Spatiotemporal Dynamic Characteristics of Land Use Intensity in Rapidly Urbanizing Areas from Urban Underground Space Perspectives

Abstract

Land use intensity (LUI) reflects the utilization status of land use. However, traditional LUI assessments have been conducted for land space governance with a primary focus on surface land. Thus far, the explicit variation and spatiotemporal characteristics of land use of underground space (LUUS), particularly the quantization of LUUS-related intensity, are not well understood. Using the case of Wuhan in China, this study takes the main urban area of Wuhan as the research area, based on the time series data of the underground space information survey of analysis units from 2002 to 2018. This reflected the distribution pattern and evolution characteristics of underground space in terms of the intensity, the concentration and spatial hot-spots by using the spatiotemporal analysis framework. The results show that: (1) The LUUS exhibits spatial characteristics of global dispersion, and local aggregation increased and expanded along the northwest–southeast direction; (2) The global spatial dependency of LUUS is strong and the degree decreases with the expansion of the scope; (3) The LUUS is mainly developed in a relatively concentrated mode, and the concentration degree decreases with time; (4) The main development area of the LUUS is gradually expanding from within the inner ring line outside the second ring road in different periods, and the spatial difference is more obvious and increasing. Our study renews the indicators of quantitative LUI evaluation based on underground spatial data. The findings refreshed the knowledge base concerning the spatiotemporal heterogeneity in terms of underground space intensity and provided new insights into spatial governance.

1. Introduction

The rapid growth of cities has led to more and more people living in urban areas [1,2,3], resulting in a rapid increase in spatial scarcity in urban central areas. This has brought continuous demand for the increase in land use intensity (LUI). However, it is becoming increasingly difficult for traditional single, two-dimensional, aboveground space utilization to meet practical needs. Cities are shifting towards an era of three-dimensional space, and they need to become more compact. By meeting more three-dimensional space needs, they can improve their ability to carry out various human activities [4,5]. However, as an important spatial carrier for urban activities, the land use of underground space (LUUS) has not yet gained sufficient recognition and understanding. The traditional view is that the strength of surface buildings can reflect the level of urban spatial urbanization, as it represents the intensity of human activities within the city. However, with the continuous improvement of urbanization, especially in the postindustrial era, human urban activities continue to shift from being two-dimensional to three-dimensional, extending vertically from the surface and above to underground [6,7,8,9]. The traditional two-dimensional and planar surface building strength cannot accurately reflect the urban spatial utilization intensity. This is especially true when measuring the differences in spatial intensity levels within cities, which shows obvious limitations and lag. It is urgent to find a new and more accurate model to meet the measurement of urbanization level differences within three-dimensional and three-dimensional cities. In areas with rapid urbanization, serious adverse consequences such as underground resource abuse, underground disaster accidents, and environmental damage continue to occur in underground spaces [10,11]. Decision-makers, scholars, and the public should urgently attach great importance to this and actively respond. This is of great significance for improving the safety and sustainability of cities and human settlements.

LUUS has always been a complex and systematic concept accompanying the process of urbanization, which integrates multiple elements such as natural resources, the social economy, the ecological environment, cultural traditions, population activities, and so on. Its connotation constantly evolves and changes due to differences in urban size, development stage, and the degree of urbanization. Relevant research on the utilization of natural conditions and engineering fields [12,13,14] in LUUS, for example, fast transdimensional Bayesian transient electromagnetic imaging for urban underground space, has been continuously undertaken. There are significant differences in socio-economic sustainability aspects [15,16,17,18] and management systems [19,20,21,22]. The evaluation of spatial performance and supply–demand ratios of urban underground space has been valued. The systems approach to urban underground space planning and management is needed. This study reviews the literature regarding urban underground planning through a systems lens.

Therefore, it is evident that the spatiotemporal pattern and characteristics of the LUUS formed by Chinese cities still need to be explored [23,24,25]. The spatial structure of LUUS cannot be examined from static urban underground space projects, as this dynamic spatiotemporal structure provides an important basis for decision-making on the utilization of LUUS at the urban level. Another important piece of knowledge drawn from reviewing previous studies is that China’s LUUS research has only focused on economically developed capital cities and eastern coastal megacities [15,17,21,26], while inland cities that are undergoing rapid urbanization have been completely ignored. Therefore, China’s conceptual discourse on LUUS is entirely based on big cities. Excluding attention to rapidly urbanizing inland areas in the LUUS assessment framework is a key knowledge gap in China’s urban policy research [27,28,29,30]. China’s new urbanization project addresses the increasingly severe challenges brought by rapid urban growth [31]. China hopes to transform its vibrant small- and medium-sized cities into people-centered, sustainable, and livable cities through new urbanization projects. Therefore, it is undeniable that Chinese policymakers need to urgently understand the spatiotemporal dynamics of LUUS from the perspective of cities undergoing a rapid urbanization process when attempting to recognize, develop, and ultimately implement sustainable growth technologies [32,33].

