Instructors: Dav Clark, Javier Rosa & guests from industry, the social sector and academia
While advances in technology abound, few in-roads have made our proximity to others a source of value to ourselves. For over ten years now, Vivek’s work as a civic engagement entreprenuer has placed him at the ever-challenging intersection of proximity and collaboration. It is here where he believes a transformational innovation will soon occur that enables you to tap your screen and get to where you next need to be for just 25 cents per mile. But carpooling will only become culturally significant if the perceived benefits outweigh the perceived costs, at scale – something that our project will aim to help establish. For some recent developments, please see the first link above.
Eight of us listed below are applying our skills, passions and business experiences to a data project. Our project will seek to test the following hypothesis - namely, that most people who visit similar locations also make common trips - by quantifying what “most”, “similar” and “common” actually mean.
Our project will enlist location leaders (“locleads”) in specific locations - a neighborhood, a school, a dorm, a BART station, a church, an office, etc. - to help our team amass the mobility data it needs. The locleads, be they neighborhood leaders, teachers, dorm resident advisors, commuters, parish priests or human resource professionals, will both collect and report their own mobility data and convince at least 10 of their location peers (“locpeers” or together with locleads, “users”) to do the same. Users will download the Moves App which our team has successfully tested. The app runs in the background and tracks your moves (places and modes of transport) without any manual intervention. Whenever convenient, users will export their collected data to their laptops and anonymously upload a sliver of their collected data to our project server, combining it with a progressively larger aggregated data set. Users will be able to view the aggregated data set at any time, if they wish to observe that their own contribution to it is both anonymous and limited.
To further protect the unknown owners of the data, our team is developing an automated way to objectify and parse the aggregated data set – essentially display it in a trip-centric format and display it chronologically – before we cleanse, integrate, analyze, visualize and present it. Our analysis will focus on applying a proximity filter (both space and time) to the aggregated data set. Every user who participates in our data project will also be given access to our resulting presentation. Finally, all of the data that was uploaded will be destroyed at the end of our project.
|Andrea Yang||@zcyang123||Senior in Statistics and Chemistry|
|Daniel Kim||@Minsu-Daniel-Kim||Junior in Computer Science and Statistics|
|Jessica Ji||@jessicajji||Freshman in Computer Science and Industrial Engineering and Operations Research|
|Joseph Fang||@sherlockjjj||Junior in Engineering Mathematics and Statistics|
|Regina Chan||@reginacpp||Junior in Statistics|
|Vivek Hutheesing||@vhutheesing||Startup Entrepreneur|
|Wanrong Zhu||@zhuwr0423||Senior in Applied Math and Statistics|
|Yangyi Lu||@luyangyi||Senior in Mathematics and Statistics|