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

Abstract. 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.

Modeling Locally, Thinking Globally

January 29, 2014

Communications Coordinator

Above figure: Locations of eastern Asian sites: two in China (western Shandong Province, China (a) and Northern Hunan Province, China (b)), two in Luoang Namtha, Laos (c).

Not all land uses are created equal. Among and between different kinds of land uses, their environmental impacts range from negligible to devastating. Which drivers—environmental, social, economic, etc.—influence the land use choices of a farmer, pastoralist, or housing developer may have once been treated as a question of local relevance, but a team of researchers is now studying them as forces of global significance.

Led by Dr. Nicholas Magliocca, computational research associate at the National Socio-Environmental Synthesis Center (SESYNC), the team has developed a computational model that is laying the foundation for understanding what motivates people’s land use decisions, on both local and global scales, based on their livelihood strategies. A scientific paper based on the research, which Magliocca wrote as a postdoctoral research associate at the University of Maryland, Baltimore County, was published January 29 in the journal PLOS ONE. Magliocca’s co-authors included Dr. Daniel G. Brown of the University of Michigan and Dr. Erle C. Ellis of the University of Maryland, Baltimore County.

Land use is often tied to a person’s means of making a living. With forces such as climate change, population growth, and economic globalization at play, livelihood strategies are changing—and those changes transform how people use land. Understanding how such forces influence the choices different land users in different regions make is the first step to supporting land uses that are environmentally and economically sustainable for generations to come.

This type of analysis isn’t easy. “The traditional mode of scientific experimentation is not feasible with real land use systems,” Magliocca says. “We’re talking about people's land and livelihoods here.”

Agent-based models—used as “virtual laboratories,” as Magliocca calls them—offer a powerful and practical means of simulating the actions and interactions of agents (in this case, individual or groups of land users) in order to assess their interactions with the larger system of which they are a part.

Land use change has been studied mostly by researchers creating highly detailed, specialized models that apply to a single location and are highly context-dependent. However, we can learn a lot about what influences land use choices through comparative research across different sites.

“That’s nearly impossible if you’re trying to compare models that were created for a single specific location,” says Magliocca. “Our modeling framework is different because it uses the same model structure, language, logic, and variables across different sites so that those sites can be compared in ways that provide us with meaningful insights. It will help us understand local decisions and activities in larger global contexts.”

“This model is a significant advance in modeling practice. Sometimes it performs well—it reproduces what you actually see on the ground—and in other cases, it misses. But when it misses, the model is informative about what’s going wrong and why it misses, which is hugely informative for the subsequent models we’re laying the foundation for.”

The team hopes to continue their work with a larger research project using volunteered, crowd-sourced local data. These data would help improve the accuracy of the models at any given site while still maintaining a global context by parameterizing the model—i.e., providing a reference for how the local data relate to global data sets already being used.

“We’d be asking local inhabitants for information such as crop prices, land prices, these sorts of things,” says Magliocca. “And we hope to create a system that then delivers the data back to them. We’ll see if that gets funded—it’d be pretty cool if it does.”

The National Science Foundation supported the research under the Integrative Graduate Education and Research Training (IGERT), East Asia and Pacific Summer Institutes for U.S. Graduate Students (EAPSI), and GLOBE Project awards. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

The National Socio-Environmental Synthesis Center—funded through a National Science Foundation grant to the University of Maryland—is an Annapolis, Maryland-based research center dedicated to solving complex problems at the intersection of human and natural systems.

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Computational Challenges

November 21, 2013

Research Associate

Over the last decade, agent-based modeling (ABM) has become a popular approach for investigating human–environment interactions (An, 2012). The recognition that humans are primary agents of change in the natural landscape (Ellis and Ramankutty, 2008)—combined with the ability of ABMs to explicitly model human decision-making—drives the popularity of this approach. A particularly interesting application of ABMs to socio-environmental systems has been to explore sudden transitions, or thresholds, that can emerge in natural systems due to human activities. A relevant example of interactions between sea level rise, rapid shoreline erosion, and beach nourishment can be found here.

In addition to being cool science, this example demonstrates a fundamental challenge of studying human–environment interactions and why computational models are becoming so important: the long time and massive spatial scales of most human–environment systems make it impossible to conduct traditional field-based research. Further, if one wants to learn something by comparing human–environment interactions across multiple locations, then either a small army of field technicians that can be deployed across sites is required, or the researcher must bring the real system into the computer via a simulation model. These cross-site comparative questions are currently some of the most compelling research questions being asked: how are human–environment systems similar or different across locations; is there something about a particular location that makes human–environment interactions more or less sustainable; and under what conditions do sudden transitions in sustainability occur?

Of course, just because it is easier to ask cross-site comparative questions with computational rather than field-based approaches, doesn't mean answering them is any easier. In fact, carrying out many iterations of computational experiments for a large set of study sites can be computationally challenging. Depending on the models and number of study sites, one could be facing days (or even ... gulp ... weeks) of computer time!

Fortunately, in this age of interdisciplinary research teams, socio-environmental researchers are finding strong allies in computer scientists. These new partnerships bring challenges for both parties. From my own experience as a socio-environmental researcher, I have had to become fluent with more computing jargon than I  knew existed in order to ask the right questions, and my computer scientist colleagues are finding new challenges with parallel computing of distributed, interacting, rule-based algorithms common in ABMs. Add large and complicated data sets for parameterizing and testing ABMs to it, and the computational challenges become more than most individual researchers can handle. Thus, finding adequate computational support is critical for progressing beyond these technical barriers, and can present opportunities to ask new research questions that about complex, large-scale socio-environmental systems that could not otherwise be asked.

Researchers interested in data-intensive and modeling-based projects are invited to submit applications to SESYNC’s newest research theme. Full details on the call for proposals can be found here.


An, L. (2012). Modeling human decisions in coupled human and natural systems: review of agent-based models. Ecological Modelling, 229, 25–36.

Ellis, E.C. and Ramankutty, N. (2008). Putting people in the map: Anthropogenic biomes of the world. Frontiers in Ecology and the Environment, 6, 439–47.

Associated SESYNC Researcher(s): 

VIDEO: Materials & Workshops for Cyberinfrastructure Education in Biology

We interviewed Dr. C. Titus Brown, Kaitlin Thaney, and Dr. Greg Wilson about a workshop recently held at the National Socio-Environmental Synthesis Center (SESYNC), "Materials & Workshops for Cyberinfrastructure Education in Biology." The workshop was aimed at improving the capacity of researchers to leverage a host of cyberinfrastrucutre capabilities.

Watch the video below:

Interactive Visualization Tools for Socio-Environmental Data

The future of user interfaces for data analysis is in the direction of larger, higher resolution screens, which present perceptually-rich and information-abundant displays. With such designs, the flood of data can be turned into a productive knowledge. Human perceptual skills are quite remarkable and largely underutilized in current information and computing systems, and visualization tools have rapidly emerged as a potent technology to support data analysis and human decision-making.


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