bodies mainly exert a negative effect on urban vitality (Table 2).

In this study, we found that the associations between land use and urban vitality vary across cities (Table 3). We believe that the main reason fort this is the differences in the urban development backgrounds of these cities; land undertakes various functional roles in different cities, which leads to distinct constraints and the potential for enhancing urban vitality in different cities [46]. In the process of urban development, achieving a balance between the expansion of built-up areas and land-use efficiency is crucial. There is a power–law relationship between built-up areas and economic development in the GBA: the GDP growth rate decreases as the built-up area increases [47]. China has taken a series of measures to strictly control land use, such as demarcating urban development boundaries in order to control the disorderly expansion of cities and protect agricultural and ecological spaces. Given the increasing scarcity of land resources and the governance of strict land control policies, improving land-use efficiency is more important than expanding the land scale for developed cities [48]. In the GBA, cities with higher vitality often have a higher level of development, and their potential for the expansion of built-up areas is very limited; some cities have reached the limit. In contrast, the socioeconomic activities of developing cities are more sensitive to changes in land area [49]. In addition, due to the scarcity of land resources, for developed cities with high levels of vitality, bare land is more likely transformed into land-use types that promote socioeconomic activities. Therefore, in this study, the increase in built-up areas was found to play a more significant role in promoting the urban vitality of developing cities with low vitality, and bare land has a promoting effect on the urban vitality of high-vitality cities (Table 3). For high-vitality cities, improving the land-use efficiency of the existing built-up area will be important for enhancing urban vitality in the future.

We found that developing cities with lower vitality had a more advanced primary industry, which provided stronger support for local economic and social development. For example, cities such as Zhaoqing and Jiangmen are important agricultural areas in the GBA. For these cities, the expansion of cropland, grassland, and water bodies provides essential natural resources for local socioeconomic activities and promotes the development of labor-intensive industries, such as agriculture, forestry, animal husbandry, and fisheries [50]. Therefore, the negative effect of the expansion of these land types on urban vitality is relatively low and may even play a promoting role (Table 3).

Wetlands form a unique natural ecosystem in cities. In addition to their significant ecological and environmental benefits, wetlands can provide various socioeconomic benefits [51]. Against different urban development backgrounds, there are differences in the supply and demand relationships of ecosystem services [52,53]. In this study, wetlands promoted the urban vitality of high-vitality cities but weakened that of low-vitality cities (Table 3). A possible reason is that for high-vitality cities with scarce natural resources, wetlands offer an important leisure and recreation space, promote the development of the ecological tourism industry, and drive the economic benefits of related industries. For low-vitality cities with relatively abundant natural resources, the public’s demand for leisure and recreation is relatively weak, and the social, cultural, and other service functions of wetlands have not been fully utilized.

Unlike existing studies that focused on individual typical cities, this study examined the relationship between urban vitality and land use in the GBA at both regional and urban scales for the first time. The differences in this relationship among cities with different developmental backgrounds were revealed and will contribute to the formulation of urban vitality enhancement strategies in the GBA from the perspective of regional collaborative and individual cities. The study also provides a reference for urban vitality research in other cities and urban agglomerations. However, this study has certain limitations. Currently, there is no unified standard for the assessment indicators and methods of urban vitality, and the POI and housing price data utilized in this study may be subject to limitations inherent in the collection technologies. This study incorporated multisource data to comprehensively assess urban vitality across four dimensions, mitigating biases caused by reliance on a single dataset and making the evaluation results more comprehensive and objective. However, when generalizing and comparing the results, potential biases caused by different assessment indicators and methods must be considered. In future research, attempting to establish a unified standard to assess urban vitality will help promote and apply the results of related studies, and introducing qualitative assessments of urban vitality, such as the subjective experiences and cultural differences of the residents, can make the evaluation results more comprehensive.

5. Conclusions

Within the GBA, urban vitality across various dimensions exhibits unique spatial distribution characteristics and core areas. The socioeconomic vitality core is in Hong Kong and Macau, and the cultural vitality core is in the Guangzhou–Foshan region. Overall, the urban vitality in the GBA exhibits a spatial distribution trend that is high in the center and low on the periphery, and there is a clear spatial spillover effect. The high urban vitality in the core areas of Guangzhou–Foshan and Hong Kong–Shenzhen–Dongguan has driven an increase in urban vitality in the surrounding areas, whereas the lower urban vitality in peripheral core areas has decreased the vitality of neighboring regions.

Urban built-up areas and non-built-up areas are both significantly correlated with urban vitality. The associations between land use and urban vitality exhibit strong spatial heterogeneity and vary across different scales, and built-up areas and cropland are key factors that affect urban vitality. At the regional scale, built-up areas have a stronger effect on areas with higher urban vitality; meanwhile, at the urban scale, built-up areas in cities with lower vitality have a greater effect; it is therefore crucial to select the appropriate scale for related studies.

