Investigation on the coupling coordination of pseudo human settlements in the urban agglomerations in eastern China

Abstract

In the information age, digital information technology has gradually become a new quality of productivity to improve international competitiveness, and the status and importance of the pseudo human settlements(PHS) constructed by digital and information has been increasing in the human settlements(HS) system. In this context, exploring the coupling coordination of the internal systems of the PHS provides a theoretical basis for promoting the comprehensive improvement of the quality of the HS in the urban agglomerations, provides a theoretical reference for rationally interpreting the new type of human-land relationship in the information age, and provide a new perspective for the study of the human settlements geography. Currently, research on PHS is in its nascent stages, therefore, we construct a theoretical framework for the coupling coordination of the “three states” of the HS, takes the internal system of PHS as the entry point, empirically analyze the spatiotemporal characteristics of the PHS coupling coordination degree within urban agglomerations in eastern China and the driving factors by using the coupling coordination degree, spatial autocorrelation, the center of gravity and the ellipse of the standard deviation, and geo-detector models. Research shows: (1) Time course: exhibits an upward “inverted L” trend. (2) Spatial pattern: exhibits a general spatial pattern of “high in the north and low in the south”. (3) Spatial correlation: shows a spatially positively correlated clustering trend. (4) Spatial evolution: demonstrates a decentralized pattern of migration from the northeast to the southwest, indicating that the coupling coordination grows faster in the southwest than in the northeast. (5) Driving factor: the development of coupling coordination degree of PHS results from multiple factors and systems. This research provides theoretical support for promoting the comprehensive improvement of the quality of PHS in the urban agglomerations in eastern China, and offers scientific reference for the construction of PHS in other regions of China.

Introduction

The world today is undergoing a revolutionary change—the information technology revolution, which is shaping social transformation in an unprecedented manner, facilitating the global realization of informatization. Big data is a significant outcome of this information technology revolution, with extensive implications for human production, and life^1,2,3,4,5; Simultaneously, it gives rise to the pseudo human settlements (PHS), constructed through digital and informational elements⁶.

Governments place significant emphasis on big data and digital economy development. The British government has established the world’s first open data institute, ODI; the United States initiated the “big data research and development program”; and China explicitly declared in the report of the Communist Party of China (CPC) “Twentieth National People’s Congress” the imperative to expedite digital economy development.

In the information age, the widespread application of digital information technology^3,7,8 makes the human cognitive space not limited to the reality human settlements (RHS), but gradually extended to the PHS. However, current research predominantly focuses on reality aspects, lack of pseudo level to adequately explain the new human-land relationships in the information age and meet the theoretical and practical needs of contemporary development⁶. Therefore, it is essential to introduce the concept of PHS constructed by figures and information, explore the coupling coordination interactive relationship of the internal subsystems of the PHS, and to reveal its spatiotemporal evolutionary characteristics as well as its driving factors, and promote the overall improvement of the quality of the PHS, so as to better explain the new human-land relationship in the information age, and to provide a scientific basis and theoretical foundation for the high-quality development of urban agglomerations’ human settlements (HS).

Literature review

PHS

The concept of “pseudo-settlements” was first introduced by Lippmann in his book Public Opinion, where he states that “pseudo-settlements are not real environments; they are images of reality environments that are reconstructed as people react to them in reality environments”⁹. At the beginning of the twenty-first century, “pseudo-settlements” were introduced into “Human Settlements Geography(HSG)”, and the concept of “PHS” was proposed, i.e., a kind of information and pseudo settlements constructed by the residents based on their own will and cognition, taking the reality environments as the “base plate”, through editing and processing, and with the help of the media⁶. With the advent of the informatization era, the importance of the information environment is increasingly prominent¹⁰, and PHS has gradually captured the attention of geographers. Existing research on PHS focuses on four main areas: (1) content studies, mainly addressing the definition of the PHS concept⁶, the characterization of its spatiotemporal evolution¹¹, and realistic features and driving mechanisms¹²; (2) relevance studies, which concentrate on the coupling coordination with the RHS^13,14; (3) scale research, primarily considering the provincial scope¹¹ as the research object. It analyzes its spatiotemporal distribution and evolution law by constructing the corresponding index system; (4) method research, mainly employing the entropy weight method⁶, two-stage analysis method¹³, and other methods.

In summary, existing research on PHS remains immatureness and still suffers from the following problems: (1) Research on PHS lacks comprehensive depth, especially in studying the coupling coordination, and driving factors of its internal system, which is the key to develop its sustainable. Therefore, this study examines the internal system of PHS to analyze the coupling coordination among its five subsystems, offering a novel perspective on PHS research. (2) The PHS research has predominantly focused on provinces and cities, neglecting urban agglomerations, counties, and other scales. However, urban agglomerations are the main area of the country’s new type of urbanization, which are an important platform to support the national economic growth, promote the coordinated development of the region, and participate in the international competition and cooperation. Consequently, this study uses urban agglomerations as a research scale in an attempt to enrich the research scale of PHS.

Coupling coordination degree

“Coupling coordination” is the process of coordinating and interacting among multiple systems or subsystems. The term “coupling” originated in physics¹⁵ and has been extensively utilized in research within economics^16,17,18, ecology^19,20,21, tourism^22,23,24, geography^25,26,27,28, and various fields. Applications in HS have concentrated on the following aspects: (1) the coupling coordination within the internal system of HS^29,30; (2) the coupling coordination among different states of HS⁶; (3) the coupling coordination between HS and the external system³¹. The application of the coupling coordination degree model in HSG enriches the research methodology of HSG and promotes the further development of HSG.

