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Article

Multi-Scenario Simulation of Urban Land Expansion Modes Considering Differences in Spatial Functional Zoning

by
Jing Yang
1,2,3,4,
Zheng Wang
1 and
Yizhong Sun
2,3,*
1
School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2
Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China
3
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
4
Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, Nanjing 210003, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2025, 14(4), 138; https://doi.org/10.3390/ijgi14040138
Submission received: 9 January 2025 / Revised: 28 February 2025 / Accepted: 19 March 2025 / Published: 24 March 2025
(This article belongs to the Topic Spatial Decision Support Systems for Urban Sustainability)
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Abstract

:
As a precious non-renewable resource, the rational utilization of land resources is crucial for global sustainable development, with urban land development scenario prediction and analysis serving as key methodologies to achieve this goal. Although previous studies have extensively explored urban land expansion simulation and scenario forecasting, further investigation is still required to simultaneously address spatial functional zoning differentiation and urban expansion mode diversity while simulating development trends under various expansion modes. In this study, we integrated major functional zones and ecological redlines to delineate urban spatial functional units and define development coefficients for construction land within each unit. Based on the spatial heterogeneity of expansion modes, the scopes of infill, sprawl, and leapfrog expansion modes were determined. Combining functional zoning and expansion mode zoning, we employed cellular automata model principles to design land conversion rules and simulate the evolution of land use under different expansion modes. Using Jiangyin City, China, as a case study, the model achieved a high simulation accuracy (kappa coefficient of 0.959), significantly outperforming comparative models. By predicting land-use patterns under different expansion scenarios and aligning with Jiangyin’s territorial planning goals, we recommend implementing infill–sprawl–leapfrog and infill–leapfrog–sprawl expansion modes. The results demonstrate that the model effectively supports the refined simulation of urban land expansion, providing a scientific basis for optimizing land resource allocation and balancing ecological protection with urban development. Future research could integrate multiple types of territorial control elements, refine land-use categories, and optimize prediction scenarios to enhance the model’s practicality and applicability.

