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CLEVis: A Semantic Driven Visual Analytics System for Community Level Events.
IEEE Computer Graphics and Applications
|February 21, 2020
Summary
CLEVis is a visual analytics system designed for community-level event (CLE) datasets. It helps frontline users explore neighborhood events and gain insights at fine-grained geographical resolutions.
Area of Science:
- Data Science
- Geospatial Analysis
- Human-Computer Interaction
Background:
- Community-level event (CLE) datasets offer rich semantic and geospatial-temporal information crucial for frontline users like police and social workers.
- Existing methods often lack the ability to explore these events at fine-grained neighborhood resolutions.
Purpose of the Study:
- To introduce CLEVis, a visual analytics system for exploring CLE datasets.
- To enable frontline users to discover insights within specific community regions of interest.
Main Methods:
- CLEVis integrates automatic algorithms with interactive visualizations to leverage semantic information.
- The system was designed and developed in collaboration with community workers and social scientists.
Main Results:
- CLEVis facilitates exploration of community events at neighborhood scales.
- The system effectively utilizes semantic information through integrated algorithms and visualizations.
Conclusions:
- CLEVis enhances the ability of frontline users to derive insights from community-level event data.
- Collaborative design ensures practical utility for real-world applications.
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