Agent-based Land Use Research Articles

Integrating health effects within an agent-based land use and transport model

IntroductionTransport is a determinant of urban health, and the past two decades have seen a rise in transport health impact assessment (HIA) tools. However, a common limitation of HIAs is the use of aggregation which ignores variations over space, time, and/or demographics. Furthermore, HIAs generally treat travel behaviour exogenously, meaning they can assess the effects of hypothetical behaviour shifts but not the policies that cause behaviour to change. MethodsThis article presents an open-source agent-based integrated land use, transport, and health impacts model that assesses health exposures and impacts related to physical activity, air pollution, and traffic injury risk. The agent-based framework allows exposures and impacts to be assessed with high spatiotemporal and sociodemographic resolution. To achieve a tight integration, the model adapts (traditionally car-oriented) transport modelling methods to capture health-relevant behaviours such as active travel more realistically. In addition, exposure modelling methods have been adapted to make use of the high spatial and temporal detail available in agent-based transport models. DemonstrationTo demonstrate this integrated model, this article presents an example simulation which evaluates the health impacts of a work from home policy in Munich. Overall, there is on average a small net health detriment for individuals required to work from home and a smaller net health benefit for everybody else. ConclusionsAgent-based modelling enables HIAs to jointly assess transport, environmental, and population health outcomes with high granularity, helping policymakers identify who benefits and suffers from proposed policies. This work demonstrates the feasibility of combining agent-based transport and health modelling methods in a seamless framework.

Integrated modelling of autonomous electric vehicle diffusion: From review to conceptual design

The future Autonomous Vehicles (AVs) are likely to be electric. We started with a review of the adoption of AVs, Electric Vehicles (EVs) and Autonomous Electric Vehicles (AEVs), as well as the six associated urban sub-systems, namely transportation, land use, environment, energy, economy, and population systems, in order to find evidence about the linkages and interactions between the diffusion of AEVs and the six sub-systems. Based on the review, we argued that an integrated urban model, which takes the linkages and interactions into account, was needed to fully understand the adoption and impacts of AEVs. Furthermore, we conducted a conceptual design of an integrated model for AEVs (without explicit modelling), and demonstrated how to update an existing agent-based Land Use and Transport Interaction (LUTI) model by incorporating AEV components. The resulting integrated model of AEVs would help different AEV-related stakeholders (e.g., local authorities) in their decision-making.

SHIFTING CULTIVATION PRACTICES IN BARAK VALLEY, INDIA – POLICY SCENARIOS FROM A SPATIALLY EXPLICIT LAND USE MODEL

Abstract. Barak valley is a region in north east part of India where the practice of shifting cultivation is quite prevalent. Population growth coupled with the geographic isolation of the area have led to an increased pressure on land and a consequent decline in forest cover. The decrease of forests observed is spatially distributed and dependent on neighborhood rules. Hence, we look towards modelling the land use change to understand the land use changes and the factors affecting them. In this paper, we modify an agent-based land use model for modelling shifting cultivation to determine how various policy changes at a larger scale might affect the shifting cultivation practice in the region at the micro level. We explore scenarios like drastic population increase and availability of irrigation infrastructure in the area. Through the scenario analysis we explore how policies play a role in agriculture patterns and influence land use patterns.

An agent- and GIS-based virtual city creator: A case study of Beijing, China

Many agent-based integrated urban models have been developed to investigate urban issues, considering the dynamics and feedbacks in complex urban systems. The lack of disaggregate data, however, has become one of the main barriers to the application of these models, though a number of data synthesis methods have been applied. To generate a complete dataset that contains full disaggregate input data for model initialization, this paper develops a virtual city creator as a key component of an agent-based land-use and transport model, SelfSim. The creator is a set of disaggregate data synthesis methods, including a genetic algorithm (GA)-based population synthesizer, a transport facility synthesizer, an activity facility synthesizer and a daily plan generator, which use the household travel survey data as the main input. Finally, the capital of China, Beijing, was used as a case study. The creator was applied to generate an agent- and Geographic Information System (GIS)-based virtual Beijing containing individuals, households, transport and activity facilities, as well as their attributes and linkages.

Land managers\u2019 behaviours modulate pathways to visions of future land systems

Attempts to influence the development of land systems are often based on detailed scenarios that constrain relevant factors, describe a range of divergent but plausible futures and identify potential pathways to visions of desirable conditions. However, a number of assumptions are usually made during this process, and one of the most substantial is that land managers display homogeneous, economically rational behaviour across space, time and scenarios. This assumption precludes the consideration of important behavioural effects and limits understanding of the feasibility of scenario-based pathways towards visions. We use an agent-based land use model to examine broad forms of behavioural variation within defined scenarios in theoretical contexts. We relate model results to stakeholder-developed visions of desired future land systems in Europe and so assess the scope for behavioural pathways towards these normative futures. We find that the achievability of visions is determined by internal inconsistencies, scenario conditions and the multifunctional potential of land uses, with a fundamental tension between large-scale land use productivity and small-scale diversity (i.e. land sparing and land sharing). Trading conditions affect this balance most strongly and represent an obvious target for governance strategies concerned with achieving multifunctional land use. However, within specific circumstances behavioural effects are strong and diverse, and can accelerate, counteract or mitigate the impacts of other drivers. This suggests that visions for the land system should focus on trade-offs, identifying those that are least strong, most acceptable and most susceptible to adjustment through behavioural or other influences.