Overall, considering either the suitability of natural conditions for underground space development or the sustainability and economy of society, it is necessary to have a scientific understanding of the distribution and evolution characteristics of developed underground space. The development of underground space has characteristics such as irreversibility, spatial dependence, and complex development and utilization. A single method makes it difficult to achieve a comprehensive and systematic understanding of the spatiotemporal pattern characteristics of underground space [34,35]. Therefore, there is an urgent need for an integrated method to identify the pattern of underground space and detect its evolution characteristics from different spatial scales, such as global and local or static and dynamic time dimensions. This will improve decision-making regarding the comprehensive utilization of urban space resources, which is particularly important and urgent for rapidly urbanized areas in developing countries with increasingly increasing spatial scarcity [15,26]. At the same time, identifying the spatial pattern of LUUS and exploring its evolution characteristics is not only closely related to sustainable urbanization and population activity space supply, but also has important practical significance for quantifying underground space allocation, guiding the flow of urban development factors, optimizing urban spatial planning, and promoting a people-oriented new urbanization transformation [36,37,38].

The main objective of this study is to: (1) Quantify the importance of LUUS in rapidly urbanizing areas from an intensity perspective; (2) Reveal the spatiotemporal dynamics of LUUS from both global and local perspectives; (3) Provide theoretical guidance for urban spatial system management and underground resource protection.

2. Study Area and Data Source

2.1. Study Area

Wuhan is located in central China, with geographical coordinates of 29°58′–31°22′ N and 113°41′–115°05′ E. It is a megacity in the middle reaches of the Yangtze River and the capital of Hubei Province. Wuhan is an important industrial, scientific and educational base, and a comprehensive transportation hub in China. The land area is approximately 8494 square kilometers and the population is approximately 12 million. As a typical rapidly urbanizing region in central China, Wuhan’s urbanization process has been accelerating since the establishment of the People’s Republic of China, especially since the reform and development. The urbanization rate of the permanent population has increased from 47.4% in 1978 to 80.29% in 2018, representing an increase of 32.89 percentage points; The permanent population of the city has increased from 5.551 million in 1978 to 11.081 million in 2018, representing a net increase of 5.53 million, with an average annual increase of 138,000 people.

According to the current “Wuhan Urban Master Plan (2010–2020)”, Wuhan City is made up of a four-level urban system consisting of the main city, new cities and new city clusters, central towns, and general towns. Among them, the main urban area is the core of the urban system, reflecting the function of gathering urban development factors. This study took the main urban area determined in the overall plan as the main research area, namely the areas within the third ring road, including the locally extended areas of Zhuankou, Miaoshan, and the Wuhan Iron and Steel Group. Due to the geographical barrier between the Yangtze River and the Han River, Wuhan City is divided into three regions: Hankou, Wuchang, and Hanyang, known as the “Three Towns of Wuhan”.

According to the “Blue Book of Urban Underground Space Development in China 2019”, the comprehensive level of underground space development in Wuhan is among the top in the country and the first in the central region. In the process of continuous rapid development, using a typical rapidly urbanizing area, the main urban area of Wuhan was used as an observation sample to study the distribution pattern and evolution characteristics of urban underground space as it has typical representativeness and an important reference value. Figure 1 shows the location of study area.

2.2. Data Sources

The data used in this study mainly come from the underground space information survey data of the Wuhan Surveying and Mapping Department, the planning unit vector space data of the Planning Department, and the population, urbanization, and other socio-economic data published by the Statistics Department on statistical products such as the “Wuhan National Economic and Social Development Statistical Bulletin” and the “Wuhan Statistical Yearbook”. In order to reflect the typicality and representativeness of the rapidly developing urbanization areas, the main urban area of Wuhan City was selected as the research area. The underground space information survey data were collected using time series data from 2002 to 2018, and the USI was calculated using the unit area of the underground space construction area. The scale and degree of underground space development were measured, and 600 planning units after merging were used as analysis samples. The inventory of USI and the increment of USI were calculated for a certain period of time in the main urban area of Wuhan City over the years.