Urban agglomerations

Cities serve as central hubs for capital, labor, and so on, whereas urban agglomerations represent an advanced stage of urban development³². W. Christaller, the German geographer, systematically defined the spatial organization and structure of urban agglomerations in 1933³³. The exploration of Chinese urban agglomerations began at the end of the twentieth century with Zhenhua Yao introducing the concept, denoting clusters of multiple cities exhibiting diverse characteristics within specified economic and environmental conditions³³. Following the introduction of the "urban agglomerations" concept, scholars globally have conducted diverse studies on urban agglomerations, exploring various perspectives.: (1) from an ecological perspective: the spatial and temporal evolution of ecological environment quality in urban agglomerations^34,35, and ecological security problems in urban agglomerations^36,37,38 have been studied; (2) from an economic perspective: the high-quality development³⁹, and the economic resilience^40,41 of urban agglomerations, have been studied; (3) from the perspective of HSG: studies have examined the quality and spatiotemporal evolution of HS in urban agglomerations^42,43, as well as the resilience of HS in urban agglomerations^40,44,45; (4) from an integrated standpoint: research has explored the correlation among economic, resources and environment in urban agglomerations⁴⁶, as well as the correlation between ecological and economic in urban agglomerations⁴⁷.

In summary, the existing research on urban agglomerations has yielded fruitful results, However, it predominantly concentrates on the reality aspects, neglecting the exploration of pseudo dimensions. And with the development of the informationization and digitization era, the pseudo environment constructed by numbers, information and so on, will occupy a more and more important position in the further development of the urban agglomerations in the future. Therefore, this study starts from the pseudo perspective to analyse the coupling coordination of the PHS of urban agglomerations, which offering a new research perspective for urban agglomerations studies.

The urban agglomerations in eastern China stands out as the most economically developed region in the country. In 2022, the total GDP of the Yangtze River Delta, Beijing-Tianjin-Hebei, and Pearl River Delta urban agglomerations exceeds 40% of the country’s total GDP, with the Yangtze River Delta urban agglomeration reaching an impressive 29 trillion yuan. The development of Urban Agglomerations in Eastern China not only leads the nation but also holds a crucial economic and strategic position; its economic progress significantly influences the overall economic development of the country. Therefore, the study focuses on the six major urban agglomerations in eastern China and constructs a theoretical framework for the coupling coordination of the “three states” of HS, takes the internal system of PHS as the entry point and constructs the evaluation index system for the PHS, incorporating coupling coordination degree to analyze the coupling coordination interactive relationship of PHS subsystems within these agglomerations; and incorporating spatial autocorrelation models to analyze the spatial correlations of coupling coordination degree of PHS subsystems within these agglomerations; and incorporating center of gravity and standard deviation ellipse models to analyze the spatiotemporal evolution patterns of coupling coordination degree of PHS subsystems within these agglomerations. Additionally, the study employ the geo-detector model to analyze the driving mechanisms behind the coupling coordination degree of PHS of urban agglomerations in eastern China, explore pathways for enhancing PHS quality, contribute to the advancement of HS discipline theory, provide theoretical support for the coupling coordination development of PHS within urban agglomerations in eastern China, and offering scientific references for the further progress and search for new growth poles in these regions.

Theoretical foundations

Theoretical connotations

The HS coupling coordination is a non-linear dynamic evolutionary process that demonstrates coupling coordination from elements → systems → states. It involves the progression from simple coupling coordination to complex coupling coordination throughout the development process.

• (1) Element coupling coordination. It involves the coupling coordination of reality elements such as climate, people, social welfare, housing, infrastructure, etc.; the coupling coordination of pseudo-elements such as Facebook, Twitter, QQ music, Tencent mobile manager, Baidu netdisk, etc.; and the coupling coordination of imaginative elements such as motorways, rivers, neighborhoods, subway stations, typical buildings, etc.; The coupling coordination of elements states the foundation and core of coupling coordination across various dimensions of the HS.

• (2) System coupling coordination. It involves the coupling coordination among reality systems such as residential, support, social, environmental, and human systems; the coupling coordination between pseudo systems such as living, entertainment, information, socialization, and tool systems; and the coupling coordination between imagery systems such as road, boundary regional, node, marker systems. The system coupling coordination plays a role in carrying on the coupling coordination of other levels of the HS.

• (3) State coupling coordination. It pertains to the coupling coordination among different states based on the elements and systems comprised of RHS, PHS, and imagery human settlements (IHS). Coupling coordination between distinct states represents the advanced stage of HS coupling coordination and signifies the high-quality stage in coupling coordination at other levels.

Theoretical framework

Based on the theories of HSG and coupling coordination, the study proposed a multi-dimensional dynamic system framework for the coupling coordination of HS from the three scales of time, space, and content, including the coupling coordination between elements, systems, and states.

• (1) Time scale. (1) spiral upward, the HS is a dynamic evolutionary system, in time, the coupling coordination degree undergoes stages of low-level disorder → intermediate balance → high-level order. (2) Wave forward, in the evolution process, if external elements or internal issues disrupt the original balance, a new evolution will occurs. The overall pattern shows “low → middle → high → low (middle) → high …”, with the evolution pattern being “low → middle → high → low (middle) → high”.

• (2) Space scale. (1) HS coupling coordination spans micro to macro geographic scales: community → township → county (district) → city → province → urban agglomeration → country → global. (2) From the plane to the three-dimensional scale, HS coupling coordination includes the RHS (PHS/IHS) coupling coordination of HS → RHS and PHS, RHS and IHS, IHS and PHS, and the "three states" coupling coordination of HS.

• (3) Content scale. (1) Explicit coupling coordination and implicit coupling coordination, explicit manifestation of different elements, systems, and states can be intuitively measured between the connection; implicit manifestation of different elements, systems, and states between the real existence but difficult to intuitively measure the connection; (2) Strong coupling coordination and weak coupling coordination, strong coupling coordination is seen in the strong connection between different elements, systems, and states of the HS. Weak coupling coordination is evident in the weak connection between different elements, systems, and states of the HS.

Facing the paradigm shift of HSG research, the coupling coordination of HS is constantly formed from elements → systems → states, plane to three-dimensional, micro and macro, obvious and hidden one of the multi-dimensional, multi-morphology coupling coordination, and finally formed the multi-dimensional, multi-morphology dynamic coupling coordination of HS theoretical system framework (Fig. 1).

