Urban expansion is the process of conversion of lands to urban [1
]. Shifts from agricultural lands [2
], forest lands [5
], water [7
], and wetlands [6
] are among the most critical transitions to urban lands. This causes adverse impacts on the physical environment and is one of the leading causes of natural ecosystem degradation [13
]. It causes loss of the agriculture and croplands, habitat fragmentation, heat island effects, and reduction of surface watercourses [14
]. It also causes changes in landscapes [17
] and urban transportation needs by increasing travel distance and commuting trips between the city and suburbs, demand for private cars, and fuel consumption [21
Land use/land cover (LULC) maps are the products of the classification of satellite remote-sensing images [23
], which enable quantitative spatiotemporal analysis across geographic regions [24
] and provide helpful information about the transformation of lands [25
] to a temporal extent. Eventually, extracting urban areas from LULC maps and modeling provides a critical investigation into urban expansion’s driving mechanisms, future trends, and LULC transitions [27
Since urban expansion is a dynamic and complex process, many spatial models have been constructed to investigate, predict, and simulate it. Simulation models can develop scenarios for future-oriented decision-making [28
] by preparing a projection of land-use changes, expecting the future urban land demands and spatial distribution of these demands [29
]. Among others, these models include the cellular automata (CA) model [30
], CA–Markov model [33
], logistic regression model [36
], agent-based model [39
] and multi-agent-based model [41
]. Of these models, CA has been the most popular model employed by researchers since its conceptualization by Ulam and Von Neumann in the 1940s [43
]. A countless number of studies on the application of CA in modeling urban expansion exist. For example, Ma et al. [45
] attempted to examine CA to simulate urban expansion in China. Putting seed data and control layers, they explored the parameters for their model and reached high accurate simulation results. White et al. [46
] employed the constrained CA model based on the development intensity or dynamic constraints in space and time to predict urban land-use dynamics. Mozaffaree Pour and Oja. [47
] modeled urban expansion in Harju County in Estonia to investigate its driving forces and predict the future trend in a regular cell space.
Integrating CA with other models increased the popularity, efficiency, and quantity of the simulation [48
] in the field of urban expansion. Integrated CA–Markov has been implemented in research, considering that the trend and pace of changes in the urban expansion are similar in the past and future. In a study performed by Li et al. [34
], they explored the capabilities of CA–Markov to simulate urban expansion in China. Prediction of urban expansion employing the CA–Markov model in Iran was performed by Jafari et al. [49
] and revealed expansion on the periphery of population centers with encroachment to the forest and agricultural lands. The integrated logistic–CA model was conducted in some research [50
], exploring the spatial feature of urban expansion and defining transition rules. In the research by Mustafa et al. [50
], they took advantage of multinominal logistic regression and CA to assess the probabilities of causative factors and neighborhood effects in the urban expansion of Belgium. To explore the relationships between land conversion and driving factors of urban expansion, Arsanjani et al. [52
] employed integrated logistic–CA–Markov models to simulate land use maps consisting of built-up lands and calculate the quantity of land-use change using a transition area matrix in Tehran, Iran.
Furthermore, several studies enhanced the CA model with agent-based models to explore the drivers of urban expansion, boost the behavioral rules by defining the dynamic agents, and determine more realistic neighborhood effects to simulate urban expansion. Mustafa et al. [29
] have considered a combination of CA–Logit and agent-based modeling to capture the dynamics of neighborhood interactions and static drivers of urban expansion in three levels of agents with homogeneous characteristics and behaviors. Liu et al. [53
] constructed a land-use simulation and decision-support system (LandSDS), integrating agent-based and CA modeling in China for two homogeneous agents. However, models of urban expansion simulation vary in terms of data requirements, mechanisms, and application scales [48
]; these models have limitations, and considerably new integrated model approaches are required to meet the complexity of urban expansion nature, which deal with spatial heterogeneity, local interactions, and neighborhood effects.
To this aim, we coupled the CA model with the agent-based model (CA–Agent model) to address the complexity of the dynamic simulation, generate heterogeneity in space, define more complicated rules, and employ the suitability analysis in a novel way.
Cellular automata as a bottom-up model is based on regular or irregular cell space depending on the complexity of the representation of reality [54
] and consider the interactions between cells and their neighbors implemented by transition rules. Defining transition rules is the critical step in CA models. While transition rules are invariant through time [32
], considering the spatial heterogeneity among the cells and uneven development in simulation requires improvements of transition rules [55
]. Applying the appropriate thresholds is a way to address the spatial heterogeneity [56
]. One of the key benefits of our CA–Agent model is implementing different transition rules manipulated by suitability thresholds for cellular agents.
Moreover, a heterogeneous neighborhood impacts the spatial heterogeneity [57
] and the model simulation results. Taking advantage of the suitability analysis, we enhanced the CA–Agent model performance to simulate the most suitable areas for new areas expansions [39
]. Eventually, the CA model is flexible with an adaptable structure capable of integrating with other models [58
Additionally, agent-based models as generative simulation modeling [60
] employ the behavioral factors of agents and their interactions with the environment and with one another to simulate the urban evolution [61
] and track the dynamic changes from one agent to the whole area [62
]. Agents could be defined as human or physical entities [63
]. We defined cells as the dynamic agents in our model. The model’s outcome emerges from agents’ interactions over time [60
] and the probability and randomness of the agent’s behavior [64
]. The CA–Agent model’s significant advancement is exploiting the Markov model to enhance the allocation of probabilities to the model. The Markov model as a stochastic model describes the state of the cells regarding their previous states by specifying a series of random values to each cell, and the results represent the probability of transition [65
]. The Markov model prepares an estimation of the quantities of LULC changes [68
] appropriate for describing the complex structure of urban systems.
