This document discusses using tagging to study sustainability issues related to noise pollution monitoring. It describes NoiseTube.net, a participatory approach that uses mobile phones to collect noise pollution data. Issues with only collecting measurement data without context are identified. The solution proposed is to enrich the context by automatically generating contextual tags based on factors like loudness, location, time, weather, etc. and combining them with user-generated tags to create a semantic profile for better exploring and understanding the large dataset of environmental measurements. A demo of NoiseTube.net is presented.

1. WP2: SONY CSL Contribution Delivrables2.4, 2.5 Tagging in the Real World Study of sustainability-related issues NicolasMaisonneuve

2. Outline Tagging usage in theartisticcommunity Tagging usage for sustainability- related issues Zexe.net (2nd year) Ikoru: Armin Linke’s Installation (during the 3 years NoiseTube.net (3rd Year)

3. Tagging usage in the real world Social

4. Tagging the user experience (in the real world) Social Location (GeoTagging)

5. Tagging the user experience (in the real world) Social Location (GeoTagging) Sustainability Pollution exposure Social justice CarbonFootprint …

7. Several campaigns for un(der)-represented communities (Taxi drivers Mexico, Disabled people Geneva, MotoboysBrazil)

8. Tagging « slices of life ».2008 - Campaign in Geneva about the life of handicapped people

9. Noise Pollution: NoiseTube.net NoiseTube Participatory approach to monitor noise pollution using mobile phones - Raising awareness (extension of zexe.net principles)- Scientific issue: lack of real data Collective Level - Adaptive sensor network at a low cost - Living map showing the shared experience to noise Green user experience - Phone = environmental instrument - Autonomy to measure noise pollution

10. Accuracy of the phone ?= Virtual noise sensor =microphone + software Sound LevelMeter Real-world experiment Experiment In lab Collaboration with Park Person equippedwithsensors After correction: error 2 db Phone + hand free kit Professional sensors

11. Issue 1: Hazard identification Only measurements, No semantic information Measurement done by real sensors Simulated map

12. Issue 1: Hazard identification Only measurements, No semantic information Measurement done by real sensors Simulated map  New tagging usage:Use people as semantic sensors

13. Issue 1: Hazard identification Contextual Tag cloud

14. Issue 2: Searching/navigating in a large dataset of environmental data Searching by value = Hard for non-experts Example: meaning of 75 dB(A) ? , lat,lng={2.34,12.5} ? Numerical space Geographical space

15. Issue 2: Searching/navigating in a large dataset of environmental data Searching by value = Hard for non-experts Numerical space Semantic space Geographical space Semantic exploration of measurements via rich context Limitation of social tagging (not enough data)  Enriching the context via automatic generation of contextual tags

16. Automatic generation of contextual Tags Neighbors Roadwork Social tagging

17. Automatic generating of contextual Tags Social tagging Roadwork Neighbors >85 dB “risky” [75, 85] “noisy” [50, 75] “Annoying” <50 dB “Quiet” Machine Tagging = set of classifiers Example : Loudness Classifier

18. Automatic generating of contextual Tags Social tagging Roadwork Neighbors Loudness Signal Pattern “High variation” “short-term risky exposure”

19. Automatic generating of contextual Tags Social tagging Roadwork Neighbors Loudness Signal Pattern Location type Street name City Name Type: “indoor” “outdoor” (with gps) Location Street name: “rue Amyot” (Google Map API) City Name: “Paris”

20. Automatic generating of contextual Tags Social tagging Roadwork Neighbors Loudness Signal Pattern Location Day Week Season Day: “Morning” , “afternoon”, “evening”,”night” Time Week: “working day” , “weekend” Season (+ GPS sensor): “summer”, “spring”

21. Automatic generation of contextual Tags Social tagging Roadwork Neighbors Temperature: Loudness Signal Pattern Location Time Temperature Winds type Weather Conditions Temperature: “freezing” , “fair”, “hot” Winds: “calm”, breeze” , “storm” type: “Cloudy”, “raining”,etc.. (At the city level)

22. Automatic generation of contextual Tags User-generated tags Roadwork Neighbors Loudness Signal Pattern Location Time Weather Machine-generated tags Semantic profile of the context

23. Automatic generation of contextual Tags Semantic exploration

24. Participatory monitoring of noise pollution using mobile phones Demo