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Article

Multi-Source Data-Based Investigation of Spatiotemporal Heterogeneity and Driving Mechanisms of Coupling and Coordination in Human Settlements in Urban Agglomeration in the Middle Reaches of the Yangtze River

by
Wenmei Wu
1,2,3,
Shenzhen Tian
1,2,3,
Hang Li
1,2,3,*,
Xueming Li
1,2,3,4 and
Yadan Wang
1,2,3
1
School of Geography, Liaoning Normal University, Dalian 116029, China
2
Center for Human Settlements, Liaoning Normal University, Dalian 116029, China
3
Research Base of Urban Agglomeration in Central South Liaoning of China Urban Agglomeration Research Base Alliance, Liaoning Normal University, Dalian 116029, China
4
University Collaborative Innovation Center of Marine Economy High Quality Development of Liaoning Province, Dalian 116029, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(17), 7583; https://doi.org/10.3390/su16177583 (registering DOI)
Submission received: 18 June 2024 / Revised: 15 August 2024 / Accepted: 28 August 2024 / Published: 2 September 2024
(This article belongs to the Special Issue Urbanization and Environmental Sustainability—2nd Edition)
Browse Figures
Versions Notes

Abstract

:
In the information age, the new wave of the information technology revolution has profoundly changed our mode of production and way of life. Pseudo human settlements (PHS), consisting of digits and information, have become increasingly important in human settlements (HS) systems, and become a strong support for the high-quality development of global HS. Against this background, clarifying the spatiotemporal heterogeneity and driving mechanisms of the coupling and coordination between the PHS and real human settlements (RHS) is of great significance to the high-quality development of HS and providing a reasonable explanation of today’s man–land relationship. Therefore, we developed a theoretical framework system for describing PHS–RHS coupling and coordination based on multi-source data such as internet socialization, public utility, and remote sensing images, etc. Taking the urban agglomeration in the middle reaches of the Yangtze River (UAMRYR), which is the key region consolidating China’s “two horizontal and three vertical” urbanization strategy, as a case study area, we have comprehensively analyzed the spatiotemporal heterogeneity of the coupling and coordination of PHS and RHS and its driving mechanism in UAMRYR during the period of 2011–2021, by comprehensively applying the modified coupling coordination degree (CCD) and other models. The results show are as follows: (1) Temporal process—The CCD exhibited a reverse L-shaped increasing trend. The CCD class varied significantly, with the extremely uncoordinated and severely uncoordinated classes present at the beginning of the study period and disappearing toward the end of the study period, while the well coordinated and highly coordinated classes were absent at the beginning of the study period and appeared toward the end of the study period. (2) Spatial pattern—The CCD exhibited an equilateral triangle-shaped, core–margin spatial pattern and a characteristic of core polarization. Overall, the spatial distribution of the CCD exhibited a characteristic of “high in the central region, low in the eastern and western regions, and balanced in the south–north direction”. (3) Dynamic evolution—The CCD increased more rapidly in the north-eastern direction than in the south-western direction; the CCD exhibited north-eastward migration and dispersion, and the spatial variability decreased. (4) Driving mechanisms—The primary factors affecting the CCD varied significantly over time. The living system was dominant in the PHS, whereas the human system was dominant in the RHS. The PHS had a greater effect than the RHS on the CCD. The study broadens the research scope of human settlements geography, establishes a scientific foundation for advancing urban HS construction in the UAMRYR, and offers theoretical support for the high-quality development of cities in the UAMRYR.

1. Introduction

One of the goals set out in the United Nations 2030 Agenda for Sustainable Development is to “make cities and human settlements inclusive, safe, resilient, and sustainable”. Similarly, the report of the 20th Communist Party of China(CPC) National Congress emphasizes “the need to develop liveable, resilient, and smart cities”; China’s 14th Five-Year Plan clearly proposes to “improve the urban and rural human settlements (HS)”. HS are community spaces enabling the survival and development of humankind. The quality of HS directly affects the sense of fulfilment and happiness of the dwellers, and suitable HS are indispensable for meeting the growing demands for a better quality of life. Traditionally, the concept of HS primarily refers to real human settlements (RHS), encompassing five systems: human, social, residential, support, and environmental. An RHS is where humans live, produce and develop collectively [1].
With the advent of a new wave of information technology, digital information technologies such as 5G, the internet, big data, and artificial intelligence have become pivotal in enhancing international competitiveness. The 14th Five-Year Plan in China repeatedly mentions the strategy of “integration of the digital and the real” and “strengthening the real with the digital”. Furthermore, the extensive application of digital information technologies, such as 5G, internet, big data, and artificial intelligence [2,3,4], has progressively expanded human cognitive space beyond RHS to pseudo-human settlements (PHS). A PHS is a kind of informatized pseudo-settlement created by residents based on their cognition and preferences, using RHS as the “base plate” through editing, processing and the use of the media, which are composed of five systems: living, entertainment, socialization, information and tools [5,6,7]. By 2023, the global number of internet users was projected to reach 5.4 billion, with an internet penetration rate of 67 per cent. The digital index will be deeply integrated into daily life; people will spend more time and satisfy their diversified needs by moving from offline to online, and the PHS will become a significant component of HS systems. The HS system is holistic, and achieving high-quality development requires the coordinated advancement of both PHS and RHS.
The urban agglomeration in the middle reaches of the Yangtze River (UAMRYR) geographically spans three provinces (Hubei, Hunan, and Jiangxi); it is located in central China and is well connected to other parts of the country. This area is integral to the development of “the Yangtze River Economic Belt” and plays a crucial role in the “rise of central China” strategy and the urbanization strategy pattern of “two horizontal and three vertical”. It occupies a significant position in China’s national strategy for socioeconomic development. Therefore, investigating PHS–RHS coupling and coordination in the UAMRYR is of great significance to the comprehensive improvement and high-quality development of the HS in the central region in particular, and China in general.
HS currently constitute a popular topic in geographic research. The existing studies on HS are characterized by the use of interdisciplinary approaches [8], multi-source data [9], and multiple scales [10]. Scholarly research on HS can be dated back to the concept of ekistics proposed by Greek scholar Doxiadis in the 1950s [1]. This concept was introduced to China in the 1990s by Wu Liangyong, who later proposed the concept of the science of HS and defined its scope [1]. Studies on HS in recent years have several characteristics. First, they have focused on the urban thermal environment [11,12,13,14,15,16], atmospheric environment [17,18], suitability of HS [19,20], and quality assessment and spatiotemporal characteristics of HS [21,22,23]. Second, studies have been performed on various geographical scales, such as the global [13], continental [24,25], national [6], urban agglomeration [16,23,26], river basin [22,27], provincial [28,29], city [30,31,32], and county/district [33,34] scales. Third, various population groups have been covered, such as all residents [34], the aged [35,36], children [37], the disabled [38], and women [39]. Finally, the methods used have included entropy weight [40], coupling coordination degree (CCD) [26,41], spatial autocorrelation [42], and system dynamics [43] approaches.
The CCD describes the strength of the interaction and level of the coordination between systems. The term “coupling” originated in physics [44] and was later introduced to other fields such as ecology [45,46,47,48] and geography [26,49,50]. Geographers have used this term to explain the interactions in the complex system of the man–land relationship. Research in this direction in recent years focused on the coupling and coordination (1) between different geographical factors, such as between resources, the economy, and ecology [51,52], between urbanization and the ecological environment [53,54] and between water, energy, and food [55,56]; (2) within the system of a geographical factor, such as the urban resilience system [57], urban production–living–ecology system [58], and urban HS system [28]; and (3) between different forms of a geographical factor, such as between a PHS and an RHS [5]. The CCD models developed by geographers have been effective in explaining the complex man–land relationship, and have promoted the development of geography.
The UAMRYR is located in central China and well connected with other parts of the country. With the launch of the “rise of central China” strategy, the UAMRYR has attracted increasing scholarly interest. Extensive research on the UAMRYR has been conducted from various perspectives: (1) an economic perspective, focusing on the economic resilience [59,60] and digital economy [61] of the UAMRYR; (2) an ecological perspective, focusing on the ecological resilience [62], ecological environmental quality [63], ecological efficiency [64], and ecological system health spatiotemporal evolution [65] of the UAMRYR; (3) a geographical perspective, focusing on the urbanization [66,67], HS [21,68], and population changes [69] in the UAMRYR; and (4) an integrated perspective, focusing on the spatial variations in the coupling degree between the economy, city, society, and environment of the UAMRYR [70], the effects of land use changes on ecosystem services [71], and the coupling and coordination between carbon emissions and high-quality economic development [72].
In summary, fruitful research has been conducted on HS and on the UAMRYR. However, some deficiencies exist. First, with the advent of the information age, the PHS consisting of digital and information constructs has completely infiltrated and influenced human daily life. However, the existing studies of HS have mainly focused on the RHS, with few focusing on the PHS. In the information age, the importance of information technology is growing, and the PHS is becoming a valuable component of urban competitiveness. Second, owing to its strategic geographic location, the UAMRYR is key to the development of the Yangtze River Economic Belt, the “rise of central China” strategy, and the urbanization strategy pattern of “two horizontal and three vertical”, occupying an important position in the Chinese national strategy for socioeconomic development. However, few studies have focused on the HS in the UAMRYR, although the quality of HS directly affects the sense of fulfilment and happiness of the dwellers. Third, the coupling and coordination within an HS are critical to its improvement and high-quality development. However, previous studies have focused on the coupling coordination between the HS and external factors, with few focusing on the coupling and coordination between the internal factors, systems, and forms of HS.
Therefore, the objectives and academic contributions of this study were (1) to examine the HS in the UAMRYR from the perspective of PHS–RHS coupling and coordination, so as to enrich the perspectives and theory of human settlements geography; (2) to analyze the spatial distribution of the PHS–RHS CCD in the UAMRYR using the geographic information system (GIS) spatial analysis method; (3) to research the spatiotemporal evolution characteristics of the PHS–RHS CCD in the UAMRYR using a center of gravity (CoG) and standard deviation ellipse (SDE) model; (4) to analyze the driving mechanisms of PHS–RHS coupling and coordination in the UAMRYR using a geographic detector model; and (5) to enrich the perspectives for man–land relationship research in the information age, and identify new directions for geographic research on HS under the national strategy of “integration of the digital and the real”, thereby providing scientific support for the coordinated development of the PHS and RHS in the UAMRYR and new reference information for decision-making on the high-quality development of Chinese urban agglomerations. The research framework of this study is presented in Figure 1.

