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Automating environmental impact analyses to improve urban planning in New York City - Jonathan Pichot (NYC Planning Labs)

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This was presented at mySociety's TICTeC Local 2019 conference, which was held on 1st November 2019 at City Hall in London. More details on the conference can be found here: https://tictec.mysociety.org/local/2019

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Automating environmental impact analyses to improve urban planning in New York City - Jonathan Pichot (NYC Planning Labs)

  1. 1. Automating environmental impact analyses in New York City
  2. 2. Hi! I’m Jonathan Pichot Technologist in government Passionate urbanist Product Manager and Developer at NYC Planning Labs @_pichot
  3. 3. Civic Tech Improving the way government builds technology Plan Tech Using technology and data for better planning outcomes
  4. 4. or, the story of CEQR App Civic tech story Public sector entrepreneurship story Future of planning story
  5. 5. Responsible for public land use review (changes to zoning) Proposing land use changes (aligned with mayoral priorities) Provide data and long-term planning resources across the city Responsible for much of the environmental review
  6. 6. Proposals that increase density (ie. change zoning) are required to analyze the environmental impact of that increased density The definition of the “environment” in this context is broad: urban design, air quality, public health, transportation (19 technical chapters)
  7. 7. Analyses range between qualitative and quantitative assessments Urban Design chapter is very qualitative, Open Space is more quantitative
  8. 8. Output of process is a report (EIS/EAS) that discloses the proposal’s affect on the environment, broadly defined In most cases, all this analysis is done by environmental consultants hired by applicants, but that work has to be double checked by the relevant city agencies. This process expensive, time-consuming, and convoluted.
  9. 9. This is Mona. Mona’s job was to verify the work of consultants producing the Public Schools analysis, determining if there is enough school capacity for future residents.
  10. 10. Much of the work was finding miscalculations, or where a consultant missed an important piece of data Lots of back and forth, emails, copies of copies of Excel Computers are good at these things
  11. 11. As it happened, before joining Labs, I worked on the same team as Mona I was looking for a new project We decided to prototype a tool to save her time (initial mandate)
  12. 12. Within a few months, early prototypes were getting rave reviews: “This is the future of CEQR” “We need this yesterday” Mona’s workload was also improving
  13. 13. Officially launched to Public Schools consultants October 2018 - Became the official way to deliver analyses to Mona for review
  14. 14. Continued good reviews after launch, running user testing sessions with consultants. Every meeting was a good meeting: “You just saved me 3 months” “Thank you”
  15. 15. CEQR App, and yours myself, officially joined Labs. Hired 3 new team members (data engineer, full-stack developer, interaction designer) “Teams before tech”
  16. 16. Project becomes bigger then tech: policy and business improvement Leadership supports agile, iterative development to policy and methodologies, not just digital product Cross-disciplinary team: web developers, designers, data engineers, policy and technical experts, business improvement analysts
  17. 17. Working model just as important as technical output: Development team (biweekly sprints) Working group (biweekly) - Includes director of EARD, Director of Business Improvement Steering Committee (quarterly) - Agency director, head counsel, COO
  18. 18. Methodology Improvements New and better data Related technical products and initiatives Technical Manual Revisions CEQR App
  19. 19. Publicly launched Public Schools analysis in October 2018 > 100 users with 1 project or more 2 projects per user (median) ~ 50% less time (~ 8 hrs / project) per analysis with CEQR App ~ $1000 / project savings ~ $200,000 savings
  20. 20. Transportation chapter - Transportation Demand Factor memo Air Quality chapter - Mapping stationary polluting sources
  21. 21. Thank you! https://labs.planning.nyc.gov @nycplanninglabs

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