The dawn of Driveless Future is coming soon. How can our urban physical and policy environment better prepare for it? How can we effectively get feedback from planning or design professionals, general public and interest groups to react to the Driveless Future and help municipalities better prepare the future, and make policy changes to better protect the general welfare of the public.
Exploring the Healthy Ride bicycle trips generated in OpenStreet PGH events (May 28th, and June 25th) to see the location correlationship between event spaces(open streets), and other venues around the city. Whether there is a concentration of trip destinations patterns throughout the time of the event.
The project is using social media data to analysis the quality of social life in three Pittsburgh neighborhoods, where are perceived as lacking of social life. The analysis will focus on three criteria to measure the quality of social life, including accessibility to culture and recreational amenities, intensity of usage in average amount of time, and social connections in the neighborhoods. The visualization will also follow the three measure to see the social activities generated at the sites.
To have a better understanding of cycling environment at Shadyside regarding physical and social characteristics through exploration of a series of urban data.
Cycling is a sustainable and healthy living choice for urbanists, and city is creating bicycle infrastructure and law enforcement to better accommodate cycling demand. To prioritize improvement areas, city planners need a comprehensive understanding of where have higher possibilities of bicycle crashes.
Our team out of Data Analytics course challenged ourselves to explore the location correlation of graffiti & murals and main streets in different Pittsburgh neighborhood, including Strip District, Downtown Culture District and Shadyside. All three neighborhoods have much different urban identities, such as dense urban core, urban commercial and neighborhood commercial district. With manual data collection and computational visualization, the data reflects certain location difference of data.