1. Introduction

China is currently experiencing an era of rapid urbanization, with the huge population growth associated with urbanization leading to a sharp change in urban spaces [1], resulting in cities facing challenges, such as layout reconstruction [2], structural adjustment, and quality improvement [3,4]. To address the demands of rapid urbanization and the need for compact and intensive urban development [5,6], the simulation of land-use change processes aids our understanding of the driving mechanisms behind urban development [7], while the prediction of land-use change scenarios facilitates decision-making optimization in urban planning [8], thereby enhancing the utilization of land resources and the sustainable development of cities [9].
Spatial differentiation theory addresses the regional variances in spatial distribution, encompassing natural, economic, and social resources [10,11]. In urban environments, this differentiation into multiple elements endows unique advantages to specific land-use functions within different regional units, manifesting in pronounced spatial disparities in the distribution of urban land functions [12,13]. Given the non-renewable nature of land, characterized by a limited quantity and fixed location, land planning departments must leverage spatial differentiation theory to direct urban spatial functional orientations. This approach enables the implementation of tailored land-use policies and actions that align with local conditions, thereby enhancing the efficiency of land-use outcomes [14,15]. Researchers have successfully delineated urban spatial functional zones based on multiple factors, including the carrying capacity of resources and the environment, current and projected development densities, locational characteristics, economic structures, and population concentrations [16,17,18]. Urban evolution is often treated as a spatially non-stationary process, making the derivation of differentiated transition rules critical for accurately modeling urban land-use changes [19,20]. Although some studies have underscored the significance of simulating land-use changes that incorporate urban spatial functional zoning into construction and construction-forbidden areas [21], variations still exist within the construction areas themselves [22]. Consequently, further investigation is required to address these disparities in spatial functional zoning and to elucidate the mechanisms driving the development and transformation of urban land use.
Urban land-use changes can be divided into three categories based on land-use types—construction land replacement (transitioning from one construction area to another), urban land expansion (transitioning from non-construction to construction areas), and urban land reduction (transitioning from construction to non-construction areas) [23,24,25]. Specifically, urban land expansion is aimed at predicting and simulating spatial extensions and growth in urban construction land [26]. Topologically, this expansion can be classified into three primary modes—infill, sprawl, and leapfrog [27,28,29]. The driving forces behind these differ significantly, with policies promoting urban renewal and reconstruction primarily driving infill expansion, although this is the least preferred method [30]. Conversely, sprawl expansion is propelled by rapid economic growth and an increasing urban population, with the development of transportation networks playing a crucial role in determining its layout and pace of development [31]. Leapfrog expansion depends substantially on the spatial arrangement of urban transport infrastructure, environmental conditions, and development potential, moderated by planning policies [32]. Spatially, infill expansion is concentrated in the urban and urban inner fringe areas, where construction is dense [33], while sprawl expansion predominantly occurs in the suburbs, in the urban external urban fringe area [34], and leapfrog expansion involves sporadic construction in the rural hinterland [29]. These modes illustrate the diverse land conversion rules driven by unique expansion mechanisms, highlighting the value of simulating zoning expansion modalities.
Over the last two decades, urban land expansion simulation has remained a focal area in geographic modeling research. For instance, the CLUE-S model integrates statistical methods and explicit spatial rules to simulate land-use changes at regional scales [35]. The FLUS model was developed by coupling cellular automata (CA) with artificial neural networks to account for the interactions between human activities and natural environments [36]. Convolutional neural networks have been combined with CA to enhance the capture of complex nonlinear relationships in urban expansion [37], and long short-term memory networks and generative adversarial networks have been introduced to address temporal dependencies and spatial complexity in urban evolution [38,39]. While diverse urban land expansion models have been constructed from multiple perspectives, they have largely overlooked the diversity of urban expansion modes—specifically, the differentiation in land conversion rules across distinct expansion modes. Moreover, spatial functional zoning inherently guides and constrains all expansion modes, yet this interplay remains underexplored. Consequently, current models fail to systematically and objectively reveal urban land expansion dynamics, leaving a critical gap to be addressed.
Urban expansion scenario simulation serves as an effective tool for optimizing land resource allocation, protecting ecosystems, mitigating urban challenges, and advancing sustainable development. Although scholars have simulated land-use trends under scenarios such as natural growth, economics-driven development, policy intervention, ecological conservation, and climate change, few have focused on spatial pattern variations under different expansion modes [40,41,42,43,44]. In fact, existing models primarily predict quantitative demands for land-use categories under predefined scenarios. However, because different land types serve different socioeconomic, ecological, and productive functions, adjusting their quantities directly impacts development goals [44]. Crucially, even with fixed quantities, divergent spatial allocation strategies yield distinct outcomes [45]. Given the pronounced spatial heterogeneity of urban expansion modes, scenario designs for different development modes inherently simulate land-use evolution under varying quantity–spatial allocation schemes, thereby addressing limitations in the current research.
Previous studies have extensively explored urban expansion modeling and land-use scenario simulation. However, they have often neglected the interactions between spatial functional zoning differentiation and expansion mode diversity, failing to systematically analyze differences in urban land pattern evolution across scenarios from an expansion mode perspective. This study was aimed at developing a novel urban expansion simulation model that would integrate the coupling effects of spatial functional zoning and expansion modes. By adjusting the model’s parameters, we designed multiple expansion scenarios to simulate and predict future land-use patterns in Jiangyin City under diverse development modes. By addressing this gap, our research provides critical theoretical support and practical foundations for optimizing urban land layouts and implementing differentiated land resource management strategies.

2. Methodology

As shown in Figure 1, this study was divided into four parts: (1) Delineation of urban spatial functional zones. Based on major functional zones (MFZs) and ecological redlines, urban spatial functional units were determined, and development coefficients were defined to reflect functional differences. (2) Delineation of land expansion mode zones. Based on the spatial heterogeneity of urban development modes, the scopes of infill, sprawl, and leapfrog expansion modes were delineated. (3) Urban expansion simulation. Combining functional zoning and expansion mode zoning, the evolution process of urban land expansion was simulated using the principles of the CA model. (4) Multi-scenario design and simulation. By adjusting second-level development coefficients in the expansion model, multiple expansion scenarios were designed to predict changes in urban land-use patterns under different expansion modes.

2.1. Study Area and Data

Jiangyin City, located north of Wuxi in Jiangsu Province and adjacent to the East China Sea, with the Yangtze River to its north and Taihu Lake to its south, occupies a crucial position within the Yangtze River Delta economic zone (Figure 2). The unique location not only provides an extensive transportation network for Jiangyin City, but also transforms it into a transportation junction connecting the north to the south and the east to the west. Swift economic advancement and the enlargement of the urban area, coupled with other factors, have rendered Jiangyin City a setting characterized by varied spatial functional layouts and complex land expansion modes during urbanization. Therefore, we selected Jiangyin City as the focus of this investigation, aiming to determine the underlying patterns and future tendencies of its urban land expansion, thereby providing scientific insights for urban planning and governance.
Here, a method for the expansion of urban land use was developed using land-use data from 2007, 2012, and 2017 from Jiangyin, ecological redline data, and MFZ data (Figure 3 and Figure 4). Notably, the MFZ data specifically pertain to the year 2012. Additionally, spatial distribution, transportation network, and digital elevation model data from Jiangyin City and its county center served as auxiliary data. This methodology allowed the simulation and prediction of changes in land use under multiple scenarios of land-use expansion.

