Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Bloomberg cites MTF data in story on CDC's anti-smoking ads for e-cigarettes

Bound says notion that foreign students are displacing U.S. students "isn't right"

Prescott says online option for access to court system can help equalize justice

Highlights

U-M ranked #1 in Sociology of Population by USN&WR's "Best Graduate Schools"

PAA 2015 Annual Meeting: Preliminary program and list of UM participants

ISR addition wins LEED Gold Certification

PSC Fall 2014 Newsletter now available

Next Brown Bag

Mon, March 23
Lundberg, State Care of the Elderly & Labor Supply of Adult Children

Comparison of empirical methods for building agent-based models in land use science

Publication Abstract

Robinson, D.T., Daniel Brown, D.C. Parker, P. Schreinemachers, M.A. Janssen, M. Huigen, H. Wittmer, N. Gotts, P. Promburom, E. Irwin, T. Berger, F. Gatzweiler, and C. Barnaud. 2007. "Comparison of empirical methods for building agent-based models in land use science." Journal of Land Use Science, 2:31-55.

The use of agent-based models (ABMs) for investigating land-use science questions has been increasing dramatically over the last decade. Modelers have moved from ‘proofs of existence’ toy models to case-specific, multi-scaled, multi-actor, and data-intensive models of land-use and land-cover change. An international workshop, titled ‘Multi-Agent Modeling and Collaborative Planning—Method2Method Workshop’, was held in Bonn in 2005 in order to bring together researchers using different data collection approaches to informing agent-based models. Participants identified a typology of five approaches to empirically inform ABMs for land use science: sample surveys, participant observation, field and laboratory experiments, companion modeling, and GIS and remotely sensed data. This paper reviews these five approaches to informing ABMs, provides a corresponding case study describing the model usage of these approaches, the types of data each approach produces, the types of questions those data can answer, and an evaluation of the strengths and weaknesses of those data for use in an ABM.

DOI:10.1080/17474230701201349 (Full Text)

Browse | Search : All Pubs | Next