2. Research Methods and Data Sources

2.1. Study Area and Data Sources

2.1.1. Brief Description of the Study Area

The UAMRYR is an extra-large national urban agglomeration, which mainly comprises the Wuhan metropolitan area, Changsha–Zhuzhou–Xiangtan urban agglomeration, and Poyang Lake urban agglomeration. It has been designated by the Chinese government as a new pole for national economic growth, a pilot zone for the new mode of urbanization in the central and western regions initiative, a demonstration zone for opening-up and cooperation in the inland regions initiative, and a leading area for building an energy-saving and environmentally friendly society initiative.
According to the Development Plan for the urban agglomeration in the middle reaches of the Yangtze River approved by the Chinese State Council in 2015, the UAMRYR covers a land area of approximately 326.1 thousand km2 and encompasses a total of 31 cities: Wuhan, Huangshi, Yichang, Xiangyang, Ezhou, Jingmen, Xiaogan, Jingzhou, Huanggang, Xianning, Xiantao, Qianjiang, and Tianmen in Hubei Province; Changsha, Zhuzhou, Xiangtan, Hengyang, Yueyang, Changde, Yiyang and Loudi in Hunan Province; and Nanchang, Jingdezhen, Pingxiang, Jiujiang, Xinyu, Yingtan, Ji’an, Yichun, Fuzhou, and Shangrao in Jiangxi Province (Figure 2).
Located in central China and well connected to other parts of the country, the UAMRYR is a major component of the Yangtze River Economic Belt and a key area for the “rise of central China” strategy, all-round reform and opening-up, and the new mode of urbanization strategy, occupying an important position in the Chinese national strategy for regional development.

2.1.2. Index System and Data Sources

The degree of coupling reflects the strength of the interactions between different systems, whereas the degree of coordination measures the level of coordinated development between different systems. Three dimensions of data on the PHS–RHS coupling and coordination in the UAMRYR were collected.
(1) The pseudo dimension: Drawing on findings from human settlements geography, urban geography, news communication, and related disciplines, and adhering to principles of spatiotemporal attributes, scientificity, representativeness, continuity, and operability, and with reference to previous researchers [5,73,74]. It constructs a PHS evaluation index system by selecting five systems of living, entertainment, information, socialization and tool, the 17 intermediate layers such as life services, information search and file storage, and 64 indicators such as WeChat, Baidu and Baidu.com, which are closely related to the life of urban residents (Table 1). The data source is the Baidu index open source website (https://index.baidu.com/v2/index.html#/ (accessed on 25 January 2024)); by entering keywords, we obtained 7,965,760 items of data from 1 January 2011 to 31 December 2021, covering 4015 days, from Wuhan, Changsha, and 31 other cities, with 64 indicators, such as Kugou music and Baidu.
(2) Real dimension: Drawing on the HS science theory proposed by Doxiadis and Wu Liangyong [1], and adhering to systematic, scientific, comprehensive, and operable principles, this study builds on prior research [28,74]. Additionally, it references the “Scientific Evaluation Criteria for Livable Cities”. In this paper, the five systems of human, residential, social, support and environmental, 13 intermediate layers such as population situation, social assets and public utilities, and 42 indicators such as urbanization rate, per capita GDP and urban registered unemployment rate, are selected to construct an RHS indicator system (Table 2). The data sources mainly include the provincial (Hunan, Hubei, and Jiangxi) statistical yearbooks and bulletins, the China City Statistical Yearbooks, and China Construction Statistical Yearbooks published in 2012–2022.
(3) Map data: Data about the administrative boundaries in the study area were obtained from the National Earth System Science Data Center (https://www.geodata.cn/main/ (accessed on 17 March 2024)). The DEM data were acquired from the Geospatial Data Cloud (https://www.gscloud.cn/ (accessed on 17 March 2024)). The water system data were extracted from the Resource and Environmental Science Data Platform (https://www.resdc.cn/ (accessed on 17 March 2024)).

