Externalizing Hidden Data Flows for Situated Analytics
Link to award abstract here.
- Niklas Elmqvist – Professor (Principal Investigator) – UMD iSchool
- Andrea Batch – Ph.D. student – UMD iSchool
- Sungbok Shin – Ph.D. student – UMD Computer Science
Our world is increasingly measuring every aspect of everyday life—weather, traffic, and ourselves—in virtually every location on Earth. Understanding all of this data could help professionals, policymakers, and citizens make our society better, fairer, and more efficient. For example, imagine hunting for a house by walking through the neighborhood in which you want to live and discovering facts about the area from social media, census data, and real estate databases. Alternatively, imagine an architect showing her clients a new office building in its actual location in the city with the wandering sun, ebb and flow of crowds, and waves of car traffic giving context. However, today’s information superhighways are not designed for using space to look up information. Put differently, in removing geographical barriers, we may be giving up local flavor; in uniting people across the globe into virtual teams, we may be losing the benefits of the human condition; and in turning the real world into abstract data, we may be creating a sterile model of our planet that looks nothing like the real one, teeming with life and enterprise. This project will investigate how to once again close the loop between data and the world whence it came by overlaying data visualizations on top of the real world using Augmented Reality, where head-mounted displays or even handheld devices combine virtual and real images. This will help people use the massive troves of data on the internet by literally putting it at their fingertips: in the world around them.
To achieve this, this project will build a framework called DataWorld for enabling these situated data streams that will be externalized using Augmented Reality, in effect blending the real world and the hidden world of data. This framework will both combine existing data from a wide variety of sources, such as social media, public databases, and popular websites, as well as enable grassroot contributions from DataWorld users on the ground. This framework will then be applied to three separate themes. (1) Public safety, where information about crime, emergencies, and current events can help users. (2) History awareness, where the situated data streams will be used to reveal the footsteps of those who came before us, such as placing old newspaper stories in their geographical context, highlighting the struggles—large and small—of the civil rights movement, and showing urban development in situ over time. (3) Civic awareness, where the situated data streams can disseminate information about current events, promote sustainability and environmentally-conscious behavior, and facilitate crowdsourced data collection at a grassroots level, fostering a form of “virtual” geocaching where data can be hidden in the world. These applications will not only provide new techniques and frameworks that contribute to our knowledge of situated data, data visualization, and Augmented Reality, but will be deployed in practice using the University of Maryland campus as a testbed.
Current Results and News
- August 15 – the official NSF award notification has come in and DataWorld has been officially funded.
- A list of publications from this project will appear here.
No datasets are likely to be published as part of this project.
Software released as part of this project will be linked here (to GitHub).
Any demos released as part of this project will be linked here.
Any educational material released as part of this project will be linked here.
This work was partially supported by the U.S. National Science Foundation award #1908605. Any opinions, findings, and conclusions expressed in this material are those of the authors and do not necessarily reflect the views of the funding agency.