banking and finance
internet of things
sports and fitness
Tangible interactive matrix for real-time computation and 3D projection mappingThe Tactile Matrix, or Tangible Interactive Matrix (TIM), ...
Making the invisible visible–inside our bodies, around us, and beyond–for health, work, and connection
Ira Winder and the Tactile Matrix won the award for best demonstration at the IEEE Future Technologies Conference.
We recently led a workshop in Saudi Arabia, with staff from the Riyadh Development Authority, to test a new version of our CityScope plat...
Looking beyond smart cities
Transforming data into knowledge
Read more about this project hereMIT City Science is working with Hafencity University to develop CityScope for the neighborhood of Rothe...
View the main City Science Andorra project profile.Research in dynamic tools, mix users (citizens, workers) amenities, services, and land...
The Mobility Futures Collaborative in the MIT Department of Urban Studies and Planning (DUSP) and the Changing Places grou...
Developed by Ira Winder with the MIT Centre for Transportation and Logistics, the model seeks to use real population data and create a si...
This project focused on pedestrian accessibility in collaboration with Singapore Centre for Liveable Cities. Researchers and planners cam...
This project is the first of two projects in collaboration with GSK. We are developing a computational simulation that allows a human use...
This is the second project from the GSK collaboration. This project considers how space and collaboration are intertwined. We are develop...
Facebook volunteers and work-at-home moms might be making city planning decisions, thanks to AI research conducted by MIT scientists. Res...
Using computer vision to examine Google Street View, the researchers analyzed how streets and blocks have changed in five American cities.
Tested with five American cities, Streetchange quantifies the physical improvement or deterioration of neighborhoods.
A recently published paper in the Proceedings of the National Academy of Sciences (PNAS) looks at factors that predict neighborhood change.
Researchers have used machine learning to quantify the physical improvement or deterioration of neighborhoods in five American cities.
Computer vision uncovers predictors of physical urban change
Paiva, Prada, W., (Eds.)., 4738, datePaiva, Prada, W., (Eds.)., 4738, datePaiva, Prada, W., (Eds.)., 4738, date