2.2. Urban Spatial Functional Zoning

Major functional zones originate from a strategy proposed in China’s 11th Five-Year Plan, and place a pronounced emphasis on accommodating geographical variation in natural, economic, and human environments [46,47]. Essentially, these zones are categorized based on three key criteria—the carrying capacity of resources and the environment, the current spatial development density, and the future development potential. Consequently, the territorial space is delineated into four distinct types of spatial units—construction areas, construction-suitable areas, construction-restricted areas, and construction-forbidden areas. A construction area is defined as a region within the territorial scope that has become a high-density locus of construction. A construction-suitable area is characterized by indices indicating an environmental carrying capacity and constructive ability that suggest it is appropriate for further development. A construction-restricted area denotes a zone where land development is conditioned by specific restrictions, with the aim of achieving a sustainable balance. Conversely, a construction-forbidden area corresponds to regions where construction activities are strictly prohibited, primarily to protect natural environments and/or locations with historical value [46]. We employed rules of segmentation and the law of spatial order to determine the development tier coefficients for construction land within these varied zones. In the construction-allowable areas, the coefficient was set at 1, indicating full potential for development. In the construction-forbidden areas, the value was set at 0, representing a complete ban on construction activities. For both the suitable and restricted construction areas, the development tier coefficients for construction land were scaled between 0 and 1, with higher values assigned to suitable areas than to restricted ones, reflecting their relative compatibility with development.
In October 2011, the term “ecological redline” was introduced by the State Council, in its Opinions on Strengthening Environmental Protection Critical Points, to cover the core concept of ecological priority. It was used to establish the strictest system of ecological protection through the clear demarcation of boundaries for zones of ecological preservation and to implement robust mechanisms for ecological supervision. The goal was to foster a harmonious coexistence between humanity and nature, ensuring a sustainable equilibrium between social development and ecological integrity [48,49]. In comparison to the MFZ approach, the spatial functional positioning of ecological redlines is relatively straightforward—it simply imposes mandatory constraints on any land development within a delineated area. Consequently, here, the development tier coefficient for construction land within ecological redlines was set at 0, indicating a total prohibition against development. Conversely, the coefficient for construction land located outside of the ecological redline area was set at 1, indicating permissible development.
The MFZ approach is characterized by its comprehensive consideration of multiple factors, incorporating various aspects of land-use planning and socioeconomic development. By contrast, ecological redlines focus singularly on ecological priority, thereby establishing zones specifically for stringent ecological protection. It is conceivable, therefore, that areas designated under ecological redlines may sometimes come into conflict with restricted zones specified in MFZ plans. Given the strict limitations placed on development within ecological redline areas, coupled with the recent national emphasis on ecological priority and sustainable development, we propose a unified approach to handling such zones of conflict. Specifically, regardless of the categories defined within MFZs, if a conflict arises with an ecological redline, the development coefficient of construction land in these areas should be set to 0. This effectively designates these areas as construction-forbidden areas. Table 1 summarizes the four categories of urban space of the spatial functional zoning units, each corresponding to a different urban construction land development coefficient.

2.3. Urban Land Expansion Mode Zoning

Given the pronounced spatial heterogeneity of urban development modes, demarcating spatial boundaries for distinct expansion modes to explore land-use conversion rules through zoning is essential in order to objectively determine the evolutionary processes of land use. The spatial differentiation in urbanization intensity has led to a classification of urban space into a distinct ring structure. This structure comprises the urban area, the urban inner fringe, the urban external fringe, and the rural hinterland [50,51]. As illustrated in Figure 5, different types of urban land-use expansion are typically associated with specific zones, with infill expansion occurring in the urban and urban inner-fringe areas, sprawl expansion in the urban external-fringe area, and leapfrog expansion in the rural hinterland. In essence, the zoning for urban land-use expansion involves clearly demarcating these distinct spatial areas—the urban area, the urban inner fringe, the urban external fringe, and the rural hinterland. We utilized previous findings that applied the adaptive kernel density method, the entropy weight method, and spectral clustering to accurately delineate the spatial units for each mode of urban land-use expansion [52].

