Spatial Morphological Characteristics and Evolution of Policy-Oriented Urban Agglomerations—Take the Yangtze River Middle Reaches Urban Agglomeration as an Example

Abstract

The Yangtze River Middle Reaches urban agglomeration (YRMRUA) is a typical representation of policy-oriented urban agglomeration in China. In this study, we analyze the morphological characteristics of the built-up area of the YRMRUA, which is the research object, and we try to clarify changes in its development and the supporting role of policies. First, we used satellite image data provided by Google Earth Engine for supervised learning to obtain images of the built-up area land needed by the research. Then, we adopted radius dimension, spatial expansion intensity, and standard deviation ellipse successively to explore the spatial morphological characteristics of the YRMRUA. The following conclusions are drawn: (1) The built-up area of the YRMRUA with significant fractal characteristics has formed two parts, with diffusion-type fractals forming within its boundary and cohesion-type fractals forming outside its boundary. In addition, the fractal dimension has exhibited a gradual decline as time has passed. (2) The YRMRUA has gradually stabilized at a low rate of expansion of 0.2% per year. From the perspective of the grid, the spatial expansion intensity presented breakthrough and expansion in the second and third stages, respectively. The hot spots of space expansion with 95% confidence gradually changed from a point-like shape to a ribbon-like shape. (3) The expansion pattern of the YRMRUA showed a tendency of “northwest–southeast”, and gradually moved towards equilibrium. (4) A policy impetus has limitations, and the stages of space shaping has resulted in spatial solidification and differentiation. In response, we put forward suggestions for the objects and ways of policy functions, in order to provide references for the development of policy-oriented urban agglomerations.

1. Introduction

In the 21st century, China’s urbanization strategy has undergone a fundamental transformation, with urban agglomeration emerging as the primary mode of urbanization instead of small towns. In the national 14th Five-Year Plan, a total of 19 urban agglomerations at various levels have been identified, highlighting their significance as a crucial approach for China to implement regional coordination strategies and to explore cross-border governance. In the realm of urban agglomeration research, the academic hot topics are in empirical studies of network relationships between cities through space of flows theory proposed by Manuel Castells. These types of studies have emphasized the correlation properties of urban features, usually inside a point, line, and network framework, and have focused on virtual spatial structure, yielding a wide range of illuminating results [1,2,3]. The newest study trend is to continuously mine innovative data [4] and to explore different correlation characteristics between cities in the fields of transportation, ecology, etc. [5,6,7].

In fact, most urban agglomerations in China have been established because of policies and have produced unique spatial forms. Even though the study of a geographical network can reflect its structure to some extent, its effect is not intuitive after all. Such characteristics also lead to research conclusions that cannot be applied to planning and construction. Recently, there has been growing advocacy among research scholars for a renewed focus on the spatial entity of an urban agglomeration. Scholars have tended to identify changes in the material spaces from the perspective of land use and cover with the help of satellite images [8,9]. By analyzing the forms of urban agglomeration, a deeper understanding of the process of spatial agglomeration and diffusion can be achieved using GIS [10,11,12], eventually leading to clear policy recommendations [13,14,15]. Research methods also continue to go deeper into the fields of artificial intelligence, sensors, and cloud computing, and optimization strategies are deeply discussed with scenario simulations [16,17]. At the same time, the outer edge of related research continues to expand into multidisciplinary fields such as system dynamics and economic physics [18]. However, at present, the following three aspects still need to be addressed: (1) research on the long-term identification of ultra-large scale urban land use, (2) thematic discussions about policy-oriented urban agglomeration, (3) integrated applications of spatial analysis and policy planning. The objectives of this study are to select a typical urban agglomeration and clarify the policy factors behind it by analyzing its spatial morphology evolution and to propose optimization strategies by comparing with existing development plans.

The Yangtze River Middle Reaches urban agglomeration (YRMRUA) serves as a prominent example among numerous policy-oriented urban agglomerations in China. Currently, the YRMRUA is confronted with a significant development dilemma. Firstly, its foundation is relatively weak compared to the Pearl River Delta urban agglomeration and the Yangtze River Delta urban agglomeration, the pioneers in China. Secondly, the driving force for development needs strengthening. Although a central region strategy has been proposed, there remains an absence of clear spatial cooperation intentions within the YRMRUA. To examine inertia and path dependence in its spatial development, as well as to identify implications and outlooks that can serve as benchmarks for other policy-oriented urban agglomerations in the world, in this paper, we use the YRMRUA as an example and conduct a retrospective analysis from a spatiotemporal perspective.

