Nicholas R. Magliocca

An economic agent-based model of coupled housing and land markets (CHALMS)

This paper describes a spatially disaggregated, economic agent-based model of urban land use, which is named for its innovative feature of coupled housing and land markets (CHALMS). The three types of agents—consumer, farmer and developer—all make decisions based on underlying economic principles, and heterogeneity of both individuals and the landscape is represented. CHALMS simulates the conversion of farmland to housing development over time, through the actions of the agents in the land and housing markets. Land and building structures in the housing bundle are treated explicitly, so the model can represent the effects of land and housing prices on housing density over time. We use CHALMS to simulate the dynamics of land-use changes as a representative suburban area grows. The presence of agent and landscape heterogeneity, stochastic processes, and path dependence require multiple model runs, and the expression of spatial dispersion of housing types, overall housing density, and land prices over time in terms of the most likely, or ‘average’, patterns. We find that CHALMS captures both the general tendency for diminishing population density at greater distances from the center city, and dispersed leapfrog patterns of development evident in most suburban areas of the US.

Synthesis in land change science: methodological patterns, challenges, and guidelines

Global and regional economic and environmental changes are increasingly influencing local land-use, livelihoods, and ecosystems. At the same time, cumulative local land changes are driving global and regional changes in biodiversity and the environment. To understand the causes and consequences of these changes, land change science (LCS) draws on a wide array synthetic and meta-study techniques to generate global and regional knowledge from local case studies of land change. Here, we review the characteristics and applications of synthesis methods in LCS and assess the current state of synthetic research based on a meta-analysis of synthesis studies from 1995 to 2012. Publication of synthesis research is accelerating, with a clear trend toward increasingly sophisticated and quantitative methods, including meta-analysis. Detailed trends in synthesis objectives, methods, and land change phenomena and world regions most commonly studied are presented. Significant challenges to successful synthesis research in LCS are also identified, including issues of interpretability and comparability across case-studies and the limits of and biases in the geographic coverage of case studies. Nevertheless, synthesis methods based on local case studies will remain essential for generating systematic global and regional understanding of local land change for the foreseeable future, and multiple opportunities exist to accelerate and enhance the reliability of synthetic LCS research in the future. Demand for global and regional knowledge generation will continue to grow to support adaptation and mitigation policies consistent with both the local realities and regional and global environmental and economic contexts of land change.

Exploring agricultural livelihood transitions with an agent-based virtual laboratory: global forces to local decision-making.

Rural populations are undergoing rapid changes in both their livelihoods and land uses, with associated impacts on ecosystems, global biogeochemistry, and climate change. A primary challenge is, thus, to explain these shifts in terms of the actors and processes operating within a variety of land systems in order to understand how land users might respond locally to future changes in broader-scale environmental and economic conditions. Using ‘induced intensification’ theory as a benchmark, we develop a generalized agent-based model to investigate mechanistic explanations of relationships between agricultural intensity and population density, environmental suitability, and market influence. Land-use and livelihood decisions modeled from basic micro-economic theories generated spatial and temporal patterns of agricultural intensification consistent with predictions of induced intensification theory. Further, agent actions in response to conditions beyond those described by induced intensification theory were explored, revealing that interactions among environmental constraints, population pressure, and market influence may produce transitions to multiple livelihood regimes of varying market integration. The result is new hypotheses that could modify and enrich understanding of the classic relationship between agricultural intensity and population density. The strength of this agent-based model and the experimental results is the generalized form of the decision-making processes underlying land-use and livelihood transitions, creating the prospect of a virtual laboratory for systematically generating hypotheses of how agent decisions and interactions relate to observed land-use and livelihood patterns across diverse land systems.