2.4. Urban Land Expansion Simulation

The main objective of this study was to construct a simulation model of urban land expansion by integrating spatial functional zoning and land expansion mode zoning. Cellular automaton models are dynamic system models that simulate the process of land-use change based on local conversion rules. Based on CA model principles, we developed an urban land expansion simulation model that comprehensively takes into account the coupling effect of spatial functional zoning and expansion modes. The acquisition of conversion rules is crucial to CA model construction [52]. Essentially, the transition rule in traditional CA models calculates the probability of land transforming into various land-use types by integrating the diverse driving forces of land use existing at the current time, thereby determining its final state in the subsequent period [53,54]. Here, we considered the neighborhood and environmental effects, and the constraining impacts of urban spatial functional zoning, combining these with the results of urban land-use expansion mode zoning to simulate the evolutionary process of different expansion modes.

2.4.1. Neighborhood Effect

Urban space constitutes a systematic and adaptively organizing environment in which the preservation or shift in the status of each individual land use is influenced by the land-use structure of its neighboring areas. The CA model asserts that each neighboring land-use status should assimilate to the central land use and converge to the same type of land use. This assimilating effect is referred to as the neighborhood effect in CA models [35]. A 3 × 3 Moore neighborhood was used in this work to calculate the probabilities of conversion from one state to another under the influence of the neighborhood effect [55]:
p 1 i , j , m = s j 3 × 3 − 1 ,
where   p 1 i , j , m refers to the neighborhood effect, specifically denoting the probability of the central cell transitioning from land-use type i to land-use type j ; m refers to the zoning unit the center cell belongs to ( m = 1 being located in the urban or urban inner-fringe area, m = 2 situated in the urban external-fringe area, and m = 3 in the rural hinterland); and s j represents the number of grid cells of land-use type j in the neighborhood.

2.4.2. Environment Effect

Numerous studies have revealed that the development of urban land use is closely associated with the local environmental effects produced by natural, social, and economic factors. A logistic regression model has been proposed to effectively measure these environmental effects on land-use evolution [56]. Here, the local environmental effects were analyzed using the logistic regression model, and were influenced by factors such as altitude, proximity to the urban center, the county center, and various classes of roads (motorways, expressways, and main and secondary roads). Additionally, the probability of land transitioning from one type to another was computed [52]:
  p 2 i , j , m = 1 1 + e − z ,     j = c o n s t r u c t i o n   l a n d 0 ,     j = o t h e r ,
Z = α + ∑ n = 1 9 β n × X n ,
where p 2 i , j , m represents the environmental effect—the probability that the central cell will undergo a change from land-use type i to land-use type j under the coupling effects of multiple environmental factors; X n signifies the value of the independent variable for the n th environmental factor; β n is the coefficient of logistic regression for the variable; and α is the regression constant term.

2.4.3. Comprehensive Transition Probability

Urban spatial functional zoning serves as a tool for planning departments to implement differentiated management and development strategies within national land spaces. As discussed in Section 2.1, these areas correspond to various levels of urban construction land development, ranging from 0 to 1 in terms of development tier coefficients. Notably, even when two land uses exhibit similar neighborhood and environmental effects, their final land-use statuses may differ if they are situated in different urban functional zones. As delineated in Equation (4), the comprehensive probability of each land use transitioning to different categories is influenced by three key factors—neighborhood effects, environmental effects, and the development tier coefficient of the functional area in which it is located:
p i , j , m = α k , m × p 1 i , j , m × p 2 i , j , m ,   j = c o n s t r u c t i o n   l a n d p 1 i , j , m × p 2 i , j , m ,     j = o t h e r s
where   p i , j , m represents the comprehensive transition probability of the center cell from land-use type i to land-use type j ;   k , m indicates the central cell in the k tier development area within subdivision unit m ; and α k , m is the development coefficient of its construction land.

2.4.4. Parameter Determination

Based on Equation (4), we calculated the probability of each cell that transitioned into a different land-use category. It was posited that each cell would most likely convert to the land-use type with the highest probability. To test this hypothesis, eight parameters had to be determined—the second- and third-level development coefficients for the urban area, the urban inner-fringe area, the urban external-fringe area, and the rural hinterland. An iterative approach was employed to fine-tune these coefficients and enhance the precision of the simulation outcomes. This process involved adjusting each parameter in increments of 0.01. The principle guiding this optimization was to achieve the highest possible precision value, thereby identifying the most effective combination of parameters. To evaluate the accuracy of the model and verify the parameter settings, the kappa coefficient—a statistical measure of inter-rater agreement for qualitative items—was utilized. This coefficient helped in calculating the model’s accuracy, setting the parameters, and assessing the simulation results [57].