Modeling the paleoclimate (ca. 6000–3200 cal BP) in eastern Anatolia: the method of Macrophysical Climate Model and comparisons with proxy data

Both paleoenvironmental and archaeological data provide mounting evidence of increasing aridification in the Near East starting with the late Early Bronze Age (ca. 4300 BP). Combining the methods of the Macrophysical Climate Model (MCM) and GIS, it is possible to obtain quantifiable values across landscapes at 100-year resolution. This research focuses on the methods of MCM, a synoptic paleoclimate model that applies statistical downscaling and compares the results with proxy data. The discussion addresses the divergent trends in precipitation and temperature in eastern Anatolia between 6000 and 3200 BP. The general climatic trends emerging from this research are then compared with those illustrated in various proxy data to verify the accuracy of the retrodictions for precipitation and temperature variables of the MCM. Results of this research are important as they may be integrated with the agent-based land use and landscape evolution models. These new approaches have great potential to demonstrate how multifaceted, long-term dynamic human–environment relationships can be defined and readapted according to the changing levels of social organization, which start a new cycle of social and economic responses with each ecological change.

LandSys II: Agent-Based Land Use–Forecast Model with Artificial Neural Networks and Multiagent Model

This paper extends the previous LandSys I to introduce artificial neural networks (ANNs) into the framework of cellular automata (CA), multiagents, and geographic information system (GIS) to forecast land-use change at the grid cell level (50×50 m). In the model, the temporal and spatial interactions of land-use change are described by CA where transition rules are defined by ANNs to reduce the tedious work of parameter calibration in LandSys I. Compared with LandSys I, an improved multiagent model in LandSys II captures both zoning policies and human decision-making behaviors. The effect of multiple human decision-making behaviors (e.g., governments, households, developers) on land-use change has been quantified. Based on the historical GIS data for Orange County, Florida, the model has a higher predictive ability (87.7%, compared to 85.7% in LandSys I) for land-use change from Year 1990 to 2000. It is also found that either increasing hidden layers in ANNs or the use of multiagent models improves prediction accuracy. A comparison between LandSys I and II indicates that both models are viable; however, LandSys II is freely transferable and is more suitable for land-use forecasting, whereas LandSys I is more appropriate for evaluating the interconnections between land use and its affecting variables.

Combining agent functional types, capitals and services to model land use dynamics

Models of land use change are becoming increasingly complex as they attempt to explore the effects of climatic, political, economic and demographic change on land systems and the services these systems produce. ‘Bottom-up’ agent based models are a useful method for exploring the effects of local processes and human behaviour, but are generally limited to small spatial scales due to the complex parameterisations involved. Conversely, ‘top-down’ land allocation models can be applied at large spatial scales, but are less adept at accounting for human behaviour and non-economic factors such as the supply of ecosystem services. Models that combine the strengths of these two approaches are required for the advancement of land use science. Here, we present an agent based land use modelling framework designed to be run over large spatial extents and to be capable of accounting for relevant forms of human behaviour, variations in land use intensities, multifunctional ecosystem service production and the actions of institutions that affect land use change. We give a full description of this framework, called CRAFTY (Competition for Resources between Agent Functional TYpes), and provide details of how it can be applied and extended, including some simple examples of its ability to model important processes of land use change. These include changes in demand for and supply of ecosystem services, variation in land use intensity and multi-functionality, and heterogeneous behaviour amongst land managers.

A Multi-Agent Model to Simulate Regional Land use Change with an Application to the Poyang Lake Area of China

In many regions both urban expansion and rural development take place simultaneously, and for the purpose of understanding the dynamic process of land use/cover change (LUCC) in such large areas, this study develops a multi—agent based land use model. Taking the Poyang Lake area of China as a typical case, this study applies the mechanism of diffusion—limited aggregation to simulate the behavior of urban agents, while rural land use is illustrated with a bottom—up based model consisting of agent and environment layers. In the agent layer, each household agent makes its own decisions on land use, and at each time interval a government agent takes control of land use by implementing policies. According to incomes and the rate of migrant workers, household agents are divided into six categories, among which different decision rules are followed. For complex LUCC in the Poyang Lake area of China from 1985 to 2005, the artificial society model developed in this study yields results highly consistent with observations. Importantly, it is shown that governmental policies can impose significant effects on the decisions of individual household agents on land use and the multi—agent—based land use model developed in this study provides a robust means for assessing the effectiveness of governmental policies.

