A M iddleware Solution for Democratizing U rban D ata

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

2. Data Democratization Leveraging a plethora of data sources Generating publicly availableinformation about the environment Allowing the cooperation of governments and citizens to induce policy changes and actions for smarter and healthier environments

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

4. Data Democratization: How? Active participatory sensing to complement passive sensing Real time learning from streaming data 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?

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

10. Early Architecture probabilistic semantic 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 monitornoise using microphones • -Static noise meters, mobile phones, tablets, social networks, user-based input

12. Urban Environmental Use Cases • Air pollution • Static sensors (e.g., NO2) • Mobile wearable sensors • Data assimilation • Safety • Static/mobile cameras • Criminal reports • Aggregate social variables

13. Next Steps 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

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