Visual Interactive Spatio-Temporal Analysis

Honorable Mention: IEEE Conference on Visual Analytics Science and Technology (VAST) Challenge – 2016

 

Abstract:

The VAST challenge was on the analysis of spatio-temporal data from sensors in a building and movement data of employees from prox cards. The challenge required detecting anomalies and finding typical patterns in both datasets as well as finding causal relation- ships between sensor and prox card data. Our entry to the challenge consisted of two tools – one for visual analysis of prox card and the other for sensor data. We processed the data using simple machine learning techniques such as clustering and association rule mining and created novel, interactive and user friendly visual analytics tools for extracting insights from the data.

 

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