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INCLUSIVE CITIES:
A CROWDSOURCING APPROACH
Elena Simperl
The city of the future
Cardiff, December 2018
DATA IS
TRANSFORMING
CITIES
Cities have access to more data
than ever to improve urban
services, create efficiencies and
reduce their environmental
footprint
Machine learning and AI can help
optimise traffic, support future
planning and improve fuel
efficiencies
A SMART CITY IS
INCLUSIVE
Citizen-, rather than
technology-centric
Participatory
Using data responsibly
SOLUTIONS
Citizen sensing
Open *
Participatory governance
Crowdsourcing
Human in the loop
THE QROWD PROJECT
3 years project funded through
Horizon 2020
8 partners from 5 European
countries, led by the University
of Southampton
Smart city solutions
Combining crowd and
computational intelligence
Piloted in transportation with
A medium-sized smart city
A leading navigation and traffic
management service provider
qrowd-project.eu
@QrowdProject
OUR APPROACH
Mix of open innovation methods to co-design pilots and encourage stakeholder
participation
Value-centric approach to platform design: personal data empowerment, open source,
building upon existing standards
Sustainable urban auditing through online and mobile crowdsourcing
Human-in-the-loop (HIL) architecture to improve the accuracy of predictions
MORE THAN JUST TECHNOLOGY
Supports deployment of
human-machine workflows
throughout
Interfaces to multiple
crowdsourcing services
Complemented by methodology
and guidelines
Data protection by design
THE METHODOLOGY
• What - Goal
• Who - Participants
• How - Implementation
• Why - Incentives
Tasks based on human
skills, not easily
replicable by machines
Most effective when
used at scale (‘open
call’), in combination
w/ machine intelligence
Example
Predicting modal split
What to crowdsource?
Use the right crowd
for the right task
Who is the crowd?
Paid crowdsourcing
Ideas competition
Citizen
sensing
Implicit vs. explicit
participation
Onsite vs. remotely
Instructions and interfaces
Quality assurance and
behavioural analysis
Time constraints
How to crowdsource?
12
Why should anyone
participate?
People do things for love,
money or glory
Love and glory keep costs
down
Money and glory deliver
faster
13
CONCLUSIONS
Explore the what, who, how, why
methodology to solve problems through
participatory methods.
To use AI and ML cities will need not just
data, but labelled data, created through
crowdsourcing.
Use the full range of approaches and
techniques to apply crowdsourcing at scale.

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Inclusive cities thecityofthefuture_cardiff_2018

  • 1. INCLUSIVE CITIES: A CROWDSOURCING APPROACH Elena Simperl The city of the future Cardiff, December 2018
  • 2. DATA IS TRANSFORMING CITIES Cities have access to more data than ever to improve urban services, create efficiencies and reduce their environmental footprint Machine learning and AI can help optimise traffic, support future planning and improve fuel efficiencies
  • 3. A SMART CITY IS INCLUSIVE Citizen-, rather than technology-centric Participatory Using data responsibly
  • 4. SOLUTIONS Citizen sensing Open * Participatory governance Crowdsourcing Human in the loop
  • 5. THE QROWD PROJECT 3 years project funded through Horizon 2020 8 partners from 5 European countries, led by the University of Southampton Smart city solutions Combining crowd and computational intelligence Piloted in transportation with A medium-sized smart city A leading navigation and traffic management service provider qrowd-project.eu @QrowdProject
  • 6. OUR APPROACH Mix of open innovation methods to co-design pilots and encourage stakeholder participation Value-centric approach to platform design: personal data empowerment, open source, building upon existing standards Sustainable urban auditing through online and mobile crowdsourcing Human-in-the-loop (HIL) architecture to improve the accuracy of predictions
  • 7. MORE THAN JUST TECHNOLOGY Supports deployment of human-machine workflows throughout Interfaces to multiple crowdsourcing services Complemented by methodology and guidelines Data protection by design
  • 8. THE METHODOLOGY • What - Goal • Who - Participants • How - Implementation • Why - Incentives
  • 9. Tasks based on human skills, not easily replicable by machines Most effective when used at scale (‘open call’), in combination w/ machine intelligence Example Predicting modal split What to crowdsource?
  • 10. Use the right crowd for the right task Who is the crowd? Paid crowdsourcing Ideas competition Citizen sensing
  • 11. Implicit vs. explicit participation Onsite vs. remotely Instructions and interfaces Quality assurance and behavioural analysis Time constraints How to crowdsource?
  • 12. 12 Why should anyone participate? People do things for love, money or glory Love and glory keep costs down Money and glory deliver faster
  • 13. 13 CONCLUSIONS Explore the what, who, how, why methodology to solve problems through participatory methods. To use AI and ML cities will need not just data, but labelled data, created through crowdsourcing. Use the full range of approaches and techniques to apply crowdsourcing at scale.