Spatial process and data models: Toward integration of agent-based models and GIS

The use of object-orientation for both spatial data and spatial process models facilitates their integration, which can allow exploration and explanation of spatial-temporal phenomena. In order to better understand how tight coupling might proceed and to evaluate the possible functional and efficiency gains from such a tight coupling, we identify four key relationships affecting how geographic data (fields and objects) and agent-based process models can interact: identity, causal, temporal and topological. We discuss approaches to implementing tight integration, focusing on a middleware approach that links existing GIS and ABM development platforms, and illustrate the need and approaches with example agent-based models.

Authors and Affiliations

1. School of Natural Resources and Environment, University of Michigan, 430 E. University, Ann Arbor, MI, 48109-1115, USA

   Daniel G. Brown & Derek T. Robinson

2. Argonne National Laboratory,  

   Michael North

3. Center for the Study of Complex Systems, University of Michigan,  

   Daniel G. Brown, Rick Riolo, Derek T. Robinson & William Rand

Corresponding author

Correspondence to Daniel G. Brown.

The authors acknowledge financial support from the U.S. National Science Foundation under the Biocomplexity in the Environment Program, Coupled Human-Natural Systems, grant # BCS-0119804. Pam Sydelko of Argonne National Laboratory contributed greatly to the description of the Red-Cockaded Woodpecker Model. The Center for the Study of Complex Systems at the Univesity of Michigan provided computer resources and other support. Daniel Miller, also of Argonne National Laboratory, contributed to the description of the Infrastructure SymSuite. Argonne National Laboratory, a U.S. Department of Energy Office of Science laboratory, is operated by The University of Chicago under contract W-31-109-Eng-38. We thank Kevin Johnston at ESRI for his support and encouragement.

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