A Middleware Solution for Democratizing Urban DataSara HachemInria Paris-Rocquencourt
Joint work with Valerie Issarny, Animesh Pathak, Vivien Mallet, Rajiv Bhatia, Alexey Pozdnukhov
May 2, 2014
Data Democratization
- Leveraging a plethora of data sources
- Generating publicly available information about the
environment
- Allowing the cooperation of governments and citizens to
induce policy changes and actions for smarter and healthier
environments
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Data Democratization: Why?
- 7 out of 10 people in cities by 2050
- Cities should evolve with evolving technologies for citizens’
well-being
- Isolated technocratic institutions to solve urban problems
- No holistic view of the problems and their solutions
- Citizens may have better insights
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Data Democratization: How?
- Active participatory sensing to complement passive sensing
- Real time learning from streaming data
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Middleware with hybrid sensing/actuation
Public urban knowledge for citizens and governments
Closing the feedback loop with citizen/government cooperation
But… Challenges remain…
- How to leverage the plethora of available sensors?
- How to assimilate data and produce significant city models?
- How to ensure citizen participation?
- How to integrate all the above in an urban middleware
solution?
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The Urban Civics middleware
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Insights
Physical Sensing
Social S
ensing
Ince
ntive
s
Incentives
Insights
Urban Sensing
- Static sensing-Widely available & highly accurate
Require high deployment costs in large city scales
- Mobile sensing-Cheaper but less accurate
-Can complement but not substitute static sensors
! Can have varying precisions according to context (e.g., in
pocket)
- Social sensing-Users’ own perspective
-Data tagging
-Automatic data extraction from social networks
! Can be very subjective
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http://www.netatmo.com/
Data Assimilation
-Integrate observations from
various data sources with
mathematical simulation models
! New sensors may introduce low benefits in densely
deployed areas
Dynamically configure observation network to task optimal
sensors based on uncertainty reduction
! Manage qualitative data while accounting for subjective
assessments
Convert to quantitative values and compare to other sources
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http://www.hzg.de
Participatory Sensing
-Proactive user involvement & citizen engagement in data
collection
! Depends on user participation rate and motivation
Provide incentives:
-Financial: e.g., redeemable goods
-Ego-centric: e.g., badges
-Altruistic: e.g., personal satisfaction
-Democratic: e.g., helping the community
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Early Architecture
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semanticprobabilistic
Machine learning
Noise: Source of Environmental Pollution
- By-product of urban transport, construction, etc.
- Adverse impacts on physical and mental health
- Environmental management challenge for smart cities
- Exploit Urban Civics to monitor noise using microphones
- Static noise meters, mobile phones, tablets, social networks, user-based input
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Urban Environmental Use Cases
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- Air pollution- Static sensors (e.g., NO2)- Mobile wearable sensors- Data assimilation
- Safety- Static/mobile cameras- Criminal reports- Aggregate social variables
Next Steps
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- Implement the Urban Civics middleware- https://urbancivics.gforge.inria.fr
- Urban-scale Experiments for noise crowd-sensing- In cooperation with the San Francisco environment department
- Exploit outcome to inform further developments and
challenges to investigate
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THANK YOU!
http://citylab.inria.fr