Figure 1

Theoretical framework of coupling coordination the “three states” of HS (“three states” refers to pseudo human settlements, reality human settlements and imagery human settlements).

Data and methods

Data sources and indicator system construction

This paper is grounded in the theoretical framework of “three states” coupling coordination of HS, as well as the background of the informationization era, people will meet the needs of diversified life from offline to online + offline combination, pseudo and reality fusion, a single state of the RHS research can not reasonably explain the phenomenon of today's HS, but the existing research on the PHS is less research. So this paper focuses on PHS coupling coordination. Following the principles of scientific rigor, operational feasibility, systematic approach, and comprehensiveness of the indicator system, and drawing on previous research⁶, and consider the five subsystems of living, entertainment, information, socialization, and tool, the study constructed the PHS evaluation indicator system by selecting 64 indicators (Fig. 2), measurement study of the level of PHS coupling coordination.

Figure 2

PHS evaluation indicator system (including five systems such as information and tool, 17 intermediate layers such as information search, network socialization and 64 indicators such as baidu and meituan).

Existing research on PHS primarily focuses on the provincial and municipal scales in China, with limited attention given to urban agglomerations, which are the main areas of the country’s new type of urbanization and an important platform for supporting national economic growth, promoting coordinated regional development, and participating in international competition and cooperation. The urban agglomerations in eastern China is the most economically developed and the highest level of openness in China. The development of urban agglomerations in eastern China has a leading role in other regions of China, and its economic development directly affects China's economic development, and it has an important strategic position in the pattern of China’s economic development, and it is a key development area in China. Therefore, chose six major urban agglomerations (Including 96 prefecture-level cities and 3 directly administered counties) in eastern China as the study area for this research (Fig. 3). The study period, was from 2011 to 2021, considered the availability and continuity of the PHS data. Administrative boundary data from the Resource Environmental Science Data Registration and Publication System⁴⁸, and pseudo data from the Baidu index open source data. The time series covers January 1, 2011, to December 31, 2021. Utilized the average value of basic data to illustrate the development trend of PHS in each year, encompassing 64 indicators, such as Tencent mobile manager, Baidu netdisk, QQ music, and so on, with a cumulative acquisition of 254,390,040 data points over 4015 days.

Figure 3

Spatial distribution and geographical division of the study area. (Including 99 cities (counties) of Shanghai, Shenzhen, Chengmai, Lingao and others in the six major urban agglomerations in eastern China, including the Yangtze River Delta.).

Methods

Coupling coordination degree model

The coupling coordination degree model can reflect the coupling coordination degree among multiple systems. Specifically, the coupling degree can measure the resonance relationship between subsystems, while the coordination degree can characterize the overall and synergistic effects among subsystems, serving as indicators to assess their coordination status¹⁹. Geography scholars often employ this model to explain the pattern and mechanism of interaction and mutual influence within complex human-land systems, therefore, this paper introduces the coupling coordination degree model to evaluate the coordination status among the five subsystems of the PHS. The commonly used coupling coordination degree specification formula is⁴⁹:

$${\text{C}} = \left[ {\frac{{\mathop \prod \nolimits_{i = 1}^{n} U_{i} }}{{\left( {\frac{1}{n}\mathop \sum \nolimits_{i = 1}^{n} U_{i} } \right)^{n} }}} \right]^{\frac{1}{n}}$$

(1)

Where $n$ is the number of subsystems, ${\text{C}}$ is coupling degree, $U_{i}$ is the value of each subsystem, its distribution interval is [0,1], so ${\text{C}}$ value interval is [0,1], the larger the value, it means that the degree of discretization between the subsystems is smaller, the higher the degree of coupling, and vice versa, the lower the degree of coupling between the subsystems.

Combining the objective facts of the five subsystems of the PHS, construct an arithmetic model of the coupling coordination degree of the PHS with the following formulas:

$${\text{C}} = \left[ {\frac{F\left( x \right) \times G\left( x \right) \times H\left( x \right) \times I\left( x \right) \times J\left( x \right)}{{\left( {\frac{F\left( x \right) + G\left( x \right) + H\left( x \right) + I\left( x \right) + J\left( x \right)}{5}} \right)^{5} }}} \right]^{\frac{1}{5}}$$

(2)

Where $F\left( x \right)$、$G\left( x \right)$、$I\left( x \right)$、$H\left( x \right)$、$J\left( x \right)$ are the comprehensive evaluation indices of the living, entertainment, information, socialization, and tool systems, respectively, which are calculated using the entropy value method. ${\text{C}}$ is the coupling degree, with values in the interval [0,1].

To further study the level of coupling coordination degree among the five subsystems of the PHS, establish a coupling coordination degree model the calculation follows the formula:

$$D = \sqrt {C \times T}$$

(3)

$${\text{T}} = {\upalpha }f\left( x \right) + \beta g\left( x \right) + \gamma h\left( x \right) + \delta i\left( x \right) + \varepsilon j\left( x \right)$$

(4)

 Where $D$ is the coupling coordination degree, $C$ is the coupling degree, ${\text{T}}$ is the comprehensive coordination index, and $f\left( x \right)$, $g\left( x \right)$, $h\left( x \right)$,${ }i\left( x \right)$, and ${ }j\left( x \right)$ are the normalized values of the comprehensive evaluation indices of the living, entertainment, information, socialization, and tool systems, respectively. Coefficients ${\upalpha }$, $\beta$, $\gamma$, $\delta$, and $\varepsilon$ are to be determined, considering all five subsystems equally important, and therefore, each coefficient is set to 0.2. Employed the median segmentation method to categorize the coupling coordination degree into 10 types²⁹.