3. Methods

Figure 2 provides the overall method framework, explaining the three main steps to complete the analysis. Firstly, the development intensity of urban underground space was defined using USI and data preprocessing was performed. Secondly, the autocorrelation of LUUS in global space was analyzed using the global Moran’s I method, and the spatial relationship between USI and RHP was explored based on bivariate local global Moran’s I. Thirdly, Gi* was used to identify the local strength features of LUUS.

3.1. Spatial Intensity—Underground Space Intensity (USI)

Underground space intensity (USI) is an indicator used to measure the scale of underground space development per unit area, reflecting the degree and overall efficiency of underground space development within a unit. Generally speaking, the larger the USI, the higher the degree of development and efficiency of unit underground space. The calculation formula is as follows:

$$USI=\frac{{\sum }_{i=1}^n{S}_i}{A_k}$$

(1)

In the formula, USI is the intensity value of underground space development, $A_k$ is the area size of unit $k$, $n$ is the total number of underground space development patches, and $S_i$ is the area of underground space development patches $i$.

3.2. Spatial Autocorrelation—Global Moran’s I

In order to obtain subtle insights through the spatial pattern measurement of urban underground space [7], GIS-based correlation analysis was used to elucidate the urban underground space based on strength correlation. The spatial pattern and impact of LUUS are largely influenced by scale, so different results may occur. Spatial autocorrelation and the global Moran’s I statistics are useful for understanding patterns. Two types of clustering are well-known, (i) global clustering and (ii) local clustering [39]. Global spatial autocorrelation can describe the distribution characteristics of spatial feature attribute values throughout the entire region, and can be characterized using global Moran’s I. The value range of the global Moran’s I is [−1, 1], and an index greater than 0 indicates a positive spatial correlation. The larger the value, the more significant the spatial correlation; An index less than 0 indicates negative spatial correlation, and the smaller its value, the greater the spatial difference; An exponent equal to zero indicates that the spatial distribution is random. The calculation formula for global Moran’s I is as follows:

$$I=\frac{n}{S_0}\frac{{\sum }_{i=1}^n{\sum }_{i=1}^n{w}_{i,j}(x_i-\overline{x})(x_j-\overline{x})}{{\sum }_{i=1}^n{(x_i-\overline{x})}^2}$$

(2)

In the formula, $w_{i,j}$ is the spatial weight between elements $i$ and $j$, $n$ is equal to the total number of elements, and $S_0$ is the aggregation of all spatial weights.

For the global Moran’s I, the standardized statistic $Z$ can be used to test whether $n$ regions have an autocorrelation relationships. The calculation formula for $Z$ is as follows:

$$Z=\frac{I-E\left(I\right)}{\sqrt{VAR\left(I\right)}}$$

(3)

Here, $E\left(I\right)$ and $VAR\left(I\right)$ are the theoretical expectations and theoretical variances, respectively. When the $Z$ value is positive and significant, it indicates the existence of positive spatial autocorrelation; When the $Z$ value is negative and significant, it indicates the existence of negative spatial autocorrelation; When $Z$ is 0, the observed values exhibit an independent random distribution. And for the distribution patterns of similar elements at different times, the larger the $Z$ value, the greater the degree of aggregation; The smaller the $Z$ value, the greater the degree of dispersion.

The bivariate Moran’s I was used to explore the spatial clustering and dispersion between underground space urbanization and urban location value, using global and local bivariate Moran’s I values for this purpose. Here, the local bivariate Moran’s I was used to examine the spatial correlation between USI and regional housing prices (RHP) in LUUS and to determine the extent to which there was spatial correlation; Local binary Moran’s I was used to draw spatial correlations between different units. More details on the theory and calculation process of global or local Moran’s I can be found in previous studies [40].