2. Study Area and Period

The YRMRUA’s level is currently recognized as national [19], it is situated in the central region of China, and it serves as a pivotal location within the Yangtze River Economic Belt [20,21]. With its natural waterway, it provides convenient access to the Yangtze River Delta urban agglomeration and the Chengdu-Chongqing urban agglomeration. The minimum bounding rectangle spans from 110°15′ to 118°28′ E and from 26°2′ to 32°37′ N, involving the provinces of Hubei, Hunan, and Jiangxi. The official planning of the YRMRUA encompasses 31 cities (including county-level cities administrated by the province) covering a land area that is over 317,000 square kilometers. The development plan of the YRMRUA aims to incorporate all the economically robust cities within the three provinces. Compared to other urban agglomerations, the YRMRUA stands out for its exceptional collage characteristics, which means that the geographic boundaries of the YRMRUA are not arbitrarily drawn, but after the growth of provincial urban agglomerations in three provinces (which are now a subgroup of the YRMRUA), they came into contact with those boundaries, which led to the intention of cooperation on the part of various governments. After this process, they reported to the State Council for investigation and pursuit of national-level urban agglomerations. Previous studies have usually divided the YRMRUA into three subgroups according to provincial boundaries and provincial spatial strategies [22,23]. The three subgroups are the Wuhan urban agglomeration (WHUA), the Chang-Zhu-Tan urban agglomeration (CZTUA), and the Poyang Lake urban agglomeration (PLUA) (Figure 1). The provincial capitals are undoubtedly at the core of the subgroups, namely Wuhan, Changsha, and Nanchang.

Despite being officially recognized late, the YRMRUA has a well-defined development trajectory and extensive policy groundwork. It can be argued that the strategic concept of various urban agglomerations in China originated from a “re-exploration” of economic zones after the reform and opening-up period (post 1978), for instance, the relationships between the Nanjing Economic Cooperation Area and the Yangtze River Delta urban agglomeration, as well as the interaction between the Chongqing Economic Cooperation Area and the Chengdu-Chongqing urban agglomeration. The YRMRUA is not an exception and can be traced back to the concept of the Wuhan Economic Cooperation Zone, which was established in 1987. To better investigate the evolution process of urban agglomeration and account for data availability, this study takes the construction of the Wuhan Economic Cooperation Zone as the starting point for research (1987), with China’s 13th Five-Year Plan’s final year (2020) serving as its research endpoint. Thus, this study covers a time range from 1987 to 2020. The developmental process of the YRMRUA throughout the research period is succinctly summarized as follows (Figure 2). The development process of the YRMRUA reveals that 1987, 2006, 2012, and 2020 serve as suitable segmentation points for investigating this region.

3. Methodology

3.1. Data Sources and Processing

The fundamental data utilized in this study were surface reflectance (SR) images of Landsat 5 and 8, which were provided by Google Earth Engine (GEE, https://explorer.earthengine.google.com/ (accessed on 3 February 2023). The data were preprocessed according to the operation process shown in Figure 3, and then the built-up area data required in this paper were obtained. The process can be divided into two main steps. Firstly, obtaining preliminary results of the YRMRUA built-up area identification through the GEE platform. Secondly, correcting data noise issues and related logical errors in the built-up area identification data using the ArcGIS Pro 3.0 software.

The following key steps are described in the above operations: (1) Prepare the labeled samples. According to a survey of the status quo of the overall urban planning and field survey, the authors selected land for functional marking in typical cities (provincial capitals and prefecture-level cities) in the YRMRUA. All the samples were divided into seven categories according to the similarity of land objects (cultivated land, forest, shrub, grassland, water, snow, bare land, construction land, and wetland). Then, according to the research content and object of this study, stratified sampling was divided into two categories (construction land and non-construction land). Taking into consideration the limit of computing memory allocated to users by GEE, a total of 33,000 patches over 1 hectare were labeled. All the samples were divided into test sets and training sets according to the principle of 3:7. (2) For machine learning, the labeled samples were uploaded to the GEE platform and divided into training and test sets. Subsequently, the full band ”region” mode was trained by random forest, and transfer learning was applied to analyze the results of 2020 spread to each year. In this process, the reason for using RF is that as compared with the SVM and NB methods provided by GEE, RF has better adaptability for large-scale land identification and noise processing. This method has very wide applicability in land use recognition [24,25,26]. After calculation, the overall accuracy was found to be 0.87 with a Kappa of 0.78 that reached the application level. (3) Detail processing. Due to the annual band synthesis and cloud removal operation, there were patchy images missing in some years. In the subsequent visual inspection, the patches with significant differences from the actual results were verified and corrected. After vectorizing the land identification results, the EliminatePolygonPart tool was used to eliminate small patches, and the spatial temporal filtering method was used to strengthen the consistency of land attributes. Due to the logic problem of the reverse growth of built-up areas, the growth sequence of built-up areas was corrected using the method of a spatio-temporal trajectory map [27,28,29]. The spatial forms of the built-up areas in four representative years are depicted in Figure 4.