2.2. Research Methods

2.2.1. Modified CCD Model

The coupling degree reflects the strength of the interactions between different systems, whereas the coordination degree measures their level of coordinated development [75]. The traditional CCD model, if the importance and weight of each subsystem are set to be consistent, will simplify the coordination degree D to the 2nd power of the product of each subsystem, thereby reducing the validity of the model’s use. The advantage of the modified CCD model is that it can spread the distribution of coupling degree C with [0, 1] as much as possible, and thus increase the differentiation degree of coupling degree C, so that the further calculated D can more reasonably represent the coupling coordination relationship with the development level measure [76]. Therefore, this study introduces the modified CCD model to construct a CCD model of PHS and RHS in order to explore the strength of the interaction between PHS and RHS and its coordinated development level in the UAMRYR. The specific calculation formula is as follows:
C = 1 − P H S − R H S 2 × min ⁡ P H S , R H S max ⁡ P H S , R H S
T = α P H S x + β R H S x ,   α + β = 1
D = C × T
where P H S and R H S are the comprehensive evaluation indices of the PHS and RHS, respectively, and are calculated using an entropy weight method [77]; C is the degree of coupling and has a value in the range of [0, 1], with a larger value of C indicating a higher degree of coupling, i.e., stronger interactions between the PHS and RHS; D is the CCD; T is the comprehensive coordination index; P H S x and R H S x are the normalized comprehensive evaluation indices of the PHS and RHS, respectively; α and β are undetermined coefficients and were both assigned a value of 0.5 in this study, as the PHS and RHS were deemed equally important; D is the CCD—by referencing a previous report [28], the CCD values were divided into 10 classes (Table 3).

2.2.2. Trend Surface Analysis

The trend surface is a smooth mathematical model that effectively captures spatial data trends over extensive areas, making it an ideal tool for revealing the spatial variation of continuously distributed phenomena [78]. In geography, it is frequently employed to model the spatial trends of population, resources, the environment, and other elements [79]. Consequently, this study utilizes it to simulate the spatial variation trend of the PHS–RHS CCD in the UAMRYR. The formula for its calculation is as follows:
Z i x i ,     y i = T i x i ,   y i + ε i
where Z i x i ,   y i denotes the CCD between PHS and RHS in the i-th city, x i ,   y i are the planar spatial coordinates (where i = 31,   T i x i ,   y i is the trend function, which responds to the general trend of the CCD variation between PHS and RHS in urban agglomerations), and ε i denotes the autocorrelation random error, reflecting the localized characteristics of the coupling and coordination of PHS and RHS [80].
In this study, a second-order polynomial is employed to calculate the trend value, using the formula:
T i x i ,   y i = β 0 + β 1 x + β 2 y + β 3 x 2 + β 4 y 2 + β 5 x y
where the β values represent the estimated coefficients of the second-order polynomial estimated from the sample data [80]; T i x i ,   y i is the same as in Equation (4).

2.2.3. Center of Gravity (CoG) and Standard Deviation Ellipse (SDE) Model

The CoG and SDE model, a spatial statistical method proposed by American sociologist Welty Lefever and colleagues, encompasses fundamental parameters such as the area of the SDE, the CoG, the standard deviation of the major and auxiliary axes, and the angle of rotation. Geographers frequently employ this model to elucidate the spatiotemporal evolution characteristics of geographic elements or attributes within a study area [81]. Consequently, this study utilizes the model to measure the spatiotemporal evolution characteristics of the CCD of the PHS and RHS in the UAMRYR.
(1) CoG model. The CoG, representing the central position of a group of geographic elements, is utilized to depict the migration direction and displacement of the central position of the CCD of the PHS and RHS of the UAMRYR. The specific calculation formula is as follows:
X = ∑ i = 1 n P i X i ¯ / ∑ i = 1 n P i     Y = ∑ i = 1 n P i Y i ¯ / ∑ i = 1 n P i
θ i − j = n π / 2 + a r c t a n y i − y j / x i − x j
D i − j = C · y i − y j 2 + x i − x j 2
where X and Y are the CoG of the CCD; X ¯ i and Y ¯ i are the geographic coordinates of the i-th city in the study area; P is the CCD of the i-th city; θ is the angle of migration of the CoG, with θ = 0 indicating eastward migration of the CoG; ( x i , y i ) and ( x j , y j ) are the coordinates of the CoG of the CCD in the i-th and j-th years, respectively; D i − j is the distance of migration of CoG; C is the ratio of conversion from geographic coordinates to projected coordinates and is conventionally assigned a value of 111.111 km [81].
(2) SDE model. The SDE is a method used to describe the distribution range of a dataset. Key parameters include the rotational angle θ , standard deviations of the major and auxiliary axes, and the area of the SDE. The area of the SDE represents the area of spatial distribution of the geographical attribute of interest. The area of the SDE indicates the spatial extent of the distribution of the CCD between the PHS and RHS. The angle of rotation θ (i.e., the angle formed from the due north direction along the clockwise direction to the major axis of the ellipse) indicates the main direction of the distribution of the CCD between the PHS and RHS. The major axis indicates the dispersion of CCD between the PHS and RHS in the main direction. The auxiliary axis represents the degree of spatial dispersion of the CCD between the PHS and RHS.

2.2.4. Geographical Detector Model

The geographical detector is a statistical method used to identify spatial dissimilarities and their driving force. It comprises four modules—dissimilarity and factor detection, interaction, risk area, and ecological detection. This method is widely employed in geography due to its ability to analyze numerical and qualitative data [82,83]. In this study, we utilize the dissimilarity and factor detection module to examine the impact of each driving factor on the CCD of the PHS and RHS in the UAMRYR. The formula for its calculation is:
q = 1 − ∑ h = 1 L N h δ h 2 N δ 2 = 1 − S S W S S T , S S W = ∑ h = 1 L N h δ h 2 , S S T = N δ 2
where h denotes the stratification of variables and factors; N h and N represent the numbers of units in the h -th layer and the entire study area, respectively; δ h 2 and δ are the variances of the CCD in the h -th layer and the entire study area, respectively; S S W and S S T are the total intra-layer variance and total variance in the entire study area, respectively; and q denotes the power of the factor of interest in explaining the CCD, and has a value in the range of [0, 1], with a larger value of q indicating a stronger explanatory power of the factor.

