Land change has great impact on regional environment and human well-being, and is also an important driver of global change (
Journal of Geographical Sciences, Volume. 30, Issue 10, 1555(2020)
Agent-based model of land system: Theory, application and modelling framework
Land change science has become an interdisciplinary research direction for understanding human-natural coupling systems. As a process-oriented modelling approach, agent based model (ABM) plays an important role in revealing the driving forces of land change and understanding the process of land change. This paper starts from three aspects: The theory, application and modeling framework of ABM. First, we summarize the theoretical basis of ABM and introduce some related concepts. Then we expound the application and development of ABM in both urban land systems and agricultural land systems, and further introduce the case study of a model on Grain for Green Program in Hengduan Mountainous region, China. On the basis of combing the ABM modeling protocol, we propose the land system ABM modeling framework and process from the perspective of agents. In terms of urban land use, ABM research initially focused on the study of urban expansion based on landscape, then expanded to issues like urban residential separation, planning and zoning, ecological functions, etc. In terms of agricultural land use, ABM application presents more diverse and individualized features. Research topics include farmers’ behavior, farmers’ decision-making, planting systems, agricultural policy, etc. Compared to traditional models, ABM is more complex and difficult to generalize beyond specific context since it relies on local knowledge and data. However, due to its unique bottom-up model structure, ABM has an indispensable role in exploring the driving forces of land change and also the impact of human behavior on the environment.
1 Introduction
Land change has great impact on regional environment and human well-being, and is also an important driver of global change (
The real world is a complex system composed of a large number of decision-making agents. One of the most important challenges facing contemporary sciences is understanding complex systems (
This paper starts from ABM theory, application, and modeling framework of land system. Based on summary and review, we conduct a case study and propose a modeling framework. The content includes four parts: 1) ABM theory, we introduce ABM theory and related concepts. 2) ABM applications, we review ABM application in urban and agricultural land systems, followed by a case study of Chinese Grain for Green Program ABM. 3) Modeling framework, based on existing model communication methods like ODD protocols, we propose modeling framework from the agent perspective of land system. 4) Discussion, challenges and future research directions are discussed.
2 ABM theoretical basis and related concepts
2.1 Complexity theory and ABM
Originated from general systems theory, complexity theory has multi-disciplinary background. It absorbs theories from mathematics, physics, genetic biology and social sciences during the process of development. Composed by many components that interact with each other, complex systems have features include path-dependence, criticality, spatial self-organization and emergence (
The agent-based model is a modeling method based on complexity theory. Farmer
2.2 Related concepts
(1) Cellular automata
As a bottom-up modeling approach, ABM traces back to cellular automata (CA) (
(2) Individual based model
ABM is also derived from individual based model (IBM) in population ecology. Ecology has a long tradition of bottom-up simulation (
(3) Object-oriented programming
Essentially ABM is a computer model. Models in natural sciences are usually quantitative, but computer languages allow the description of qualitative conditions (such as if… then…), thereby allowing the expression of behavior rules in ABM. Scientists believe that ABM is related to object-oriented programming (OOP) in computer sciences in the 1980s (
3 Application of ABM in land system and case study
ABM has a very wide range of applications, including business organization, economics, infrastructure, group events, society and culture, terrorism, military, biology, and ecosystems (
3.1 Urban land use change models
Cities are the areas where human use the land most intensively on the earth surface. Urban land changes during the development process of the city. Schelling and Sakoda were the pioneers in the research of decision-making of urban land use agent. They independently proposed models in 1970 to simulate the residential segregation caused by individuals’ slight difference in preference to neighbors (
With the deepening of research, urban ABM has become more meticulous and diversified, and sociological and economic theories have been widely applied.
Methods in ABM for studying urban development issues are also constantly being developed and improved, and gradually tend to couple ABM with other approaches, such as Bayesian networks, genetic algorithms, analytic hierarchy process, game theory (
3.2 Agricultural land use change models
In addition to cities, agriculture is another significant way human use land. Farmers cut down forests, plant crops, and run farms. Their behaviors are more direct and diversified than urban land use behaviors. In addition, compared with the city, the rural landscape has a more prominent interaction between human and nature. Therefore, researchers often build agricultural ABM based on specific natural conditions, socio-economic conditions, and local policies. They usually investigate local knowledge in the research area. Research goals are generally to address specific issues of the region or to provide support for agricultural policymaking.