2.5. Multi-Scenario Design for Urban Land Expansion

Another research objective of this study was to simulate the changing trends in urban land-use patterns under different expansion mode scenarios, with the design of the scenario evolution rules being key to this. Although existing research has divided urban land expansion into three modes—infill, sprawl, and leapfrog—the evolution of urban spaces cannot be described by any single mode. The actual dynamic involves a combination of these three modes, albeit the predominance of each mode varies throughout the urban evolutionary process. Based on the foregoing analysis, it is evident that, regardless of the type of expansion mode, the expansion should be confined to second-level development zones within the confines of diverse urban zoning schemes. As outlined in Table 2, we employed a step of 0.2 to design various urban land expansion scenarios by modulating the second-level development coefficient, α 2 , across different districts. The higher the development coefficient, the more influential the corresponding development mode in guiding the urban growth process.

3. Results

3.1. Spatial Pattern of Urban Functional Zoning

As shown in Figure 6, we identified conflicts between the MFZs and the ecological redlines in Jiangyin City. Subsequently, the MFZ map was adjusted, and an updated urban spatial functional zoning map of Jiangyin was created. In addition, the construction land development tiers of different urban spaces were determined.

3.2. Analysis of Urban Land Expansion Simulation

3.2.1. Analysis of Model Parameters

The model employed in this work relied on land-use data from 2007 and 2012, combined with the urban functional zoning map and all driving factors, with the optimal parameters being determined through iterative methods. As can be seen in Table 3, the second-level development coefficient of urban construction land was higher in the urban, urban inner-fringe, and urban external-fringe areas, while being lower in the rural hinterland. First, the coefficient of land development for construction purposes was higher in the urban and fringe areas because the urban center was consistently regarded as a location-driven factor influencing urban land expansion. Moreover, the likelihood of urban land expansion is generally inversely proportional to proximity to the city center. Secondly, the development of the urban center and urban inner-fringe area was categorized under the infill expansion mode, with the development of the urban external-fringe area falling under the sprawl expansion mode, and that of the rural hinterland under the leapfrog expansion mode. The analysis indicated that the second-level development coefficients for the urban external-fringe, urban, and urban inner-fringe areas and the rural hinterland in Jiangyin City gradually decreased (0.89 > 0.84 > 0.79 > 0.23, respectively) from 2007 to 2012, suggesting that Jiangyin’s expansion mode transitioned from intensive to less intensive, characterized by a blend of sprawl, infill, and leapfrog modes.

3.2.2. Analysis of Simulated Results

Assuming Jiangyin’s land use would continue to develop following the pattern of sprawl–infill–leapfrog from 2012 to 2017, based on the parameters in Table 3, a predicted land-use change map for 2017 was generated using the 2012 land-use status data as a foundation [Figure 7a]. Upon comparing this to the actual 2017 land-use data, as shown in Figure 7b, errors in the simulation results were primarily observed in the urban external-fringe areas from 2012. However, statistics revealed that 15.99% of the urban inner-fringe area transitioned to urban area, 8.25% of the urban external-fringe area transitioned to inner-fringe area, 6.6% of the rural hinterland changed to urban external-fringe area, and no new discrete external-fringe areas were noted in the rural hinterland in 2017. Therefore, our findings suggest that development in Jiangyin has predominantly been characterized by infill in the urban and urban inner-fringe areas, with sprawl serving an auxiliary role. Thus, the prevailing development pattern is identified as infill–sprawl–leapfrog. Assuming the second-level development coefficients for Jiangyin’s urban, urban inner-fringe, and urban external-fringe areas and rural hinterland to be 0.84, 0.89, 0.79, and 0.23, respectively, the CA model proposed here was used to simulate the development of the infill–sprawl–leapfrog expansion mode [Figure 7c]. By comparison [Figure 7d], it was found that the error rate of the simulation results, after adjusting the development mode, had significantly declined.
Table 4 presents the simulated accuracy of two expansion patterns—the sprawl–infill–leapfrog expansion mode (Model 1) and the infill–sprawl–leapfrog expansion mode (Model 2). Upon comparison, it is evident that the simulation accuracy of Model 2 is significantly improved in both the urban inner and external fringes of the city, with the global simulation accuracy reaching 95.9%. This improvement underscores the vital role of accurately identifying the urban expansion mode in advance to enhance simulation accuracy. Moreover, we also employed a traditional CA model as a benchmark, which did not account for urban spatial zoning or specific expansion modes. In this traditional model, the coefficient for the development of construction land was set to 1 and no zoning simulations were conducted. The results from both the zoning-specific and global comparisons indicate that the model proposed here achieves superior simulation accuracy. This further substantiates the necessity of incorporating considerations of functional zoning and urban expansion modes when exploring the transformational trajectory of urban land use.