The global bivariate Moran’s I value ranges from −1 to 1, where the positive values and negative values refer to the positive and negative spatial correlation between the USI and RHP, respectively. Generally, a Moran’s I value of 0.2 or higher is regarded as highly clustered. The local bivariate Moran’s I is applied to identify four types of spatial correlations at the grid level: High–high (H–H) clusters indicate a high USI surrounded by high RHP; high–low (H–L) clusters indicate a high USI surrounded by low RHP; low–high (L–H) clusters indicate a low USI surrounded by high RHP; and low–low (L–L) clusters indicate a low USI surrounded by low RHP. Furthermore, grids referring to H–H clusters were extracted to identify the area where urban land density and urban thermal environment are closely related to each other. The small number represents the standard deviation of USI and RHP, respectively. When the absolute values of I and I’ approach 1, they have a stronger spatial correlation. In addition, clustering maps are generated through local binary Moran’s I calculations to reflect the spatial correlation between the USI of a specific spatial unit and the RHP of adjacent spaces.

3.3. Spatial Clusters—Hot-Spot Analysis (Getis-Ord Gi*)

The Gi* indicator can express the second-order distribution attributes of geographical phenomena [23]. The Gi* statistical method can detect statistically significant distribution hot-spots in urban underground spaces. Hot-spots are caused by the geographical order or location of high-value objects. A significantly positive “GiZScore” expresses that the value around a spot is comparatively higher than the mean and represents a hot-spot, whereas a significantly negative “GiZScore” represents a cold-spot. Generally speaking, high-value objects frequently aggregate in local space to form hot-spots. Locality is a local spatial autocorrelation index based on distance weight matrix, which can detect high-value clustering and low-value clustering. High-value clustering areas are called hot-spots, while low-value clustering areas are called cold-spots. This statistic calculates the local sum of a certain element and its adjacent elements within a given distance range, compares the calculation results with the sum of all elements, and is used to analyze the degree of clustering of attribute values at the local spatial level. The calculation formula is as follows:

$$G_i^{\mathrm{*}}=\frac{{\sum }_{j=1}^n{W}_{ij}\left(d\right)X_i}{{\sum }_{j=1}^n{X}_i}(j\ne i)$$

(4)

In the formula: $X_i$ is the feature attribute value of the $j$th spatial unit and n is the total number of elements; If the distance between the $i$-th and $j$th spatial units is within the given critical distance $d$, they are considered to be neighbors. The element in the spatial weight matrix is 1, otherwise the element is 0. The local Gi* index can also be standardized:

$$Z(G_i^{\mathrm{*}})=\frac{G_i^{\mathrm{*}}-E\left(G_i^{\mathrm{*}}\right)}{\sqrt{VAR\left(G_i^{\mathrm{*}}\right)}}$$

(5)

In the formula: $E\left(G_i^{*}\right)$ is the mathematical expected value and $VAR\left(G_i^{*}\right)$ is the coefficient of variation. When it is positively significant, it indicates that the values around spatial unit $i$ are relatively large, i.e., the hot-spot area; When it is negatively significant, it indicates the low-value spatial agglomeration of the spatial unit $i$, i.e., the cold-spot area.

4. Results and Analysis

4.1. Spatial and Temporal Changes in LUUS

According to USI time series data, the annual increase in USI in the research area showed a gradual trend from 2002 to 2018, with an average growth rate of 0.005 per year and a median of 0.003 per year. The maximum growth rate occurred in 2017, reaching 0.016 per year. The cumulative amount of USI shows an exponential growth trend, increasing by nearly 35 times in less than 20 years (Figure 3).

To facilitate the comparison of changes in USI during different periods, this study selected five data periods from 1999 to 2002, 2003 to 2006, 2007 to 2010, 2011 to 2014, and 2015 to 2018 for calculation and analysis, and used ArcGIS for spatialization (Figure 4). It can be seen that the overall intensity of underground space development in the first two periods was relatively low, with only a partial increase. However, compared to the previous two periods, the intensity of underground space development was greatly strengthened between 2007 and 2010, and the scope of construction was expanded, with significant spatial differences. Since 2011, the development intensity of underground space has increased significantly, and the construction scale and space scope have increased significantly, with the largest increase occurring between 2015 and 2018.

From the cross-sectional data of USI (Figure 3), it can be seen that during the period from 2002 to 2018, the overall intensity of underground space development showed an increasing trend. In the legend, 0 and 1.43 represent the minimum and maximum values of USI, respectively. However, in the early stage (2002), it was concentrated in the central local area and had a low level of development and utilization. In the early stage (2006), the distribution pattern remained basically stable, and the development intensity increased significantly. Afterwards, the development and construction of underground space expanded outward from the main city center The intensity of development and utilization has greatly increased and this trend has been continued. After nearly a decade of large-scale and high-intensity development and construction, except for a few areas, the intensity of underground space development within the research area has significantly increased.