3. Results Analysis

3.1. Temporal Evolution of the PHS–RHS CCD in UAMRYR

The CCD exhibited a reverse L-shaped increasing trend. In 2011–2021, the PHS–RHS CCD in the UAMRYR varied significantly and exhibited an overall reverse L-shaped increasing trend. In China’s resource-based cities, the CCD variations of PHS and RHS exhibit similar characteristics [84]. The mean CCD increased from 0.1732 in 2011 to 0.3747 in 2021, i.e., 0.2015. The CCD in 2011 was the lowest (0.1732), and that in 2020 was the highest (0.3765) (Figure 3).
The CCD exhibited a polarized distribution. In 2011–2021, the CCDs of the 31 cities exhibited an overall box-shaped, concentrated distribution with a low degree of dispersion. The CCDs of most cities were of the same level. A few outliers existed, i.e., Wuhan, Changsha, and Nanchang, while the CCDs of a few cities were significantly higher than those of other cities, exhibiting a polarization phenomenon. However, Wuhan, Changsha, and Nanchang are notable outliers, exhibiting significantly higher CCD levels than other cities, indicative of a polarization phenomenon. This is because Wuhan, Changsha, and Nanchang, as provincial capitals, possess markedly higher economic development levels than other cities. During the study period, the per capita GDPs of Wuhan, Changsha, and Nanchang were RMB 109,438, RMB 116,208, and RMB 75,034, respectively, which are all significantly higher than the regional average of RMB 55,323. This provides a robust foundation for the HS construction. Additionally, Wuhan, Changsha, and Nanchang have advanced infrastructures, such as network and communication, and transportation; at the same time, they also have good science and technology, as well as talent concentration, all of which provide good support for their HS construction. Similar characteristics are observed in the CCD of PHS and RHS in the three north-eastern provinces of China, where provincial capitals exhibit much higher CCD levels than other regions [74].
The CCD class varied significantly. The PHS–RHS CCD in the UAMRYR increased continuously from 2011 to 2021, with the dominant class changing from severely uncoordinated to moderately uncoordinated and then slightly uncoordinated. The dominance of the slightly uncoordinated class increased. The extremely uncoordinated and severely uncoordinated classes were present at the beginning of the period, but disappeared toward the end. The well-coordinated and highly coordinated classes were absent at the beginning of the period but appeared toward the end (Figure 4).

3.2. Spatial Pattern of the PHS–RHS CCD in the UAMRYR

3.2.1. Spatial Pattern

The spatial distributions of the PHS–RHS CCD in the UAMRYR at four temporal cross-sections (2011, 2014, 2018, and 2021) were plotted using ArcGIS 10.8 (Figure 5).
The PHS–RHS CCD in the UAMRYR exhibited an equilateral triangle-shaped, core–margin spatial pattern. During the study period, the PHS–RHS CCDs in the 31 cities of the UAMRYR increased but were at a low level overall. Few cities had coordinated CCDs, whereas many cities had uncoordinated CCDs, and these were extensively distributed. The number of severely uncoordinated cities decreased, and the number of slightly uncoordinated cities increased. The coordinated cities were mainly scattered at the central nodes of the three key areas (Wuhan metropolitan area, Changsha–Zhuzhou–Xiangtan urban agglomeration, and Poyang Lake urban agglomeration). These cities included Wuhan, Changsha, and Nanchang. The uncoordinated cities were distributed continuously in the areas surrounding the central nodes of the three key areas. Overall, the coordinated and uncoordinated cities exhibited an equilateral triangle-shaped, core–margin distribution, with the coordinated cities located at the apexes of the triangle and uncoordinated cities located along its edges. The urban resilience of the UAMRYR shows a similar spatial distribution pattern [85].
The CCD exhibited a core-polarization spatial pattern. (1) Strong PHS–RHS coordination and interaction were observed in Wuhan, Changsha, and Nanchang. In 2011–2021, the mean PHS–RHS CCDs in these three cities were 0.8140, 0.7421, and 0.5609 (corresponding to the well-coordinated, moderately coordinated, and preliminarily coordinated classes, respectively), much higher than those in the other 28 cities in the UAMRYR. (2) The classes of the CCDs in these three cities governed the overall level of coordination and interaction in the UAMRYR. Coordinated CCDs, especially higher-level coordinated CCDs, first appeared in these three cities. Specifically, the barely coordinated, preliminarily coordinated, moderately coordinated, and well-coordinated classes appeared only in these three cities. The highly coordinated class appeared only in Wuhan. (3) The low PHS–RHS CCDs in the UAMRYR were mainly distributed in Tianmen, Xiantao, and Qianjiang, and the mean CCDs in these three cities in 2011–2021 were 0.1848, 0.1976, and 0.2006, respectively. Although increasing, the highest class of coordination achieved in these cities was moderately uncoordinated. This considerable difference in CCD indicates a core polarization spatial distribution of the CCDs in the UAMRYR. In summary, core polarization was a dominant feature of the PHS–RHS CCD spatial distribution in the UAMRYR from 2011 to 2021.
The spatial distribution was high in the central region, low in the eastern and western regions, and balanced in the south–north direction (Figure 6). (1) The spatial trend in the east–west direction was reverse U-shaped, i.e., arching in the middle and low at the two ends. During the study period, the degree of arching first increased and then decreased, i.e., the variation in the strength of the PHS–RHS interaction between the central region and eastern and western regions first increased and then decreased. Additionally, a spatial trend of the CCD being high in the eastern region and low in the western region existed, i.e., the eastern region outperformed the western region in the CCD. (2) The CCD exhibited a flat spatial trend in the north–south direction, which varied insignificantly during the study period, i.e., the strength of the PHS–RHS interaction in the UAMRYR was consistent in the north–south direction.

3.2.2. Analysis of Inter-Provincial Variations

The 31 cities in the YRMRCR address during the study period belonged to three provinces: Hubei, Hunan, and Jiangxi. The spatial distributions of the PHS–RHS CCD in these three provinces are compared in Figure 7. The results show that the CCD exhibited a markedly heterogeneous spatial distribution. Specifically, the HS quality in Hunan Province was better than that in Jiangxi and Hubei Provinces. The mean CCD in Hunan was the highest (0.3507), followed by those in Jiangxi (0.3004) and Hubei (0.2997). Among the 10 cities with the highest CCDs, six belonged to Hunan (accounting for 75% of the cities in Hunan), two belonged to Jiangxi (20% of the cities in Jiangxi), and two belonged to Hubei (15% of the cities in Hubei). For within-province variations, the difference between the highest CCD (0.7421, in Changsha City) and the lowest CCD (0.2451, in Yiyang) in Hunan was 0.497, that in Jiangxi was 0.3359 (0.5609 in Nanchang and 0.2250 in Yingtan), and that in Hubei was 0.6292 (0.8140 in Wuhan and 0.1848 in Tianmen).

3.3. Dynamic Evolution of the PHS–RHS CCD in the UAMRYR

3.3.1. CoG Migration Analysis

Based on the CoGs of the PHS–RHS CCD in the UAMRYR at different time points, estimated using the CoG model, the distances and directions of CoG migration were plotted using the spatial statistics module of ArcGIS (Figure 8).
During the study period, the CoG of the CCD mainly migrated in Xianning City, moving north-eastward by 6.4 km, indicating that the CCD increased more rapidly in the north-eastern region than in the south-western region. Specifically, the CCD migrated south-eastward by 4.6 km from 2011 to 2014, indicating more rapid increases in the south-eastern region during this period. It then migrated north-eastward by 3.9 km from 2014 to 2018 and by 1.5 km from 2018 to 2021, indicating more rapid increases in the north-eastern region.

