CLEVis: A Semantic Driven Visual Analytics System for Community Level Events

Chao Ma, Ye Zhao, Andrew Curtis, Farah Kamw, Shamal AL-Dohuki, Jing Yang, Suphanut Jamonnak, Ismael Ali

External link (DOI)
View presentation:2021-10-27T18:00:00ZGMT-0600Change your timezone on the schedule page
2021-10-27T18:00:00Z
Exemplar figure, described by caption below
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.
Fast forward

Direct link to video on YouTube: https://youtu.be/EVfDSX6M_KA

Abstract

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.