4.2. Spatial Correlation of the LUUS

Using global Moran’s I to analyze the global characteristics of underground spatial pattern evolution can reflect the characteristics and laws of global scale underground spatial distribution. According to the global Moran’s I calculation results of the increase in USI in the study area from 1999 to 2018 (

Table 1. Global spatial autocorrelation of the USI variations.

Parameter Item	T1	T2	T3	T4	T5
Moran’s I	0.364247	0.194655	0.100642	0.189021	0.161530
Expected I	−0.001669	−0.001669	−0.001669	−0.001669	−0.001669
z-score	14.544254	7.609100	3.978042	7.325619	6.330082
p-value	0.000000	0.000000	0.000069	0.000000	0.000000

), the global Moran’s I of the increase in USI in the study area was all positive. In addition, the results of the global Moran’s I test were also significant, indicating that the underground space construction in Wuhan City during these periods was not randomly distributed, but showed spatial clustering between similar values, and all showed a positive spatial autocorrelation. Between 1999 and 2002, the global Moran’s I value reached its maximum, reaching 0.364247, indicating that the underground space in Wuhan City was mainly under centralized construction on a global scale from 1999 to 2002. Since 2002, the global Moran’s I has decreased and remained relatively stable. This indicates that although the underground space development has maintained a trend in centralized construction on a global scale from 2002 to 2018, the degree of concentration has decreased compared to the previous spatial characteristics. For ease of expression, the five time stages are expressed as T1 (1999–2002), T2 (2003–2006), T3 (2007–2010), T4 (2011–2014), and T5 (2015–2018).

According to the global Moran’s I (Table 1) of the USI, the global Moran’s I values of the USI at the five time points from 1999 to 2018 were all positive. Furthermore, the results of the global Moran’s I test were also significant, indicating that the USI at different time points within the main urban area of Wuhan City exhibits strong spatial dependence globally. This indicates that there is a positive mutual influence between the underground space development in adjacent areas. The global Moran’s I values of USI at two time points in 2002 and 2006 were 0.365612 and 0.416172, respectively. These were significantly higher than the global Moran’s I values at other time points. Before 2006, the scale of underground space construction in Wuhan was relatively small and concentrated in the main city center, while the underground space outside the main city center had not been fully developed. After 2006, the development speed of underground space continued to accelerate and began to spread outward from the main city center, and it was no longer limited to the scope of the main city center. Therefore, in 2010, the global Moran’s I value of USI showed a significant decrease compared to 2006. Since then, the momentum of underground space expansion in Wuhan has continued, but the degree of agglomeration has decreased. The global Moran’s I value has decreased at both time points since 2010. This indicates that the expansion pattern of underground space became relatively scattered after 2010.

Regional housing price (RHP) can comprehensively reflect the location value of a region, which can be used to detect the spatial correlation between urban location value and USI.

To reflect the spatial relationship between LUUS and urban location value, USI and RHP were used to represent the degree of LUUS and urban location value, respectively. The relationship between USI and RHP was explored using bivariate local Moran’s I. The blue and red colors in Figure 5 represent the low- and high-value regions of the bivariate, respectively. From the graph, it can be seen that the high-value spatial clustering of USI and RHP during the study period was mainly distributed in the central areas of Hankou and Wuchang cities, while the low-value clustering area was mainly distributed in the northwest region. The peripheral areas of cities such as those in the southwest and northeast reflect the dependence of urban location value on the development and utilization of underground space.

4.3. Spatial Clusters of the LUUS

Starting from the two time dimensions of static and dynamic dimensions, this study investigated the local Getis Ord Gi * statistical values of the accumulation and variation in USI at different periods and time points, in order to characterize the clustering characteristics of static and dynamic USI. The z-score and p-value of the local Getis Ord Gi * statistical values can identify the spatial clustering positions of high- or low-value elements. When the z-score of the element is high and the p-value is small, it indicates a high-value spatial clustering. If the z-score is low and negative, and the p-value is small, it indicates a low-value spatial clustering. The higher the z-score (or lower), the greater the degree of clustering. However, when the z-score is close to zero, it indicates that there is no significant spatial clustering.

