What is Zoetrope?
Zoetrope is an open-source tool created to facilitate the collection, processing, and analysis of Google Street View data across time and geographies. The tool aims to serve the diverse community of city enthusiasts, urban planners, researchers, and others interested in exploring the richness of temporal street view imagery, without having to navigate single year panoramas through manually interfacing with Google Maps and Street View. For example, a Zoetrope user can browse individual street addresses or sample from neighbourhoods to look at how an address or the neighbourhood's streetscape changes over the years, potentially identifying physical indicators of growth, gentrification, or dilapidation in recent years.
Zoetrope was originally developed as part of the Urban Displacement Project at UC Berkeley and is currently hosted by the School of Cities at the University of Toronto. Zoetrope's public GitHub repository is available as part of the School of Cities organization. As an open-source project, Zoetrope is also accessible for programmers seeking to adapt and customize their own Zoetrope; however, Zoetrope is not taking contributions at this time. Zoetrope creates a graphical user interface (GUI) to improve access to street view panoramas over time grabbed using the streetview python library by Adrian Letchford and the Google Maps API.
Why is it called Zoetrope?
The name Zoetrope was chosen to reflect the magic of early advances in animation, which created the illusion of motion by rotating static images over a cylinder. Similarly, the Zoetrope tool hopes to provide new perspectives of the places around us and the changes that occur to them over time.
About the Developers
Q Chen is a data analyst at the University of Toronto's School of Cities, and currently maintains Zoetrope and develops new features for the tool.
Shayan Ghosh is a software engineer at Amazon Web Services and was the primary developer of Zoetrope during his undergraduate studies at UC Berkeley. He is passionate about equitable housing and transportation.
Moulay-Zaidane Draidia is a data scientist at Laurel interested in the socially driven and conscientious use of Machine Learning. He first began work on Zoetrope as an undergraduate student at UC Berkeley, eager to explore how people perceived changes in their environments.