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Predictive Analytics for Cities
- 7. Predictability, therefore, hints towards the existence of natural laws that might not yet have been able to characterize or discover. -- Cesar Hidalgo atlas.media.mit.edu
- 10. asthmapolis.com
- 11. sfpark.org
- 13. Capacity planning Emergency vehicle deployment Traffic flow Weather Budget analysis Crime prevention Education etc
- 14. Some examples from the Internet
- 15. ads
- 16. fraud
- 17. security
- 18. security
- 19. privacy
- 20. privacy
- 22. “pulling” standardization ? ? ? 2005 GTFS GTFS GTFS 2012 GTFS GTFS GTFS GTFS
- 23. TRUST
- 24. Bureaucracy Regulation Friction TRUST
- 25. Bureaucracy Transparency Regulation Accountability Friction Innovation TRUST
Notes de l'éditeur
- Predictive Analytics. Knowing what’s going to happen based on what has happened. Science Fiction. Minority Report. Exciting, and creepy.\n
- It’s been a big year for predictive analytics. Nate Silver for President!\n
- Predictive analytics told us -- long ago -- that an outcome like what happened with Superstorm Sandy was possible, and likely.\n
- We are just wrapping our heads around analytics -- and we are perhaps moving from reactive analytics to predictive analytics.\n
- Cesar Hidalgo at MIT has done great work in this area -- visualizing connections among data, in order to spot patterns that numbers alone wouldn’t tell us. These patterns are the hints at the underlying “natural law” that is a predictive guide.\n
- The “syrup smell” in NYC was an exercise in analytics -- tracing the source of the smell based on the time + location of 311 calls.\n
- The million dollar block project shows us where and how much we’re spending on incarceration. This is not predictive, but it’s hugely insightful looking backwards.\n
- \n
- San Francisco’s SFpark system can predict when & where it’ll be good to park.\n
- Ginger.io can predict when we’ll be depressed, based on other data patterns.\n
- There are infinity use cases within cities for better prediction.\n
- As always, I like to point to the internet as a guide. The internet has been doing analytics for a while, and there are some lessons to be learned.\n
- We see lots of prediction in the ad space. Target famously knew that one customer was pregnant before her family did.\n
- Prediction is also a big deal in the area of online fraud. Sift Science can essentially create a “4d fingerprint” that can detect fraud before it occurs.\n
- CloudFlare uses predictive algorithms as early warning against denial of service attacks.\n
- Deeply embedded in all this is the issue of privacy. Projects like TOS-DR and Collusion are starting to visualize the privacy impacts of analytics.\n
- One issue facing the civic info space is authentication & authorization -- oauth has blazed a trail for this that has gained a lot of traction in the private sector.\n
- It should be noted that the best approaches to standardization are driven by an outside “magnetic” force -- Google transit’s role in opening up the transit data space is an important case study.\n
- Lastly -- we’re at a moment in time where we have new approaches for establishing trust. This is important as we consider how best to regulate this fast moving space. In the old days, adding friction up front was the best way to establish trust. Nowadays -- with information liquidity like never before -- we can take a more “innovation friendly” approach -- using transparency to establish accountability and trust.\n
- Lastly -- we’re at a moment in time where we have new approaches for establishing trust. This is important as we consider how best to regulate this fast moving space. In the old days, adding friction up front was the best way to establish trust. Nowadays -- with information liquidity like never before -- we can take a more “innovation friendly” approach -- using transparency to establish accountability and trust.\n