According to the calculation of Getis Ord Gi * for the development intensity of underground space, there were significant differences in the distribution of hot-spots in the development intensity of underground space during different periods (Figure 6). Between 1999 and 2002, two significant underground space development hot-spots (Hankou and Wuchang) emerged, and the scale of hot-spots in Hankou was much larger than that in the Wuchang region; During the period from 2003 to 2006, the scale of Hankou hot-spots continued to expand, but the interior of the hot-spots was, in relative terms, no longer dense. Instead, a smaller hot-spot area appeared in Wuchang, and the original hot-spot area further expanded in scale; During the period from 2007 to 2010, almost all of the hot-spots for underground space development had been concentrated in the central area of the main city at the intersection of the two rivers in Wuhan. Wuchang had formed a large-scale development hot-spot area north of Guoguohu, Hankou had formed three moderate and relatively concentrated development hot-spots, and at the same time, one development hot-spot area had appeared in Hanyang. From 2011 to 2014, there was only one development hot-spot left in Hankou, while the development hot-spot in Hanyang basically disappeared, and the development hot-spot area in Wuchang experienced relative displacement. The trend of expanding the scope of underground space development hot-spots from 2015 to 2018 is obvious, with multiple relatively small and dispersed underground space development hot-spots being added, most of which appeared in Hankou and shifted towards the edge of the main city. Compared to previous periods, the trend in underground space development hot-spots spreading from the center to the outside during this period is evident. With the continuous strengthening of the overall development intensity of underground space in recent years, lower-level areas of local underground space development and construction have emerged, forming a cold-spot area for USI.

According to the cross-sectional data analysis, the spatial distribution and agglomeration characteristics of hot-spots in underground space development are relatively obvious. In 2002, the two development hot-spots in Hankou appeared at the intersection of the two rivers and Wuchang. In 2006, the two development hot-spots in Hankou showed a merging trend. Meanwhile, the scale of Wuchang development hot-spots also expanded, and began to shift in 2010, with both scale and intensity expanding. However, the development hot-spots in Hankou showed a dispersed trend and expanded along the Yangtze River. At the same time, the scale of developing hot-spots at the intersection of the two rivers in Hanyang was relatively small. In 2014, the development hot-spots in Hankou showed a dispersed trend, while the scale of hot-spots in Wuchang and Hanyang continued to expand. In 2018, the development intensity of underground space in Wuhan remained stable, and the spatial pattern of cold and hot-spots remained stable, while the development hot-spots along the Yangtze River in the Hanyang region tended to weaken.

As the intensity of existing underground space development continues to increase, the spatial polarization effect of USI becomes more apparent, and new cold-spots for underground space development and utilization are constantly forming. The spatial differences in USI are also becoming increasingly significant.

5. Discussion

5.1. Research Findings

The spatial autocorrelation analysis using Moran’s I and LISA plots also confirmed the diversity of LUUS growth in the study area. The increase in value in the first five periods confirms the overall growth of USI, while the sudden decrease in value in the last period indicates the dispersion of USI growth [23].

This shows strong spatial heterogeneity, a strong correlation with surface value, a high correlation with population, and lag, i.e., it is unable to fully describe the dynamics of urbanization.

(1) Overall, the temporal characteristics of LUUS in the research area have gone through three stages: the basic cultivation period (before 2006), the slow growth period (2007–2010), and the rapid development period (2011–2018), reflecting the overall trend of increasing USI in rapidly urbanizing areas. From a spatial distribution perspective, underground space construction has gradually spread outward from the central area of the main city, and the coverage of underground space development is becoming larger, It is worth mentioning that the development and construction of underground space in leading industrial areas such as Qingshan District and the Zhuankou Economic and Technological Development Zone have always been at a relatively low level. This reflects, to some extent, the industrial zone’s low dependence and demand for underground space. Overall, the increment of USI and the distribution characteristics of existing space are relatively similar, reflecting a significant increase in the variation in USI in different periods compared to the cumulative amount. This indicates the rapid development trend in underground space development and utilization intensity in the research area during the research period.

(2) The spatial correlation characteristics of LUUS show a strong spatial dependence on the development intensity of underground space at different time points within the main urban area, showing a characteristic of concentrated and contiguous development. Moreover, the degree of urbanization of urban underground space is consistent with the location value of urban surfaces in the space. In other words, the higher the degree of urbanization of underground space, the greater the urban location value.