Long-Term, Large-Scale Morphodynamic Effects of Artificial Dune Construction along a Barrier Island Coastline

Abstract Interactions between human manipulations and landscape processes can form a dynamically coupled system because landscape-forming processes affect humans, and humans increasingly manipulate landscape-forming processes. Despite the dynamic nature of sandy barrier islands, economic incentive and recreational opportunities attract humans and development. Storm-driven sediment-transport events that build barrier islands constitute hazards to humans and infrastructure, and manipulations aimed at preventing or mitigating such events link human actions and long-term island morphodynamics. To explore how the behavior of a natural barrier island differs from one in which humans are dynamic system constituents, we use a numerical model of storm-driven sediment redistributions within the shoreface/island/back-barrier system and human rearrangements of sediment within the subaerial barrier island. In a modeled natural system, periods of dune growth and island stability, initiated by stochastic lulls in storm activity, alternate with stormy periods, in which shoreline erosion and frequent overwash regulate dune heights. When humans are included in the model, overwash deposits are removed from the island, and artificially high dunes are rebuilt. These manipulations tend to filter moderate overwash events. However, with shoreline erosion and rising sea level, these manipulations promote lower and narrower islands in the long term, so that when dunes are overtopped, the sediment redistributions are more severe. Thus, the coupled human/barrier system exhibits wider swings between increased island stability and sudden island displacements. Increasing the height of artificially maintained dunes increases the rate of island narrowing and, therefore, infrastructure relocation, and increases the need for sediment to be imported from outside the system.

Meta-studies in land use science: Current coverage and prospects

Land use science has traditionally used case-study approaches for in-depth investigation of land use change processes and impacts. Meta-studies synthesize findings across case-study evidence to identify general patterns. In this paper, we provide a review of meta-studies in land use science. Various meta-studies have been conducted, which synthesize deforestation and agricultural land use change processes, while other important changes, such as urbanization, wetland conversion, and grassland dynamics have hardly been addressed. Meta-studies of land use change impacts focus mostly on biodiversity and biogeochemical cycles, while meta-studies of socioeconomic consequences are rare. Land use change processes and land use change impacts are generally addressed in isolation, while only few studies considered trajectories of drivers through changes to their impacts and their potential feedbacks. We provide a conceptual framework for linking meta-studies of land use change processes and impacts for the analysis of coupled human–environmental systems. Moreover, we provide suggestions for combining meta-studies of different land use change processes to develop a more integrated theory of land use change, and for combining meta-studies of land use change impacts to identify tradeoffs between different impacts. Land use science can benefit from an improved conceptualization of land use change processes and their impacts, and from new methods that combine meta-study findings to advance our understanding of human–environmental systems.

Cross-Site Comparison of Land-Use Decision-Making and Its Consequences across Land Systems with a Generalized Agent-Based Model

Local changes in land use result from the decisions and actions of land-users within land systems, which are structured by local and global environmental, economic, political, and cultural contexts. Such cross-scale causation presents a major challenge for developing a general understanding of how local decision-making shapes land-use changes at the global scale. This paper implements a generalized agent-based model (ABM) as a virtual laboratory to explore how global and local processes influence the land-use and livelihood decisions of local land-users, operationalized as settlement-level agents, across the landscapes of six real-world test sites. Test sites were chosen in USA, Laos, and China to capture globally-significant variation in population density, market influence, and environmental conditions, with land systems ranging from swidden to commercial agriculture. Publicly available global data were integrated into the ABM to model cross-scale effects of economic globalization on local land-use decisions. A suite of statistics was developed to assess the accuracy of model-predicted land-use outcomes relative to observed and random (i.e. null model) landscapes. At four of six sites, where environmental and demographic forces were important constraints on land-use choices, modeled land-use outcomes were more similar to those observed across sites than the null model. At the two sites in which market forces significantly influenced land-use and livelihood decisions, the model was a poorer predictor of land-use outcomes than the null model. Model successes and failures in simulating real-world land-use patterns enabled the testing of hypotheses on land-use decision-making and yielded insights on the importance of missing mechanisms. The virtual laboratory approach provides a practical framework for systematic improvement of both theory and predictive skill in land change science based on a continual process of experimentation and model enhancement.

