Monthly Archives: January 2013

New Paper: Pattern-Oriented Modeling in Multi-Scale ABMs of Land Change

TGIS_screen_captureA particular challenge of investigating the causes of land-use change is the multi-scale nature of factors that influence land-use decisions. In an increasingly globalized world, land-use choices and livelihood strategies are linked to local AND regional to global forces. But attempts to incorporate such multi-scale causation in land change models often run into significant knowledge and data gaps – especially when trying to link incomplete and/or low quality global data to individual agents’ decision-making processes.

Figure4_mainOne way forward, which my co-author Dr. Erle Ellis and I present in this new open-access article in Transactions in GIS, is to use pattern-oriented modeling (Grimm et al., 2005) within an agent-based virtual laboratory to experimentally bound the possible values of uncertain parameters. By targeting characteristic patterns tied to important individual- and landscape-level processes – the selection of which are informed by theory, data, or both – ABMs can be designed and tested to be more realistic despite data limitations. We propose that this experimental method can help overcome significant data gaps, and help land change scientists begin to quantify some global trends in local land change processes.

Comments welcome!

Abstract

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.

Looking forward to reading and reviewing this. Also, a sure sign that the field of agent-based modeling is maturing.

GIS and Science

AgentAnalyst-frontcover-300dpiAgent Analyst: Agent-Based Modeling in ArcGIS [PDF]

Contributors: Kevin M. Johnston (Editor), Daniel G. Brown, Nicholson Collier, Hamid R. Ekbia, Mary Jo Fraley, Elizabeth R. Groff, Michelle A. Gudorf, Naicong Li, Arika Ligmann-Zielinska, Michael J. North, Derek T. Robinson, and Nathan Strout

Agent Analyst: Agent-Based Modeling in ArcGIS is an introduction to agent-based modeling using an open-source software called Agent Analyst, which is compatible with ArcGIS software. This workbook’s step-by-step exercises, written by agent-based modeling experts, demonstrate how to create agent-based models using points, polygons, rasters, and representative networks. Key topics include creating, manipulating, and scheduling actions and fields. The book shows how to implement basic-to-complex decision making by agents, and demonstrates the code to capture these decisions. Agent Analyst: Agent-Based Modeling in ArcGIS includes exercises, case studies, and code necessary to begin building agent-based models in ArcGIS Desktop 10. You can download Agent Analyst: Agent-Based Modeling in ArcGIS by…

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