Details

Autor(en) / Beteiligte

• Min, Sitao
• Ahuja, Ritesh
• Liu, Yingzhe
• Zaidi, Abbas
• Phu, Catherine
• Nocera, Luciano
• Shahabi, Cyrus
• Meng, Xiaofeng
• Lu, Chang-Tien
• Huang, Yan
• Xie, Xing
• Shekhar, Shashi
• Wang, Fusheng

Titel

CrowdMap: Spatiotemporal Visualization of Anonymous Occupancy Data for Pandemic Response

Ist Teil von

• Proceedings of the 29th International Conference on Advances in Geographic Information Systems, 2021, p.630-633

Ort / Verlag

New York, NY, USA: ACM

Erscheinungsjahr

2021

Quelle

ACM Digital Library

Beschreibungen/Notizen

• CrowdMap is an anonymous occupancy monitoring system developed in response to the COVID-19 pandemic. CrowdMap collects, cleans, and visualizes occupancy data derived from connection logs generated by large arrays of Wi-Fi access points. Thus, CrowdMap is a passive digital tracking tool that can be used to reopen buildings safely, as it helps actively manage occupancy limits and identify utilization trends at scale. Occupancy monitoring is possible at various levels of resolution over large spatial (e.g., from individual rooms to entire buildings) and temporal (e.g., from hours to months) extents. The CrowdMap web-based front-end implements powerful spatiotemporal querying and visualization tools to quickly and effectively explore occupancy patterns throughout large campuses. We will demonstrate CrowdMap and its spatiotemporal GUI that was deployed for an entire university campus with data continuously being collected since summer 2020.