Spatial autocorrelation model

The first law of geography posits that phenomena in geographical space exhibit interdependencies, with closer proximities displaying stronger correlations. Spatial autocorrelation is an crucial indicator to assess whether the attribute value of a certain element is significantly correlated with its attribute values in neighboring spatial points, it is categorized into global and local spatial autocorrelation⁵⁰. Global spatial autocorrelation describes the spatial distribution patterns of attribute values across an entire region, enabling analysis of overall spatial correlation and differentiation through index calculations⁵¹. Therefore, this study introduces the global autocorrelation to reveal the overall spatial characteristics of the coupling coordination degree among PHS subsystems in urban agglomerations in eastern China. The calculation follows the formula^16,52:

$${\text{Moran}}{\prime} {\text{s I}} = \frac{{n \cdot \mathop \sum \nolimits_{i = 1}^{n} \mathop \sum \nolimits_{j = 1}^{m} w_{ij} \left( {x_{i} - \overline{x}} \right)\left( {x_{j} - \overline{x}} \right)}}{{\mathop \sum \nolimits_{i = 1}^{n} \mathop \sum \nolimits_{j = 1}^{m} w_{ij} \mathop \sum \nolimits_{i = 1}^{n} \left( {x_{i} - \overline{x}} \right)^{2} }}$$

(5)

$$z = \frac{I - E\left[ I \right]}{{\sqrt {VAR\left( I \right)} }}$$

(6)

Where $n$ is the number of municipalities, $m$ is the number of neighboring municipalities in the municipality $i$, $w_{ij}$ is an element in the spatial weight matrix $w$. When the municipality $i$ is adjacent to the municipality $j$, $w_{ij}$ = 1, and vice versa, $w_{ij}$ = 0, $x_{i}$ and $x_{j}$ are the values of coupling coordination degree on municipalities $i$ and $j$; $\overline{x}$ is the mean value of the coupling coordination degree for all municipalities. Standardized $z$-values are employed to test the significance level of global ${\text{Moran}}{\prime} {\text{s I}}$. $E\left[ I \right]$ and $VAR\left( I \right)$ denote the expectation and variance of ${\text{Moran}}{\prime} {\text{s I}}$, respectively.

Local autocorrelation can reveal the heterogeneous characteristics of spatial differences in order to comprehensively reflect the trend of spatial differences in PHS⁵¹. Therefore, this study introduces local spatial autocorrelation and integrates it with the LISA cluster map to detect the specific location of the spatial cluster of the coupling coordination degree of PHS subsystems of the urban agglomerations in eastern China. The calculation follows the formula¹⁶:

$${\text{Moran}}{\prime} {\text{s I}} = \frac{{x_{i} - \overline{x}}}{{S^{2} }} \cdot \mathop \sum \limits_{j = 1}^{m} w_{ij} \left( {x_{j} - \overline{x}} \right)$$

(7)

$${\text{S}}^{2} = \frac{1}{n - 1}\mathop \sum \limits_{j = 1,j \ne i}^{m} \left( {x_{i} - \overline{x}} \right)^{2}$$

(8)

Where ${\text{S}}^{2}$ denotes the variance and the rest of the meaning is the same as in Eq. (5).

Center of gravity and standard deviation ellipse models

The center of gravity and standard deviation ellipse model, proposed by American sociologist Welty Lefever and others, is a spatial statistical method used to analyze spatial patterns of socio-economic and natural environmental elements⁵³. The method can characterizes the evolution of the coupling coordination degree of PHS subsystems within urban agglomerations in eastern China by quantitatively portraying key parameters of the standard deviation ellipse, including its area, the center of gravity, standard deviations of the major and auxiliary axes, and the angle of rotation⁵⁴.

• (1) Center of gravity model: This model is employed to analyze the direction and distance of the center of gravity migration of the coupling coordination degree among the PHS subsystems in the urban agglomerations in eastern China. The specific calculation formula is provided below:

  $${\text{X}} = \mathop \sum \limits_{i = 1}^{n} P_{i} \overline{{X_{i} }} /\mathop \sum \limits_{i = 1}^{n} P_{i} {\text{ Y}} = \mathop \sum \limits_{i = 1}^{n} P_{i} \overline{{Y_{i} }} /\mathop \sum \limits_{i = 1}^{n} P_{i}$$

  (9)
  $$\theta_{i - j} = n\pi /2 + arctan\left[ {\left( {y_{i} - y_{j} } \right)/\left( {x_{i} - x_{j} } \right)} \right]$$

  (10)
  $$D_{i - j} = C \cdot \sqrt {\left( {y_{i} - y_{j} } \right)^{2} + \left( {x_{i} - x_{j} } \right)^{2} }$$

  (11)

  Where $X$, $Y$ represent the centers of gravity of the coupling coordination degree of the PHS subsystems in urban agglomerations in eastern China. $\overline{X}_{i}$, $\overline{Y}_{i}$ denote the geographic coordinates of the $i$ th municipal area within the region. $P_{i}$ signifies the value of the PHS coupling coordination degree of each municipal area. ${\uptheta }$ denotes the angle of the center of gravity movement. $\left( {x_{j} ,y_{j} } \right)$ represents the coordinates of the center of gravity, reflecting the coupling coordination degree of the PHS subsystems in urban agglomerations in eastern China for the year $i$, $j$ coordinates of the center of gravity, When ${\uptheta }$ = 0, the center of gravity moves due east. $D_{i - j}$ represents the distance of the center of gravity movement. $D_{i - j}$ denotes the conversion rate of geographic coordinates, typically calculated as projected coordinates, with a standard value of 111.111 km.

• (2) Standard deviation ellipse: primarily comprises the rotation $\theta$, standard deviations along the major and auxiliary axes. The area of the ellipse denotes the spatial distribution range of the geographic elements, while the rotation angle $\theta$ signifies the primary direction of the distribution. Additionally, the major axis represents the dispersion of elements along the primary direction.