3.2. Methods

The built-up area land data were used to analyze the spatial form of the YRMRUA. The characteristics of urban agglomeration spatial form were analyzed in three directions: fractal, expansion, and equilibrium. The degree of space self-organization can be judged by the fractal characteristics of space, spatial expansion can express the vitality of space, and spatial equilibrium can express the key direction of spatial development and observe the sustainability of spatial structure. The three previously mentioned aspects were studied through a radius dimension analysis, a spatial expansion intensity analysis, and a standard deviation ellipse analysis, respectively. Subsequently, we discuss the development plan and results of the YRMRUA (Figure 5).

3.2.1. Radius Dimension Analysis

Since the German mathematician Hausdorff proposed the fractal theory in 1910, the model has been widely used in the field of social science [30,31,32,33], and has derived many types. From the perspective of fractal theory, the process of urban agglomerations growing to maturity and finally becoming integrated contains a lot of trends of self-adaptation and self-organization. Currently, urban agglomerations undergoing growth in China exhibit varying degrees of fractal characteristics. Given that the YRMRUA initiative spans across three provinces and aims to establish a “three poles and three circles” urban system, it is more appropriate to employ the radius dimension for evaluation, as all parties involved exhibit a relatively balanced distribution along the upward development axis. Compared with other various definitions of fractal dimensions, the radius dimension can better measure the equilibrium of urban space expansion from the geographical center, and its expression is as follows (Formula (1)):

$$S\left(r\right)=k{r}^D$$

(1)

where $S\left(r\right)$ is the built-up area of a circle with a specified center and various radius $r$. The fractal dimension is $D$, and $k$ is the coefficient. Formulas (2) and (3) can be obtained by taking the logarithmic formula and circular area formula [34] as follows:

$$\ln S\left(r\right)=\ln k+D\ln r$$

(2)

$$P\left(r\right)=\frac{dS\left(r\right)}{dA\left(r\right)}\propto r^{D-2}$$

(3)

In the above formula, $P\left(r\right)$ refers to the density of the built-up area at the radius $r$ from the center of the circle, where $A\left(r\right)$ refers the area of the whole circle at the radius $r$. Therefore, if the two are equal, it indicates that the concentric circle is full of built-up area. The calculation results of the radius fractal dimension $D$ can be judged by the following table (

Table 1. Interpretation of the fractal dimension D value.

$\mathit{D}$	Interpretation
(−$\infty$,2)	It reflects the trend of density distribution decaying from the center to the periphery
2	At this time, the dimension is a critical value, reflecting a uniform distribution from the center to the periphery
(2,$+\infty$)	It reflects the increasing density distribution from the center to the periphery

Note: The final value of D is bounded by 2. As the value of D increases, the change trend becomes more pronounced.

):

The midpoint of the region’s center should be selected for analyzing the radius dimension of the YRMRUA. To ensure completeness in constructing the radius method for the YRMRUA, a maximum radius of 480 km should be established. According to previous research conventions, 16 circles were constructed with an initial radius (30 km) and the radius of each subsequent circle was increased by 30 km (

Table 2. Circle radius division in different circle IDs.

Circle ID	Circle Radius /km	Circle ID	Circle Radius /km
1	30	9	270
2	60	10	300
3	90	11	330
4	120	12	360
5	150	13	390
6	180	14	420
7	210	15	450
8	240	16	480

and Figure 6). Based on this partition, the radius division $\left(r\right)$ of the circle required for calculating the radius dimension and the cumulative result $S\left(r\right)$ of built-up area in the corresponding circular space are derived.

3.2.2. Spatial Expansion Intensity Analysis

A more comprehensive review of the spatial structure of the YRMRUA necessitates an analysis of spatial expansion intensity. The spatial expansion intensity index was used to summarize the urban spatial growth and development difference. The calculation formula is as follows (Formula (4)):

$$I=\frac{S_t-S_0}{A_{i}t}\times 100$$

(4)

where $I$ represents the intensity of urban spatial expansion within the specified research period, and the result is a percentage value; $S_0 \mathrm{and} S_t$ represent, respectively, the built-up area of time $0$ and time $t$; $A_i$ denotes the spatial area of the research unit (includes administrative units and custom grid units); $t$ represents the temporal duration of the study period.