3.3. Prediction of Multi-Scenario Urban Land Expansion

Figure 8 illustrates the projected evolution of land-use patterns in Jiangyin by 2027 under various expansion mode scenarios. Under the infill–sprawl–leapfrog mode, there is no significant increase in construction land, nor are there any striking changes in the land-use layout. By contrast, the sprawl–infill–leapfrog and sprawl–leapfrog–infill modes result in substantial changes to the land-use layout, particularly in the north near the Yangtze River, southwest of the central zoning, and on the fringes of the urban southwest area. The leapfrog–infill–sprawl and infill–leapfrog–sprawl modes exert a minor influence on Jiangyin’s overall land-use layout, with the latter showing especially minimal impact. However, the leapfrog–sprawl–infill mode has the most significant effect on the city’s land-use layout, notably leading to a requisitioning of the urban inner- and external-fringe areas as well as arable land in the rural hinterland, which are then converted into construction land.

4. Discussion

Based on the predictive outcomes, we further analyzed the specific impacts of various expansion modes on the anticipated land-use configuration of Jiangyin City and the underlying factors responsible for these impacts. By overlaying the urban spatial functional zoning map with the urban spatial expansion mode zoning map, we identified that second-level development zones—which define the land expansion mode by adjusting the construction land development coefficient—were primarily located in the urban external-fringe area and the rural hinterland. The development of the expansion mode was predominantly based on infill expansion, which occurred primarily in the urban area, encompassing the majority of first-level development zones and the urban inner-fringe area. Notably, the conversion of non-construction land in these zones was unlikely to significantly impact the urban pattern [58]. Although this development mode enhances urban spatial compactness, optimizes resource allocation, and improves land-use efficiency [59], it can also introduce challenges, such as increased traffic congestion in the city and reduced green and public spaces [60]. The sprawl–infill–leapfrog and sprawl–leapfrog–infill expansion modes originated from sprawl development in the urban external-fringe area—a process that frequently occupies significant amounts of cultivated and ecological land, inevitably leading to the persistent outward growth of the urban periphery and significantly impacting the urban land-use pattern. Research has indicated that, while sprawl development effectively promotes local economic growth and diverse space utilization [61,62], it also leads to problems such as inefficient land use and increased administrative challenges [63,64]. The leapfrog–infill–sprawl and leapfrog–sprawl–infill expansion modes primarily rely on leapfrog expansion, with the rural hinterland targeted by this method forming the core of the second-level development zones. However, the unique characteristics of the areas undergoing sprawl shape the impact of the second development mode, which, compared to the first model—augmented by sprawl expansion—exerts a more pronounced influence on land-use patterns. Furthermore, these expansion modes will likely lead to the significant fragmentation of urban land in the rural hinterland, potentially resulting in inefficient land resource utilization [65]. Moreover, the second expansion mode generates greater resource waste. Consequently, due to variations in urban spatial functional zoning, different development patterns result in diverse impacts on land-use configurations, potentially leading to various “urban diseases” and necessitating differing management approaches in urban planning [66].
The Jiangyin City Master Plan (2011–2030) explicitly states that Jiangyin is expected to develop a spatial layout characterized by “a concentrated area along the northern riverbank and an ecological open space in the south”. Based on the results of various scenarios, it has been observed that the implementation of sprawl–infill–leapfrog, sprawl–leapfrog–infill, and leapfrog–sprawl–infill expansion modes could lead to extensive discrete urban development in the south of Jiangyin, contradicting the goal of the ecological open area in the south. Although the implementation of infill–sprawl–leapfrog, leapfrog–infill–sprawl, and infill–leapfrog–sprawl modes will not damage the ecological area, these approaches promote the agglomeration development of riverfront and eastern lands in Jiangyin. However, upon comparing the results of the three expansion modes, it is apparent that the leapfrog–infill–sprawl mode performs worse than the other two modes in terms of clustering along the river. Based on the predictions, the land-use patterns for the infill–sprawl–leapfrog and infill–leapfrog–sprawl expansion modes resemble the spatial patterns intended by the planning goals and expectations, making it difficult to ascertain a superior option. Therefore, we recommend that Jiangyin City adopt both these land-use expansion modes over the next decade.
Here, we have provided a method with broad applicability for urban expansion simulation by integrating the differences in urban spatial functional zoning and the diversity of expansion modes. The urban expansion modes and the spatial differentiation of urban land-use functions discussed here are common phenomena that occur during the urban development process. Previous studies have found that infill, sprawl, and leapfrog expansions coexist simultaneously in different regions [67,68,69]. It has also been proposed that mixed expansion modes represent a common feature of urban development in the context of smart-city evolution [70]. Although this study took Jiangyin City in China as an example, and delineated the functional zoning of urban spaces by integrating the main functional zones and ecological redlines proposed for China, international cases have demonstrated similar territorial control approaches. For instance, the Netherlands’ National Spatial Strategy divides territories into urban, agricultural, and ecological zones [71], while Portland’s (United States) Urban Growth Boundary explicitly separates urban development from ecological protection areas [72]. Japan has implemented differentiated development policies through its National Land Formation Plan [73], and the United States’ San Francisco Bay Area’s Greenbelt Alliance has employed ecological boundaries for habitat protection [74]. Therefore, by adapting local territorial control elements to replace the specific spatial zoning framework used in this study, the proposed model can be effectively applied to urban expansion research in various regions. Conversely, the model is not suitable for areas lacking formal territorial control systems.
Although this study produced several pertinent findings, there remain limitations requiring further investigation. First, current territorial control systems incorporate six guidance dimensions—positional, structural, regional, boundary, indicator, and directory controls [75,76]. While spatial functional zoning constitutes regional control [77,78,79], comprehensive simulation requires the integration of multiple control types to better reflect planning impacts on urban expansion patterns. Second, the differentiation in expansion characteristics among land-use types (residential, commercial, industrial, etc.) remains underexplored [80,81]. Future work should analyze type-specific expansion patterns and their interrelationships under spatial zoning constraints. Third, ecological protection requirements need stronger integration in expansion modeling [82,83]. Developing multi-scenario frameworks incorporating ecological forecasting could better assess the environmental impacts of alternative expansion strategies.

