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Visual Spatial Analytics and Trusted Information for Effective Decision Making

Published: 05 November 2019 Publication History

Abstract

Information, not just data, is key to today's global challenges. To solve these challenges requires not only advancing geospatial and big data analytics but requires new analysis and decision-making environments that enable reliable decisions from trustable, understandable information that go beyond current approaches to machine learning and artificial intelligence. These environments are successful when they effectively couple human decision making with advanced, guided spatial analytics in human-computer collaborative discourse and decision making (HCCD). Our HCCD approach builds upon visual analytics, natural scale templates, traceable information, human-guided analytics, and explainable and interactive machine learning, focusing on empowering the decisionmaker through interactive visual spatial analytic environments where non-digital human expertise and experience can be combined with state-of-the-art and transparent analytical techniques. When we combine this approach with real-world application-driven research, not only does the pace of scientific innovation accelerate, but impactful change occurs. I'll describe how we have applied these techniques to challenges in sustainability, security, resiliency, public safety, and disaster management.

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  • (2020)Algorithm of Pre-whitening on the Basis of the Polynomial Canonical Expansion of Random Sequences2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET)10.1109/TCSET49122.2020.235402(107-112)Online publication date: Feb-2020

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cover image ACM Conferences
SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
November 2019
648 pages
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2019

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Author Tags

  1. human-computer collaborative disclosure and decision making (HCCD)
  2. trusted information
  3. visual spatial analytics

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  • Invited-talk
  • Research
  • Refereed limited

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SIGSPATIAL '19
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SIGSPATIAL '19 Paper Acceptance Rate 34 of 161 submissions, 21%;
Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

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  • (2020)Algorithm of Pre-whitening on the Basis of the Polynomial Canonical Expansion of Random Sequences2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET)10.1109/TCSET49122.2020.235402(107-112)Online publication date: Feb-2020

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