Geo-detector model

The coupling coordination of PHS subsystems is influenced by various factors such as economics and infrastructure. Therefore, traditional methods for analyzing this problem require numerous prerequisite assumptions. Geo-detector are statistical methods that detect spatial dissimilarities and can uncover underlying driving forces. Initially applied in studying factors influencing endemic diseases, they are now widely used to study the spatial distribution mechanisms in geography due to their fewer constraints on prerequisites⁵⁵. Therefore, this paper introduce the factor detector in geo-detectors to explore the influence of driving factors on the coupling coordination degree of PHS subsystems. The calculation follows the formula:

$${\text{q}} = 1 - \frac{{\mathop \sum \nolimits_{h = 1}^{L} N_{h} \delta_{h}^{2} }}{{N\delta^{2} }} = 1 - \frac{SSW}{{SST}},SSW = \mathop \sum \limits_{h = 1}^{L} N_{h} \delta_{h}^{2} , SST = N\delta^{2}$$

(12)

Where h = 1,…, L is the stratification, indicating the categorization or partitioning, of the variable Y-factor X, $N_{h}$ and $N$ is the number of cells in stratum h and the whole region, respectively, $\delta_{h}^{2}$ and $\delta^{2}$ are the variance of the Y-values in stratum $h$ and the whole region, respectively, and ${\text{SSW}}$ and ${\text{SST}}$ are the sum of the variances within the stratum and the total variance in the whole region, respectively. A higher ${\text{ q}}$ signifies a stronger explanatory capacity of the driving factor X on the coupling coordination degree of the PHS subsystems, while a lower ${\text{q}}$ indicates the opposite.

Results

Time course of coupling coordination

The study results indicating the average value of the coupling coordination degree exhibits an upward trend resembling an “inverted L”. From 2011 to 2021, the coupling coordination degree among the five subsystems of the PHS urban agglomerations in eastern China has undergone noticeable changes, exhibiting an upward trend resembling an “inverted L”. The average value of the coupling coordination degree fluctuated from 0.1469 in 2011 to 0.3242 in 2021. The main reason for this trend is socio-economic development and the continuous improvement of new infrastructures, such as networks and communications, which provide a realistic basis for PHS development in terms of facilities and finances. The growth rate of “first fast and then slow” is primarily due to the initially low coupling coordination degree, which is relatively easy to improve, whereas in the later stages of development, this degree reaches a higher level and enters a relative bottleneck period, thus remains relatively stabilizing the overall level.

The difference in coupling coordination degree values exhibits a trend resembling a “saddle-type”. During the study period, the coupling coordination degree difference value between urban agglomerations demonstrated a “saddle-type” trend of change, with the largest is 0.2410 in 2015 and the smallest is 0.1387 in 2012. (Fig. 4a). The reason for this phenomenon is the large disparity in economic development among different regions in the previous period. For instance, the Pearl River Delta urban agglomeration was the first to develop due to reform and opening up and other national policies, resulting in rapid socio-economic development that provided a solid foundation for PHS development. Conversely, the Beibu Gulf urban agglomeration, being geographically remote with underdeveloped infrastructure and a lagging economy, resulted in comparatively poorer PHS development. In later years, with China's implementation of poverty alleviation and other strategies, the economies of urban agglomerations like Beibu Gulf have gradually improved. Moreover, new infrastructure such as networks and communications has been constantly enhanced, laying a foundation for PHS development.

Figure 4

Coupling coordination degree of PHS and its type in the urban agglomeration in eastern China from 2011 to 2021.

Significant alterations occurred in the dominant type of coupling coordination from 2011 to 2021. The evolutionary order follows a sequence of severe(moderate) disorder → moderate disorder → mild disorder → mild(near) disorder → mild disorder. During this period, the overall level of coupling coordination was low in 2011–2013, characterized by the dominant types being severe disorder and moderate disorder. In contrast, the overall level was high in 2014–2021, with the dominant type being mild disorder. (Fig. 4b).

Spatial pattern of coupling coordination

Employed ArcGIS to map the spatial pattern of the coupling coordination degree of PHS in urban agglomerations in eastern China, and results show that:

• (1) Overall spatial pattern of “high in the north and low in the south”. From 2011 to 2021, the coupling coordination degree among the PHS subsystems of the urban agglomerations in eastern China exhibited significant variations; However, the spatial pattern of “high in the north and low in the south” persisted over an extended period. Throughout the study period, the mean coupling coordination degree the PHS of the Beijing-Tianjin-Hebei, Shandong Peninsula, Yangtze River Delta, Guangdong-Fujian-Zhejiang coastal, Pearl River Delta, and BeiBu Gulf urban agglomerations were 0.3441, 0.2877, 0.3164, 0.2567, 0.3799, 0.1814, respectively. The type of coupling coordination evolved from mild disorder to moderate and severe disorders from north to south (Fig. 5). The primary reason for this phenomenon is that the Beijing-Tianjin-Hebei and Yangtze River Delta urban agglomerations in the northern region have a high level of socio-economic development, and new infrastructure such as networks and communications are perfect, providing favorable conditions for PHS development; in contrast, the Beibu Gulf urban agglomerations in the southern region are characterized by mountainous terrain and comparatively underdeveloped economies and infrastructures, leading to less conducive conditions for PHS development.

• (2) The Pearl River Delta urban agglomeration has consistently held the top rank in terms of coupling coordination degree over an extended period. From 2011 to 2021, the coupling coordination degree of the Pearl River Delta urban agglomeration surpassed the other five urban agglomerations, reaching its peak coupling coordination degree in 2015 at 0.4389 and its lowest value in 2012 at 0.2438. The type of coupling coordination degree shifted from moderate disorder to near disorder. This phenomenon is primarily attributed to the Pearl River Delta urban agglomeration, as a pioneer region of China's reform and opening up, has undergone rapid economic growth and continuous improvement of infrastructure, becoming the “leading” region of China's economic development, and providing favorable realistic conditions for PHS development in terms of finance, facilities and so on.

Figure 5

Spatial pattern of coupling coordination degree of PHS in urban agglomerations in eastern China from 2011 to 2021.

