Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review

Parker, Dawn C.; Manson, Steven M.; Janssen, Marco A.; Hoffmann, Matthew J.; Deadman, Peter

doi:10.1111/1467-8306.9302004

Cited by 1,490 publications

(911 citation statements)

References 140 publications

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“…A common approach to simulate LUCC as a result of variations in individual decisions and actions is the use of agent-based modelling (ABM) (Matthews et al 2007;Parker et al 2003Parker et al , 2008Robinson et al 2007). ABM makes the modelling of interactions between both human and natural systems possible by defining different decision-making units or agents.…”

Section: Introductionmentioning

confidence: 99%

“…Agents can have different internal characteristics and strategies, and can interact with other agents and their environment (Bonabeau 2002;Sawyer 2003). Although, the use of ABM offers the potential for understanding and exploring LUCC processes (Matthews et al 2007;Parker et al 2003), their relevance to predict LUCC has been limited by the inherent complexity of the processes that they try to address and by high data requirements (Couclelis 2002;Verburg 2006). Because of this complexity, the data requirements and the diversity of farming systems within agricultural regions, ABM has mainly been implemented in simulating local scale LUCC processes (e.g.…”

Section: Introductionmentioning

confidence: 99%

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An agent-based approach to model land-use change at a regional scale

et al. 2009

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Land-use/cover change (LUCC) is a complex process that includes actors and factors at different social and spatial levels. A common approach to analyse and simulate LUCC as the result of individual decisions is agent-based modelling (ABM). However, ABM is often applied to simulate processes at local scales, while its application in regional studies is limited. This paper describes first a conceptual framework for ABM to analyse and explore regional LUCC processes. Second, the conceptual framework is represented by combining different concepts including agent typologies, farm trajectories and probabilistic decision-making processes. Finally, the framework is illustrated through a case study in the Netherlands, where processes of farm cessation, farm expansion and farm diversification are shaping the structure of the landscape. The framework is a generic, straightforward approach to analyse and explore regional LUCC with an explicit link to empirical approaches for parameterization of ABM.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An agent-based approach to model land-use change at a regional scale

et al. 2009

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“…It enables researchers to study emergent system-level patterns and outcomes from generalized interactions at the individual agent level [4][5][6]. ABMs have been widely applied in many fields, including ecology, biology, social science, economic science, computer science, and the geospatial sciences [7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

4D-SAS: A Distributed Dynamic-Data Driven Simulation and Analysis System for Massive Spatial Agent-Based Modeling

Guan

et al. 2016

IJGI

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Significant computation challenges are emerging as agent-based modeling becomes more complicated and dynamically data-driven. In this context, parallel simulation is an attractive solution when dealing with massive data and computation requirements. Nearly all the available distributed simulation systems, however, do not support geospatial phenomena modeling, dynamic data injection, and real-time visualization. To tackle these problems, we propose a distributed dynamic-data driven simulation and analysis system (4D-SAS) specifically for massive spatial agent-based modeling to support real-time representation and analysis of geospatial phenomena. To accomplish large-scale geospatial problem-solving, the 4D-SAS system was spatially enabled to support geospatial model development and employs high-performance computing to improve simulation performance. It can automatically decompose simulation tasks and distribute them among computing nodes following two common schemes: order division or spatial decomposition. Moreover, it provides streaming channels and a storage database to incorporate dynamic data into simulation models; updating agent context in real-time. A new online visualization module was developed based on a GIS mapping library, SharpMap, for an animated display of model execution to help clients understand the model outputs efficiently. To evaluate the system's efficiency and scalability, two different spatially explicitly agent-based models, an en-route choice model, and a forest fire propagation model, were created on 4D-SAS. Simulation results illustrate that 4D-SAS provides an efficient platform for dynamic data-driven geospatial modeling, e.g., both discrete multi-agent simulation and grid-based cellular automata, demonstrating efficient support for massive parallel simulation. The parallel efficiency of the two models is above 0.7 and remains nearly stable in our experiments.

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“…Land use change models as a tool are used to show where, when and how changes could arise in the future in order to adapt current public policy [9,10]. In the past decades, different models have been developed to exhibit and quantify land use changes [11,12,13,14], mainly in land-use change (LUC) models. In this paper In recent decades, remote sensing data and Geospatial Information System (GIS) have been widely applied for identification and analyses of land use change in metropolitan area [15,16,17,18,19,20,21].…”

Section: Introductionmentioning

confidence: 99%

Urban Growth Modeling Using Anfis Algorithm: A Case Study for Sanandaj City, Iran

Mohammady

Delavar

Pijanowski

2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Global urban population has increased from 22.9% in 1985 to 47% in 2010. In spite of the tendency for urbanization worldwide, only about 2% of Earth's land surface is covered by cities. Urban population in Iran is increasing due to social and economic development.The proportion of the population living in Iran urban areas has consistently increased from about 31% in 1956 to 68.4% in 2006.Migration of the rural population to cities and population growth in cities have caused many problems, such as irregular growth of cities, improper placement of infrastructure and urban services. Air and environmental pollution, resource degradation and insufficient infrastructure, are the results of poor urban planning that have negative impact on the environment or livelihoods of people living in cities. These issues are a consequence of improper land use planning.Models have been employed to assist in our understanding of relations between land use and its subsequent effects. Different models for urban growth modeling have been developed. Methods from computational intelligence have made great contributions in all specific application domains and hybrid algorithms research as a part of them has become a big trend in computational intelligence. Artificial Neural Network (ANN) has the capability to deal with imprecise data by training, while fuzzy logic can deal with the uncertainty of human cognition. ANN learns from scratch by adjusting the interconnections between layers and Fuzzy Inference Systems (FIS) is a popular computing framework based on the concept of fuzzy set theory, fuzzy logic, and fuzzy reasoning. Fuzzy logic has many advantages such as flexibility and at the other sides, one of the biggest problems in fuzzy logic application is the location and shape and of membership function for each fuzzy variable which is generally being solved by trial and error method. In contrast, numerical computation and learning are the advantages of neural network, however, it is not easy to obtain the optimal structure. Since, in this type of fuzzy logic, neural network has been used, therefore, by using a learning algorithm the parameters have been changed until reach the optimal solution. Adaptive Neuro Fuzzy Inference System (ANFIS) computing due to ability to understand nonlinear structures is a popular framework for solving complex problems. Fusion of ANN and FIS has attracted the growing interest of researchers in various scientific and engineering areas due to the growing need of adaptive intelligent systems to solve the real world problems.International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1/W3, 2013 SMPR 2013, 5 -8 October 2013 This contribution has been peer-reviewed. The peer-review was conducted on the basis of the abstract. 493In this research, an ANFIS method has been developed for modeling land use change and interpreting the relationship between the drivers of urbanization. Our study area is the city of Sanandaj located in the west of Iran. Landsat imag...

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Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review

Cited by 1,490 publications

References 140 publications

An agent-based approach to model land-use change at a regional scale

An agent-based approach to model land-use change at a regional scale

4D-SAS: A Distributed Dynamic-Data Driven Simulation and Analysis System for Massive Spatial Agent-Based Modeling

Urban Growth Modeling Using Anfis Algorithm: A Case Study for Sanandaj City, Iran

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