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Mapbox Cities
Mentorship program for cities to get smarter
at making data-driven decisions
Open by default
Urban challenge...
Improving cities faster
Return on Investment (RoI)
Organizations can expect increased
RoI when investing in open source
te...
Open Source
for Cities:
Get better, faster!
Copy what
works.
Repeat.
Alarming rise in traffic fatalities
2014
Traffic fatalities in the
United States
2015
Sharp increase of traffic
fatalities...
Alarming rise in traffic fatalities
2014
Traffic fatalities in the
United States
2015
Sharp increase of traffic
fatalities...
Vision Zero “No loss of life is
acceptable.”
Vision Zero DC
Data - driven decision making
www.dcvisionzero.com
Washington, DC
Census data on race and ethnicity in DC
Goal: Act faster
OPEN
DATA
POLICY ACTION
Time
Timeline 2017
Dec 2016
Start conversations
with DDOT
Kicking off
In-person meeting
in DC
Smart Cities
Week DC
Deadline to ...
How can we
better prioritize
where measures are needed?
DC’s crash data
150,000+ entries
45+ attributes
Data quality
Cause of errors obscured by
complex internal processes
Format...
Assumption 1
More vehicles or pedestrians
= more opportunities for incidents
Assumption 2
Higher speeds = more crashes
Assumption 3
More shops, restaurants & schools
= higher frequency of crashes
CC BY-ND 2.0 by Daniel M. Hendricks | Flickr
Data available
opendata.dc.gov
Crash data (before 2017)
Census data
Intersection data
DDOT + Howard University Traffic Dat...
Modeling Collision Frequency
Various conditions
Traffic counts
Employment data census block
Intersections
School locations...
Lively urban streets.
More accidents.
Intersection
density
matters.
CC BY-ND 2.0 by Sonara Arnav | Flickr
Higher speeds
More incidents
= unrelated
School locations
Crash frequency
= unrelated
Why took it so long?
Dec 2016
Start conversations
with DDOT
Talking legalese
Finally we agreed that no
contract is ok, sin...
Team involved
Ramya
FARS data analysis, front
end
Bangalore
Bhargav Dana
Data analysis,
preparation
Bangalore
Testing addi...
Final team
Rasagy
UX design, data
visualization
Bangalore
Eric Morgan
Data modelling.
Creator of tippecanoe.
San Francisco...
Take aways
working with local
government on (open)
data-driven tools
MVPs
Prototype - “Show don’t just tell.”
Deadlines
Set external deadlines
Passion
Assemble a diverse team that’s passionat...
Newsletter mapbox.com/cities
Questions christina@mapbox.com
Traffic safety - answering tough questions with open data
Traffic safety - answering tough questions with open data
Traffic safety - answering tough questions with open data
Traffic safety - answering tough questions with open data
Traffic safety - answering tough questions with open data
Traffic safety - answering tough questions with open data
Traffic safety - answering tough questions with open data
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Traffic safety - answering tough questions with open data Slide 1 Traffic safety - answering tough questions with open data Slide 2 Traffic safety - answering tough questions with open data Slide 3 Traffic safety - answering tough questions with open data Slide 4 Traffic safety - answering tough questions with open data Slide 5 Traffic safety - answering tough questions with open data Slide 6 Traffic safety - answering tough questions with open data Slide 7 Traffic safety - answering tough questions with open data Slide 8 Traffic safety - answering tough questions with open data Slide 9 Traffic safety - answering tough questions with open data Slide 10 Traffic safety - answering tough questions with open data Slide 11 Traffic safety - answering tough questions with open data Slide 12 Traffic safety - answering tough questions with open data Slide 13 Traffic safety - answering tough questions with open data Slide 14 Traffic safety - answering tough questions with open data Slide 15 Traffic safety - answering tough questions with open data Slide 16 Traffic safety - answering tough questions with open data Slide 17 Traffic safety - answering tough questions with open data Slide 18 Traffic safety - answering tough questions with open data Slide 19 Traffic safety - answering tough questions with open data Slide 20 Traffic safety - answering tough questions with open data Slide 21 Traffic safety - answering tough questions with open data Slide 22 Traffic safety - answering tough questions with open data Slide 23 Traffic safety - answering tough questions with open data Slide 24 Traffic safety - answering tough questions with open data Slide 25 Traffic safety - answering tough questions with open data Slide 26 Traffic safety - answering tough questions with open data Slide 27 Traffic safety - answering tough questions with open data Slide 28 Traffic safety - answering tough questions with open data Slide 29 Traffic safety - answering tough questions with open data Slide 30 Traffic safety - answering tough questions with open data Slide 31 Traffic safety - answering tough questions with open data Slide 32 Traffic safety - answering tough questions with open data Slide 33 Traffic safety - answering tough questions with open data Slide 34 Traffic safety - answering tough questions with open data Slide 35 Traffic safety - answering tough questions with open data Slide 36 Traffic safety - answering tough questions with open data Slide 37 Traffic safety - answering tough questions with open data Slide 38
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Presentation by Christina Franken at Open Belgium 2018 - http://2018.openbelgium.be/session/traffic-safety-answering-really-tough-question-open-data