Spatial correlation of coupling coordination

Global spatial autocorrelation analysis

Use ArcGIS to test the spatial correlation of the coupling coordination degree of PHS in urban agglomerations in eastern China, and results show that all Moran's I > 0, P < 0.05, and Z > 1.96, passing the significance test throughout the study period (Table 1, Fig. 6). This indicates that, from 2011 to 2021, there exists a positive spatial correlation and a clustering trend in the coupling coordination degree among PHS subsystems in urban agglomerations in eastern China.

Table 1 Global moran’s I index and testing of coupling coordination in 2011–2021.

Figure 6

Global Moran’s I index of coupling coordination degree of urban agglomerations in eastern China.

From 2011 to 2021, the global Moran's I for the coupling coordination degree among PHS subsystems in urban agglomerations in eastern China exhibited a fluctuating downward trend, the cluster trend continuously weakens, and the regional gap narrows.

Local autocorrelation

After conducting the global autocorrelation test for the coupling coordination degree among PHS subsystems in 99 cities (counties) within the urban agglomerations in eastern China, employed ArcGIS Pro software to illustrate the local autocorrelation LISA cluster distribution of the coupling coordination degree in these urban agglomerations during 2011 and 2021, which presented five types of clusters (Fig. 7):

• (1) “High-high” cluster type signifies that the city and its neighboring cities exhibit a high coupling coordination degree, with a positive spatial correlation. Dongguan, Shenzhen, Langfang, Nantong, Jiaxing, Huizhou, and Zhongshan have consistently belonged to this category, while Tianjin and Suzhou transitioned from “high-high” clusters to non-significant type, and Guangzhou and Foshan evolved from non-significant to “high-high” clusters. Cities of this type are primarily situated in economically developed areas with well-established infrastructures and abundant resources, which are facilitate to improvement the PHS coupling coordination.

• (2) “High-low” outlier implies that the city has a high coupling coordination degree, while the neighboring cities exhibit a low coupling coordination degree, resulting in a negative spatial correlation; this category is less prevalent. In 2011, Nanning and Haikou were areas of this type; by 2021, only Nanning retained this classification, while Haikou transformed into a “low-low” cluster type. Which should give full play to the radiating role of this type of city and drive the development of neighboring cities.

• (3) “Low–high” outlier type indicates that the city has a low coupling coordination degree, while its neighboring cities exhibit a high coupling coordination degree; in 2011, Chengde and Zhuhai exemplified this pattern; by 2021, Zhuhai transitioned from this type to a “non-significant” area, and Zhaozhou shifted from a “non-significant” area to this type. These areas are primarily situated near "high-high" clusters, where there is a significant loss of human and material resources, which has a negative impact on local development, thus forming a relative “valley areas”.

• (4) “Low-low” cluster type denotes a low coupling coordination degree among the city and its neighboring cities. And is predominantly found in the southern part of the Beibu Gulf urban agglomeration and the northwestern part of the Guangdong-Fujian-Zhejiang coastal urban agglomeration. In 2011, this type included Shangrao, Zhanjiang, and other eight cities (counties); by 2021, the count increased to ten, incorporating Haikou through the evolution from the “high-low” outlier type, Fuzhou and Nanping through the evolution from the “insignificant” type, and Fangchenggang transitioning from this type to the “insignificant” type. The type of region is more remote geographic location, inconvenience, economic backwardness, is less conducive to promotion the coupling coordination degree.

• (5) Non-significant type suggests a less robust relationship between them. Which has the highest number of municipalities. 75 municipalities were in this type of area in 2011, occupying 75.75% of the study area, and 74 municipalities were in this type of area in 2021, occupying 74.75% of the study area.

Figure 7

Local autocorrelation LISA cluster distribution of PHS coupling coordination degree in urban agglomerations in eastern China.

Spatiotemporal evolution of coupling coordination

Center of gravity migration analysis

Utilizing the spatial statistics module of ArcGIS, the center of gravity of the coupling coordination degree between the PHS subsystems of the urban agglomerations in eastern China was measured for each period based on the center of gravity model. Additionally, the distance and direction of their migration were analyzed, and the corresponding trajectory maps were generated (Fig. 8).

Figure 8

Center of gravity and standard deviation ellipse distribution of the coupling coordination degree of PHS urban agglomerations in eastern China from 2011 to 2021.

Overall, the center of gravity of the coupling coordination degree between the PHS subsystems of urban agglomerations in eastern China is situated within the territory of the Yangtze River Delta urban agglomeration from 2011 to 2021 and demonstrates a tendency to shift from the northeast to the southwest, with a total displacement of 42.58 km. This suggests that the coupling coordination degree of the PHS subsystems in the southwest direction of the urban agglomerations in eastern China exhibits a faster growth rate than that in the northeast direction during the study period. This is primarily because the northeastern region exhibits a higher coupling coordination degree and a larger base, rendering its improvement more challenging compared to the southwestern region; moreover, China’s proposed “Western Development” and other policies also provide favorable conditions for the improvement of the PHS in the southwestern region.

Standard deviation elliptic analysis

Using the spatial statistics module of ArcGIS, the standard deviation ellipse model was employed to calculate and identify the parameters of the standard deviation ellipse of the coupling coordination degree among the PHS subsystems of the urban agglomerations in eastern China in different periods, and the standard deviation ellipse change map was plotted (Fig. 8).

From 2011 to 2021, the coupling coordination degree within the PHS subsystems in the urban agglomerations in eastern China exhibited a decentralized trend shifting towards the southwest, and the coupling coordination degree in the southwest of the urban agglomerations grew at a faster rate. (1) From the perspective of the corner θ, the corner θ of the standard deviation ellipse shows a gradually increasing trend from 2011 to 2021, with the northeast-southwest pattern. This suggests that the coupling coordination degree of cities situated in the northeast of the ellipse’s axis grows slower than those in the southwest. (2) In terms of the distribution range of the standard deviation ellipse, from 2011 to 2021, the standard deviation of the main axis and the auxiliary axis increased by 21.58 km and 6.24 km, respectively. The standard deviation ellipse area expanded by 11,489.95 km², indicating a decentralized migration trend towards the southwest among the PHS subsystems in the urban agglomerations in eastern China. This is due to the fact that the southwestern region, which used to have a low coupling coordination degree, has experienced sustained economic development and the enhancement of infrastructure such as networks and communications, bolstered by Chinese policies like “Western Development” and “Poverty Eradication and Struggle”. This has provided favorable conditions for the gradual improvement of the PHS in the southwestern region, thereby gradually narrowing the gap with the northeastern region.