(3) The characteristics of cold-spots and hot-spots in LUUS space can be seen. In the early stage, LUUS hot-spots were mainly concentrated in areas with a high construction intensity of existing underground spaces. In the later stage, with the continuous acceleration of urbanization and expansion of urban activity space, the development hot-spots of underground spaces gradually expanded from the center to the outside.

The recognition of underground space form and the study of spatial patterns are the prerequisites and foundation for promoting the rational and efficient utilization of underground space, and the comprehensive utilization of aboveground and underground space. This study evaluated the spatiotemporal dynamic characteristics of LUUS in the research area from three dimensions: development intensity, spatial agglomeration, and spatial hot-spots. Some spatial units have single-dimensional problems, while others are limited to development intensity, spatial agglomeration, and spatial hot-spots. There are varying degrees of problems in multiple dimensions such as spatial hot-spots.

5.2. Limitation

This study had the following limitations: (1) As a complex spatial system that carries elements of urban development, underground space is composed of and characterized by multiple spatial elements, multiple spatial structures, and multiple spatial functions. A single dimension of underground space has significant limitations in adapting to the complexity and uncertainty of the real world, and needs to be considered from the perspective of aboveground space, multidimensional definition, and comprehensive consideration of natural resource conditions and socio-economic development needs. (2) The form and spatial pattern of underground space also have characteristics such as scale dependence and system complexity. This study only selected planning units for research on development intensity, spatial aggregation, and hot-spot detection. To scientifically reveal the characteristics and evolution of underground space form and patterns, it is necessary to conduct in-depth exploration on multiple scales and with element coupling. Future research will focus on multiple dimensions such as strength, function, and type of scale Research should be conducted on the coupling between underground and aboveground spaces on multiple scales such as the patch scale. Adopting, adopting nonlinear machine learning methods to support spatial decision-making [41].

6. Conclusions

This study proposes a spatiotemporal dynamic feature problem of LUUS that differs from traditional two-dimensional flat cities, and uses different methods to quantify the growth pattern of LUUS in an emerging city in China. This study provides important insights and references for the urbanization patterns of rapidly urbanizing regions worldwide. The results indicate that this model of USI does not always follow the existing single center, but rather develops from the urban center area to its surrounding areas. Therefore, not only has USI been discovered in major urban centers, but its impact can also be seen in the surrounding areas of their cities. The diversity of USI growth confirms the dispersion of USI growth in the study area. With rapid urbanization occurring in different periods, this pattern becomes more pronounced in the northwest and southeast regions of the study area. These results indicate that it is necessary to consider the utilization of underground space in the central urban area when making important decisions to improve urban spatial utilization. Although the existing problems caused by rapid urbanization cannot be quickly eliminated, understanding the impact of unplanned growth on the formation of USI at the level of urban central areas is crucial for planning purposes. This study may be helpful to decision-makers and rational planners in land resource allocation.

Based on the survey data of underground space information from 2002 to 2018, from the perspectives of time series and cross-section, gradient analysis, directional analysis, spatial hot-spot analysis, and spatial autocorrelation analysis were used to study the spatiotemporal pattern of USI in 600 analysis units in the main urban area of Wuhan. Overall, the USI in the study area showed a rapid increase in intensity and a continuous expansion in scope, with the increasingly polarized spatial variation characteristics of spatial differences. The following three results were observed:

(1) The development intensity of underground space in the study area shows the characteristics of spatial heterogeneity; the degree of global dispersion increases and the degree of local heterogeneity increases. The spatial distribution law states that the development intensity of underground space gradually decreases from the center to the outside; however, it was observed that the intensity in the northwest–southeast direction was higher than that in other directions.

(2) The intensity of underground space development shows a strong spatial dependency relationship of mutual influence on a global scale, but as the scope of underground space development continues to expand, the spatial dependency relationship decreases. The most obvious hot-spot development areas have been formed near the Fruit Lake area within the central ring road of the main urban area, while significant cold-spots have been formed in the Qingshan Industrial Zone and the Zhuankou Economic and Technological Development Zone outside the third ring road.

(3) The development activities of underground space in different periods show strong spatial dependence in local areas, with an overall trend in the relatively concentrated development mode, but overall, decreasing in concentration over time; The key areas for underground space development in different periods have gradually expanded from within the inner ring road to outside the second ring road, with significant spatial differences and a continuous increasing trend.