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Traffic safety - answering tough questions with open data

  1. 1. Mapbox Cities Mentorship program for cities to get smarter at making data-driven decisions Open by default Urban challenges New technologies
  2. 2. Improving cities faster Return on Investment (RoI) Organizations can expect increased RoI when investing in open source technology (World Bank) Traditional “closed-by-default” Open Source at least 200%
  3. 3. Open Source for Cities: Get better, faster!
  4. 4. Copy what works.
  5. 5. Repeat.
  6. 6. Alarming rise in traffic fatalities 2014 Traffic fatalities in the United States 2015 Sharp increase of traffic fatalities by 7.2% A wave of ‘Vision Zero’ initiatives... 35,09232,675
  7. 7. Alarming rise in traffic fatalities 2014 Traffic fatalities in the United States 2015 Sharp increase of traffic fatalities by 7.2% 35,09232,675 37,461 2016 Number continued to rise... ?
  8. 8. Vision Zero “No loss of life is acceptable.”
  9. 9. Vision Zero DC
  10. 10. Data - driven decision making www.dcvisionzero.com
  11. 11. Washington, DC
  12. 12. Census data on race and ethnicity in DC
  13. 13. Goal: Act faster OPEN DATA POLICY ACTION Time
  14. 14. Timeline 2017 Dec 2016 Start conversations with DDOT Kicking off In-person meeting in DC Smart Cities Week DC Deadline to present some insights to the public Open Belgium Sharing our take aways 2016 2017 April - Aug - Oct Nov Dec 2018 - Mar
  15. 15. How can we better prioritize where measures are needed?
  16. 16. DC’s crash data 150,000+ entries 45+ attributes Data quality Cause of errors obscured by complex internal processes Format changes Split data into two separate sets that couldn’t be joined b/c of #1
  17. 17. Assumption 1 More vehicles or pedestrians = more opportunities for incidents
  18. 18. Assumption 2 Higher speeds = more crashes
  19. 19. Assumption 3 More shops, restaurants & schools = higher frequency of crashes CC BY-ND 2.0 by Daniel M. Hendricks | Flickr
  20. 20. Data available opendata.dc.gov Crash data (before 2017) Census data Intersection data DDOT + Howard University Traffic Data Center Traffic counts Mapbox Mobile sensor data (speeding)
  21. 21. Modeling Collision Frequency Various conditions Traffic counts Employment data census block Intersections School locations Mapbox speed data* Density of crashes
  22. 22. Lively urban streets. More accidents.
  23. 23. Intersection density matters. CC BY-ND 2.0 by Sonara Arnav | Flickr
  24. 24. Higher speeds More incidents = unrelated
  25. 25. School locations Crash frequency = unrelated
  26. 26. Why took it so long? Dec 2016 Start conversations with DDOT Talking legalese Finally we agreed that no contract is ok, since only using open data. Testing datasets With distributed team, no clear guidance from the DDOT team Smart Cities Week DC Deadline to present some insights to the public Open Belgium Sharing our take aways 2016 2017 April - Aug - Oct Nov Dec 2018 - Mar Smart City Expo Barcelona Presenting the project
  27. 27. Team involved Ramya FARS data analysis, front end Bangalore Bhargav Dana Data analysis, preparation Bangalore Testing additional datasets Washington, DC Michele Research, data prep, statistics PhD, King’s College London Ryan Technical scope of the project San Francisco
  28. 28. Final team Rasagy UX design, data visualization Bangalore Eric Morgan Data modelling. Creator of tippecanoe. San Francisco Data analysis, data prep. Creator of turf.js San Francisco Mikel Local coordination, data prep Washington, DC
  29. 29. Take aways working with local government on (open) data-driven tools
  30. 30. MVPs Prototype - “Show don’t just tell.” Deadlines Set external deadlines Passion Assemble a diverse team that’s passionate about the cause Feedback Give recommendations to improve data quality Plan B Have an alternative in case not all data is accessible
  31. 31. Newsletter mapbox.com/cities Questions christina@mapbox.com

Presentation by Christina Franken at Open Belgium 2018 - http://2018.openbelgium.be/session/traffic-safety-answering-really-tough-question-open-data

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