3.3.2. SDE Analysis

The parameters of the SDE of the PHS–RHS CCD in different years were calculated using the SDE model and the Spatial Statistics module of ArcGIS, and the evolutions of the SDE were plotted (Figure 8).
Overall, from 2011 to 2021, the rotational angle of the SDE θ decreased by 2.54°. This indicates that the CCD in the north-east direction grows faster than that in the south-west direction. The standard deviations of the major and auxiliary axis increased by 7.31 km and 0.12 km, respectively, and the area increased by 11.61 km2; these changes indicate that the CCD shows a decentralized trend of migrating to the north-east, which means that the spatial clustering degree of the CCD of the PHS and RHS of the UAMRYR decreased during the study period, and spatial variability was narrowed. Specifically, from 2011 to 2014, the θ decreased by 3.22°, the standard deviations of the major axis decreased by 0.39 km, the standard deviations of the auxiliary axis increased by 2.01 km, and the area increased by 2.55 km2. These changes indicate that the CCD increased more rapidly in the north-eastern direction than in the southwestern direction, and exhibited a trend of northeastward migration and dispersion, while the spatial variability narrowed. From 2014 to 2018, the θ increased by 3.01°, the standard deviations of the major axis increased by 7.67 km, the standard deviations of the auxiliary axis decreased by 1.51 km, and the area increased by 9.68 km2. This shows that the CCD increased more slowly in the north-eastern direction than in the south-western direction, and exhibited a trend of south-westward migration and dispersion, and the spatial variability narrowed. From 2018 to 2021, the θ decreased by 2.33°, the standard deviations of the major axis and auxiliary decreased by 0.0087 km and 0.39 km, respectively, and the area decreased by 0.62 km2. This shows that the CCD increased more rapidly in the north-eastern direction than in the south-western direction, and exhibited a trend of south-westward migration and concentration, while the spatial variability increased.

3.4. Driving Mechanisms of PHS–RHS Coupling and Coordination in the UAMRYR

3.4.1. Factor Analysis

Considering that PHS–RHS coupling and coordination are affected by the combined effects of multifaceted factors, factor detection analysis was performed using a geographical detector. By referencing previous studies [75,82], and based on a comprehensive consideration of the weights of the indices and expert opinions, the 12 PHS and 12 RHS factors with the highest weights were selected as the detection factors.
The dominant factors affecting the PHS–RHS CCD in the UAMRYR varied significantly between years (Figure 9). For the PHS, the dominant driving factors evolved from Baidu Maps, Toutiao, Alipay, Jingdong, and Qidian in 2011 to Baidu Maps, Goufang Network, Zhihu, Jingdong, and Baidu Netdisk in 2014; to MeituPic, Baidu, WeChat, Alipay, and Baidu Maps in 2018; and to MeituPic, Tencent Video, WeChat, Baidu Netdisk, and Baidu in 2021.
For the RHS, the driving factors shifted from the number of public transport vehicles per 10,000 people, urbanization ratio, per capita gross domestic product, number of public library books per 100 people, and ratio of amount of investment in real estate developments in 2011 to urbanization ratio, number of public transport vehicles per 10,000 people, average salary of full-time employees, number of public library books per 100 people, and ratio of amount of investment in real estate developments in 2014; to number of internet users per 10,000 people, average salary of full-time employees, urbanization ratio, number of public transport vehicles per 10,000 people, and ratio of amount of investment in real estate developments in 2018; and to number of public transport vehicles per 10,000 people, ratio of amount of investment in real estate developments, urbanization ratio, average salary of full-time employees, and per capita GDP in 2021. These driving factors may be positive, such as the number of public transport vehicles per 10,000 people and urbanization ratio, or negative, such as sulfur dioxide emissions per 10,000 people and urban registered employment rate.

3.4.2. System Analysis

The PHS–RHS coupling and coordination in the UAMRYR were influenced by the combined effects of various systems. The explanatory power of these systems with respect to the CCD varied significantly in different periods (Figure 10).
For the pseudo dimension, in 2011, the ranking of the systems in terms of their explanatory power with respect to the CCD was as follows: living system > entertainment system > socialization system > tool system > information system. In 2014, the ranking changed to living system > tool system > information system > socialization system > entertainment system. In 2018, the ranking was entertainment system > information system > living system > socialization system > tool system. In 2021, the ranking changed to living system > socialization system > tool system > information system > entertainment system. Notably, among the pseudo systems, the living system exhibited the highest explanatory power for the CCD, consistently ranking first in 2011, 2014, and 2021. This predominance is attributed to the significant relevance and impact of the living system on people, and its extensive audience. In contrast, systems such as the tool and information systems demonstrate relatively lower relevance and smaller audience groups.
For the real dimension, in 2011, the ranking of the systems in terms of their explanatory powers with respect to the CCD was as follows: human system > support system > social system > residential system > environmental system. In 2014, the ranking became human system > social system > support system > residential system > environmental system. In 2018, the ranking was support system > human system > social system > residential system > environmental system. In 2021, the ranking changed to human system > social system > residential system > environmental system > support system. Notably, the human system within the real system exhibited the highest explanatory power for the CCD, consistently ranking as the primary driving system in 2011, 2014, and 2021. This prominence is attributed to the human element being the core of the HS, serving as the fundamental driver for the coupling and coordination of the PHS and RHS, and forming the foundation for the development and construction of other systems.

3.4.3. Morphological Analysis

The correlation coefficients of the PHS and RHS with the CCD in the UAMRYR were estimated using SPSS 26 and other software. In 2011–2021, the PHS and RHS in the UAMRYR were strongly positively correlated with the CCD, with the PHS having stronger positive feedback on the CCD (Figure 11). (1) During the study period, the mean correlation coefficients of the PHS and RHS with the CCD were 0.98 and 0.83, respectively, and both passed the significance test. Thus, the PHS and RHS were significantly positively correlated with the CCD, with the PHS more significantly positively correlated with the CCD. (2) During the study period, the coefficient of determination of the PHS exhibited a markedly increasing trend, whereas the RHS displayed a markedly decreasing trend. Thus, the effect of the PHS on the CCD increased, whereas the effect of the RHS on the CCD decreased.

3.4.4. Discussion of Mechanisms

Based on a comprehensive analysis of the driving factors, systems, and patterns behind the PHS–RHS coupling and coordination in the UAMRYR, the driving mechanisms were investigated to provide scientific criteria for the all-round improvement of the PHS–RHS coupling and coordination and HS in the UAMRYR.
For the PHS, the living system was the dominant driving system of the PHS–RHS coupling and coordination. In 2011, 2014, and 2021, the living system ranked first in terms of its explanatory power with respect to the CCD. Additionally, representative factors of the living system, such as Baidu Maps, ranked high in most years in terms of their explanatory power with respect to the CCD.
For the RHS, the human system was the dominant driving system behind the PHS–RHS coupling and coordination. In 2011, 2014, and 2021, the human system ranked first among the PHS systems in terms of its explanatory power with respect to the CCD. Additionally, representative factors of the human system, such as the urbanization ratio, ranked high in most years in terms of their explanatory power with respect to the CCD.
The economic development level was a major factor affecting the PHS–RHS coupling and coordination. Economic factors such as the per capita GDP and average salary of full-time employees ranked high in most years in terms of their effects on the CCD. Additionally, cities with high per capita GDPs and average salaries of full-time employees, such as Wuhan, Changsha, and Nanchang, had high CCDs, whereas cities with low per capita GDPs and average salaries of full-time employees, such as Tianmen and Loudi, had low CCDs.

