CLEVis: A Semantic Driven Visual Analytics System for Community Level Events
CLEVis: A Semantic Driven Visual Analytics System for Community Level Events
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DOI：
10.1109/mcg.2020.2973939
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发表时间：
2021-03-01
影响因子：
1.8
通讯作者：
Ali, Ismael
中科院分区：
文献类型：
--
作者：
Ma, Chao;Zhao, Ye;Ali, Ismael
社区级事件(CLE)数据集，如犯罪事件的警方报告，包含丰富的事件情况的语义信息，并在地理空间-时间上下文中进行描述。对于警察和社会工作者等一线用户来说，它们对于发现和检查社区社区的洞察力至关重要。我们提出了CLE数据集的邻域视觉分析系统Clevis，以帮助一线用户探索社区感兴趣区域的事件洞察力，即细粒度的地理分辨率，例如当地餐馆、教堂和学校周围的小社区。Clevis通过集成自动算法和交互式可视化来充分利用语义信息。Clevis的设计和开发是通过与现实世界的社区工作者和社会科学家的扎实合作进行的。案例研究和用户反馈提供了真实世界的数据集和应用程序。
Community-level event (CLE) datasets, such as police reports of crime events, contain abundant semantic information of event situations, and descriptions in a geospatial-temporal context. They are critical for frontline users, such as police officers and social workers, to discover and examine insights about community neighborhoods. We propose CLEVis, a neighborhood visual analytics system for CLE datasets, to help frontline users explore events for insights at community regions of interest, namely fine-grained geographical resolutions, such as small neighborhoods around local restaurants, churches, and schools. CLEVis fully utilizes semantic information by integrating automatic algorithms and interactive visualizations. The design and development of CLEVis are conducted with solid collaborations with real-world community workers and social scientists. Case studies and user feedback are presented with real-world datasets and applications.