In addition, some researchers studied the effectiveness of ecologic protection policies, such as China’s natural forest protection project (
Different agricultural ABMs have obvious local characteristics. They are more like case diagnosis of specific issues in regional sustainable development or natural resource management. In recent years, participatory modeling has gained popularity. The researchers organized different stakeholders such as local villagers, governments, and enterprises to participate directly in the formulation of model rules. They reproduce the decision-making process through interactive methods like games.
3.3 ABM case study of Grain for Green Program
The Grain for Green Program (GGP) as a representative of ecological engineering has had an important impact on land use patterns in China. Hengduan mountainous region is a key area to implement the GGP. Based on the ABM modeling framework and process, we built a spatial ABM for the GGP implementation. Taking Tongdu Town, Dongchuan District, Yunnan Province as the research area, based on census data, geographic data, and survey data, we simulated the GGP implementation process from 2010 to 2015. Our simulation results also include the annual income of farmers and households, and the spatial distribution of farmers’ and government’s willingness to return cropland (
We represented three types of agents in the model including farmers, farmer households and the government. Farmers have attributes such as age, fertility, and mortality. Households are basic units of decision-making. Households and the government each determine their willingness to return the cropland in accordance with the changes in expected income and policies. On one hand, by calculating the annual income from working after participating in the GGP and the annual income from planting before participating in the GGP, combined with the subsidy from the government, we can get the income change, and further the willingness of the households to return their land. On the other hand, the government determines the willingness considering the slope, soil fertility, ecological importance and ease of project implementation. Then we calculate the total willingness based on the willingness of two sides. The parcel is used as simulation space unit. Based on the government’s annual quota of the GGP, any cultivated land in the area that meets the requirements of the GGP (slope greater than 25 degrees) would be sorted in descending order to the total willingness and implement the returning until the quota is completed. According to the model framework and decision rules of each agent type, we built a computer model with Java in the Repast (
Figure 1.
Figure 2.
Different from the traditional land use models, this model directly represents the agents’ behaviors of decision-making and the parameters are defined at agent level. Its advantage lies in reproducing the process of returning cropland to forest instead of the phenomenon. Subsequent research can further simulate the implementation of GGP and land change patterns under different policy scenarios. The results can provide a basis for policy formulation and promote the sustainable development of environment in mountain areas.
4 ABM modeling framework
4.1 Model communication and ODD protocols
ABM is more of a modeling concept than technology. Multidisciplinary researchers build models to solve specific problems in different regions. They differ very much in theoretical basis, structures and details. The feature of ABM that mixing qualitative and quantitative methods in rule setting means it cannot be expressed in mathematical language transparently. Therefore, effective communication has always been a problem faced by researchers. Using Unified Modeling Language (UML) in software development can promote model communication, but it is not a specialized solution for ABM.
To facilitate model evaluation, comparison and communication, ABM researchers believe that a specific protocol should be followed at the beginning of model building.
Figure 3.
4.2 Agent-based modeling framework and implementation process of land system from the agent perspective
For modelers, the most important point that ABM is different from other models is to represent reality from the agent perspective. Based on this, we propose the agent-based modeling framework of land system from agent perspective (
Figure 4.
In the agent subsystem, agent selection and classification should be performed first to determine the basic components of the model. Each type of agent is classified according to its attributes. For example, in the urban land change model, residents can be sub-divided based on attributes like income and age. In the agricultural land model, farmers can be classified by full-time or part-time, whether they fall into the category of labor force. Then the decision-making willingness and ability of each type of agents are determined according to the attributes. The ability determines the possible decision-making options in a period of time, and the willingness determines the preference for specific options of the agents (
The land system is a spatial system, so building ABMs for land systems requires the design of environment subsystems. First, in order to represent the environment, we can choose CA square girds, polygon grids or vector layers. Then environment units should be given geographical attributes, such as land cover/use type, altitude, environmental quality, etc.