The associations between land use and urban vitality also vary significantly among cities, where high-vitality and low-vitality cities have different drivers. The reason for this is the inconsistent service functions of land in different cities. In high-vitality cities, the expansion space for built-up areas, which is an important carrier of socioeconomic activities, is limited; hence, its promotional effect on urban vitality is constrained. Conversely, natural landscapes, particularly wetlands, exert a notably positive influence on urban vitality. The key carriers of agricultural activities, including cropland, grassland, and water bodies, are associated with a more pronounced negative effect on urban vitality. For low-vitality cities, built-up areas serve as an effective catalyst for enhancing urban vitality. In addition, land types that are integral to agricultural, forestry, animal husbandry, and fishing operations, such as cropland, water bodies, and grassland, likely foster urban vitality or exert a mitigated negative influence. In the context of the increasingly evident development of urban agglomerations, it is necessary to shift the research perspective on urban vitality from urban built-up areas to regional land use.

The construction of the GBA is a strategic initiative of national importance for China. Assessing urban vitality and analyzing the associations between land use and urban vitality at both regional and urban scales in the GBA can provide essential support to gauge the progress and identify challenges in the development of the GBA. Such analyses also provide valuable, targeted insights into formulating urban vitality enhancement strategies that are attuned to the diverse developmental contexts of the cities in the GBA. Furthermore, this study can provide a reference for urban vitality research in other regions confronting similar challenges of uneven development.

Author Contributions

Conceptualization, C.D. and Z.Y.; methodology, C.D. and Y.W.; formal analysis, C.D. and Z.Y.; data curation, Y.W.; writing—original draft preparation, C.D.; writing—review and editing, Z.Y.; visualization, Y.W.; supervision, D.Z. and J.W.; project administration, D.Z. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

All data that support the findings of this study are available upon request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Location of the study area.

Figure 3. LISA agglomeration of urban vitality in the GBA.

Figure 4. Urban vitality cold- and hotspot distribution in the GBA based on hotspot analysis.

Figure 5. Impact of land use on urban vitality based on the results of the GWR model.

Table 2

Regression coefficients of the OLS model and GWR model.

ExplanatoryVariables	OLS(R2 = 0.46, p < 0.001)	GWR (R2 = 0.76)
		Mean Β Value	Positive Rate	Negative Rate
Cropland	−0.1310 ***	0.1571	0.1164	0.8836
Grassland	−0.0086	0.1511	0.5943	0.4057
Shrubland	−0.0040	2.0672	0.3254	0.6746
Wetland	0.0162 *	1.9349	0.4316	0.5684
Water bodies	−0.0620 ***	0.0780	0.4387	0.5613
Built-up areas	0.6547 ***	0.2300	0.9995	0.0005
Bare land	0.0313 ***	0.1913	0.4270	0.5730

***: Difference is significant at the 0.001 level (2-tailed). *: Difference is significant at the 0.05 level (2-tailed). The mean β was calculated from the absolute values of the β coefficients.

Table 3

Standardized regression coefficients for the impact of land use on urban vitality based on OLS models.

City	Cropland	Grassland	Shrubland	Wetland	Water Bodies	Built-Up Areas	Bare Land
Shenzhen	−0.2159 ***	0.0018	−0.0628 *	0.3980 ***	−0.1398 ***	0.3908 ***	0.0855 **
Dongguan	−0.2406 ***	−0.1881 ***	−0.0140	0.1870 ***	−0.2344 ***	0.5453 ***	0.0233
Hong Kong	−0.1908 ***	0.2111 ***	−0.1343 **	0.0097	−0.1605 ***	0.3026 ***	0.1110 **
Foshan	−0.1444 ***	−0.2170 ***	0.0121	0.0622 *	0.0001	0.6917 ***	0.0198
Guangzhou	−0.1648 ***	−0.2108 ***	0.0004	−0.0162	−0.0203	0.7245 ***	0.0111
Zhongshan	−0.2146 ***	−0.1960 ***	0.0057	0.0292	−0.1837 ***	0.6213 ***	−0.0516
Zhuhai	−0.2118 ***	0.2216 ***	−0.1446 ***	0.0958 *	−0.2327 ***	0.1492 ***	−0.1024 *
Huizhou	−0.0490 ***	0.1012 ***	−0.0205	−0.0989 ***	0.0182	0.6719 ***	−0.0184
Jiangmen	−0.1490 ***	0.1035 ***	−0.0243 *	0.0038	−0.0373 *	0.7013 ***	−0.0547 ***
Zhaoqing	−0.0987 ***	0.0461 ***	0.0022	−0.2357 ***	0.0954 ***	0.7633 ***	0.0196 *

***: Difference is significant at the 0.001 level (2-tailed). **: Difference is significant at the 0.05 level (2-tailed). *: Difference is significant at the 0.05 level (2-tailed).

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