A computational approach to understanding spatial and temporal granularities in agent-based modeling

Epidemic agent-based models simulate individuals in artificial societies capable of moving, interacting, and transmitting disease amongst themselves. Due to limitations in data and computation, epidemic models simulating tens of millions of individuals often coarsen the finest representations of space and time–termed spatial and temporal granularities in this thesis. This dissertation examines and overcomes a set of computational challenges to investigate a fundamental problem in spatially explicit epidemic agent-based modeling. This research demonstrates that coarsening spatial and temporal granularities influence both computational tractability and epidemic ABM processes. By focusing on the nexus of space, time, and process my dissertation improves understanding of the interrelationships and trade-offs between space and time as they relate to spatial processes using an epidemic modeling case study.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Geography

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2013

Author dc:creator

• Shook, Eric

Contributors dc:contributor

• Wang, Shaowen
• Hannon, Bruce M.
• Kale, Laxmikant V.
• McLafferty, Sara L.

• agent-based model (abm)
• spatial and temporal granularities
• parallel abm

Statement dc:rights

• Copyright 2013 by Eric A. Shook. Chapter 2 is published in the International Journal of Geographic Information Science. The publisher grants authors the right to include the article in a dissertation as listed here: http://journalauthors.tandf.co.uk/permissions/reusingOwnWork.asp

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/45637

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/45637