1. Introduction
An urban agglomeration is a highly developed spatial form of an integrated city and is one of the distinguishing features of modern civilization. Defined as an agglomerated radial area comprising one or two core cities and several microcities, an urban agglomeration exhibits close socioeconomic ties. This framework fosters a high level of spatial interaction between cities, contributing to the collective development of a region [
1]. However, such high expansion impacts the urban landscape, particularly by significantly reducing ecological and productive space, leading to serious ecological and environmental problems [
2,
3,
4,
5]. As the World Commission on Environment and Development (WCED) proposed, national environmental policies and urban development should align with sustainable development goals [
6]. Modeling and predicting urban expansion while providing policy-oriented recommendations for the rational control of urban expansion and the coordinated sustainable development of urban ecology have become the focus of current research [
7,
8,
9]. Understanding the relationship between urban expansion and complex networks of urban agglomerations necessitates exploring the mechanisms of urban expansion [
10]. This information can further assist policymakers in formulating policy-oriented recommendations [
11].
Urban growth is a complex process [
12]. Since the emergence of the first theoretical approach to cellular automata (CA)-based models of urban expansion in the 1980s [
13], there has been a proliferation of models and methods for simulating urban expansion, which have evolved to the micro-dynamic model stage [
9]. Many scholars have developed various land use simulation models to understand better the land use change process under different scenarios. These models include the conversion of land use and its effects at a small regional extent (CLUE-S) model [
14], the land-use scenario dynamics (LUSD) model [
15], multi-agent system models of land use/cover change (MAS/LUCC models) [
16], etc. Among these, the most widely used method is CA, which has successfully explored various urban phenomena. It is widely used to simulate dynamic urban expansion and optimize urban spatial structures [
17]. However, its limitation includes its weaknesses in the quantitative aspect and its inability to include the driving forces of urban growth in the simulation process [
18]. In contrast, artificial intelligence (AI)-based methods offer the advantage of capturing the nonlinearity and heterogeneity of urban growth. Their improvement over traditional CA has achieved good results in urban growth simulations [
19]. Many scholars have simulated land-use dynamics by combining artificial intelligence methods with CA models. For instance, Qiang et al. combined and improved artificial neural networks (ANN) and the CA method to simulate complex LULC dynamics in a vulnerable coastal region with high accuracy [
20]. Feng et al. proposed a machine-learning CA model based on a least-squares support vector machine (LS-SVM) to simulate urban growth, which can capture the spatial complexity of urban dynamics and improve simulation accuracy [
21]. Liu proposed an urban growth model using a self-adaptive genetic algorithm (SAGA) to optimize (CA), which outperformed the logistics-CA model [
22]. Liao et al. proposed a neighbor decay cellular automata model based on particle swarm optimization (PSO-NDCA) with a higher prediction accuracy for built-up land [
23]. Urban expansion is influenced by many complex and variable factors, such as social, economic, natural, and policy factors, with high uncertainty levels. This makes it difficult to simulate urban expansion accurately. The future land-use simulation (FLUS) model was proposed by Xiaoping [
24], who considered climate, natural, and socioeconomic factors. The model performs top-down system dynamics and bottom-up meta-automata interactions, integrates ANNs, and introduces adaptive competition and inertia mechanisms for land-use simulation. This makes it more accurate than other recognized systems such as CLUE-S and CA, and it has been widely used to simulate and predict land use/cover changes under the influence of human activities and natural conditions [
25].
Most current studies use CA to simulate urban expansion, treating the city as an individual entity, favoring internal factors, and ignoring interactions between cities. The interflow of urban space (e.g., the flow of people, transport, and information) is increasingly important in driving urban expansion in metropolitan areas [
26,
27]. Therefore, it is necessary to consider spatial interactions between cities when modeling large-scale urban expansion [
28]. Urban mobility intensity serves as a valid indicator of the intensity of socioeconomic interactions among member cities in metropolitan cluster areas [
29,
30]. Furthermore, with urbanization, there is a need to scientifically control the scale of cities to coordinate their sustainable development with the surrounding ecological environment [
8]. Hence, urban development under ecological constraints is an effective way to address and mitigate the ecological impact of rapid and uncontrolled urbanization [
31]. For example, Ma [
7] integrated ecological correlation into CA for urban growth simulation, and Li et al. [
32] introduced conservation priorities based on standardized values of green infrastructure assessment into CA. They simulated and explored the impact of the ecological environment on the urbanization process to better coordinate the relationship between urban expansion and ecological protection.
In general, current studies incorporating urban interactions and ecological constraints into urban expansion simulations mainly manifest in two ways. For small regional scales, such as individual cities, ecological constraints are primarily incorporated into urban growth boundaries or urban expansion simulations under ecological constraints [
6,
7,
32,
33]. These studies consider conflicts between urban expansion and environmental protection. However, studies on urban simulation at a large regional scale of urban agglomerations primarily integrate the interflow effects between cities in urban expansion simulations [
28,
34] and study the effects of spatial interactions between cities on urban growth.