5. Conclusions

In conclusion, the urban expansion simulation model developed in this study accounts for the heterogeneity of spatial functions and the diversity of land expansion modes, providing significant practical value for urban planning and land resource management. The model’s core strength lies in its ability to capture the complex interactions between functional zones and expansion modes, offering a powerful tool for optimizing land-use allocation and supporting sustainable development. Specifically, the model achieves refined simulation of urban land expansion processes through four key steps—spatial functional zoning delineation, expansion mode partitioning, land expansion model construction, and multi-scenario simulation. Here, Jiangyin was selected as the research area. The proposed method was employed to confirm that the global simulation accuracy could reach 95.9% by considering urban spatial functional zoning and urban land expansion modes. Meanwhile, the layout of land use in Jiangyin over the next ten years is expected to show significant differences among various expansion mode scenarios. From the perspective of forming a planning pattern of a concentrated area along the northern riverbank and an ecological open space in the south, we recommend that Jiangyin implement both the infill–sprawl–leapfrog and infill–leapfrog–sprawl expansion modes.
The proposed method is applicable to countries or regions with territorial functional zoning regulations, such as China, Japan, the Netherlands, and the United States. It not only enhances the precision of urban planning, but also ensures context-specific development strategies tailored to local conditions. Future research should prioritize incorporating multi-category territorial regulatory elements, refining land-use classifications, and optimizing scenario designs. These efforts will further improve the model’s practicality and applicability, offering more robust support for sustainable urban development.

Author Contributions

Conceptualization, Jing Yang; methodology, Jing Yang and Zheng Wang; data acquisition, Jing Yang and Zheng Wang; software, Yizhong Sun; formal analysis, Jing Yang and Yizhong Sun; writing and review, Jing Yang; editing, Zheng Wang; visualization, Jing Yang and Zheng Wang; supervision, Yizhong Sun. 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 (Nos. 42301483, 42371408), the Natural Science Foundation of Jiangsu Province (No. BK20230372), the Natural Science Research of Jiangsu Higher Education Institutions of China (No. 22KJB170018), the China Postdoctoral Science Foundation (No. 2024M751464), the Key Laboratory of Land satellite Remote sensing Application, Ministry of Natural Resources of the People’s Republic of China (No. KLSMNR-G202311), the Foundation of Key Lab of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education (No. 2021VGE03), and the Key Laboratory of Land satellite Remote sensing Application, Ministry of Natural Resources of the People’s Republic of China (No. KLSMNR-G202311).