Both agent subsystem and environment subsystem change dynamically. The update of the environment subsystem is driven by natural changes and agents’ influence on the environment. The update of agent system is affected by the learning and evolution of agents on one hand, and by the environment’s feedback on the other hand. By characterizing agent features, behaviors and interactions and representation of environment a conceptual ABM of land system is built.
Based on the conceptual model, decision rules should be formulated as mathematically as possible to build a theoretical model. Further, a computer model can be constructed with a modeling platform (or independent development using a programming language). Before application, the model should be checked by sweeping the parameters. Theoretical simulation can help reveal whether the characteristics shown by the model are consistent with assumptions and common sense. At the same time, sensitivity analysis can be performed. Then the model can be parameterized with empirical data, and the actual land system change can be simulated. The modeling results should be compared with actual data like remote sensing data to validate the model (
Figure 5.
5 Discussion
The ABM simulates land change drivers through representing decision-making behaviors of micro agents. Macro land change and spatial patterns are emergent results. In this way, the ABM has its irreplaceable role in all these land system modeling approaches. Its value lies not in higher simulation accuracy, but in its ability to promote a better understanding of land change mechanisms and processes. Due to its multi-source nature, the ABM is different in model design, programming, parameterization, calibration and implementation from traditional methods. There are also many challenges, such as representation of agents’ behaviors, application in large scale, verification and model reuse.
5.1 Strengthen the representation of agent behaviors
The core of the agent-based modeling is the design of agent behavior rules. How to better capture the agent behaviors is a key issue for ABM researchers. There are many different ways to characterize behaviors of agents such as the commonly used utility functions and statistical methods. Genetic algorithms and neural networks have also been integrated into ABM to define agent behaviors. When multiple methods are coupled, it is necessary to solve the spatial and temporal inconsistency. Agent classification can aggregate agents into class, further defining the behaviors of each class. In addition, it is necessary to consider the agent’s ability to perceive the environment, whether the agent is fully or bounded rational. The integration of sociological and psychological theories helps to characterize the agent’s cognitive, emotional, and belief dynamics. Another important thing to consider is to find balance between theoretical basis and empirical observation (
In representing the behavior of agents, the detailed description of behaviors in economic models (such as the process of expectation formation and proactive behavior) has not been well integrated into the land change ABM. More work needs to focus on establishing links between spatial economic models and land change ABM. The parameterizing process of agent decision-making simulation urgently requires standardized methods to improve efficiency (
5.2 Application of ABM at larger scales
Large-scale ecological models and climate models have been widely used. Biophysical processes are well represented, while human activities are often oversimplified in the models. Coupling the ABM with the ecological model is a possible solution. However, most of current ABMs focus on the local scale. Local decision rules in the model cannot be directly applied to a larger spatial range or a finer spatial resolution (
5.3 Validation of ABM
Model validation checks the validity and its degree of the model by comparing the simulation results with actual data, which is an important part of the model evaluation. Validation of land change models includes pattern validation and structural validation. The pattern validation is to compare the simulated land use patterns with the actual land use patterns. The actual land use patterns can be obtained by classification of remote sensing data. Methods like point-to-point comparison, kappa coefficient, and landscape index are commonly used. Structural validation is to justify the rationality of the model mechanism and process, which is still a challenge (
5.4 Reusability
From the perspective of the construction process of the ABM, the reusability of ABMs is poor. A specific model cannot usually be applied to other regions. Compared with simple abstract models, highly complex and data-dependent models are more opaque to users (
In summary, the land system ABM has unique advantages and significant positions, but there are still a lot of challenges, and its methodology is still in the process of formation. This means that land system ABM needs more attention from researchers and modelers; on the other hand, researchers also need to take a cautious attitude when adopting this method.
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Erfu DAI, Liang MA, Weishi YANG, Yahui WANG, Le YIN, Miao TONG. Agent-based model of land system: Theory, application and modelling framework[J]. Journal of Geographical Sciences, 2020, 30(10): 1555
Received: Mar. 31, 2020
Accepted: Jun. 30, 2020
Published Online: Apr. 21, 2021
The Author Email: DAI Erfu (daief@igsnrr.ac.cn)