Although many studies have analyzed the relationship between urban expansion and urban interactions or ecological constraints, few have incorporated both urban interactions and ecological constraints into urban agglomeration expansion simulations. While gradually promoting urban development, it is also necessary to reduce the negative impact of urban expansion on the ecological environment. Therefore, considering the dual factors of ecological constraints and urban interactions is necessary for studying urban agglomeration expansion.
Simultaneously, analyzing urban agglomeration expansion patterns and driving mechanisms has gradually become a hot research topic [
35], and scholars posit that natural, transportation location, socioeconomic, and policy factors are the main factors driving urban expansion [
36,
37,
38,
39]. However, the driving factors of urban growth and their influences vary in different regions and development stages [
40]. Understanding the main determinants of regional urban sprawl can help planners formulate more locality-oriented measures to control urban expansion in an orderly manner [
38].
In summary, this study constructs a new approach to land use simulation that couples gravitational field models and ecological constraints to incorporate urban interactions and ecological constraints as driving variables in urban expansion simulation models. First, urban strength and flow data were integrated to calculate urban spatial field strength. Although prior studies [
28,
34,
41] have utilized gravitational field models to measure the strength of spatial interactions between cities, as intercity flows play an increasingly important role in the evolution of urban agglomerations [
27,
28], the individual flow indicators of cities cannot fully reflect the spatial interactions that occur between them. Therefore, this study integrates human, traffic, and information flow data between cities and calculates cities’ comprehensive gravitational spatial field strength by combining the time–cost distance. Then, the ecological quality of the region was comprehensively evaluated from two perspectives: the remote sensing ecological index (RSEI) and the ecological resistance surface (ERS) of urban expansion, which is taken as the ecological constraint of urban expansion. Finally, the FLUS model incorporates the gravitational field model and ecological constraints into the conversion rules of the FLUS model to predict the conversion probability of urban units more accurately. The feasibility of this study’s methodology was verified by simulating the land-use dynamics of an intercity cluster in Henan Province and exploring the individual and coupled effects of the different drivers measured using the optimal parameters-based geographical detector (OPGD) model. This study provides rational suggestions for controlling urban expansion and coordinating sustainable development in both society and nature.
5. Discussion
The factors influencing urban land use change are diverse and influenced by intercity interactions, the ecological environment, and local urban drivers. This study has implications for planning urban agglomerations in a macro sense and for the sustainable development of reconciling urban expansion with ecological conservation.
Currently, most studies examine cities as individuals, while urban development is not geographically or functionally isolated by local administrative boundaries [
59]. The expansion of urban land in a city is greatly affected, not only by local driving forces, but also by intercity factors such as the distant influences of neighboring cities, especially the surrounding regional core cities in metropolitan areas [
60]. In addition, some scholars have focused on the impact of ecology on urban expansion. However, most have considered only one focal region or city as the object of study [
6,
7,
8,
9,
32,
33], and there are few large-scale studies on the correlation between urban expansion and ecology. This study combines intercity interactions and the ecological environment for urban expansion research, which has vital practical and research significance.
Previous studies have indicated that the ecological environment and the strength of the urban gravity field are the two most significant factors influencing the expansion of urban agglomerations [
28]. The simulation results of this study also demonstrate an improvement in the accuracy of simulating urban agglomerations. However, we observed a decrease in simulation accuracy when incorporating the urban spatial field strength factor after coupling it with ecological constraints. This decrease can be attributed to two factors. Firstly, the urban spatial field strength has been shown to enhance the accuracy of simulating developed cities [
28], as these cities tend to have higher levels of urbanization and stronger urban spatial field intensities, attracting most resources to their vicinity, thereby exerting a stronger driving force on urban expansion. Secondly, the local ecological environment also plays a role. Ecologically rich areas require greater consideration for conservation needs, imposing constraints on urban expansion. The significant “potential difference” between ecological constraints and inter-city gravitational drivers allows ecological factors to dominate, thus diminishing the simulation accuracy.
This study explored the driving mechanisms of various factors using the OPGD model so that a more intuitive understanding of the influencing mechanisms of factors associated with the urban expansion can guide solving the problems existing policies and related factors may face. The analysis of factor-driven mechanisms of urban expansion indicated that intercity mobility significantly affects urban growth, as shown in previous studies [
41,
61].