After the spatial expansion intensity results are obtained, the Getis-Ord $G_i^{*}$ analysis was used to calculate the significant spatial cold spots and hot spots [35,36]. Then, the spatial expansion of the built-up area was divided into two groups, i.e., “hotspots region” or “coldspots region” [37,38,39], representing areas with strong development momentum or insufficient development momentum. The formula for the Getis-Ord $G_i^{*}$ analysis is as follows (Formulas (5) and (6)):

$$G_i^{*}=\frac{{{\displaystyle \sum }}_{j=1}^n\left[W_{ij}\left(d\right)\right]X_j}{{{\displaystyle \sum }}_{j=1}^n{X}_j}$$

(5)

where $X_j$ is the coordinate of the $j$ spatial plaques, $n$ represents the total number of spatial plaques, and $W_{ij}$ is the spatial weight matrix. $W_{ij}\left(d\right)$ is a function that determines whether it is adjacent according to the distance $d$ (adjacent is 1, nonadjacent is 0), and the normalized formula after $G_i^{*}$ is as follows (Formula (6)):

$$Z\left(G_i^{*}\right)=\frac{{{\displaystyle \sum }}_{j=1}^n{W}_{ij}{X}_j-\overline{X}{{\displaystyle \sum }}_{j=1}^n{W}_{ij}}{\sqrt{\frac{{{\displaystyle \sum }}_{j=1}^n{X}_j^2}{n}-{\left(\overline{X}\right)}^2}\sqrt{\frac{n{{\displaystyle \sum }}_{j=1}^n{W}_{ij}^2-\left({{\displaystyle \sum }}_{j=1}^n{W}_{ij}^2\right)}{n-1}}}$$

(6)

When the value of $Z\left(G_i^{*}\right)$ in the formula is positive, the significant area represents a hot spot; a negative value of $Z\left(G_i^{*}\right)$ represents a cold spot. A value close to 0 for $Z\left(G_i^{*}\right)$ indicates no significant spatial correlation. After several parameter adjustments, when the spatial concept relationship of “fixed distance range” is adopted in ArcGIS and the distance is set to 20 km, the regional relationship between the three growth periods of the YRMRUA can display hot and cold spots simultaneously.

3.2.3. Standard Deviation Ellipse Analysis

The standard deviation ellipse (SDE), initially proposed by D. Wilty Lefever in 1926, offers a highly generalized approach to describing the location and distribution characteristics of geographical elements, allowing for an intuitive understanding of the evolution of spatial structure elements. The purpose of utilizing the SDE analysis is to assess the overall equilibrium characteristics of the YRMRUA and changes in the development center of gravity trajectory. It has been widely used in the research of spatial form [40,41,42,43]. The SDE method consists of the following set of calculations (Formulas (7)–(9)):

(a) Center of gravity:

$$\overline{X}=\frac{{{\displaystyle \sum }}_{i=1}^n{w}_i{x}_i}{{{\displaystyle \sum }}_{i=1}^n{w}_i},\overline{Y}=\frac{{{\displaystyle \sum }}_{i=1}^n{w}_i{y}_i}{{{\displaystyle \sum }}_{i=1}^n{w}_i}$$

(7)

(b) Angle:

$$tan\theta =\frac{\left({{\displaystyle \sum }}_{i=1}^n{w}_i^2{\tilde{x}}_i^2-{{\displaystyle \sum }}_{i=1}^n{w}_i^2{\tilde{y}}_i^2\right)+\sqrt{{\left({{\displaystyle \sum }}_{i=1}^n{w}_i^2{\tilde{x}}_i^2-{{\displaystyle \sum }}_{i=1}^n{w}_i^2{\tilde{y}}_i^2\right)}^2+4{{\displaystyle \sum }}_{i=1}^n{w}_i^2{\tilde{x}}_i{\tilde{y}}_i}}{{{\displaystyle \sum }}_{i=1}^{n}2w_i^2{\tilde{x}}_i{\tilde{y}}_i}$$

(8)

(c) Standard deviation:

$$\begin{array}{c}{\sigma }_x=\sqrt{\frac{2{{\displaystyle \sum }}_{i=1}^n{\left(w_i{\tilde{x}}_{i}cos\theta -w_i{\tilde{y}}_{i}sin\theta \right)}^2}{{{\displaystyle \sum }}_{i=1}^n{w}_i^2}}\\ {\sigma }_y=\sqrt{\frac{2{{\displaystyle \sum }}_{i=1}^n{\left(w_i{\tilde{x}}_{i}sin\theta -w_i{\tilde{y}}_{i}cos\theta \right)}^2}{{{\displaystyle \sum }}_{i=1}^n{w}_i^2}}\end{array}$$

(9)

In the formula above, $\left(x_i, y_i\right)$ is the spatial location of the research object with weight $w_i$. ($\overline{X},\overline{Y}$) is the location of the center of gravity, while $\left({\tilde{x}}_i, {\tilde{y}}_i\right)$ is the deviation of the study subject to the center of gravity location.

