Instalment 4: Data Driven Cities (Big Data and Social Physics)

MOOC Summaries - Big Data and Social Physics - Data Driven CitiesInstalment 4: Data Driven cities

“… Towards Deep Data… Curating Innovation at MIT…” 
(Source)

 

Summaries

  • Intro Video
  • Towards Deep Data
  • Curating Innovation at MIT

Intro Video

  • Take the ideas of social physics that we saw applied to individuals and to companies and apply it to cities i.e. social physics in action at the scale of cities, data driven cities
  • How can we use the big data from all the people moving around (e.g. cellphones, and credit cards, and EZPass driving payments etc).
  • Can examine the popular places are, if it the city is nicely mixed up, flow of  ideas, groups that don’t talk to each other, “tribes” (they don’t know each other but they go to the same place and develop similar habits) etc
    • Examples of tribes: people who like dresses of a certain color, similar attitudes towards risk and credit card payments, diabetics and patterns of lifestyle etc.
    • Use these to devise better health, financial, even fashion solutions and ideas.
  • Took data from 300 cities and explored the flow and mixing of ideas in the cities to say how innovative the cities are, where the innovation comes from, and use it to predict other indicators such as GDP.
    • Found that GDP could be predicted almost exactly, and it wasn’t just education, class structure, specialization etc; it was the banging of ideas together from different communities that resulted in innovation that raised the GDP.
  • Knowing that lets you design better transportation infrastructure, IT infrastructure that support sharing experiences and ideas.
Chop Chop MOOCs’ summary of Big Data and Social Physics

Towards Deep Data

  • Going beyond big data towards deep data.
  • Need to go deeper to study not just a few ways but the myriad of ways we communicate (call, text, switch channels etc) and travel (where, how, measurable?), which are all very complex.
  • Use that data to understand if for example, someone is in debt, if it has anything to do with the places and people one has connections with?
  • Or to predict where you might go, and use that to understand mobility better, or to give nudges to modify/improve their behavior, or to create new services.
  • In doing all these, privacy must be studied and understood too, including giving users access to their data and encouraging them to understand what they are sharing with whom, for what purpose, and how the data will be used.
  • This is an amazing time because this is the first time we can study human behavior with such unprecedented resolution.
Chop Chop MOOCs’ summary of Big Data and Social Physics

Curating Innovation at MIT

  • Share some lesson lessons in curating innovation in MIT.
  • Each decade saw a doubling of the number of ventures, and also revealed a lot about where these ventures happen.
  • MIT as a university with our leadership invested in first Technology Square, later Cambridge Center, University Park, and more.
  • Over 500 start-ups and emerging growth companies in Kendall Square in just one building that MIT today owns.
  • A crude way of looking innovation is through the Innovation Pipeline metaphor; it’s more complicated and not simply linear but the idea of going from creative ideation to real world impact is central to academia, and MIT has the related infrastructure and support network.
  • Other initiatives: Entrepreneurship Competition,  licensing office, and MIT Media Lab.
  • Also learnt lessons from students working with one another across disciplinary boundaries, and looking at past companies as live case studies.
Chop Chop MOOCs’ summary of Big Data and Social Physics

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photo: depositphotos/swillklitch

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