CyberGIS-Jupyter for spatially explicit agent-based modeling: A case study on influenza transmission

Jeon Young Kang; Jared Aldstadt; Alexander Michels; Rebecca Vandewalle; Shaowen Wang

doi:10.1145/3356470.3365531

CyberGIS-Jupyter for spatially explicit agent-based modeling: A case study on influenza transmission

Jeon Young Kang, Jared Aldstadt, Alexander Michels, Rebecca Vandewalle, Shaowen Wang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

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
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	https://doi.org/10.1145/3356470.3365531
Publication status	Published - 5 Nov 2019
Event	2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2019 - Chicago, United States Duration: 5 Nov 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/Territory	United States
City	Chicago
Period	5/11/19 → …

Bibliographical note

Publisher Copyright:
© 2019 Association for Computing Machinery.

Keywords

Agent-Based Modeling
CyberGIS
Influenza
Replicability
Reproducibility

Access to Document

10.1145/3356470.3365531

Cite this

Kang, J. Y., Aldstadt, J., Michels, A., Vandewalle, R., & Wang, S. (2019). CyberGIS-Jupyter for spatially explicit agent-based modeling: A case study on influenza transmission. In H. Kavak, J.-S. Kim, & S. Wise (Eds.), Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2019 (pp. 32-35). (Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2019). Association for Computing Machinery, Inc. https://doi.org/10.1145/3356470.3365531

Kang, Jeon Young ; Aldstadt, Jared ; Michels, Alexander et al. / CyberGIS-Jupyter for spatially explicit agent-based modeling : A case study on influenza transmission. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2019. editor / Hamdi Kavak ; Joon-Seok Kim ; Sarah Wise. Association for Computing Machinery, Inc, 2019. pp. 32-35 (Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2019).

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title = "CyberGIS-Jupyter for spatially explicit agent-based modeling: A case study on influenza transmission",

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.",

keywords = "Agent-Based Modeling, CyberGIS, Influenza, Replicability, Reproducibility",

author = "Kang, {Jeon Young} and Jared Aldstadt and Alexander Michels and Rebecca Vandewalle and Shaowen Wang",

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Kang, JY, Aldstadt, J, Michels, A, Vandewalle, R & Wang, S 2019, CyberGIS-Jupyter for spatially explicit agent-based modeling: A case study on influenza transmission. in H Kavak, J-S Kim & S Wise (eds), Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2019. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2019, Association for Computing Machinery, Inc, pp. 32-35, 2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2019, Chicago, United States, 5/11/19. https://doi.org/10.1145/3356470.3365531

CyberGIS-Jupyter for spatially explicit agent-based modeling: A case study on influenza transmission. / Kang, Jeon Young; Aldstadt, Jared; Michels, Alexander et al.
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 › peer-review

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AU - Kang, Jeon Young

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AU - Michels, Alexander

AU - Vandewalle, Rebecca

AU - Wang, Shaowen

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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.

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BT - Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2019

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A2 - Kim, Joon-Seok

A2 - Wise, Sarah

PB - Association for Computing Machinery, Inc

Y2 - 5 November 2019

ER -

Kang JY, Aldstadt J, Michels A, Vandewalle R, Wang S. CyberGIS-Jupyter for spatially explicit agent-based modeling: A case study on influenza transmission. In Kavak H, Kim JS, Wise S, editors, Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2019. Association for Computing Machinery, Inc. 2019. p. 32-35. (Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2019). doi: 10.1145/3356470.3365531

CyberGIS-Jupyter for spatially explicit agent-based modeling: A case study on influenza transmission

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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