1. A Middleware Solution for Democratizing Urban Data Sara Hachem Inria Paris-Rocquencourt Joint work with Valerie Issarny, Animesh Pathak, Vivien Mallet, Rajiv Bhatia, Alexey Pozdnukhov May 2, 2014

2. 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 2

3. 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 3

4. Data Democratization: How? -Active participatory sensing to complement passive sensing -Real time learning from streaming data 4 Middleware with hybrid sensing/actuation Public urban knowledge for citizens and governments Closing the feedback loop with citizen/government cooperation

5. 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? 5

6. The Urban Civics middleware 6 Insights Physical Sensing Social Sensing Incentives Insights

7. 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 7 http://www.netatmo.com/

8. 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 8 http://www.hzg.de

9. 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 9

10. Early Architecture 10 semantic probabilistic Machine learning

11. 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 11

12. Urban Environmental Use Cases 12 -Air pollution -Static sensors (e.g., NO 2 ) -Mobile wearable sensors -Data assimilation -Safety -Static/mobile cameras -Criminal reports -Aggregate social variables

13. Next Steps 13 -Implement the Urban Civics middleware -https://urbancivics.gforge.inria.frhttps://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

14. 14 THANK YOU! http://citylab.inria.fr