Data Availability Statement

The data used in this paper are available upon request from the corresponding author via email.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Flowchart for multi-scenario simulation of urban land expansion modes.
Figure 1. Flowchart for multi-scenario simulation of urban land expansion modes.
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Figure 2. The location of the study area.
Figure 2. The location of the study area.
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Figure 3. Spatial distribution of land-use patterns in Jiangyin City.
Figure 3. Spatial distribution of land-use patterns in Jiangyin City.
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Figure 4. Spatial distribution in Jiangyin City: (a) ecological redline areas; and (b) major functional zones (MFZs).
Figure 4. Spatial distribution in Jiangyin City: (a) ecological redline areas; and (b) major functional zones (MFZs).
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Figure 5. Schematic diagram showing the spatial distribution of urban land expansion modes.
Figure 5. Schematic diagram showing the spatial distribution of urban land expansion modes.
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Figure 6. Spatial functional zoning in Jiangyin City.
Figure 6. Spatial functional zoning in Jiangyin City.
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Figure 7. Simulation results of land-use change in Jiangyin City in 2017: (a) sprawl–infill–leapfrog mode (Model 1); (b) comparison of the results of Model 1 with real land-use data for 2017; (c) infill–sprawl–leapfrog mode (Model 2); and (d) comparison of the results of Model 2 with real land-use data for 2017.
Figure 7. Simulation results of land-use change in Jiangyin City in 2017: (a) sprawl–infill–leapfrog mode (Model 1); (b) comparison of the results of Model 1 with real land-use data for 2017; (c) infill–sprawl–leapfrog mode (Model 2); and (d) comparison of the results of Model 2 with real land-use data for 2017.
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Figure 8. Forecast results of urban land expansion: (a) infill–sprawl–leapfrog mode; (b) sprawl–infill–leapfrog mode; (c) leapfrog–infill–sprawl mode; (d) infill–leapfrog–sprawl mode; (e) sprawl–leapfrog–infill mode; and (f) leapfrog–sprawl–infill mode.
Figure 8. Forecast results of urban land expansion: (a) infill–sprawl–leapfrog mode; (b) sprawl–infill–leapfrog mode; (c) leapfrog–infill–sprawl mode; (d) infill–leapfrog–sprawl mode; (e) sprawl–leapfrog–infill mode; and (f) leapfrog–sprawl–infill mode.
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Table 1. Construction land development coefficients in urban spatial functional zoning.
Table 1. Construction land development coefficients in urban spatial functional zoning.
Development TierAreaDevelopment Coefficient
Level 1Urban construction area α 1 = 1
Level 2Urban construction-suitable area α 2 ∈ (0, 1)
Level 3Urban construction-restricted area α 3   ∈   ( 0 ,   1 )   &     α 2 > α 3
Level 4Combination of construction- forbidden and ecological redline areas α 4   = 0
Table 2. Parameters for the scenario design of urban land expansion modes.
Table 2. Parameters for the scenario design of urban land expansion modes.
Expansion Mode (Leading–Auxiliary–Secondary)Development Coefficient
Urban AreaInner-Fringe AreaExternal-Fringe AreaRural Hinterland
Infill–Sprawl–Leapfrog0.80.60.40.2
Sprawl–Infill–Leapfrog0.60.40.80.2
Leapfrog–Infill–Sprawl0.60.40.20.8
Infill–Leapfrog–Sprawl0.80.60.20.4
Sprawl–Leapfrog–Infill0.40.20.80.6
Leapfrog–Sprawl–Infill0.40.20.60.8
Table 3. Development coefficient of construction land in different expansion mode zones.
Table 3. Development coefficient of construction land in different expansion mode zones.
AreaDevelopment Coefficient of Construction Land
Second LevelThird Level
Urban area0.84-
Inner-fringe area0.790.08
External-fringe area0.890.32
Rural hinterland0.230.19
Table 4. Comparison of simulation accuracy for different regions.
Table 4. Comparison of simulation accuracy for different regions.
AreaMethods
Model 1Model 2Comparison Model
Urban area0.9210.9210.825
Inner-fringe area0.8970.9650.701
External-fringe area0.7250.9060.846
Rural hinterland0.9600.9600.852
Global area0.8830.9590.806
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Yang, J.; Wang, Z.; Sun, Y. Multi-Scenario Simulation of Urban Land Expansion Modes Considering Differences in Spatial Functional Zoning. ISPRS Int. J. Geo-Inf. 2025, 14, 138. https://doi.org/10.3390/ijgi14040138

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Yang J, Wang Z, Sun Y. Multi-Scenario Simulation of Urban Land Expansion Modes Considering Differences in Spatial Functional Zoning. ISPRS International Journal of Geo-Information. 2025; 14(4):138. https://doi.org/10.3390/ijgi14040138

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Yang, Jing, Zheng Wang, and Yizhong Sun. 2025. "Multi-Scenario Simulation of Urban Land Expansion Modes Considering Differences in Spatial Functional Zoning" ISPRS International Journal of Geo-Information 14, no. 4: 138. https://doi.org/10.3390/ijgi14040138

APA Style

Yang, J., Wang, Z., & Sun, Y. (2025). Multi-Scenario Simulation of Urban Land Expansion Modes Considering Differences in Spatial Functional Zoning. ISPRS International Journal of Geo-Information, 14(4), 138. https://doi.org/10.3390/ijgi14040138

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