Crowdsourcing Approaches for Smart City Open Data Management

Crowdsourcing Approaches for
Smart City Open Data Management
Edward Curry & Adegboyega Ojo
Insight @ NUI Galway
ed.curry@insight-centre.org
www.edwardcurry.org

About Me
• Researcher in both Computer
Science and Information
Systems
• Green and Sustainable IT
Research Group Leader in
DERI/Insight NUI Galway

Some Background
Multi-year research on state
of research and practice of
smart cities to inform Next
Generation Smart City Design
and Policy
Part of an International Smart Cities
Research/Practice Consortium
composed of international research
teams from the US, Canada, Mexico,
Colombia, China and Ireland.

Designing Next Generation Smart City
Initiatives - SCID
Ojo, A., Curry, E., and Janowski, T. 2014. “Designing Next Generation Smart City Initiatives - Harnessing
Findings And Lessons From A Study Of Ten Smart City Programs,” in 22nd European Conference on
Information Systems (ECIS 2014)

Open Data as a Smart City Imitative

Ojo, A., Curry, E., and Sanaz-Ahmadi, F. 2015. “A Tale of Open Data Innovations in Five Smart
Cities,” in 48th Annual Hawaii International Conference on System Sciences (HICSS-48)

Open Data Powering Smart Cities
Economy Energy Environment Education
Health &
Wellbeing
Tourism Mobility Grovenance

An Open Innovation Economy
Initial findings of the study are consistent and
support the notion of an open data oriented
smart city as an:
“Open Innovation Economy”
We are now investigating Crowdsourcing as a
means of increasing Citizen engagement and
participation within a smart city’s open
innovation ecosystem

Introduction to Crowdsourcing
 Coordinating a crowd (a large group of workers)to do
micro-work (small tasks) that solves problems (that
computers or a single user can’t)
 A collection of mechanisms and associated
methodologies for scaling and directing
crowd activities to achieve goals
 Related Areas
 Collective Intelligence
 Social Computing
 Human Computation
 Data Mining
A. J. Quinn and B. B. Bederson, “Human computation: a survey and taxonomy of a growing field,” in
Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems, 2011, pp. 1403–
1412.

When Computers Were Human
 Maskelyne 1760
Used human computers to
created almanac of moon
positions
– Used for shipping/navigation
Quality assurance
– Do calculations twice
– Compare to third verifier
D. A. Grier, When Computers Were Human, vol.
13. Princeton University Press, 2005.

ReCaptcha
 OCR
 ~ 1% error rate
 20%-30% for 18th and 19th
century books
 40 million ReCAPTCHAs
every day” (2008)
 Fixing 40,000 books a day

Enterprise Examples
 Categorize millions of products on eBay’s catalog
with accurate and complete attributes
 Combine the crowd with machine learning to
create an affordable and flexible catalog quality
system
 Understanding customer sentiment for launch
of new product around the world.
 Implemented 24/7 sentiment analysis system
with workers from around the world.
 90% accuracy in 95% on content

Spatial Crowdsourcing
 Spatial Crowdsoucring requires a person to travel to a
location to preform a spatial task
 Helps non-local requesters through workers in targeted spatial
locality
 Used for data collection, package routing, citizen actuation
 Usually based on mobile applications
 Closely related to social sensing, participatory sensing, etc.
 Early example Ardavark social search

Sensing
Credits: Albany Associates, stuartpilrow, Mike_n (Flickr)
Computation Actuation
Human Powered
Smart Cities
Leverages human capabilities in conjunction
with machine capabilities for optimizing
processes in the cyber-physical-social
environments

Citizen Sensors
“…humans as citizens on the ubiquitous Web, acting as
sensors and sharing their observations and views…”
 Sheth, A. (2009). Citizen sensing, social signals, and enriching human
experience. Internet Computing, IEEE, 13(4), 87-92.
Air Pollution

Crowdsourcing Approaches for Smart City Open Data Management

Haklay, M., 2013, Citizen Science and Volunteered Geographic Information – overview and typology of participation in Sui,
D.Z., Elwood, S. and M.F. Goodchild (eds.), 2013. Crowdsourcing Geographic Knowledge: Volunteered Geographic
Information (VGI) in Theory and Practice . Berlin: Springer.
23

Human vs Machine Affordances
Human
 Visual perception
 Visuospatial thinking
 Audiolinguistic ability
 Sociocultural awareness
 Creativity
 Domain knowledge
Machine
 Large-scale data
manipulation
 Collecting and storing
large amounts of data
 Efficient data movement
 Bias-free analysis
R. J. Crouser and R. Chang, “An affordance-based framework for
human computation and human-computer collaboration,” IEEE
Trans. Vis. Comput. Graph., vol. 18, pp. 2859–2868, 2012.

Generic Architecture
Platform/Marketplace
(Publish Task, Task Management)
Workers
Requestors
1.
2.
4.
3.

Core Design Questions
Goal
What
Workers Who Why Incentives
How
Process
Malone, T. W., Laubacher, R., & Dellarocas, C. N.
Harnessing crowds: Mapping the genome of collective intelligence. MIT Sloan Research Paper 4732-09, (2009).

Setting up a Crowdsourcing Process
1 – Who is doing it?
 Hierarchy (Assignment), Crowd (Choice)
2 – Why are they doing it?
 Money ($$££), Glory (reputation/prestige), Love (altruism, socialize,
enjoyment), Unintended by-product (e.g. re-Captcha, captured in
workflow), Self-serving resources (e.g. Wikipedia, product/customer
data), Part of their job description
 Determine pay and time for each task
 Marketplace: Delicate balance (Money does not improve quality but can increase
participation)
 Internal Hierarchy: Engineering opportunities for recognition: Performance review, prizes for
top contributors, badges, leaderboards, etc.
3 – What is being done?
 Creation Tasks: Create/Generate/Find/Improve/ Edit / Fix
 Decision (Vote) Tasks: Accept/Reject, Thumbs up / Down, Vote
4 – How is it being done?
 Identify the workflow: Integrate in workflow (“rating” algorithm)
 Identify the platform (Internal/Community/Public)
 Identify the Algorithm (Data quality, Image recognition, etc.)

Summary
29
Analytics &
Algorithms
Entity Linking
Data Fusion
Relation Extraction
Human
Computation
Relevance Judgment
Data Verification
Disambiguation
Better Data
Internal Community
- Domain Knowledge
- High Quality Responses
- Trustable
Web Data
Databases
Sensor Data
Programmers Managers
External Crowd
- High Availability
- Large Scale
- Expertise Variety

References & Further Information
 Ojo, A., Curry, E., and Janowski, T. 2014. “Designing
Next Generation Smart City Initiatives - Harnessing
Findings And Lessons From A Study Of Ten Smart City
Programs,” in 22nd European Conference on Information
Systems (ECIS 2014)
 Ojo, A., Curry, E., and Sanaz-Ahmadi, F. 2015. “A Tale
of Open Data Innovations in Five Smart Cities,” in 48th
Annual Hawaii International Conference on System
Sciences (HICSS-48)
 Curry, E., Freitas, A., and O’Riáin, S. 2010. “The Role of
Community-Driven Data Curation for Enterprises,” in
Linking Enterprise Data, D. Wood (ed.), Boston, MA:
Springer US, pp. 25–47.

Crowdsourcing Approaches for Smart City Open Data Management

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