4. Results

4.1. The Spatial Fractal Characteristics

Before analyzing the radius dimension, it is necessary to observe the built-up area in each circle. It was observed that while there were significant differences in the total built-up area contained in all the circles, almost all the circles exhibited exponential growth patterns (Figure 7). Notably, the built-up areas in Circles 5–7 (located at distances of 150 km, 180 km, 210 km, and 240 km from the center of the region, respectively) dominated all the circles and reached a spatial magnitude of 2000–2500 km². Although the innermost and outermost layers did not reach a magnitude of 100 km², they still maintained a very high growth rate.

For the second step of the radius dimension analysis, a linear regression analysis was performed. The cumulative results of the above circle radius and built-up area were solved by linear fitting. The fitting results were obtained, as shown in

Table 3. Calculation of the YRMRUA radius dimension.

	1987	2006	2012	2020
D	2.2130	2.1849	2.1407	2.0951
R2	0.9479	0.9472	0.9486	0.9501

, and then the radius dimension of the YRMRUA could be calculated. The table reveals that the D value exceeds 2 but exhibits a gradual decline, indicating that the trend of increasing density from the center to the periphery of the built-up area of the YRMRUA gradually eases. Meanwhile, although R² has reached 0.9, it is still far from the empirical standard of fractal theory (0.996), indicating that its fractal characteristics are not particularly pronounced. In order to highlight the fractal features within different radius circles, we employed the segmented regression method for further analysis of this issue.

The segmented regression analysis employs piecewise.linear, a function of R4.2, and estimates the 95% confidence interval by using the bootstrap method (set to 1000 iterations) to automatically determine the breakpoint. The results show that the fractal dimensions of the radius in 1987, 2006, 2012, and 2020 all produce breaks between the 7th and 8th circle layers and are divided into two sets (Figure 8a–d). The first set exhibits a diffused fractal pattern, while the second set displays a cohesive fractal pattern. It is noteworthy that from 1987 to 2020, the D values of both the first set and the second set exhibited a continuous decrease, from 2.809 to 2.637 and from 0.597 to 0.532, respectively, indicating that the YRMRUA forms a balanced distribution in the inner circle and continues to diffuse in the outer circle.

4.2. The Spatial Expansion Characteristics

According to the formula for measuring the intensity of space expansion, we obtained the results shown in

Table 4. The intensity of built-up area expansion.

The Period Division	The Average Annual Increase in Built-Up Area	The Average Annual Expansion Intensity
1987–2006	197.76 km2	0.06%
2006–2012	705.99 km2	0.22%
2012–2020	662.20 km2	0.21%

. The findings indicate that built-up area expansion in the YRMRUA underwent a process of initial growth followed by stabilization across the three periods. Specifically, the average annual growth areas during the three periods were 197.76 km², 705.99 km², and 662.20 km², respectively, with corresponding expansion intensities of 0.06%, 0.22%, and 0.21%. This spatial expansion intensity is far from that of urban agglomerations in China’s coastal areas.

Next, we gridded the expansion intensity of the built-up area to observe its spatial distribution. Given that the spatial extent of the YRMRUA is from two to three times that of the same type of national urban agglomerations, we adopted a grid unit size of 3 km based on previous empirical studies on urban regional form. The spatial expansion intensity was classified using natural break points. Based on the results of spatial expansion intensity, it can be observed that Wuhan, Changsha, Nanchang, and Xiangyang exhibited a point expansion pattern between 1987 and 2006, which remained within the boundaries of their respective main urban areas (Figure 9a). From 2006 to 2012, the central areas of core cities reached saturation, while peripheral regions experienced increasing expansion intensity, resulting in a “hollow circle” pattern of urban growth (Figure 9b). This also meant that the urban region began to generate. Then, from 2012 to 2020, there was still a trend of expansion in the core urban fringe area, but the expansion intensity tended to be flat. In contrast, land urbanization dominated by counties began to rise, forming a scattered layout (Figure 9c). In general, the high value region of spatial expansion intensity appeared between 2006 and 2012 (up to 12.90%), while the diffusion phenomenon of spatial expansion intensity appeared between 2012 and 2020. Similarly, the above changes are also reflected in the results of the Getis-Ord $G_i^{*}$ analysis (Figure 9d–f). At the end of the study, the spatial expansion of the YRMRUA forms a z-shaped structure (Figure 9f). Specifically, in addition to the early-stage band-type characteristics observed in the “Wuhan-Ezhou-Huangshi-Huanggang” region, other areas such as “Wuhan-Tianmen-Xiantao-Qianjiang”, “Xiangyang-Jingmen-Jingzhou-Yichang” within the WHUA, and “Jiujiang-Nanchang-Shangrao” within the PLUA also exhibited band-type development.

