Monthly Archives: December 2012

Story Map of Global Crop and Land-Use Data

ESRI_story_map

Source: ESRI Story Map and the Institute of the Environment, U. of Minnesota.

I’ve used these global crop and land-use data many times, as they are excellent data sets by Foley et al (2011) and Monfreda et al. (2008). But this visualization in the form of an ESRI story map gives the data new life and power. An excellent and thought-provoking presentation, which is made all the better with its interactive qualities.

I will have to explore such a presentation for conveying the results of spatial ABMs.

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Decision-Making and Data

Cross_scale_dataThe popularity of agent-based modeling has exploded in the past decade – and for good reason. More and more researchers are recognizing that human decision-making – as messy and unpredictable as it seems – is an incredibly powerful driver of human and natural system dynamics. Agent-based models (ABMs) offer the means to test our understanding of decision-making processes and their consequences with more sophisticated (and often more realistic) simulations. Despite this growing popularity, though, ABMs introduce new and profound challenges in their use, testing, and interpretation.

A question that I am confronted with regularly – and have been thinking a lot about recently – is this: “What are the spatial and temporal scales of human-decision-making relevant to the land change phenomenon I want to study?”

I will likely return to this question in some form or another many times on this blog, but lately I am concerned with the data dimensions of this question. For example, if one wants to model household-driven land use changes in frontier regions (e.g. Parker et al., 2008), then land-use decisions at the household level are probably your likely target. However, what if you want to apply this model over a large region? With lots of agents? Or have it be general enough to apply across many sites in different land-use systems ( a la virtual laboratory!)? Then the relevant level of decision-making becomes murkier, especially when considering the data that will be needed to parameterize and test the model. In this example, you might need to combine household surveys, regional land-use/cover data, census data, and perhaps some global data such as market influence (Verburg et al., 2006).

Much has been written on this topic, and as there are many facets to this challenge, there are also many different ways to address it. The various perspectives on this issue seem to consistently fall into categories:

  • How to coordinate different sources of data?
  • What types of data should be used to test the model?
  • What is the focal scale of analysis?

These questions are often raised in the context of using ABMs in combination with a geographic information system (GIS). Crooks and colleagues (2008), Parker (2005), and Brown and colleagues (2005) are good sources to begin answering these questions.

For our purposes here, I’ll ask a simpler question: How does one get these different kinds of data that cover different scales and resolutions to play nicely together?

Coordinating the scale and/or resolution of spatial data in a GIS with those of the decision-making processes in an ABM can be quite tricky. So, as the modeler, you have to make some choices about how closely the model should be “coupled” with the GIS: 1) Spatial data layers can be used simply to initialize an ABM by parameterizing the simulated landscape; 2) GIS and the ABM can be “loosely coupled” such that spatial data files are updated independently and then passed back and forth at designated times; or 3) the ABM can be fully embedded within the GIS (e.g. the new Agent Analyst tool with Repast). The choice of either of these methods will depend on factors such as the research question and the computational expense of passing files between systems. Whichever path one chooses, though, the challenge of coordinating low resolution spatial data with fine-grain social data remains.

Arguably a more important consideration, then, is the level of abstraction within the model. I have recently toyed with this question in a model in which my agents represent aggregations of households in settlements rather than individual households. This, of course, has some conceptual limitations. However, it has important conceptual and practical advantages as well; in particular, the ability to represent a landscape-level entity generally by avoiding some social interactions among households that are notoriously difficult to generalize. This representation is certainly controversial (and probably nonsense to some household livelihoods researchers), but considering the types of data needed to address regional land-use questions, an agent representation that works nicely with global datasets is not entirely a bad thing.

Thus, I will plant this seed: In what situations does a settlement-level agent act as a reasonable representation of land-use decision-making? This is an ongoing topic of research for me, and will undoubtedly lead to subsequent posts. Stay tuned!

References

Brown, D.G., Riolo, R., Robinson, D.T., North, M., and Rand, W. (2005). Spatial process and data models: Toward integration of agent-based models and GIS. Journal of Geographical Systems, 7:25–47.

Crooks, A. T., Castle, C. J. E., and Batty, M. (2008), Key Challenges in Agent-Based Modelling for Geo-spatial Simulation. Computers, Environment and Urban Systems, 32(6): 417-430.

