Abstract
Despite extensive efforts on achieving reproducible agent-based models (ABMs) to improve the capability of this widely adopted methodology, it remains challenging to reproduce and replicate pre-existing ABMs, due to a number of factors such as diverse computing resources and ABMs platforms. In this study, we propose to employ CyberGIS-Jupyter for spatially explicit ABMs. CyberGIS-Jupyter is a cyberGIS framework to achieve data-intensive, reproducible, and scalable geospatial analytics using Jupyter Notebook based on advanced cyberinfrastructure. Influenza transmission in the city of Miami, Florida, USA was used as a case study. In the model, Influenza is transmitted through the contact networks of individual human agents, which are constructed based on commuting behaviors. CyberGIS-Jupyter can support one not only to conduct collaborative and transparent modeling, but also to perform modeling simulation on advanced cyberinfrastructure resources. It may contribute to boosting the reproducibility and replicability of ABMs.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2019 |
| Editors | Hamdi Kavak, Joon-Seok Kim, Sarah Wise |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 32-35 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781450369565 |
| DOIs | |
| State | Published - Nov 5 2019 |
| Event | 2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2019 - Chicago, United States Duration: Nov 5 2019 → … |
Publication series
| Name | Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2019 |
|---|
Conference
| Conference | 2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2019 |
|---|---|
| Country | United States |
| City | Chicago |
| Period | 11/5/19 → … |
Fingerprint
Keywords
- Agent-Based Modeling
- CyberGIS
- Influenza
- Replicability
- Reproducibility
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Vision and Pattern Recognition
Cite this
CyberGIS-Jupyter for spatially explicit agent-based modeling : A case study on influenza transmission. / Kang, Jeon Young; Aldstadt, Jared; Michels, Alexander; Vandewalle, Rebecca; Wang, Shaowen.
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2019. ed. / Hamdi Kavak; Joon-Seok Kim; Sarah Wise. Association for Computing Machinery, Inc, 2019. p. 32-35 (Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2019).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - CyberGIS-Jupyter for spatially explicit agent-based modeling
T2 - A case study on influenza transmission
AU - Kang, Jeon Young
AU - Aldstadt, Jared
AU - Michels, Alexander
AU - Vandewalle, Rebecca
AU - Wang, Shaowen
PY - 2019/11/5
Y1 - 2019/11/5
N2 - Despite extensive efforts on achieving reproducible agent-based models (ABMs) to improve the capability of this widely adopted methodology, it remains challenging to reproduce and replicate pre-existing ABMs, due to a number of factors such as diverse computing resources and ABMs platforms. In this study, we propose to employ CyberGIS-Jupyter for spatially explicit ABMs. CyberGIS-Jupyter is a cyberGIS framework to achieve data-intensive, reproducible, and scalable geospatial analytics using Jupyter Notebook based on advanced cyberinfrastructure. Influenza transmission in the city of Miami, Florida, USA was used as a case study. In the model, Influenza is transmitted through the contact networks of individual human agents, which are constructed based on commuting behaviors. CyberGIS-Jupyter can support one not only to conduct collaborative and transparent modeling, but also to perform modeling simulation on advanced cyberinfrastructure resources. It may contribute to boosting the reproducibility and replicability of ABMs.
AB - Despite extensive efforts on achieving reproducible agent-based models (ABMs) to improve the capability of this widely adopted methodology, it remains challenging to reproduce and replicate pre-existing ABMs, due to a number of factors such as diverse computing resources and ABMs platforms. In this study, we propose to employ CyberGIS-Jupyter for spatially explicit ABMs. CyberGIS-Jupyter is a cyberGIS framework to achieve data-intensive, reproducible, and scalable geospatial analytics using Jupyter Notebook based on advanced cyberinfrastructure. Influenza transmission in the city of Miami, Florida, USA was used as a case study. In the model, Influenza is transmitted through the contact networks of individual human agents, which are constructed based on commuting behaviors. CyberGIS-Jupyter can support one not only to conduct collaborative and transparent modeling, but also to perform modeling simulation on advanced cyberinfrastructure resources. It may contribute to boosting the reproducibility and replicability of ABMs.
KW - Agent-Based Modeling
KW - CyberGIS
KW - Influenza
KW - Replicability
KW - Reproducibility
UR - http://www.scopus.com/inward/record.url?scp=85075601851&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075601851&partnerID=8YFLogxK
U2 - 10.1145/3356470.3365531
DO - 10.1145/3356470.3365531
M3 - Conference contribution
AN - SCOPUS:85075601851
T3 - Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2019
SP - 32
EP - 35
BT - Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2019
A2 - Kavak, Hamdi
A2 - Kim, Joon-Seok
A2 - Wise, Sarah
PB - Association for Computing Machinery, Inc
ER -