4.3. The Spatial Equilibrium Characteristics

In the ArcGIS platform, we utilized the DirectionalDistribution tool with a parameter set to one standard deviation in order to generate the SDE and its center of gravity deviation trajectory for each year’s spatial expansion intensity (Figure 10). The spatial distribution of built-up area in the YRMRUA exhibits a northwest–southeast pattern, which is gradually diminishing over time. The SDE area of the YRMRUA exhibits frequent fluctuations and experiences a significant decrease during the third stage of the study period. Meanwhile, the standard deviation distance of the X-axis decreases slightly from 273.41 km to 245.05 km, while that of the Y-axis increases slightly from 159.21 km to 176.32 km. Furthermore, the angle exhibits a gradual decrease from 130° to 122°, indicating a shift in the spatial expansion’s dominance from south to north towards east to west (Figure 11). The position of the center of gravity of the ellipse is always close to the junction of the three subgroups, forming a migration track from Xianning to Jingzhou. The above results show that the distribution of spatial expansion of urban agglomerations has changed from dispersion to cohesion and from skewness to balance, and the progress and effectiveness of the construction in the WHUA is slightly better than that of the other two subgroups.

A detailed comparison from the perspective of subgroups reveals that the total amount and change process of the standardized ellipse difference area of the three subgroups are relatively consistent, distributed in the area interval of 30,000–60,000 km² (Figure 11a). In addition, the X-axis of the three subgroups fluctuates between 120 and 160 km, and the Y-axis fluctuates between 50 and 100 km. In terms of the shape of the standard deviation ellipse, the WHUA and the PLUA exhibit greater stability, with the CZTUA displays a higher variability (Figure 11b–d). Regarding the shift in the center of gravity of the ellipse, the WHUA continues to move eastward, resulting in a “one city dominates” situation around Wuhan. Similarly, the CZTUA and the PLUA also exhibit this trend. The elliptical centers of both subgroups shift north–south and east–west, respectively, in Changsha and Nanchang, indicating that core cities within each subgroup still occupy an absolute central position during built-up area expansion.

5. Discussions

5.1. Limited Impetus from Policy Enactment

First of all, it is necessary to recognize that the development of the YRMRUA and the promulgation of policies have a certain correlation in time, but it is also necessary to understand the limitations of the role of policy promotion. As shown in Figure 6, the jump point of built-up area growth generally appeared around 2010. However, the changes before and after the policy application points for the YRMRUA in 2006 and in 2012 were not obvious. There are many factors behind the policies, such as economy, transportation, and culture. For example, since 2009, the opening of the Beijing-Guangzhou Line has promoted the WHUA. China’s macroeconomic slowdown has had a retarding effect on the YRMRUA from 2012. The large cultural differences between provinces make the flow of factors slow. Policies at the regional level are not perfect and have different effects in different regions [44,45,46]. During 1987–2006, cities in the YRMRUA were still recovering from economic reforms and lacked a clear understanding of the urban region. Since 2006, the policy highlands have shifted to the east and west of China, rendering the strategy of the Rise of Central China indispensable. The subgroups were established by each province in response to national policy, and the process of constructing these subgroups during this period was both autonomous and competitive. However, entering the second decade of the 21st century, the number and intensity of policies concerning the YRMRUA gradually increased, but its development did not accelerate further. Since 2012, the subgroups have established official cooperation; however, due to implementation of the ecological civilization concept, the Yangtze River protection strategy, and national territory development planning transformation policies, there are relatively stringent requirements on spatial utilization. Meanwhile, the industries of economically robust coastal cities did not promptly relocate to the central region, resulting in a challenging maintenance of the YRMRUA’s spatial expansion rate during this period.

5.2. Spatial Solidification by Building a Tri-Pole Structure

The results of the spatial fractals show that even though the D value has certain changes in the two categories (the rates of change are −6.12% and −10.89, respectively), the discontinuity point is always between 5 and 6 on the axis of ln(r), indicating that a spatial self-organization pattern has been established. We define this scenario as spatial solidification. The triangular configuration, which is typically stable in the physical realm, often encounters challenges when applied to urban contexts. The divergence in development intentions among various regional policy stakeholders often impedes the establishment of a cohesive developmental trajectory. As in the YRMRUA, more than 5% of the spatial expansion intensity is concentrated around core cities. Previous research has demonstrated that urban agglomerations encounter numerous conflicts during the construction and management phases [47], which always affect the size and capacity of urban agglomerations [48]. Additionally, the full potential of multi-poles can only be realized with a stronger population base and infrastructure in place [49]. In fact, whether it is the spontaneously formed metropolitan belt in Western countries after World War II or the relatively mature urban agglomeration in China, both demonstrate the emergence of a “uni-pole” pattern around the primary city. Alternatively, the other major type is that there are two adjacent economically strong cities in an urban agglomeration, thereby constructing a “dual-pole” structure. Typical examples of the former include the economic dominance of New York in the northeast coast of the United States. Cases of the latter include the joint dominance of the Chinese cities of Chongqing and Chengdu in the Chengdu-Chongqing urban agglomeration.

