A Virtual Reality Approach for Exploring Spatio-Temporal Urban Data

Abstract

One of the biggest challenges in computing nowadays is to extract relevant information from ever-growing datasets. Applications such as smart cities, transportation planning, control of epidemics, and citizen engagement in public governance can heavily benefit from the analysis of large volumes of urban data. Despite advances in AI and Data Mining, sometimes they are not enough. Data visualization allows us to apply our human visual understanding capabilities and domain knowledge to this process, and to explore the data without necessarily knowing beforehand what information we are looking for. We hypothesize that immersive and stereoscopic Virtual Reality (VR) environments, coupled with natural embodied interaction, will better support the exploration of inherently three-dimensional spatio-temporal data representations. Through the expansion of an immersive technique we have recently proposed, and iterative user evaluations employing real-world datasets, we will investigate this hypothesis and identify the most efficient design choices for interaction and collaboration.