4. Discussion

4.1. Spatiotemporal Characteristics of PHS–RHS Coupling and Coordination

In terms of the temporal variation characteristics, from 2011 to 2021, the PHS–RHS CCD exhibited an increase, with the number of coordinated cities increasing and the number of uncoordinated cities decreasing, corroborating the findings of Li Guozhu et al. on the coupling and coordination between PHS and RHS in 114 resource-based cities in China [84]. The gradual improvement in the HS of the UAMRYR and the rising living standards of its residents can be attributed to regional socio-economic development, the widespread adoption of the internet, and enhanced infrastructure. These factors have significantly supported HS construction. Additionally, the government’s increased focus on high-quality and sustainable HS development, as outlined in the Communist Party of China’s (CPC’s) Fourteenth Five-Year Plan, has provided essential policy support and guidance for enhancing HS quality.
Regarding the spatial characteristics, from 2011 to 2021, an equilateral triangle-shaped core–margin spatial pattern was evident, with central-node cities such as Changsha and Wuhan exhibiting high CCDs and the surrounding cities exhibiting low CCDs, indicating core polarization. This aligns with the spatial pattern of urban resilience in the UAMRYR as studied by Yin Jianjun et al. [85]. Similarly, the spatial distribution pattern of urbanization and HS coupling and coordination in the UAMRYR, as examined by Xu Jiang et al. [86], exhibits comparable characteristics. This is because Wuhan and Changsha, being provincial capitals, benefit from clustered advantageous resources and superior economic and infrastructure conditions [60], providing a robust foundation for the high-quality development of HS. During the study period, the spatial gap in CCD gradually narrowed, indicating the enhanced regional coordination of HS within the UAMRYR. This improvement is attributed to the “Development Plan for the UAMRYR”, introduced in 2015, which emphasizes infrastructure connectivity, coordinated industrial development, and the sharing of public services as key tasks, thereby offering policy support for the coordinated development of the UAMRYR.

4.2. Driving Mechanisms of PHS–RHS Coupling and Coordination

The level of economic development is a key factor influencing the coupling and coordination of PHS and RHS, aligning with the findings of Xue Qirui et al. [6] in their study on the factors influencing PHS and RHS in China. Temporally, the per capita GDP of the UAMRYR increased from RMB 34,348 in 2011 to RMB 79,500 in 2021, with the corresponding CCD between PHS and RHS rising from 0.17 in 2011 to 0.37 in 2021. Spatially, the top three cities in the UAMRYR by per capita GDP are Changsha (RMB 116,208), Wuhan (RMB 109,438), and Yichang (RMB 85,903), with corresponding CCD rankings of second, first, and fourth, respectively. Therefore, enhancing regional economic development remains a priority for future governmental efforts to provide robust and practical support for the high-quality development of HS.
The PHS, RHS and CCD exhibit a strong positive correlation. The feedback from PHS to CCD is notably stronger and demonstrates an increasing trend, aligning with previous research on PHS and RHS coupling and coordination in the three north-eastern provinces of China [74]. This shows that the enhancement of the quality of the HS is the result of the joint enhancement of the PHS and RHS, but with the arrival of the information age and the development of the internet, big data and other technologies, the status of the PHS in the HS system is constantly improving. Therefore, in the future construction of cities, attention should be paid not only to the construction of real environments such as the economy and transportation, but also to the construction of pseudo-environments such as networks and information.

4.3. Policy Recommendations

(1) Giving full play to the radiating role of the “three-center” cities. The CCDs of the PHS and RHS in the UAMRYR exhibit a “core–edge” spatial pattern characterized by a “positive triangle”. Significant disparities in CCD are observed among regions, with Wuhan, Changsha, and Nanchang, as the central cities of the UAMRYR, displaying markedly higher CCD values than other cities. Thus, it is imperative to bolster intraregional cooperation and exchanges, and to augment industrial and technological support from the central cities of Wuhan, Changsha, and Nanchang to other regions. This approach aims to disseminate expertise and methodologies in urban HS construction, thereby actualizing the principle of “leveraging strengths to mitigate weaknesses” and fostering the high-quality, coordinated development of the overall HS within the UAMRYR.
(2) Optimizing the HS of urban agglomeration “according to local conditions”. The CCD of the PHS and RHS in the cities of UAMRYR is uneven, and the difference is very large. By 2021, only two cities, Wuhan and Changsha, belonged to the high-quality coordination type, and Nanchang, as the capital city of Jiangxi Province, only reached the barely coordinated type of coupling coordination. Between Wuhan and Changsha there is still a certain gap, and in general, only four cities belong to the coordinated type, which accounts for only 1/8 of the cities of the UAMRYR, and the overall quality of development needs to be urgently upgraded. Consequently, the following recommendations are made to ensure promotion.
First of all, it is necessary to make great efforts to improve the HS quality of Nanchang, the capital city of the province, to reach a state on a par with Wuhan and Changsha, in order to give better play to the radiating role of the “three central” cities. Nanchang, situated in East China near developed regions such as Shanghai and Zhejiang, is strategically positioned at the intersection of the Beijing–Kowloon and Shanghai–Kunming Railways. This advantageous location necessitates leveraging its connectivity to enhance exchanges and cooperation with neighboring areas, thereby fostering economic development, attracting related industries, creating employment opportunities, and establishing a robust foundation for improving the quality of HS.
Secondly, it is necessary to vigorously improve the quality of the HS in cities with a low CCD, and increase the proportion of coordinated cities in the region. These cities, having developed later, exhibit relatively low levels of economic development and outdated infrastructure. However, they possess a favorable ecological environment and abundant tourism resources. It is imperative to leverage these resource advantages to vigorously develop the tourism industry, thereby stimulating local economic growth and infrastructure development. This transformation of resource advantages into economic benefits will provide essential support for the construction of HS. Concurrently, maintaining their ecological advantages is crucial.
(3) Focus on the synergistic development of PHS and RHS. PHS, RHS and the CCD of HS exhibit a strong positive correlation, and PHS is more influential on the CCD. Therefore, to achieve high-quality development of the HS, the government must enhance economic development, create more employment opportunities, and improve the environmental management of air, water, and soil. Additionally, it should strengthen infrastructure for transportation and housing, and bolster social security to ensure a good RHS. Concurrently, it is essential to enhance cyber–environmental governance by introducing and enforcing appropriate laws and regulations on cyber use, thereby providing a safe and healthy PHS.