5.3. Continuous Spatial Patterns Are Difficult to Realize

In the results of Section 4.2, slow expansion areas less than 0.13% (1987–2006), 0.33% (2006–2012), and 0.24% (2012–2020) in Figure 9 occupy the vast majority. This also indicates that, over a long period of time, it will be difficult for the YRMRUA to form a continuous spatial expansion model. In addition, hotspots with a 99% confidence level also indicate that their spatial expansion trend is limited. China has implemented a nationwide reform program for territorial spatial planning, which mandates all cities to rigorously control the amount of land used for development projects. The region of the YRMRUA failed to benefit from the initial dividends of reform and opening up; therefore, it is difficult to achieve a coherent spatial form. Current major developments are still dominated by large-scale oriented ideas, which are destined to be difficult to realize. Additionally, the majority of the literature has focused on the macro-region of urban agglomerations, neglecting spatial units, such as county-level units and township-level units [50]. In fact, the expansion of central urban areas will inevitably drive the transformation of county-level cities into regional subcenters [51]. It can be observed that, since 2012, a significant number of county-level cities surrounding prefecture-level cities have gradually emerged in the YRMRUA, presenting promising prospects for the transformation and advancement in the new era. Therefore, the YAMRUA is poised to embark on a divergent trajectory from its conventional course, which is a nebulous distribution rather than a contiguous development.

5.4. The Subgroup Strategy Has Not Bridged the Spatial Gap

The results of the SDE show that the X-axis of the three subgroups is stable within the range of 120–200 km, the Y-axis is stable within the range of 50–100 km, and the angle is stable within the range of 60–150 km, indicating that the cooperation intention and cooperation path among the three groups are not clear. It can be summarized that the three subgroups have not changed their development direction because of YRMRUA policy. In general, the predominant characteristic of urban agglomeration in China lies in its marginal expansion [52], while the space available for development in the YRMRUA is very limited. Although the YRMRUA has a planned area that is over 300,000 square kilometers, hills and mountains within the first four circles in the fractal analysis actually become natural barriers between cities; and therefore, the YRMRUA cannot fully generate convenient communication. In response to this situation, the official development plan in 2015 proposed the construction of a ring-shaped development belt encircling the central green heart of the urban agglomeration. However, each subgroup still plans a radial development axis centered on its respective core city. This type of axis, devoid of primary and secondary distinctions, essentially maintains a circular structure. As a result, although the YRMRUA locally produces the characteristics of band expansion, it is imperfect and slow. Moreover, the provincial strategic pattern often presents alternative options, such as the dual-axis structure of “Wuhan-Xiangyang” and “Wuhan-Yichang” in Hubei province. Thus, recent efforts to construct urban agglomerations in the province have surpassed the established planning route of the YRMRUA.

6. Policy Suggestions

It should be clear that the YRMRUA should have its own unique path of development. Fortunately, based on the findings regarding spatial expansion, it is evident that the embryonic form of urban agglomeration is gradually taking shape. This robustly counters numerous arguments that suggest the YRMRUA will not ultimately evolve into an urban agglomeration. Therefore, we conclude that the YRMRUA fits the current era’s development strategy based on the results and discussions. Our policy suggestions include the following:

First, respect the role of the market. Industrial construction is an inevitable path for urban agglomerations to return to their essence. This is also the most typical difference between urban agglomerations and metropolitan areas. Improvement of an urban agglomeration must be based on a solid economic foundation and mature division of labor and a cooperation network. Over the course of the YRMRUA’s 34-year history, policies have overly squeezed the market space, making it too difficult to identify the YRMRUA. For example, Anhui Province has tried to join the YRMRUA, and the CTZUA and the PLUA within the YRMRUA have tended to break away. These attempts are not supported by any policy. Therefore, putting the choice back in the hands of the market can improve the identification and recognition of the YRMRUA to a certain extent. In addition, it must be emphasized that the government also needs to proactively face the transformation and provide sustainable development strategies to meet the needs of the new era, such as TOD and green transport policies [53,54].