Driving factor of coupling coordination

Factor analysis

Based on the fact that coupling coordination development among the PHS subsystems of urban agglomerations in eastern China is influenced by various factors. In this paper, on the basis of reference to the research results of the previous researchers^15,29,47, taking into account the weight of the indicators and the size of the coefficient of variation, the 12 indicators with the highest weights and coefficients of variation are selected as the detection factors, and the geo-detector is utilized for the analysis of the factor detection.

Research has found that the dominant drivers influencing the coupling coordination of PHS urban agglomerations in eastern China varied significantly over time. Specifically, the dominant driving factors from 2011 to 2021 were music, video, work-study, news browsing, and internet socialization. In 2011, travel, information search, work-study, instant messaging, and financial management were the dominant driving factors. In 2013, the dominant driving factors were news browsing, work-study, file storage, internet socialization, and instant messaging. In 2015, it were influenced by music, news browsing, work-study, video, and financial management. In 2018, the dominant drivers were travel, instant messaging, financial management, work-study, and file storage. In 2021, it were mainly influenced by video, instant messaging, financial management, music, and news browsing (Fig. 9).

Figure 9

Q-value of the driving factors of PHS coupling coordination in urban agglomerations in eastern China from 2011 to 2021. (PHS1-PHS12 represent the detection factors of Wechat, Iiqiye, and Netease cloud music, respectively).

System analysis

The results show that the ability of the systems to explain the coupling coordination varies significantly over time. The ranking of the systems' ability to explain the coupling coordination from 2011 to 2021 is as follows: entertainment system > tool system > socialization system > information system > living system; In 2011, the ranking of the systems' ability to explain the coupling coordination is information system > socialization system > tool system > entertainment system > living system; evolved in 2013: entertainment system > socialization system > information system > tool system > living system; evolved in 2015: socialization system > entertainment system > tool system > information system > living system; evolved in 2018: entertainment system > tool system > socialization system > living system > information system; evolved in 2021: tool system > socialization system > entertainment system > living system > information system (Fig. 10).

Figure 10

The detection q-value of the PHS coupling coordination drive system in the urban agglomerations in eastern China from 2011 to 2021.

Driving factor explored

The coupling coordination of PHS was notably influenced by factors such as music, video consumption, news browsing, and financial management and were the dominant factors affecting the coupling coordination several times during the study period.

The entertainment system is the dominant driving system for the coupling coordination of PHS. During the study, the explanatory ability of the entertainment system for the coupling coordination of the PHS has been strong, and the entertainment system was the dominant driving system for the coupling coordination in 2013, 2018, and 2011–2021.

Socialization systems have the next best ability to explain the coupling coordination of PHS. Throughout the study period, the socialization system exhibited the second-highest explanatory capability for the coupling coordination of PHS, eventually emerging as the dominant driving system in 2015.

Discussion

Temporal Characterization of PHS coupling coordination degree

From 2011 to 2021, coupling coordination degree of PHS in urban agglomerations in the eastern China has shown a fluctuating upward trend overall, consistent with the findings of Li Guozhu et al⁵⁶. This trend is closely tied to China's recent social and economic development and continuous improvements in network and communication infrastructures¹³, laying a solid foundation for PHS development. Moreover, this is also indispensable to China’s national policy support, exemplified by the Communist Party of China (CPC) “Twentieth National People’s Congress” report put forward to build livable, resilient, smart cities, for the PHS to enhance the favorable policy conditions.

Spatial characterization of PHS coupling coordination degree

Spatial pattern

From 2011 to 2021, the overall spatial pattern of the coupling coordination degree of PHS in urban agglomerations in eastern China was “high in the north and low in the south”. This phenomenon is closely tied to the level of regional economic development. Urban agglomerations like Beijing-Tianjin-Hebei and the Yangtze River Delta in the northern region exhibit higher economic development levels, boasting favorable conditions for livelihoods, welfare, resources, environment, and infrastructure³⁹, so the real conditions for PHS development are superior. Conversely, southern urban agglomerations such as the Beibu Gulf are characterized by mountainous terrain, lower economic development levels, and less developed transportation and network infrastructures^57,58, thus affecting the development of PHS to varying degrees.

The Pearl River Delta urban agglomeration have consistently ranked first in terms of coupling coordination degree of PHS. This is attributed to the close proximity of the Pearl River Delta urban agglomeration to Hong Kong and Macao, their geographical advantages, high level of socio-economic development, robust scientific and technological innovation capacity, sound infrastructure, and China’s national policy support³⁹, all of which create favorable conditions for PHS development.

Spatial correlation

From 2011 to 2021, the coupling coordination degree of PHS in the urban agglomerations in eastern China exhibited a positive spatial correlation and demonstrated a clustering pattern. Among them, the “high-high” cluster type is predominantly concentrated in the Pearl River Delta and Yangtze River Delta urban agglomerations. Take the Pearl River Delta urban agglomeration as an example: Shenzhen, the first special economic zone of China’s reform and opening up, located in the Pearl River Delta urban agglomeration, has been developing rapidly with its favorable geographic location, China’s national policy support, and socio-economic development, and at the same time driving the rapid development of neighboring cities, so the region’s overall PHS is superior.

