The history of Los Angeles urbanism is entangled with conversations around air quality monitoring. However, the complexity of its acquisition and visualization has removed the general public from understanding its nuances. Environmental data has been generally visualized as tabular data or two-dimensional plots that have traditionally only interested environmental scientists and meteorologists. With the growth of DIY culture, along with accessibility to readily available electronics, a new generation of scientists, policymakers, and active citizens, have begun exploring alternative methods of environmental data acquisition and analysis. In this context, we have developed USC AiR to enable an immersive visualization of microclimatic data. USC AiR is an Augmented Reality (AR) data visualization mobile application which communicates with I3’s IoT air quality sensors throughout the USC campus and displays the real-time data as interactive visualizations. Location-based AR technologies have the capability to enhance our understanding of socio-spatial dynamics. These technologies also have the capability to revolutionize the field of data visualization. Our research extends the existing mobile AR capabilities to work with real-time and high-resolution air quality data. USC AiR augments the data from CCITI testbed sensors on to a 3D model of the USC campus. This application has two modes:

• The Aerial Mode, is activated by scanning any USC student card and allows one to see the location and data of all the CCITI testbed sensors throughout the campus.

• The First-person Mode, is set up to guide the user to the nearest CCITI sensor based on their geolocation. This mode accesses the user’s camera view and creates a color gradient, ranging from clear to saturated red based on nearest sensor readings. Once within a close range, the user can access an immersive visualization of the sensor data, as well as information about the sensing unit. In this mode, the user can snap a photo of the setup and upload to their Twitter account with suggested hashtags. The user also can plant virtual trees near each sensor, in order to trigger further action from the campus community and planning committee. These virtual trees are stored in the application’s database and will appear next time when another user visits the location.

USC AiR allows the user to switch between these modes at any time, enhancing their understanding of the data and its immediate context. Designing these actions allow us to develop an engaging and context-aware air quality sensing and visualization workflow. The goal is to raise awareness about the urgency of ubiquitous environmental sensing and motivate the formulation of locally informed urban policies. The USC AiR is developed using Unity framework, and it currently runs on Android devices. To access the sensor data from CCITI testbed, we used MQTT, a publish subscribe messaging broker. Our future work includes extending the application to support "news-friendly" or "social-media-friendly" outputs to share the data with the members of the community.

Project Leaders: Biayna Bogosian , Gowri Sankar Ramachandran

Development Team: Kunal Vasudeva, Sushanth Ikshwaku Sriramaraju, Jay Patel, Shubhesh Amidwar, Lavanya Malladi, and Rohan Doddaiah Shylaja, and Nishant Revur Bharath Kumar

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