Visible to the public Visual Spatial Analytics and Trusted Information for Effective Decision Making

TitleVisual Spatial Analytics and Trusted Information for Effective Decision Making
Publication TypeConference Paper
Year of Publication2019
AuthorsEbert, David S.
Conference NameProceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Date Publishednov
PublisherAssociation for Computing Machinery
Conference LocationChicago, IL, USA
ISBN Number978-1-4503-6909-1
KeywordsCollaboration, composability, Human Behavior, human-computer collaborative disclosure and decision making (HCCD), information assurance, Metrics, policy-based governance, pubcrawl, resilience, Resiliency, Scalability, trusted information, visual spatial analytics
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.

URLhttps://dl.acm.org/doi/10.1145/3347146.3368469
DOI10.1145/3347146.3368469
Citation Keyebert_visual_2019