Simple or complicated agent-based models? A complicated issue

Agent-based models (ABMs) are increasingly recognized as valuable tools in modelling human-environmental systems, but challenges and critics remain. One pressing challenge in the era of Big Data and given the flexibility of representation afforded by ABMs, is identifying the appropriate level of complicatedness in model structure for representing and investigating complex real-world systems. In this paper, we differentiate the concepts of complexity (model behaviour) and complicatedness (model structure), and illustrate the non-linear relationship between them. We then systematically evaluate the trade-offs between simple (often theoretical) models and complicated (often empirically-grounded) models. We propose using pattern-oriented modelling, stepwise approaches, and modular design to guide modellers in reaching an appropriate level of model complicatedness. While ABMs should be constructed as simple as possible but as complicated as necessary to address the predefined research questions, we also warn modellers of the pitfalls and risks of building mid-level models mixing stylized and empirical components. We clarify the terms complexity and complicated in the context of ABM.We comprehensively discuss pros and cons of simple and complicated ABMs.We identify challenges and pitfalls for simple and complicated ABMs.We provide recommendations and good practices for dealing with complicatedness.

Using Pattern‐oriented Modeling (POM) to Cope with Uncertainty in Multi‐scale Agent‐based Models of Land Change

Local land-use and -cover changes (LUCCs) are the result of both the decisions and actions of individual land-users, and the larger global and regional economic, political, cultural, and environmental contexts in which land-use systems are embedded. However, the dearth of detailed empirical data and knowledge of the influences of global/regional forces on local land-use decisions is a substantial challenge to formulating multi-scale agent-based models (ABMs) of land change. Pattern-oriented modeling (POM) is a means to cope with such process and parameter uncertainty, and to design process-based land change models despite a lack of detailed process knowledge or empirical data. POM was applied to a simplified agent-based model of LUCC to design and test model relationships linking global market influence to agents’ land-use decisions within an example test site. Results demonstrated that evaluating alternative model parameterizations based on their ability to simultaneously reproduce target patterns led to more realistic land-use outcomes. This framework is promising as an agent-based virtual laboratory to test hypotheses of how and under what conditions driving forces of land change differ from a generalized model representation depending on the particular land-use system and location.

From meta-studies to modeling: Using synthesis knowledge to build broadly applicable process-based land change models

This paper explores how meta-studies can support the development of process-based land change models (LCMs) that can be applied across locations and scales. We describe a multi-step framework for model development and provide descriptions and examples of how meta-studies can be used in each step. We conclude that meta-studies best support the conceptualization and experimentation phases of the model development cycle, but cannot typically provide full model parameterizations. Moreover, meta-studies are particularly useful for developing agent-based LCMs that can be applied across a wide range of contexts, locations, and/or scales, because meta-studies provide both quantitative and qualitative data needed to derive agent behaviors more readily than from case study or aggregate data sources alone. Recent land change synthesis studies provide sufficient topical breadth and depth to support the development of broadly applicable process-based LCMs, as well as the potential to accelerate the production of generalized knowledge through model-driven synthesis.

Effects of alternative developer decision-making models on the production of ecological subdivision designs: experimental results from an agent-based model

Approaches to residential development have clear effects on the surrounding environments, including those on habitat protection, water quality, transportation and congestion costs, and loss of public open space. Ecological subdivision designs (ESDs) are a means to mitigate some of the most negative effects of low-density dispersed land-use patterns, yet there is not widespread adoption of these alternative approaches to subdivision development. In this paper we attempt to improve understanding of how developers make decisions over development designs and what influences those decisions. Using an agent-based model of residential-housing and land markets, the effects of different developer-decision frameworks on development designs and land use are assessed. The importance of uncertainty in the outcome of new designs, such as ESDs, and the effect of that uncertainty on the cost of credit are possible explanations for the prevalence of conventional, low-density development types, and may be impeding adoption of ESDs.