Secondly, construct dual-pole urban systems. The dominant scattered distribution pattern of the YRMRUA is actually an inter-group competition. The core cities within each subgroup continue to attract resources within the province, hindering deeper cooperation. Determination of the “tri-pole” structure by the YRMRUA during a period of weak economic foundation resulted in a significant lag behind expectations for the plan’s implementation. Currently, Wuhan and Changsha hold an economic advantage over Nanchang, while the provinces of Hubei and Hunan, based on where they are situated, share a rich historical connection (known as the Liang Hu Region). Therefore, prioritizing the establishment of a “dual-pole” structure by combining the WHUA and the CZTUA might serve as a pivotal factor in facilitating the development of the YRMRUA.

Third, focus on county-level units. The development experiences of the Pearl River Delta urban agglomeration and the Yangtze River Delta urban agglomeration have proven the following facts: Professional counties and towns in an urban agglomeration rely on upstream and downstream businesses in the industrial chain, integrate a variety of innovative resources to adapt to market development, continuously improve their own innovation capabilities, and support the modernization of the entire region. In this process, surplus labor from villages continues to flow into enterprises, thereby expanding the scale of county-level cities. Consequently, a stable specialized division of the labor market is formed (e.g., smallware products in Yiwu and hardware products in Yongkang). Currently, some industries in the YRMRUA have achieved significant breakthroughs in terms of scale and type [55], resulting in the formation of industrial clusters led by photoelectric chips and its affiliated industries, automobile manufacturing, biomedicine, and equipment manufacturing [56]. This cluster is constantly engaged in industrial iteration and upgrading. Therefore, it is crucial for relevant stakeholders to strategically allocate development space in county-level cities and proactively plan for future industrial transfers.

Fourth, adjust the subgroup strategy. The key to reshaping the YRMRUA collaboration lies in reconfiguring subgroups into a banding structure, while taking into consideration the pivotal role of transportation and infrastructure in this process. In fact, the Middle Reaches of the Yangtze River, the region where the YRMRUA is situated possesses significant comparative advantages over other urban agglomerations. Firstly, in terms of physical geography, the Yangtze River’s mainstream and tributaries such as the Hanjiang River, Xiang River, and Ganjiang River have established a convenient water network system, rendering the Middle Reaches of the Yangtze River as the most prosperous area for waterway transportation. Secondly, regarding transportation infrastructure, it has been highly developed in this region with various facilities advancing together to form a three-dimensional transportation network that includes high-speed rail and air travel as its backbone. This has effectively promoted and stabilized major city developments [57]. Returning its strategic focus to the construction and optimization of transportation infrastructure will be a significant breakthrough for the YRMRUA, as it promotes the development of subgroup passageways along major transportation channels and facilitates greater interaction between urban and rural areas.

7. Conclusions

Policy-oriented urban agglomerations often fall into difficulties due to the lack of clear development paths. It is necessary to systematically sort out development logic from the perspective of time and space and based on a GIS system. The YRMRUA is one of many policy-oriented urban agglomerations. Taking the built-up area of the YRMRUA as the research object, this paper reviews its spatial form evolution characteristics from 1987 to 2020, and draws the following conclusions:

(1) The area of the YRMRUA with significant fractal characteristics forms two parts. A dividing line is observed at a distance of 210 km from the geometric center of the region, with diffusion-type fractals forming within this boundary and cohesion-type fractals forming outside it. The shape dimension of the two components has decreased year by year, indicating that the trend of spatial morphology evolution has been internal equilibrium and external diffusion.

(2) The YRMRUA has gradually stabilized at a low rate of expansion of 0.2% per year. In the analysis of the grid, the high value region of spatial expansion intensity appears in 2006–2012, and the phenomenon of spatial expansion intensity diffusion appears in 2012–2020. The Getis-Ord $G_i^{*}$ analysis showed that the hot spots in the spatial expansion had a point-like distribution before 2006, and then changed to a ribbon-like distribution.

(3) The results obtained from the standard deviation ellipses indicate that spatial expansion tends towards a “northwest–southeast” direction and gradually approaches equilibrium over time. The three subgroups expand around their respective core cities but fail to demonstrate any trend towards joint expansion.

(4) We believe that in the development process of the YRMRUA, a policy impetus is very limited. Under the existing policy background, spatial solidification and differentiation of the YRMRUA space continue. Based on this, our recommendations include respecting the role of the market, constructing dual-pole urban systems, focusing on county-level units, and adjusting the subgroup strategy.

In terms of the limitations and shortcomings of this study, this paper only reflects the spatial form of urban agglomerations; the relationship between factor agglomeration and flow based on spatial entities needs to be further explored. Future research should focus on the basic role of major transportation infrastructure in the remodeling of spatial structure. On the one hand, it should focus on the role of traffic stations in the compact and intensive development of surrounding construction land, and on the other hand, it should explore the guiding role of transportation networks in the growth direction of construction land.