The urban mobility data used in this study are a combination of population movement, search activity, and transportation connections, which reflect not only population interactions between cities, but also travel intentions and transportation connection conditions. In addition, urban expansion is a complex process, and the urbanization process generally destroys the ecological environment. This study uses RSEI and ERS to represent ecological quality factors, the former reflects the ecological quality status of the region [
33] and the latter can reflect the spatial movement trend of urban expansion under ecological constraints [
9]. From a macro perspective, urban growth is positively influenced by urban mobility, greater than the impact of ecological factors (RSEI) and other factors. Ecological constraints also have a significant impact on urban expansion. From a micro perspective, urban inflow is a stronger driver of urban expansion than the outflow of cities because urban inflow represents a city that is more attractive to other cities and, therefore, has more development opportunities. The driving effects of the ecological constraint factors, ERS and RSEI, ranked first and fifth, respectively, with ERS playing the strongest role. From the perspective of two-factor interaction, except for the weakening effect when ERS interact with other factors, all other two-factor interactions are enhanced, indicating that ERS is dominant in the urban expansion drive, which also indirectly explains why simulation accuracy decreases after the introduction of urban mobility factors in ecologically rich areas. The research results show that the urban gravitational field and ecological constraints have an important influence on urban expansion, which jointly affects cities’ scale and expansion direction and needs to be considered in urban planning and management. At the same time, the research results also provide a theoretical and practical basis for sustainable urban development and have certain reference values for formulating relevant policies and plans. According to the conclusion that urban interactions and ecological constraints jointly influence urban expansion, each region should consider local conditions and check and balance between socioeconomic development and the ecological environment, especially in areas with rapid urban development and a complex ecological environment. Rigid constraints can be applied to control urban expansion in important food-producing areas or areas with fragile ecologies, thereby achieving harmonious urbanization and ecological protection.
This study simulates urban land expansion in Henan Province by proposing a new framework for land use simulation with a coupled gravitational field model and ecological constraints and explores the effects of different factors on urban expansion. This may be useful for urban development and planning; however, some shortcomings exist. First, the expansion of urban agglomerations was influenced by policies in addition to topography, economy, and ecology. Policy data were not included in our experimental design due to a lack of data availability. Second, for the models adopted in this study, the urban flows in the micro-driving force analysis were derived from the calculated results based on the gravity model, which were different from the actual flow data and might have led to biased results. This study idealized urban spatial field strength, used constant values for the parameters, and ignored the influence of spatial and temporal disparities [
62,
63]. However, many scholars have adopted this calculation method because reasonable fixed parameters can reflect the overall field intensities of different cities [
64]. In addition, this study uses the coupled gravitational field model and ecologically constrained land use model to simulate and predict urban use changes in Henan Province in 2020 and 2030. However, this does not mean that the model is applicable to other regions, and comparing the absolute contributions of the spatial interaction among inter-cities, ecological constraints, and other factors on urban expansion requires additional empirical and historical data to design a highly accurate model. The urban expansion simulation should be based on the actual ecological environment and social background and should be carried out to ensure the practicability of urban expansion [
65] through the construction of social, ecological, economic, and other multi-indicators and multi-dimensional analysis. In future studies, to gain a more comprehensive and in-depth understanding, we propose the application of more detailed data to enable a more detailed examination. For example, multi-source heterogeneous spatial data can be fused to rationalize policies meticulously into urban simulation models to build more accurate models that reproduce the urban expansion modeling process.
6. Conclusions
This study constructed a new framework for land-use simulation with a coupled gravitational field model and ecological constraints from the perspective of urban interactions and the ecological environment. The experiment was conducted in Henan Province, China, to verify the feasibility of the method and to use the OPGD model to reveal the driving mechanism of urban land use change in the spatiotemporal pattern of Henan Province. The main conclusions are as follows:
(1) The proposed method can improve simulation accuracy at the scale of cities and urban agglomerations, particularly for the central cities of urban agglomerations (e.g., Zhengzhou);
(2) For local areas, the simulation results of the method are different when the gravitational field model and ecological constraints are coupled; the simulation results are better for urban developed areas after coupling the gravitational field model, and the simulation results are better for ecologically developed areas after coupling ecological constraints. Therefore, for cities with smaller scales and rich ecological resources, the simulation effect may be better if only a single factor of coupling ecological constraints is considered;
(3) The prediction of urban expansion in Henan Province in 2030 shows that inter-urban interaction forces and ecological condition constraints incorporated into urban expansion simulations can be better coordinated. The urban land use in Henan Province will increase from 2010 to 2030, and the urban land demand in Henan Province will reach 28,268.83 km2 by 2030;
(4) Through the analysis of the driving mechanism of urban land expansion, it was found that the intensity of urban spatial intensity and ecological constraints are two important factors affecting the expansion of urban agglomeration: urban interaction has a promoting effect on urban expansion, while the need for ecological protection has a restraining effect on urban expansion, and both work jointly to affect the expansion behavior of urban agglomeration.
To address the sustainable development issues of urban expansion and ecological protection, future urban development planning needs to consider both urban interactions and ecological environmental protection factors based on the actual socioeconomic and ecological environment and proactively take targeted measures to coordinate the relationship between urban, ecological, and sustainable development according to local conditions.