Spatial evolution

From 2011 to 2021, the coupling coordination degree of PHS in the southwestern of the urban agglomerations in eastern China increased faster rate compared to the northeastern direction, and the overall spatial disparities in the region narrowed and coordination increased. This cannot be separated from the support of the Communist Party of China (CPC) and China’s national policies, such as the establishment of the Beibu Gulf urban agglomeration in 2017 and the establishment of the Hainan Free Trade Zone in 2018. These initiatives have significantly boosted socio-economic development in the southwestern region, thereby further promoting the PHS enhancement; The communist party of China(CPC) “Nineteenth National People’s Congress” and “Twentieth National People’s Congress” report have clearly stated the need to promote coordinated regional development, providing policy guidance for narrowing regional development disparities.

Policy implications

• (1) Give full play to the radiation-driven role of “high-value zones”. There is substantial variation in the coupling coordination degree of PHS across urban agglomerations in eastern China, with the Pearl River Delta and Yangtze River Delta urban agglomerations being significantly higher than the other urban agglomerations, and the disparities within the urban agglomerations are also notable, with the coupling coordination degree of Beijing, Shanghai, Guangzhou, and Shenzhen being significantly higher than that of the other cities. Thus, enhancing the radiation impact of these “high-value zones” on surrounding regions, implementing strategies of bringing the high to the low, and improving the overall quality of PHS in the urban agglomerations of eastern China are imperative.

• (2) Create a multi-level synergistic development model. The level of coupling coordination of PHS varies among cities and urban agglomerations in the urban agglomerations in eastern China. In terms of urban agglomerations, by 2021, only the Pearl River Delta urban agglomeration has reached the level of near-disorder, while all other urban agglomerations are in a state of disorder; in terms of cities, by 2021, only 17 cities have reached the level of coordination, and the overall level of development needs to be urgently improved. Therefore, it is crucial to devise appropriate countermeasures based on the varying levels of coupling coordination among cities and urban agglomerations, in order to foster the synergistic development of regional PHS:

  • 1) For cities and urban agglomerations at the coordinated level, these regions boast developed economies, well-established networks, communications, and other infrastructures, and the coupling coordination degree of PHS has reached a high level. However, further enhancements can be achieved by bolstering urban services, upgrading digital infrastructure, and developing smart cities to further enhance PHS.

  • 2) For cities and urban agglomerations at the dis-coordinated level, these areas are relatively backward in terms of economy and infrastructure. Firstly, they should capitalize on their tourism resources and geographical advantages, developing tourism, foreign trade, and other industries, and strengthen economic construction to provide an economic basis for residents to engage in pseudo activities such as online shopping and entertainment. Secondly, there is a need to enhance the development of new infrastructures like networks and communications, which will provide technical and service support for residents engaged in pseudo activities.

  • 3) Multifaceted promotion of the comprehensive enhancement of PHS. Focus not only on enhancing the “hard environment” of PHS through economic development and improving infrastructure like networks and communications to support financial and technological prerequisites for participating in pseudoscience activities. Simultaneously, attention should be given to cultivating the “soft environment” conducive to PHS development, including enhancing humanistic support and setting reasonable working hours, so as to provide time and energy for people to participate in pseudo activities.

Limitations and prospects

• (1) Comprehensive research content. This paper primarily focuses on the PHS as the research object, while the HS is a complex giant system, studying only PHS cannot adequately explain the intricate human-land relationships. Future research will comprehensively investigate the coupling coordination within the broader HS framework, incorporating RHS and IHS, to foster holistic development of the HSG.

• (2) More in-depth research content. This paper focuses on a preliminary study of the spatio-temporal characteristics and driving factors influencing the coupling coordination of PHS. It has not yet conducted an in-depth analysis of the underlying mechanisms driving these results. Future studies will delve into the complex interactions among different elements and systems, examining how these interactions influence outcomes.

• (3) Diversified data sources. The data in this paper are primarily pseudo data such as information search, online social networking, etc. Future research will incorporate economic, social and other real data to analyze the real factors affecting the spatiotemporal evolution of the PHS.

Conclusion

The rapid development of information technology has diversified people’s lives, and the study of a single state of RHS can no longer adequately interpret the new human-land relationship in the information age, and can’t satisfy the evolving theory and practice of HSG. Therefore, this paper constructs a theoretical framework for the coupling coordination of the “three states” of the HS, takes the five subsystems of the "PHS" as the entry point, and builds a PHS evaluation index system. Coupling coordination and spatial autocorrelation and other models are employed to explore the spatiotemporal characteristics of PHS coupling coordination degree and its driving factors in the urban agglomerations in eastern China from 2011 to 2021, revealing the coupling coordination interactions of subsystems within PHS, in order to achieve an overall improvement in the quality of PHS. This study provides theoretical support for enhancing the overall quality of PHS in the urban agglomerations in eastern China, offers a scientific reference for PHS development in other regions of China, contributes theoretical support into the interpretation of new human-land relationships in the information age, and presents a new perspective for the study of HSG. The main conclusions drawn are as follows:

• (1) Time course: the mean of the coupling coordination degree exhibits an upward “inverted L” trend, and the difference shows a “saddle” trend; the dominant type of the coupling coordination evolves from a severe disorder to a mild disorder.

• (2) Spatial pattern: the coupling coordination degree exhibits a general spatial pattern of “high in the north and low in the south”, but the coupling coordination degree of the Pearl River Delta urban agglomeration, which is located in the south, has long been in the first place.

• (3) Spatial correlation: the coupling coordination degree of PHS of the urban agglomerations in eastern China has a positive spatial correlation and exhibits a clustering trend from 2011 to 2021; during the study period, the clustering trend is weakening and the regional gap is narrowing.

• (4) Spatial evolution: from 2011 to 2021, the coupling coordination degree of PHS in the southwestward of the urban agglomerations in eastern China grows faster than that in the northeastward, and the regional differentiation narrows.

• (5) Driving factor: the development of coupling coordination degree of PHS results from multiple factors and systems, with work-study, video, news browsing, etc. being the dominant driving factors and the entertainment system being the dominant driving system.