Extending agent-based land-use/-cover change models to tropical deforestation: a focus on farm-based decision making in tropical subsistence farming

Subsistence agriculture continues to be the mainstay activity in most tropical regions, and opinions on how it impacts tropical deforestation vary significantly. The sparse literature available on subsistence farm-based models highlights the existing methodological gap in the ability of present-day agent-based models (ABMs) to simulate the impact of subsistence production on frontier tropical deforestation. Though issues concerning agent specification and its variability have been successfully dealt with, gaps exist in the explicit incorporation and articulation of linkages of subsistence production and consumption theories on the one hand and frontier tropical deforestation on the other hand. This demands explicit determination of the decision-making process and the objective functions for subsistence farmers. Once simulated, the trajectories of deforestation and its trigger mechanisms can be understood. Overall, farm-based ABMs have the potential to allow insightful understanding of the small and slow individual homogeneous subsistence practices that have resulted in massive deforestation in the tropics.

Toward an understanding of provenance in complex land use dynamics

Landscapes are complex adaptive spatial systems driven by biophysical and socioeconomic processes that are shaped by the cumulative behavior of interacting, but independent decision-makers. Agent-based modeling has been identified as an effective paradigm for the study of such systems because of its ability to represent behavior and interaction at the individual level. These interactions are inherently associated with cause–effect relations in the transition of land use systems through time. Elucidating specific cause and effect relations from model results, however, poses a significant challenge to the research community because of complicated model structures and the complex processes that drive system dynamics. In this article, we call for provenance-based agent-based models of land use dynamics to support the capture and tracking of complex cause–effect relations. We illustrate the importance of explicitly considering provenance in agent-based modeling through the development of a spatially explicit agent-based land use simulation framework. This framework is designed to simulate complex land use dynamics in southwest Montana, USA. Land managers are modeled as geospatial agents who interact with their spatially explicit environments. These agents exchange opinions about land use controls through social networks in an attempt to form consensus on land use policies. The heterogeneous opinion behavior of landowner agents and complicated social structural relationships produce complex dynamics. We conduct experiments to demonstrate the need to explicitly consider provenance in agent-based models of land use systems and its ability to promote our understanding of complex adaptive land use systems.

Linking MODFLOW with an Agent‐Based Land‐Use Model to Support Decision Making

The U.S. Geological Survey numerical groundwater flow model, MODFLOW, was integrated with an agent-based land-use model to yield a simulator for environmental planning studies. Ultimately, this integrated simulator will be used as a means to organize information, illustrate potential system responses, and facilitate communication within a participatory modeling framework. Initial results show the potential system response to different zoning policy scenarios in terms of the spatial patterns of development, which is referred to as urban form, and consequent impacts on groundwater levels. These results illustrate how the integrated simulator is capable of representing the complexity of the system. From a groundwater modeling perspective, the most important aspect of the integration is that the simulator generates stresses on the groundwater system within the simulation in contrast to the traditional approach that requires the user to specify the stresses through time.

Ontologies for transparent integrated human-natural system modelling

We propose an approach to modular agent-based land use modelling, based on ontologies in their computer science sense: formal representations of conceptualisations. The approach is primarily aimed at addressing the issue of model transparency. Human-natural systems models involve large numbers of submodels, making them difficult to understand for those not involved in their construction. We show that using ontologies to represent the structure and state of a simulation model improves transparency in two ways: First, the information about the structure and state is decoupled from the simulation software and can be independently processed. Second, the logics on which ontologies are based reflect more commonsense understandings of the relationships among concepts than those of computer programming languages.

Adding ecosystem function to agent-based land use models

The objective of this paper is to examine issues in the inclusion of simulations of ecosystem functions in agent-based models of land use decision-making. The reasons for incorporating these simulations include local interests in land fertility and global interests in carbon sequestration. Biogeochemical models are needed in order to calculate such fluxes. The Century model is described with particular attention to the land use choices that it can encompass. When Century is applied to a land use problem the combinatorial choices lead to a potentially unmanageable number of simulation runs. Century is also parameter-intensive. Three ways of including Century output in agent-based models, ranging from separately calculated look-up tables to agents running Century within the simulation, are presented. The latter may be most efficient, but it moves the computing costs to where they are most problematic. Concern for computing costs should not be a roadblock.

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

Agent-based modelling is an approach that has been receiving attention by the land use modelling community in recent years, mainly because it offers a way of incorporating the influence of human decision-making on land use in a mechanistic, formal, and spatially explicit way, taking into account social interaction, adaptation, and decision-making at different levels. Specific advantages of agent-based models include their ability to model individual decision-making entities and their interactions, to incorporate social processes and non-monetary influences on decision-making, and to dynamically link social and environmental processes. A number of such models are now beginning to appear-it is timely, therefore, to review the uses to which agent-based land use models have been put so far, and to discuss some of the relevant lessons learnt, also drawing on those from other areas of simulation modelling, in relation to future applications. In this paper, we review applications of agent-based land use models under the headings of (a) policy analysis and planning, (b) participatory modelling, (c) explaining spatial patterns of land use or settlement, (d) testing social science concepts and (e) explaining land use functions. The greatest use of such models so far has been by the research community as tools for organising knowledge from empirical studies, and for exploring theoretical aspects of particular systems. However, there is a need to demonstrate that such models are able to solve problems in the real world better than traditional modelling approaches. It is concluded that in terms of decision support, agent-based land-use models are probably more useful as research tools to develop an underlying knowledge base which can then be developed together with end-users into simple rules-of-thumb, rather than as operational decision support tools.