Coupling these models in a GIS environment is a novel approach that leads to a better understanding of the dynamics of urban expansion driving forces. The main distinction of the CA–Agent model compared to the previous models is implementing dynamic factors [61
], enhancing the spatial behavioral rules [41
] of the autonomous cellular agents, their decisions concerning the neighboring cells, and probabilities of spatial changes. Additionally, agents acting in a cellular space can change their behaviors over time, so it is possible to understand the evolution of spatial patterns [32
]. Additionally, the CA–Agent model can highlight the spatiotemporal dynamics of urban expansion at the local level.
As the process of expanding the cities mainly occurs in the immediate neighboring lands from main cities [61
], in line with the observations of Mozaffaree Pour and Oja [74
] on the expansion of Tallinn in Harju County, we considered the buffer of 15 km from the center of Tallinn. The buffer of 15 km was chosen as this is where most of the new development occurred between 2000 and 2018, and the nearest cities and surrounding municipalities started expanding.
It is important to note that until 1990 (during the Soviet period), the use of land adjacent to Tallinn was relatively strictly regulated, and agricultural land change into settlements was almost excluded. Urbanization-related land-use changes were not happening around Tallinn due to state-established limiting regulations. After regaining the independence of Estonia in 1991, regarding the land reform and revision of planning principles on the one hand, and economic growth and increase in personal wealth on the other hand, the location of new constructions considerably changed to a scattered form in Tallinn’s neighboring suburbs. At the same time, wealthy people moved to the suburbs to improve their living conditions in detached houses [75
]. Thus, the consequences of urban expansion require effective decision making implementing such models in spatial planning.
To monitor the footprints of urban expansion, we used SCP (Semiautomatic Classification Plugin) in the open-source software QGIS 3.10 (Free Software Foundation, Boston, MA, USA). Using SCP allows the possibility of downloading the satellite data directly, processing the data, classifying supervised and unsupervised remote sensing images, and post-processing the data [78
]. To run the CA–Agent model and prepare the simulation, we employed the Repast platform and AgentAnalyst extension in ArcMap 10.6 (Esri, California, USA). Further, to analyze the Markov model and validate the simulation result, we used IDRISI TerrSet software (Clark Labs, Worcester, MA, USA).
This paper is structured as follows. Section 2
represents the study area in Tallinn and its 15 km buffer zone, the data collection, processing, analyzing, and framework of the CA–Agent model. The third section shows the results of model implementation in the study area and validation. Section 4
discusses the output and innovation of the proposed approach, and finally, the paper concludes with an overview of the whole process.
Trade-offs between agriculture, forest, and built-up lands need simulation modeling approaches to link expansion orientation and quantify spatial planning activities. In this paper, we employed the CA–Agent model to monitor the process of urban expansion and simulate urban expansion in Tallinn and its 15 km buffer zone. Application of the CA–Agent model for spatial planning activities let us consider how implementing different driving factors, probability of changes, and modifying thresholds will affect the outputs of the model and the distribution of the built-up over time. Correspondingly, spatially explicit consequences of urban expansion support the effective decision making across the cellular agent’s characteristics and rules of behavior in spatial planning.
It is essential to highlight that even though the built-up area in the study area increased by 25.03%, the population of Tallinn decreased by 10.19% between 1990 and 2018. The dominating migration has been from Tallinn into surrounding municipalities (feeding the new settlement areas) and from other regions of Estonia to Tallinn.
In this regard, modeling the factors affecting urban expansion in Tallinn area is critical to detect future expansion trends. The integrated CA–agent model in our research allowed us to synthesize the probabilities and enhance the rules into one model. This capability allowed the cellular agents to decide the probability of spatial transition and the rules of behavior on interacting with the other cellular agents and their environment. The main conclusion that can be notified is that taking advantage of cellular-based modeling, adjacent neighborhood information, and Markovian transition probability provides the simulated map of urban expansion in 2018 with a Kappa degree of 0.86, which confirms a relatively high accuracy of the implemented model components. Consequently, the model was considered “approved” for simulating the urban expansion in 2030. Thus, implementing the CA–Agent model in the study area illustrated the temporal changes of land conversion and represented the present spatial planning results requiring regulation of urban expansion encroachment on agricultural and forest lands.
This conclusion follows that urban expansion is a dynamic spatial process affected by different physical drivers. We considered the suitability factors of “distance to Tallinn”, “distance to main roads”, “distance to local roads” and “neighborhood status” and six constraints, namely, “main lakes”, “railways”, “watercourses”, “main roads”, “airport”, and “wetlands”. It best suits the simulation results to consider the social and economic factors that could be conducted in future studies. Altogether, the modeling proves urban expansion as a result of unplanned sprawl.