A Graph-Based Locality-Aware Approach to Scalable Parallel Agent-Based Models of Spatial Interaction

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review


Colleges, School and Institutes

External organisations

  • University of North Carolina at Charlotte


A great potential exists for mitigating the computational costs of spatially
explicit agent-based models (SE-ABMs) by taking advantage of parallel and
high-performance computing. However, spatial dependency and heterogeneity of
interactions between agents pose challenges for parallel SE-ABMs to achieve good scalability. This chapter summarizes an application of the principle of data locality to tackle these challenges by extending a theoretical approach to the representation of the spatial computational domain. We propose and formalize a graph-based locality-aware approach to scalable parallelization of SE-ABMs. To demonstrate the applicability of this approach, two sets of experimentation are laid out and a locality-aware algorithm is designed to facilitate the study of model scalability. The results of simulation experiments illustrate the advantage of our approach to scalable parallel agent-based models of spatial interaction.


Original languageEnglish
Title of host publicationAdvances in Geocomputation:
Subtitle of host publicationGeocomputation 2015--The 13th International Conference
EditorsDaniel A. Griffith, Yongwan Chun, Denis J. Dean
Publication statusPublished - 5 Jan 2017
EventGeoComputation 2015: The 13th International Conference - University of Texas at Dallas, Dallas, Texas, United States
Duration: 20 May 201523 May 2015

Publication series

NameAdvances in Geographic Information Science
ISSN (Print)1867-2434
ISSN (Electronic)1867-2442


ConferenceGeoComputation 2015
CountryUnited States
CityDallas, Texas
Internet address


  • Locality awareness, Parallel agent-based models, Spatial interaction