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_Data Visualization for Virtual Reality Cities

A new paradigm for creating immersive experiences in virtual reality by combining real cities with geography-based data.

The biggest difference between virtual reality and flat, map-based data visualizations is that you can be placed “inside” the data, rather than above it. We’ve experienced an unexpected intimacy from these visualizations, along with a sense of scale… The subjective point of view is also more cinematic than a map, making it potentially more powerful for storytelling.

Brian Chirls, Creative Developer

This toolkit includes four three-dimensional representations of data placed in a virtual world.

You can select one data visualization at a time.  Search for a location or use the W-A-S-D keys to move through the cities. Be sure to close other windows or applications on your computer to maximize the frame rate.

For use with Oculus Rift: Load this site in a WebVR build of either Firefox or Chrome. When the Rift is connected, hit the “Enter” key or click the “VR” button to enter full-screen VR mode. Hit “Escape” to exit VR.

To use a smartphone as a controller: Scan (or right-click and copy) the QR code for a link to open in a mobile version of Chrome or Firefox. Touch and drag in any direction to move around the space. You will move in whatever direction you’re dragging, taking into account the direction the device is pointing. You can shake the phone quickly to turn around 180 degrees.

The Data Visualizations

Average Income: There are two views representing average income, either as vertical bars or as hemispherical bubbles. The data come from the 2010 US census, so this one only works inside the United States. Each bar or bubble represents the average for a single census tract. Because each data point is already an average, we don’t see the highest and lowest values on their own. Even so, differences in income across a city are clear.

Population Particles: Inspired by the Racial Dot Map, the second visualization shows population broken down by race, with each person represented as a color-coded particle, like a snowflake or bit of pollen floating in the air. This one also comes from the 2010 US census. Reducing everyone to a single color based on race is perhaps over-simplistic and any attempt to do so demonstrates the complexity of race, but watching the colors change as one flies around the country seems a startling and effective way to show the diversity – or lack thereof – of each neighborhood.

NYPD Personal Injury Claims: This view plots every personal injury claim made against the New York City Police Department in 2013. The visual is simple: each claim is represented as a red cylinder, similar to the income bars. Looking out across the city shows great disparity across neighborhoods, probably about as well as a 2-D map does. But at lower elevation, I caught myself trying to navigate heavily-affected areas like Harlem and The Bronx while avoiding any of the incidents and found it impossible.

Bike and Run GPS Logs: The GPS data behind this visualization comes from a friend who is an avid triathlete. Vizicities makes it easy to import the GPX files generated by tracking devices. It’s possible to have users follow the paths, but the GPS data are imperfect and sometimes paths go through buildings. Without a lot of data clean-up, it would be an unpleasant experience. I chose to simply draw the paths as colored lines and let the user follow them manually. With the mobile phone control for movement, the result is a nice simulation of what it might be like to ride through the city.

Project at a Glance :

Company : POV
Year : 2015
Proprietary or Open-source : Open-source
Skill levels : JavaScript,WebGL
Costs : MIT License
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