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Technology and Urbanism

LiDAR Scan of Drummond Street


Changing cities with Virtual Reality 


How can advances in virtual reality and 3D scanning improve participatory urban design?

Using the High Speed Rail 2 (HS2) project in the UK as a case study, the paper analyzes the need for more inclusive community engagement in urban development projects. Virtual reality is examined as a more inclusive medium for architectural representation, as research has shown that it is more readily comprehended by non-designers. Furthermore, the advent of low-cost virtual reality equipment like Google Cardboard and Project Tango have made it a viable technology for participatory design schemes. The paper uses a design research project based on the concept of a digital palimpsest as a tool to investigate how stakeholders in the HS2 project can communicate needs, solutions, and facilitate debate in a participatory design process. Our research suggests that virtual reality can become a tool for inclusive community engagement if the strategy is designed to be intuitive and accessible.


Was invited to present this research at two conferences in Brussels. Full text available here.



I participated in a 2 day design sprint organized by the Gehl Institute. My teammate and I were awarded runners-up.


The goal — design an app that helps volunteers collect vital information about how public spaces are used to contribute to Gehl Institute’s PublicLife Database.

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HappenNow allows researchers to quickly snap a photo of a public space and add layers of information by identifying visitors and tapping on categories.

Using Unity, I coded a demo app.


Our design was driven by four objectives.


Horizontal data capture

Instead of trying to associate all types of data to each visitor, one by one, input one type of data for all visitors, one category at a time.

It is easier for the mind to focus on one data type, and it creates complete data sets by category for all visitors if observation time is short or interrupted.



As researchers verify data sets, send updates to contributors and challenge them to improve their R-value



Scrub images of Personally Identifiable Data before uploading them to the database.


Massively Scalable

First, it speeds up accurate data collection by people in the field.

Second, it acts as a training set for a computer-vision based machine learning algorithm.

Finally, the app can be primarily used to collect photos that are analyzed automatically.


Machine learning algorithms require high-quality training sets.

With this in mind, researchers need robust tools that can transition from facilitating accurate coding of data to collecting large volumes of raw input.