Big Data and Big Cities
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Presentation by Ed Glaeser (with Scott Kominers, Mike Luca and Nikhil Naik) Signatures in Regional Science Vienna (August, 2016)
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Big Data and Big Cities
- 2. Outline • Part 1: Big Data and Urban Ques+ons – Too much big think on big data • Part 2: Measuring City Life when data is missing – Look forward to my lecture later – Measuring the impact of water in Zambia • Part 3: Using Big Data to Improve City Services – Modest model on tournaments vs. consultants – Report on a hygiene tournament in Boston
- 3. Big Data and Big Ques+ons about Ci+es • How does urban development impact the economy? – Shocks to people vs. shocks to place • How the physical city interact with social outcomes? – Measuring the physical city with big data • How much do people value urban ameni+es? – Measuring ameni+es and beCer con+ngent valua+on • How can public policy improve the quality of urban space? – Merging government ac+ons with physical measures
- 4. Examples of Big Data • Much finer geographic records (the IRS data) • Similar data from private providers (corelogic) • Novel data sets on tradi+onal outcomes (Zoona) • Novel data sets on rela+vely new things (Yelp) • Completely different data on things we had barely thought about before (Google Streetview)
- 5. What’s It Good For • Big data does not intrinsically solve any of the causal inference issues that we have long worried about. • It does make it possible to measure more things (hygiene, streetscapes) in more places in more ways. • IRS records provide the mother-of-all-panel sets, which is par+cularly useful for spa+al interven+ons – The right way to judge empowerment zones, for example, would be to use the panel structure
- 7. Measuring the Previously Unmeasurable • We are used to having public sources for data on the most basic economic outcome: income • This is typically not true in the developing world, especially sub-Saharan Africa. • Especially false for a usable panel • Example # 1 Zoona data, water and health • Example # 2 Google Streetview: essen+ally nightlights on steroids
- 16. Yelp • Luca 2011: high ra+ngs increase revenue for independent restaurants • Chevalier and Mayzlin 2006: Barnes & Noble, Amazon and online book orders • Ghose et al 2011: TripAdvisor and hotel reserva+ons
- 18. Yelp Search
- 20. Some background • Los Angeles in 1997… – Pos+ng à • higher scores • lower rates of foodborne illness • Jin and Leslie (2003) – Major success story of disclosure • NYC in 2010 • Yet, a lot has changed…
- 24. Theory • Tournaments make sense when: – the probability of a breakthrough, 𝜑, is high; – when the baseline low-skilled outcome, 𝑞 , is not that bad; – and when the best outcome, 𝑞↓𝑚𝑎𝑥 , is par+cularly good. • Wage inequality makes tournaments more appealing. • Tournaments are unaCrac+ve for ensuring ¯𝑞 . ” Tournaments may be becoming more aCrac+ve!
- 26. Conjecture • Inspec+on and disclosure policies can be enhanced by working with social media: – Social media is a poten+al plarorm for disclosure – Op+mal disclosure is a func+on of what people are saying on social media • Design disclosure of hygiene viola+ons through Yelp plarorm Use Yelp review text to guide inspec+ons. – Inspec+ons are fairly random, but they don’t have to be!
- 27. Why restaurant hygiene inspec+ons? • Data and technology have changed – Policy has remained the same • Disclosure side – Market with very liCle informa+on – Early success story of disclosure (Jin and Leslie 2003), so known poten+al impact • Ideal sesng for informa+on design ques+ons – What condi+ons cause pos+ng to work? – What are the behavioral factors underlying customer response? • Scope for improving policy – Dai and Luca 2016
- 28. Hygiene Inspec+ons • Process and scoring varies (some+mes a lot) by city • In SF: – restaurants inspected roughly 2X per year. – viola+ons classified as major (lots of rats) and minor (a rat) – final score between 0 and 100 • In Boston: – Restaurants inspected at least once per year – Viola+ons classified as minor, major, and severe – Un+l now, no grades • Goal: – Iden+fy risks – Shut down worst offenders, enforce clean up
- 29. Essen+ally a predic+on problem • Which restaurant is most likely to have a viola+on? • By targe+ng inspec+ons, can be more efficient: – Iden+fy more risks, or, – Reduce number of inspec+ons • Eg: 1 random annual inspec+on for each restaurant, plus targeted
- 30. Treatment: Inspection Results on Yelp
- 32. Distribution of Hygiene Scores
- 33. Hygiene Scores by Restaurant Price
- 34. Yelp Ratings Predict Hygiene Scores
- 36. Tournament: • Cosponsored with Yelp • Supported by City of Boston • Combined Yelp data with Boston inspec+on results: – Objec+ve to predict viola+ons. – Weights chosen by city (minor = 1, major = 2, severe = 5). – Evaluated using RMSLE
- 37. Tournament: Rewards Place Prize Amount 1st $3,000 2nd $1,000 3rd $1,000 Prize money provided by Yelp
- 45. Results • > 500 signups • Development phase: – ~55 completed at least one entry – ~450 sets of predic+ons • Evalua+on phase: – 23 submiCed final algorithms – During this +me, Boston inspected 364 restaurants
- 46. The Winner
- 47. The Runner Up
- 50. Ongoing work • Launching a trial – Starts this month • Incorpora+ng into day-to-day inspec+ons • Ongoing challenges: – Other city goals? – Gamability? – Transferability?
- 51. Epilogue • Results of the Algorithm were given to inspectors to improve accuracy. • Then we looked at how they did using their own best prac+ces vs. the fancy algorithm vs. a really simple algorithm. • The fancy algorithm does help– but the simple algorithm gets most of the way there. • In some things, gesng the basics right is far more important than too much fancy math.