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tFUTURES: Computational Steering for Geosimulations

Published: 07 November 2017 Publication History

Abstract

Geographic modeling using geosimulations is a popular approach to explore outcomes from interacting geographic processes in a region. Geosimulations account for space, time, and complex spatial and spatiotemporal relationships to explore "what-if" scenarios and their potential impact in a region. However, current approaches to geosimulation limit manipulating model input and exploring alternative scenarios by controlling the simulation at runtime. Furthermore, lack of runtime support for visualization hinders the ability to view the current state of a simulation to provide meaningful steering input. In this paper, we propose a computational steering system for geosimulations, called tFUTURES, that allows users to specify steering input and execute steering actions at runtime. The core of the proposed system includes: (i) Visualization Service that provides a minimal web-based user interface; (ii) Monitoring Server that receives and handles user-initiated steering actions; and (iii) Steering Client that executes the steering actions by altering the control flow of the geosimulation at runtime. Further, we develop versioning and checkpointing features for the system to support: (i) concurrent execution paths of a geosimulation based on varying inputs in a time-step; and (ii) controlled execution with the ability to pause, advance or rollback a geosimulation. To evaluate our computational steering system, we modify the FUTURES Urban Growth Model (UGM) geosimulation to support user-initiated steering input and steering actions from a web browser. Experimental results demonstrate minimal system overhead with observed end-to-end steering latency of about 5 and 11 seconds for a single time-step of the simulation when measured in a local and distributed computing environment, respectively.

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Cited By

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  • (2021)Automated In Situ Computational Steering Using Ascent’s Capable Yes-No MachineISAV'21: In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization10.1145/3490138.3490144(24-30)Online publication date: 15-Nov-2021
  • (2018)FUTURES-DPEProceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/3274895.3274948(464-467)Online publication date: 6-Nov-2018

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cover image ACM Conferences
SIGSPATIAL '17: Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
November 2017
677 pages
ISBN:9781450354905
DOI:10.1145/3139958
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 November 2017

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View all
  • (2021)Automated In Situ Computational Steering Using Ascent’s Capable Yes-No MachineISAV'21: In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization10.1145/3490138.3490144(24-30)Online publication date: 15-Nov-2021
  • (2018)FUTURES-DPEProceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/3274895.3274948(464-467)Online publication date: 6-Nov-2018

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