Agent-based land-use models: a review of applications

Matthews, Robin; Gilbert, Nigel; Roach, Alan; Polhill, Gary; Gotts, Nick

doi:10.1007/s10980-007-9135-1

Cited by 700 publications

(413 citation statements)

References 54 publications

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“…This is in line with the statement of several authors that uncertainty needs to be quantified, represented and included in the outputs of ABM Parker et al 2003), and even in policy-making processes (Bradshaw and Borchers 2000). Finally, by including the diversity between and within agent types, this conceptual framework includes part of the diversity of decision-making processes, which is an essential characteristic of the human-environmental system (Köbrich et al 2003;Matthews et al 2007).…”

Section: Resultssupporting

confidence: 70%

“…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%

See 2 more Smart Citations

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: Resultssupporting

confidence: 70%

Section: Introductionmentioning

confidence: 99%

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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“…Including land exchange between different categories of farmers, as well as between farmers and other types of land users, is challenging because the various agents within the land market have different criteria with respect to land value and other financial and legal positions that require different types of data evidence (Geller and Moss 2008). This may also explain why many contemporary land exchange models do not model categorical land-use change since they are limited to rather homogeneous populations of agents (Matthews et al 2007). We start by describing the case study area and the data that was used in the model development and calibration.…”

Section: Introductionmentioning

confidence: 99%

Land-use change arising from rural land exchange: an agent-based simulation model

et al. 2014

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Introduction Land exchange can be a major factor driving land-use change in regions with high pressure on land, but is generally not incorporated in land-use change models. Here we present an agent-based model to simulate land-use change arising from land exchange between multiple agent types representing farmers, nature organizations, and estate owners. Methods The RULEX model (Rural Land EXchange) was calibrated and applied to a 300 km 2 case study area in the east of the Netherlands. Decision rules about which actor will sell and buy land, as well as which specific land to buy or sell are based on historical observations, interviews, and choice experiments.Results A reconstruction of land-use change for the period 2001-2009 demonstrates that RULEX reproduces most observed land-use trends and patterns. Given that RULEX simulates only one mechanism of land-use change, i.e. land exchange, it is conservative in simulating change. Conclusions With this model, we demonstrate the potential of incorporating land market processes in an agent-based, land-use change model. This supports understanding of land-use change that is brought about by ownership change, which is an important process in areas where pressure on land is high. The soundness of the process representation was corroborated by stakeholders within the study area. Land exchange models can be used to assess the impact of changes in climate, markets, and policy on land use change, and help to increase effectiveness of alternative land purchasing strategies by stakeholders or spatial planning policy.

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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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Agent-based land-use models: a review of applications

Cited by 700 publications

References 54 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

Land-use change arising from rural land exchange: an agent-based simulation model

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

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