4.4. Limitations and Future Work

The limitations of and future work raised by this paper mainly include the following aspects:
(1) With the development of the information age, the form of HS has evolved from a single RHS to a variety of forms such as PHS and imagery human settlements (IHS) [87]. This study exclusively examines the spatiotemporal heterogeneity and driving mechanisms of the coupling and coordination of HS in the UAMRYR, focusing on PHS and RHS. Future research will investigate the interaction mechanism of the “three states” of HS at the “PHS–RHS–HIS” level;
(2) The data utilized in this study were primarily derived from the Baidu index and various statistical yearbooks in China, rather than first-hand field survey data, which may results in a slight deviation between the research findings and the actual situation. Consequently, future studies should aim to obtain first-hand data through questionnaires and other methods to enhance the accuracy of research on PHS and RHS;
(3) This study predominantly employs traditional research methods such as CCD, trend surface analysis, and geographic detectors, and will incorporate more advanced and innovative methods for assessing the state of HS development in future research;
(4) This study focuses on the UAMRYR region, characterized by a uniform geographical background. Notably, there are variations in the interaction mechanism between PHS and RHS across different geographical contexts. Future research will examine the interaction mechanism of HS within the unique geographic settings of resource cities, tourist cities, developed countries, and developing countries, considering diverse geographic backgrounds;
(5) Given that the Baidu Index and China Statistical Yearbook data possess distinct Chinese characteristics and are predominantly applicable to research within China, their international applicability is limited. Consequently, future research will utilize global data sources such as Google and the World Bank database to advance PHS and RHS research on a worldwide scale.

5. Conclusions

With the advent of the information age, advances in internet and big data technologies have allowed the human cognitive space to extend beyond the RHS. With people spending more time online, the PHS, consisting of digital and information constructs, is becoming increasingly important in the HS system. The RHS alone cannot yield a reasonable explanation of the present HS or meet the needs for advancing theory and practice. Therefore, we developed a theoretical framework to describe PHS–RHS coupling and coordination, and performed an empirical analysis of the heterogeneity and driving mechanisms of PHS–RHS coupling and coordination in the UAMRYR in 2011–2021, using multiple sources of data on online networking, scientific, educational, cultural and health activities, and remote sensing images, employing modified CCD, trend surface, CoG and SDE, and geographical detector models. This has elucidated the interactive dynamics between the coupling and coordination of the PHS and RHS, offering a reference basis for the construction of the HS in the UAMRYR and future urban planning. Additionally, it draws insights relevant for HS construction in other regions of China, and provides theoretical support for the development of a beautiful China. Our key findings can be summarized as follows:
(1) Temporal process—During the study period, the mean PHS–RHS CCD in the UAMRYR exhibited a reverse L-shaped increasing trend and a polarized distribution. The CCD class varied significantly, with the extremely uncoordinated and severely uncoordinated classes present at the beginning of the study period and disappearing toward the end, and the well-coordinated and highly coordinated classes absent at the beginning of the study period and appearing toward the end;
(2) Spatial pattern—During the study period, the PHS–RHS CCD in the UAMRYR exhibited an equilateral triangle-shaped, core–margin spatial pattern and core polarization, with the coordinated cities scattering at central nodes of the three key regions and uncoordinated cities distributing continuously in the surrounding areas of these central nodes. The spatial distribution was high in the central region, low in the eastern and western regions, and balanced in the south–north direction, with more stable trends of surface variations in the south–north direction. At the provincial scale, the CCD exhibited marked spatial heterogeneity, with the HS quality in Hunan province being higher than that in Jiangxi and Hubei provinces;
(3) Dynamic evolution—During the study period, the CoG of the PHS–RHS CCD in the UAMRYR migrated north-eastward, with the rotational angle of the SDE decreasing and the area increasing. Thus, the CCD increased more rapidly in the north-eastern direction than in the south-western direction, the coupling and coordination exhibited a trend of north-eastward migration and dispersion, and the spatial variability decreased.
(4) Driving mechanisms—The dominant factors affecting the PHS–RHS CCD in the UAMRYR varied significantly over the years. The living system was the dominant driving system of the PHS, whereas the human system was the dominant driving system of the RHS. Both the PHS and RHS were strongly positively correlated with the CCD, with the PHS having a stronger positive effect on the CCD.