Parker, D.C.  (2005). Integration of geographic information systems and agent-based models of land use: Prospects and challenges. GIS, Spatial Analysis, and Modeling (2005): 403-422.

Verburg PH, Ellis EC, Letourneau A (2011) A global assessment of market accessibility and market influence for global environmental change studies. Environ Res Lett, 6.

Friday Features

Welcome to Friday Features! Or something like that … name to be determined. Regardless of the name, here’s the deal: A couple times a month I will post a series of links – a sort of what’s “trending” in the world of agent-based modeling (ABM), land change science, and/or sustainability science. So, without further ado ….

Thought-provoking …

  • A Wired Science post about the legacy of medieval agricultural land-use patterns. An excellent example of how economic rationale manifests itself as striking land-use patterns. Source: Wired Science, Tim De Chant.
  • Another Wired Science post about land-use patterns. Clear evidence Brasíliafor the importance of institutions in shaping land-use patterns and the need to better model institutional agents – a common problem in the ABM world. Source: Wired Science, Betsy Mason.

Interesting academic articles …

CFP: Multi-Agent-Based Simulation 2013

MABS_2013

MABS’13 – The Fourteenth International Workshop on Multi-Agent-Based Simulation Multi-Agent Simulation In conjunction with AAMAS 2013, St. Paul, Minnesota, USA 6th-10th May 2013

Submission deadline: February 3, 2013 For more information: https://sites.google.com/site/mabsworkshop/

Aims and scope. The meeting of researchers from MAS engineering and the social/economic/organizational sciences is extensively recognized for its role in cross-fertilization, and has undoubtedly been an important source of inspiration for the body of knowledge that has been produced in the MAS area. Multi-Agent Based Simulation (MABS) is a vibrant inter-disciplinary area which brings together researchers within the agent-based social simulation community (ABSS) and the Multiagent Systems community (MAS).

The range of technical issues that MABS has dealt with and continues to deal with is diverse and extensive and includes:

Simulation methodologies: – standards for MABS – methodologies and simulation languages for MABS – simulation platforms and tools for MABS – visualization and analytic tools – approaches for large-scale simulations – scalability and robustness in MABS

Simulation of social and economic behavior: – formal and agent models of social behavior – cognitive modeling and social simulation – game theory and simulation – social structure: social networks and simulating organizations – simulating social complexity (e.g. structures and norms, social order, emergence of cooperation and coordinated action, self-organization, the micro-macro link)

Applications: – MABS in environmental modeling – agent-based experimental economics – participative-based simulation – MABS and games

MABS WORKSHOP SERIES The workshop is a continuation of the International Workshop series on Multi-Agent-Based Simulation (MABS). Further details of the previous workshops can be found at http://www.pcs.usp.br/~mabs/.

Important Dates February 3, 2013 – Deadline for paper submission. March 1, 2013 – Acceptance or rejection notification is sent to authors. March 10, 2013 – Deadline for authors sending their revised contribution, according to reviewers’ remarks. May 6-57, 2013 – MABS 2013 workshop takes place.

*This is an abbreviated version. The original CFP can be found at  OpenABM.

About the Agent-Based Virutal Labs (ABVLs) blog

Welcome to the Agent-Based Virtual Labs blog!Landscape_fig

This blog will cover issues relating broadly to the social, economic, and cultural interactions that are  changing the planet’s surface and climate. In particular, these issues will be explored with posts relating to agent-based modeling (ABM), and how ABMs can be used as virtual laboratories to ask questions about peoples’ motivations for observed behaviors that would be impossible to ask any other way. Along the way, topics informing the creation, use, and testing of ABMs will be included, as well as my areas of application of ABVLs such as land-use change, livelihoods in developing countries, and sustainability.

This blog will also be a hub of information for those interested in ABMs or my subject areas of interest. Following the navigation menu will lead you to collections of links for learning and teaching resources, other ABVLs-relevant blogs, and my research topics. Also, at regular intervals, posts will appear that contain an annotated list of links dedicated to topics ‘trending’ in the ABVLs world.

I hope you enjoy and please drop me a line!