Slides from the talk given at UbiComp 2016 conference in Heidelberg (Germany)

1. The Night is Young: Urban
Crowdsourcing of Nightlife Patterns
Darshan Santani
Idiap Research Institute and EPFL Switzerland
Joint work with Joan Isaac-Biel, Florian Labhart, Jasmine Truong,
Sara Landolt, Emmanuel Kuntsche and Daniel Gatica-Perez

3. A large-scale mobile crowdsourcing study to capture,
examine, and provide insights on nightlife patterns of
youth in Switzerland.
Youth@Night
Alcohol Epidemiologists
Urban Geographers
Computer scientists

4. Study Design
Lausanne
Zurich
Location
2 major Swiss nightlife hubs
Excellent public transportation
Drinking in public places is legal
High cost of a night out

5. Study Design
Lausanne
Zurich
Location Recruitment Mobile Apps
Weekend Nights
between 8PM to 4AM
Two major nightlife hubs
in Switzerland
On street recruitment of
youth aged 16-25 years

6. MobileDataCollectionFramework
Place Functional Attributes Ambiance
Drink Attributes Social
Place Survey
Drink Survey
Video Survey
SurveyData(EMA)
Sensor Data
MobileSensor
andLogs
Interviews
andSurveys

7. Collected Dataset
Study Duration: September – November 2014
Fridays and Saturdays between 8PM and 4AM
Each person was requested to contribute for 10 nights
Each check-in contains place, drink and video responses

8. Spatial Coverage
Participants Home Locations: 128 unique postal codes
11 states (cantons) of Switzerland
Lausanne
Zurich

9. Research Questions
RQ1: What are the places and social contexts in which
young people hang out during night?
RQ2: What are the connections between automatically
extracted physical ambiance and in-situ vs. external
observations?

10. RQ1: What are the places and social contexts in
which young people hang out during night?

11. Participants Demographics
Balanced gender ratio (48% female)
Over 83% of participants reported to be
living with their parents
63% go out at least once per weekend
40% reported living within the city limits
of either Zurich or Lausanne
Population is different than those reported in previous UbiComp research
62% are below the
age of 20
[1] Yohan Chon et al. “Understanding the Coverage and Scalability of Place-centric Crowdsensing”, UbiComp' 13
[2] Rui Wang et al. “SmartGPA: How Smartphones Can Assess and Predict Academic Performance of College Students”, UbiComp' 15

12. User Contributions
894 videosAvg. check-ins per participant: 6.5

13. Place Analysis
Private Places
Public Places
47.3% check-ins
52.7% check-ins
PBS: Public Parks,
Plazas, Squares
Public Place Categories

14. Place Analysis
83% live with their parents
Going out is relatively costly
Videos recorded at homes clearly have an
intimate, unfiltered flavor
Youth@Night Foursquare
Representativeness of Social Media

15. Place Analysis
83% live with their parents
Going out is relatively costly
Videos recorded at homes clearly have an
intimate, unfiltered flavor
Youth@Night Foursquare
Representativeness of Social Media

16. Place Analysis
83% live with their parents
Going out is relatively costly
Videos recorded at homes clearly have an
intimate, unfiltered flavor
Youth@Night Foursquare
Representativeness of Social Media

17. Comparing Spatial Coverage
Youth@Night
FoursquareMix

18. Social Context and Activities
For most of the public places, the majority
of check-ins were reported to be with
fewer than three people
Alcohol Consumption
Home Drinking: 48% of private check-ins
with alcohol consumption
Street Square Drinking: 84% of check-ins
with alcohol consumption in the PBS
category

19. Findings from RQ1
Youth spend a considerable amount of time hanging
out away from mainstream nightlife areas
Crowdsensing with young workers can capture places
along the full spectrum of social and ambiance context

20. RQ2: What are the connections between automatically
extracted physical ambiance and in-situ vs. external
observations?
Conceptualize this problem in terms of
Brunswik’s lens model

21. Video Content Analysis
● Video Corpus
● 843 videos; Mean duration: 9.4 seconds
● Public Places: 69% vs. Public Places: 66%
● Video Crowdsourcing Compliance – 32% of check-ins with no video
● Safety, Ethics and Social reasons – ~30% each
● Automatic Feature Extraction
● Loudness (AEL) as audio power using the audio channel of videos
● Brightness (AEB) as average brightness of videos in the YUV color space
● Manual Coding of Videos
● Rate the loudness and brightness after watching the videos

22. In-situ Ambiance
Loudness Brightness
Bars and clubs were reported to more crowded, louder, and darker

23. Automatically Extracted Ambiance
Private places are quieter than public places
Clubs, events, and bars are found to be the
loudest places
Clubs and bars to be the darkest places
together with PBS across all place types
Travel category have the highest median
brightness
Loudness Brightness
Diversity of ambiance in home environments may reflect different social settings

24. Physical Ambiance Feature Comparison
To examine the reliability of different crowd-workers
(in-situ and ex-situ) in comparison with automatic feature extraction
Are in-situ self-reports reliable?
What can be considered the “ground-truth”?

25. Brunswik Lens Model
In-situ Perceived
Ambiance
Manually Perceived
Ambiance
AutomaticallyExtractedAmbiance
Cue Validity (rv
) Cue Utilization (ru
)
Higher rv
and ru
is obtained if what is automatically perceived was equally
perceived by both in-situ and external observers respectively

26. Feature Reliability
Cue utilization effect sizes are overall
higher than for cue validity for both
loudness and brightness
Effect sizes of public and private
places are comparable
In-situ External

27. Findings from RQ2
Basic automatic audio-visual features are informative of ambiance
External observers consistently assess loudness and brightness
Automatic ambiance described more accurately the perception of
external online observers than that of participants in-situ

28. With the help of young workers, mobile crowdsourcing allows to
understand of patterns of physical mobility, activities, and social
context of youth population
The resulting data is diverse across place types, social, ambiance
context, and video content.
Taken together, the data and analysis will enable new research
directions in human geography and alcohol epidemiology
Overall Conclusions

29. Q & A
Email: dsantani@idiap.ch
Twitter: @SabMayaHai