Author Contributions

Conceptualization, X.L. and S.T.; methodology, W.W.; software, W.W.; validation, W.W., S.T. and H.L.; formal analysis, H.L. and W.W.; resources, W.W. and S.T.; data curation, X.L. and S.T.; writing—original draft preparation, W.W., H.L. and Y.W.; writing—review and editing, W.W. and S.T.; visualization, W.W. and H.L.; supervision, S.T. and X.L.; project administration, X.L. and S.T.; funding acquisition, S.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (42201221), Liaoning Province Natural Science Foundation Project (2023-MS-254); Liaoning Province Social Science Planning Fund Project (L22CJY016); Dalian Federation of Social Sciences (2022dlskzd037).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the paper.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Research framework.
Figure 1. Research framework.
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Figure 2. Geographical location and scope of the study area. (a) Indicating the location of the Yangtze River Basin in China. (b) Indicating the location of the UAMRYR in the Yangtze River Basin. (c) Indicating the scope of the UAMRYR.
Figure 2. Geographical location and scope of the study area. (a) Indicating the location of the Yangtze River Basin in China. (b) Indicating the location of the UAMRYR in the Yangtze River Basin. (c) Indicating the scope of the UAMRYR.
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Figure 3. Temporal evolution of the PHS–RHS CCD in the UAMRYR in 2011–2021.
Figure 3. Temporal evolution of the PHS–RHS CCD in the UAMRYR in 2011–2021.
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Figure 4. Temporal variations in the classes of the PHS–RHS CCD in UAMRYR in 2011–2021.
Figure 4. Temporal variations in the classes of the PHS–RHS CCD in UAMRYR in 2011–2021.
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Figure 5. Spatial distribution of the PHS–RHS CCD in the UAMRYR in 2011–2021. (a–d) The CCD classes of different cities in different years. (e–h) The CCD values of different cities in different years.
Figure 5. Spatial distribution of the PHS–RHS CCD in the UAMRYR in 2011–2021. (a–d) The CCD classes of different cities in different years. (e–h) The CCD values of different cities in different years.
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Figure 6. Trend surface analysis of the PHS–RHS CCD in the UAMRYR in 2011–2021: (a) 2011; (b) 2014; (c) 2018; (d) 2021.
Figure 6. Trend surface analysis of the PHS–RHS CCD in the UAMRYR in 2011–2021: (a) 2011; (b) 2014; (c) 2018; (d) 2021.
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Figure 7. Inter-provincial variations in the PHS–RHS CCD in the UAMRYR.
Figure 7. Inter-provincial variations in the PHS–RHS CCD in the UAMRYR.
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Figure 8. CoG and SDE distributions of the RHS-PHS CCD in the UAMRYR in 2011–2021.
Figure 8. CoG and SDE distributions of the RHS-PHS CCD in the UAMRYR in 2011–2021.
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Figure 9. Q-values of driving factors of PHS–RHS coupling and coordination in UAMRYR in 2011–2021: (a) PHS; (b) RHS.
Figure 9. Q-values of driving factors of PHS–RHS coupling and coordination in UAMRYR in 2011–2021: (a) PHS; (b) RHS.
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Figure 10. Q-values of driving systems of PHS–RHS CCD in UAMRYR in 2011–2021: (a) PHS; (b) RHS.
Figure 10. Q-values of driving systems of PHS–RHS CCD in UAMRYR in 2011–2021: (a) PHS; (b) RHS.
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Figure 11. Correlation coefficients of PHS and RHS with CCD in UAMRYR in 2011–2021: (a–d) PHS; (e–h) RHS.
Figure 11. Correlation coefficients of PHS and RHS with CCD in UAMRYR in 2011–2021: (a–d) PHS; (e–h) RHS.
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Table 1. Index systems for the PHS.
Table 1. Index systems for the PHS.
Criterion LayerIntermediate LayerIndicator Layer
Living systemOnline shoppingDianping 0.0176; Dangdang 0.0127; Gome 0.0111; JD 0.0238; Taobao 0.0226; VIPshop0.0174
Travel Baidu maps 0.0222; Amap 0.0159; Trip 0.0176; 12306 railway 0.0119
Life serviceAnjuke 0.0209; Lianjia 0.0251; Sofang.com 0.0203; Goufang.com 0.0275; 58.com 0.0220; Meituan 0.0182; Elema 0.0177
Entertainment systemMusicQQ music 0.0144; Kugou music 0.0139; Kuwo music 0.0079; Netease cloudmusic 0.0171; Himalaya 0.0128
VideoIqiyi 0.0151; Tencent video 0.0227; Youku 0.0188; Mango TV 0.0123
ReadQQ reading 0.0072; Start reading 0.0207
PlayFun fest 0.0145; League of legends 0.0142
Information systemNews browsingSina news 0.0086; Sohu news 0.0100; Tencent news 0.0128; Toutiao 0.0170; Netease news 0.0085
Weather forecastMoweather 0.0083; 2345 weather 0.0103
Information searchUC browser 0.0142; QQ browser 0.0103; Quark 0.0251; Baidu 0.0254
Socialization systemInternet socializationMicro blog 0.0225; Zhihu 0.0258; Kwai 0.0145; Bilibili 0.0207
Instant messagingQQ 0.0162; Wechat 0.0173; China unicom 0.0130; China mobile 0.0171; China telecom 0.0101
Tool systemWork-studyMicrosoft office 0.0092; Youdao 0.0118; WPS office 0.0143
Financial managementChina construction bank 0.0126; Agricultural bank of china 0.0102; Alipay 0.0184
Network security360 security 0.0088; Tencent mobile manager 0.0086
Face-lifting photoMeitu 0.0144; Beautycam 0.0124; Photoshop 0.0083
File storageBaidu netdisk 0.0164; Thunder 0.0163; Tencent cloud 0.0145
Table 2. Index systems for the RHS.
Table 2. Index systems for the RHS.
Criterion LayerIntermediate LayerIndicator Layer
Human systemPopulation situationUrbanization rate 0.0287;Natural population growth rate 0.0038; Gender ratio 0.0171; Ratio of permanent population to registered residence population 0.0198
Employment statusUrban registered unemployment rate 0.0274; The proportion of population in the tertiary industry 0.0191; Per capita built-up area 0.0280; Average price of newly built housing 0.0040
Residential systemLand resourcePopulation density in urban areas 0.0180; The proportion of urban construction land to built-up area 0.0079; Per capita built-up area 0.0280; Average price of newly built housing 0.0040
Residential statusUrban per capita housing construction area 0.0178; The proportion of investment in real estate development 0.0383; Per capita income to housing price ratio 0.0172
Social systemEnterprise statusPer capita liabilities of industrial enterprises above designated size 0.0059
Social assetsPer capita Gross Domestic Product 0.0449; Gross Domestic Product Index 0.0055; Per capita social fixed assets investment 0.0289
Financial budgetThe proportion of social security and employment expenditure to local public finance expenditure 0.0198
People’s LivesAverage salary of on duty employees 0.0379; Average annual disposable income per capita for urban residents 0.0337; Consumer Price Index for Residents 0.0098
Social securityThe proportion of urban basic pension insurance participants 0.0338
Support systemScience, education, culture and healthPublic library collections per 100 people 0.0596; Number of beds in medical institutions per thousand population 0.0227; Number of practicing (assistant) physicians per thousand population 0.0217; Teacher–student ratio in higher education institutions 0.0547; Number of ordinary higher education institutions per 10,000 people 0.0962; Primary and secondary school full-time teachers bear the burden of student numbers 0.0144
Public utilitiesPer capita urban road area 0.0344; Urban water usage penetration rate 0.0014; Urban gas penetration rate 0.0039; Urban sewage centralized treatment rate 0.0043; Harmless treatment rate of household waste 0.0012; 10,000 people have access to public transportation 0.0729; Number of internet users per 10,000 people 0.0508; Number of road lights per kilometer 0.0390
Environmental systemEnvironmental pollutionPer capita total industrial wastewater discharge 0.0059; Per capita sulfur dioxide emissions per 10,000 people 0.0069; Dust emissions per 10,000 people 0.0028
Environmental feedbackPer capita park green space area 0.0213; Green coverage rate in built-up areas 0.0077; Air quality excellence rate 0.0111
Table 3. Classification of CCD values.
Table 3. Classification of CCD values.
CCDClassCCDClass
0.00–0.10Extremely uncoordinated0.50–0.60Barely coordinated
0.10–0.20Severely uncoordinated0.60–0.70Preliminarily coordinated
0.20–0.30Moderately uncoordinated0.70–0.80Moderately coordinated
0.30–0.40Slightly uncoordinated0.80–0.90Well-coordinated
0.40–0.50Marginally uncoordinated0.90–1.00Highly coordinated
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Wu, W.; Tian, S.; Li, H.; Li, X.; Wang, Y. Multi-Source Data-Based Investigation of Spatiotemporal Heterogeneity and Driving Mechanisms of Coupling and Coordination in Human Settlements in Urban Agglomeration in the Middle Reaches of the Yangtze River. Sustainability 2024, 16, 7583. https://doi.org/10.3390/su16177583

AMA Style

Wu W, Tian S, Li H, Li X, Wang Y. Multi-Source Data-Based Investigation of Spatiotemporal Heterogeneity and Driving Mechanisms of Coupling and Coordination in Human Settlements in Urban Agglomeration in the Middle Reaches of the Yangtze River. Sustainability. 2024; 16(17):7583. https://doi.org/10.3390/su16177583

Chicago/Turabian Style

Wu, Wenmei, Shenzhen Tian, Hang Li, Xueming Li, and Yadan Wang. 2024. "Multi-Source Data-Based Investigation of Spatiotemporal Heterogeneity and Driving Mechanisms of Coupling and Coordination in Human Settlements in Urban Agglomeration in the Middle Reaches of the Yangtze River" Sustainability 16, no. 17: 7583. https://doi.org/10.3390/su16177583

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